SlideShare a Scribd company logo

exceRpt: A computational exRNA-seq analysis pipeline

β€’
β€’
E
exrna

exceRpt: A computational exRNA-seq analysis pipeline

Science
1 of 26
Download to read offline
exceRpt
 a computational exRNA-seq analysis pipeline Rob Kitchen Yale 2015 - 04 - 23 1
Extracellular RNA Communication NIH Common Fund > data management
 > reference profiles
 > exRNA biogenesis
 > exRNA biomarker
 > exRNA therapy currently 30 funded projects exRNA.org 2
exRNA-seq analysis software 3 small RNA-seq
 analysis toolkit exceRpt quantification (RPM) of
 miRNA, tRNA, piRNA, 
 snoRNA, circularRNA,
 & long transcript fragments long RNA-seq
 analysis toolkit RSEQTools quantification (RPKM) of 
 [multi-exon] transcripts
agenda 1. exceRpt smallRNA-seq analysis suite
 - filtering, QC, and alignment
 - quantification & normalisation
 - visualisations
 - differential expression (soon) 2. use-case involving 345 samples 3. downstream analysis
 - miRNA-mRNA targets from miRTarBase
 - network analysis
 - enrichment analysis
 - pathway analysis
exRNA-seq analysis software 5 small RNA-seq
 analysis toolkit exceRpt quantification (RPM) of
 miRNA, tRNA, piRNA, 
 snoRNA, circularRNA,
 & long transcript fragments long RNA-seq
 analysis toolkit RSEQTools quantification (RPKM) of 
 [multi-exon] transcripts
for a typical cellular sample… 99% pass adapter removal 1% map to calibrator oligos 1% fail adapter removal 3% rRNA contamination 88% map to host genome 75% map to known miRNAs 7% taken on to exogenous smRNA mapping 12% novel smRNAs? contaminants? all smRNA-seq reads 1. remove 3’adapters 2. map to calibrator oligos 3. map to 45S & 5S rRNA 4. map to host genome smRNA-seq analysis workflow 1% tRNA, snoRNA piRNA, Rfam β€’ exRNA samples typically much noisier β€’ cascade of read-alignment steps mitigates contamination 6
.fq.gz.fq .sra sequenced read (50nt) smallRNA (~20-30nt) adapter (~20-30nt) AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC optional: map to spike-in library map to UniVec contaminants read length analysisfastQC map to 
 tRNAs, piRNAs, & gencode map to host genome
 (hg19 hg38 mm10) sRNABench map to 
 endogenous miRNAs map to 45S, 5S, & mitochondrial rRNAs unmapped reads unmapped reads unmapped reads preprocessing filtering QC input endogenous
 alignment unmapped reads map to 
 exogenous miRNAs exogenous
 alignment map to all genomes in ensembl & NCBI β€’ automatic pre- processing and QC of sequence reads β€’ absolute quantitation by quantification of exogenous spike-in sequences β€’ explicit rRNA filtering & QC β€’ quantify many different smallRNA types β€’ choice of 3 end-points exceRpt unmapped reads unmapped reads standard formats remove adapters
 + primers for reads >15nt
 
 map using bowtie2 hierarchical mapping using bowtie choreographed by sRNABench map using STAR read-quality filter 1 2 3 1 2 3
effect of filtering β€’ what happens to alignments when we individually remove upstream libraries? β€’ gencode appears to be a subset of the repetitive elements β€’ quality filter, UniVec, and repetitive elements have the largest impact 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 0.0% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 4.2% 3.8% 0.0% 3.8% 3.8% 3.8% 3.8% 3.8% 3.8% 88.8% 85.8% 83.3% 79.5% 79.5% 79.5% 79.5% 79.5% 79.5% 65.2% 62.3% 62.5% 58.8% 58.8% 58.8% 58.8% 58.8% 58.8% 52.3% 47.9% 47.9% 48.5% 48.5% 48.5% 48.5% 48.5% 48.5% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.4% 0.4% 0.4% 0.0% 0.4% 0.4% 0.4% 0.4% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 4.6% 4.8% 4.5% 3.9% 3.8% 0.0% 3.7% 3.7% 3.7% 3.9% 5.8% 4.6% 3.5% 3.5% 0.0% 3.4% 3.4% 3.4% 1.5% 1.8% 2.3% 1.3% 1.2% 4.7% 0.0% 1.2% 1.2% 1.2% 1.2% 1.3% 1.0% 1.0% 4.2% 0.0% 1.0% 1.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.7% 19.4% 18.4% 18.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.6% 19.4% 18.4% 18.4% 1.4% 2.0% 1.3% 1.3% 1.3% 1.4% 1.7% 1.3% 1.3%exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter successfully_clipped input SRR822433_1_noQualFilter SRR822433_2_noUniVec SRR822433_3_noRiboRNA SRR822433_4_notRNA SRR822433_5_nopiRNA SRR822433_6_noGencode SRR822433_7_noRE SRR822433_8_noCirc SRR822433_FullPipeline Sample Stage Read pancreas placenta skin temporal neocortex 0.00 0.25 0.50 0.75 1.00 ReadFraction
effect of filtering β€’ what happens to alignments when we individually remove upstream libraries? β€’ gencode appears to be a subset of the repetitive elements β€’ quality filter, UniVec, and repetitive elements have the largest impact 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 0.0% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 4.2% 3.8% 0.0% 3.8% 3.8% 3.8% 3.8% 3.8% 3.8% 88.8% 85.8% 83.3% 79.5% 79.5% 79.5% 79.5% 79.5% 79.5% 65.2% 62.3% 62.5% 58.8% 58.8% 58.8% 58.8% 58.8% 58.8% 52.3% 47.9% 47.9% 48.5% 48.5% 48.5% 48.5% 48.5% 48.5% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.4% 0.4% 0.4% 0.0% 0.4% 0.4% 0.4% 0.4% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 4.6% 4.8% 4.5% 3.9% 3.8% 0.0% 3.7% 3.7% 3.7% 3.9% 5.8% 4.6% 3.5% 3.5% 0.0% 3.4% 3.4% 3.4% 1.5% 1.8% 2.3% 1.3% 1.2% 4.7% 0.0% 1.2% 1.2% 1.2% 1.2% 1.3% 1.0% 1.0% 4.2% 0.0% 1.0% 1.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.7% 19.4% 18.4% 18.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.6% 19.4% 18.4% 18.4% 1.4% 2.0% 1.3% 1.3% 1.3% 1.4% 1.7% 1.3% 1.3%exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter successfully_clipped input SRR822433_1_noQualFilter SRR822433_2_noUniVec SRR822433_3_noRiboRNA SRR822433_4_notRNA SRR822433_5_nopiRNA SRR822433_6_noGencode SRR822433_7_noRE SRR822433_8_noCirc SRR822433_FullPipeline Sample Stage Read pancreas placenta skin temporal neocortex 0.00 0.25 0.50 0.75 1.00 ReadFraction
β€’ what happens if we run the smallRNA libraries through the pipeline as if they were reads? β€’ miRNAs include all species, but most are highly conserved β€’ 1/20 piRNAs are rRNA mappability 100.0% 100.0% 100.0% 100.0% 100 100.0% 100.0% 100.0% 100.0% 100 0.0% 0.0% 0.0% 0.0% 0.0 0.3% 0.3% 0.3% 0.0% 0.0 NA% NA% NA% NA% NA 0.0% 0.0% 0.0% 0.0% 0.0 0.1% 0.2% 0.1% 5.0% 0.0 99.6% 99.5% 99.6% 95.0% 100 75.5% 75.5% 73.9% 94.9% 84. 32.5% 32.4% 27.7% 0.0% 0.0 0.6% 0.6% 0.3% 0.0% 0.0 0.0% 0.0% 0.0% 0.2% 84. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 94.6% 0.0 0.0% 0.0% 0.0% 0.1% 0.0 7.8% 7.7% 8.3% 0.0% 12. 4.9% 4.9% 5.8% 0.0% 3.2 8.1% 7.9% 7.5% 0.0% 0.5 8.9% 9.1% 7.4% 0.0% 25. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 0.0% 0.0 18.7% 18.7% 20.8% 0.0% 0.0 13.6% 13.6% 14.9% 0.0% 0.0 1.4% 1.4% 1.4% 0.0% 0.0 1.2% 1.2% 1.4% 0.0% 0.0exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter clipped input miRBase21_hg19_FullPipeline miRBase21_hg38_FullPipeline miRBase21_mm10_FullPipeline piRNAs_hg38_FullPipeline Sample Stage
100.0% 100.0% 100.0% 100.0% 100 100.0% 100.0% 100.0% 100.0% 100 0.0% 0.0% 0.0% 0.0% 0.0 0.3% 0.3% 0.3% 0.0% 0.0 NA% NA% NA% NA% NA 0.0% 0.0% 0.0% 0.0% 0.0 0.1% 0.2% 0.1% 5.0% 0.0 99.6% 99.5% 99.6% 95.0% 100 75.5% 75.5% 73.9% 94.9% 84. 32.5% 32.4% 27.7% 0.0% 0.0 0.6% 0.6% 0.3% 0.0% 0.0 0.0% 0.0% 0.0% 0.2% 84. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 94.6% 0.0 0.0% 0.0% 0.0% 0.1% 0.0 7.8% 7.7% 8.3% 0.0% 12. 4.9% 4.9% 5.8% 0.0% 3.2 8.1% 7.9% 7.5% 0.0% 0.5 8.9% 9.1% 7.4% 0.0% 25. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 0.0% 0.0 18.7% 18.7% 20.8% 0.0% 0.0 13.6% 13.6% 14.9% 0.0% 0.0 1.4% 1.4% 1.4% 0.0% 0.0 1.2% 1.2% 1.4% 0.0% 0.0exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter clipped input miRBase21_hg19_FullPipeline miRBase21_hg38_FullPipeline miRBase21_mm10_FullPipeline piRNAs_hg38_FullPipeline Sample Stage mappability β€’ what happens if we run the smallRNA libraries through the pipeline as if they were reads? β€’ miRNAs include all species, but most are highly conserved β€’ 1/20 piRNAs are rRNA
total reads by biotype 12 ● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● IG_D_gene IG_J_gene IG_J_pseudogene TR_J_pseudogene circularRNA transcribed_unitary_pseudogene translated_unprocessed_pseudogene TR_J_gene IG_C_pseudogene TR_C_gene translated_processed_pseudogene TR_V_pseudogene IG_C_gene IG_V_pseudogene pseudogene IG_V_gene 3prime_overlapping_ncrna non_coding non_stop_decay polymorphic_pseudogene Mt_rRNA TR_V_gene rRNA transcribed_processed_pseudogene unitary_pseudogene known_ncrna tRNA transcribed_unprocessed_pseudogene exogenous_miRNA sense_overlapping unprocessed_pseudogene TEC sense_intronic Mt_tRNA processed_pseudogene misc_RNA snRNA snoRNA nonsense_mediated_decay exogenous_genomes antisense processed_transcript piRNA retained_intron lincRNA RE protein_coding miRNA 1eβˆ’02 1eβˆ’01 1e+00 1e+01 1e+02 1e+03 1e+04 1e+05 1e+06 1e+07 readCount biotype
13 ●● ● ● ● ● ● ● ● ● ● ● ●● known_ncrna tRNA ribed_unprocessed_pseudogene exogenous_miRNA sense_overlapping unprocessed_pseudogene TEC sense_intronic Mt_tRNA processed_pseudogene misc_RNA snRNA snoRNA nonsense_mediated_decay exogenous_genomes antisense processed_transcript piRNA retained_intron lincRNA RE protein_coding miRNA ● ●● ● ● ● ●● ● ● ● ● ● e+00 1e+01 1e+02 1e+03 1e+04 1e+05 1e+06 1e+07 readCount β€’ large contribution from miRNA and mRNA β€’ also some signal from exogenous sequences total reads by biotype
exceRpt @ Genboree β€’ extremely simple to use (1 input, 1 output) β€’ can process multiple samples in parallel β€’ very customisable (choice of smRNA libs, calibrators, etc) 14 input output
β€’ exceRpt output from multiple samples can be combined, compared, and tested for differential abundance β€’ features:
 - intuitive QC visualisations
 - filtering & normalisation
 - differential expression β€’ processed expression data 
 are excel, R, matlab, & 
 geneSpring compatible downstream analysis 15 0e+00 1e+07 2e+07 3e+07 4e+07 0 50 100 150 read length (nt) #reads readβˆ’length distributions read length ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● 1eβˆ’03 1e+00 1e+03 1e+06 sample Readspermillion(RPM) miRNA abundance distributions (RPM) miRNARPM ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● 1eβˆ’02 1e+01 1e+04 1e+07 sample Readcount miRNA abundance distributions (raw counts) miRNAcount
agenda 1. exceRpt smallRNA-seq analysis suite
 - filtering, QC, and alignment
 - quantification & normalisation
 - visualisations
 - differential expression (soon) 2. use-case involving 345 samples 3. downstream analysis
 - miRNA-mRNA targets from miRTarBase
 - network analysis
 - enrichment analysis
 - pathway analysis
17 Downstream Analyses: Overview PathwaysGenboree Focused Β  set Β of Β  Β  interactions All Β known Β  interactions PD Β vs Β PDD Β diff Β exp Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
18 1. Query Interactions: Genboree
19 2. Network Analysis: Cytoscape Upregulated Β and Β  downregulated Β in Β PD Β  with Β dementia Β relative Β  to Β PD Β alone. Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
20 2. Network Analysis: Cytoscape
55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 21 2. Network Analysis: Cytoscape colour or filter miRNA-mRNA connections based on type of experimental support e.g. luciferase, CLIP, etc.
55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 22 3. Enrichment Analysis: BiNGO
23 4. Pathway Analysis: WikiPathways wikipathways.org
55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 24 Downstream Analyses: Summary PathwaysGenboree Focused Β  set Β of Β  Β  interactions All Β known Β  interactions PD Β vs Β PDD Β diff Β exp Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
summary β€’ trivial to use exceRpt thanks to:
 - automated, linear workflow
 - GUI, processing, & data/result storage on Genboree β€’ average runtime for high quality RNA-seq sample:
 - 1hr for endogenous-only analysis
 - 2hr 30mins for endogenous+exogenous alignments β€’ downstream network analysis tools directly integrated in genboree and automatically accept exceRpt results
 
 25 exRNA.orggenboree.org
Acknowledgements Data Integration and Analysis Component (DIAC) members of the Data Management Resource and Repository (DMRR) Mark Gerstein Aleks Milosavljevic Joel Rozowsky Matt Roth Sai Lakshmi
 Subramanian William
 Thistlethwaite Alex PicoFabio
 Navarro

Recommended

Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...
Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...
Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...exrna
Β 
exRNA Data Analysis Tools in the Genboree Workbench
exRNA Data Analysis Tools in the Genboree WorkbenchexRNA Data Analysis Tools in the Genboree Workbench
exRNA Data Analysis Tools in the Genboree Workbenchexrna
Β 
Communicating Science For Policy
Communicating Science For PolicyCommunicating Science For Policy
Communicating Science For PolicyRECOUP
Β 
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...Joseph Hughes
Β 
BioSB meeting 2015
BioSB meeting 2015BioSB meeting 2015
BioSB meeting 2015hansjansen9999
Β 
Rnaseq basics ngs_application1
Rnaseq basics ngs_application1Rnaseq basics ngs_application1
Rnaseq basics ngs_application1Yaoyu Wang
Β 
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...JuliΓ‘n Urbano
Β 
160627 giab for festival sv workshop
160627 giab for festival sv workshop160627 giab for festival sv workshop
160627 giab for festival sv workshopGenomeInABottle
Β 

Recommended

Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...
Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...
Refining Gene Ontology to Include Terms and Relationships Relevant to exRNA C...exrna
Β 
exRNA Data Analysis Tools in the Genboree Workbench
exRNA Data Analysis Tools in the Genboree WorkbenchexRNA Data Analysis Tools in the Genboree Workbench
exRNA Data Analysis Tools in the Genboree Workbenchexrna
Β 
Communicating Science For Policy
Communicating Science For PolicyCommunicating Science For Policy
Communicating Science For PolicyRECOUP
Β 
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...Joseph Hughes
Β 
BioSB meeting 2015
BioSB meeting 2015BioSB meeting 2015
BioSB meeting 2015hansjansen9999
Β 
Rnaseq basics ngs_application1
Rnaseq basics ngs_application1Rnaseq basics ngs_application1
Rnaseq basics ngs_application1Yaoyu Wang
Β 
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...
What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Fea...JuliΓ‘n Urbano
Β 
160627 giab for festival sv workshop
160627 giab for festival sv workshop160627 giab for festival sv workshop
160627 giab for festival sv workshopGenomeInABottle
Β 
Introduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seqIntroduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seqTimothy Tickle
Β 
Introduction to bioinformatics
Introduction to bioinformaticsIntroduction to bioinformatics
Introduction to bioinformaticsAndrzej Stefan Czech
Β 
2015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and22015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and2Dan Gaston
Β 
Phd2013 lyamin
Phd2013 lyaminPhd2013 lyamin
Phd2013 lyaminPositive Hack Days
Β 
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88MppsPhd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88MppsAlexander Lyamin
Β 
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 MppsАлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 MppsPositive Hack Days
Β 
ASU
ASUASU
ASUDataWorks Summit/Hadoop Summit
Β 
So you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression AnalysisSo you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression AnalysisUniversity of California, Davis
Β 
2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekinge2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekingeProf. Wim Van Criekinge
Β 
05 costa
05 costa05 costa
05 costafruitbreedomics
Β 
Protein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to PrintingProtein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to PrintingSCHOTT
Β 
PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...Valerie Wood
Β 
Back Up QA - Session 4
Back Up QA - Session 4Back Up QA - Session 4
Back Up QA - Session 4megha G
Β 
Chicago stats talk
Chicago stats talkChicago stats talk
Chicago stats talkAlistair Boettiger
Β 
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...Yandex
Β 
RMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdfRMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdfGounderKirthika1
Β 
Poster-microRNA Quantitation_Keystone 2008
Poster-microRNA Quantitation_Keystone 2008Poster-microRNA Quantitation_Keystone 2008
Poster-microRNA Quantitation_Keystone 2008Christie Hughes, MPH, MT(ASCP)
Β 
20110524zurichngs 1st pub
20110524zurichngs 1st pub20110524zurichngs 1st pub
20110524zurichngs 1st pubsesejun
Β 
Brock peters single tube long fragment read technology
Brock peters single tube long fragment read technologyBrock peters single tube long fragment read technology
Brock peters single tube long fragment read technologyGenomeInABottle
Β 
Lecture6.pptx
Lecture6.pptxLecture6.pptx
Lecture6.pptxgregcaporaso
Β 
Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksSΓ©rgio Sacani
Β 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoSΓ©rgio Sacani
Β 

More Related Content

Similar to exceRpt: A computational exRNA-seq analysis pipeline

Introduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seqIntroduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seqTimothy Tickle
Β 
Introduction to bioinformatics
Introduction to bioinformaticsIntroduction to bioinformatics
Introduction to bioinformaticsAndrzej Stefan Czech
Β 
2015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and22015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and2Dan Gaston
Β 
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88MppsPhd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88MppsAlexander Lyamin
Β 
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 MppsАлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 MppsPositive Hack Days
Β 
So you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression AnalysisSo you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression AnalysisUniversity of California, Davis
Β 
2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekinge2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekingeProf. Wim Van Criekinge
Β 
Protein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to PrintingProtein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to PrintingSCHOTT
Β 
PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...Valerie Wood
Β 
Back Up QA - Session 4
Back Up QA - Session 4Back Up QA - Session 4
Back Up QA - Session 4megha G
Β 
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...Yandex
Β 
RMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdfRMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdfGounderKirthika1
Β 
20110524zurichngs 1st pub
20110524zurichngs 1st pub20110524zurichngs 1st pub
20110524zurichngs 1st pubsesejun
Β 
Brock peters single tube long fragment read technology
Brock peters single tube long fragment read technologyBrock peters single tube long fragment read technology
Brock peters single tube long fragment read technologyGenomeInABottle
Β 
Lecture6.pptx
Lecture6.pptxLecture6.pptx
Lecture6.pptxgregcaporaso
Β 

Similar to exceRpt: A computational exRNA-seq analysis pipeline (20)

Introduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seqIntroduction to Single-cell RNA-seq
Introduction to Single-cell RNA-seq
Β 
Introduction to bioinformatics
Introduction to bioinformaticsIntroduction to bioinformatics
Introduction to bioinformatics
Β 
2015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and22015 Bioc4010 lecture1and2
2015 Bioc4010 lecture1and2
Β 
Phd2013 lyamin
Phd2013 lyaminPhd2013 lyamin
Phd2013 lyamin
Β 
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88MppsPhd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Phd2013 lyamin Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64, Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ 14.88Mpps
Β 
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 MppsАлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
АлСксандр Лямин. HOWTO. Высокий ΠΏΠ°ΠΊΠ΅Ρ‚Ρ€Π΅ΠΉΡ‚ Π½Π° x86-64: Π±Π΅Ρ€Π΅ΠΌ ΠΏΠ»Π°Π½ΠΊΡƒ Π² 14,88 Mpps
Β 
ASU
ASUASU
ASU
Β 
So you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression AnalysisSo you want to do a: RNAseq experiment, Differential Gene Expression Analysis
So you want to do a: RNAseq experiment, Differential Gene Expression Analysis
Β 
2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekinge2015 bioinformatics score_matrices_wim_vancriekinge
2015 bioinformatics score_matrices_wim_vancriekinge
Β 
05 costa
05 costa05 costa
05 costa
Β 
Protein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to PrintingProtein Microarrays: Approaches to Printing
Protein Microarrays: Approaches to Printing
Β 
PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...PomBase conventions for improving annotation depth, breadth, consistency and ...
PomBase conventions for improving annotation depth, breadth, consistency and ...
Β 
Back Up QA - Session 4
Back Up QA - Session 4Back Up QA - Session 4
Back Up QA - Session 4
Β 
Chicago stats talk
Chicago stats talkChicago stats talk
Chicago stats talk
Β 
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
ΠœΠΈΡ…Π°ΠΈΠ» Π•ΠΏΠΈΡ…ΠΈΠ½ β€” Π‘ΡƒΡ‚Ρ‹Π»ΠΎΡ‡Π½ΠΎΠ΅ Π³ΠΎΡ€Π»Ρ‹ΡˆΠΊΠΎ. ΠΊΠ°ΠΊ Π½Π°ΠΉΡ‚ΠΈ ΡƒΠ·ΠΊΠΈΠ΅ мСста сСрвиса ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡ΠΈΡ‚...
Β 
RMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdfRMR-Nirma-NGS-Heena.pdf
RMR-Nirma-NGS-Heena.pdf
Β 
Poster-microRNA Quantitation_Keystone 2008
Poster-microRNA Quantitation_Keystone 2008Poster-microRNA Quantitation_Keystone 2008
Poster-microRNA Quantitation_Keystone 2008
Β 
20110524zurichngs 1st pub
20110524zurichngs 1st pub20110524zurichngs 1st pub
20110524zurichngs 1st pub
Β 
Brock peters single tube long fragment read technology
Brock peters single tube long fragment read technologyBrock peters single tube long fragment read technology
Brock peters single tube long fragment read technology
Β 
Lecture6.pptx
Lecture6.pptxLecture6.pptx
Lecture6.pptx
Β 

Recently uploaded

Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksSΓ©rgio Sacani
Β 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoSΓ©rgio Sacani
Β 
Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...RohitNehra6
Β 
Orientation, design and principles of polyhouse
Orientation, design and principles of polyhouseOrientation, design and principles of polyhouse
Orientation, design and principles of polyhousejana861314
Β 
Zoology 4th semester series (krishna).pdf
Zoology 4th semester series (krishna).pdfZoology 4th semester series (krishna).pdf
Zoology 4th semester series (krishna).pdfSumit Kumar yadav
Β 
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCR
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCRStunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCR
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCRDelhi Call girls
Β 
Presentation Vikram Lander by Vedansh Gupta.pptx
Presentation Vikram Lander by Vedansh Gupta.pptxPresentation Vikram Lander by Vedansh Gupta.pptx
Presentation Vikram Lander by Vedansh Gupta.pptxgindu3009
Β 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)Areesha Ahmad
Β 
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxkessiyaTpeter
Β 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPirithiRaju
Β 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PPRINCE C P
Β 
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCE
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCESTERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCE
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCEPRINCE C P
Β 
GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)Areesha Ahmad
Β 
Biological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfBiological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfmuntazimhurra
Β 
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINChromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINsankalpkumarsahoo174
Β 
Green chemistry and Sustainable development.pptx
Green chemistry and Sustainable development.pptxGreen chemistry and Sustainable development.pptx
Green chemistry and Sustainable development.pptxRajatChauhan518211
Β 
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...SΓ©rgio Sacani
Β 
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...SΓ©rgio Sacani
Β 
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...anilsa9823
Β 

Recently uploaded (20)

Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disks
Β 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on Io
Β 
Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...
Β 
Orientation, design and principles of polyhouse
Orientation, design and principles of polyhouseOrientation, design and principles of polyhouse
Orientation, design and principles of polyhouse
Β 
Zoology 4th semester series (krishna).pdf
Zoology 4th semester series (krishna).pdfZoology 4th semester series (krishna).pdf
Zoology 4th semester series (krishna).pdf
Β 
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCR
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCRStunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCR
Stunning βž₯8448380779β–» Call Girls In Panchshil Enclave Delhi NCR
Β 
Presentation Vikram Lander by Vedansh Gupta.pptx
Presentation Vikram Lander by Vedansh Gupta.pptxPresentation Vikram Lander by Vedansh Gupta.pptx
Presentation Vikram Lander by Vedansh Gupta.pptx
Β 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)
Β 
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
Β 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Β 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C P
Β 
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCE
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCESTERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCE
STERILITY TESTING OF PHARMACEUTICALS ppt by DR.C.P.PRINCE
Β 
GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)
Β 
Biological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfBiological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdf
Β 
CELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdfCELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdf
Β 
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINChromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Β 
Green chemistry and Sustainable development.pptx
Green chemistry and Sustainable development.pptxGreen chemistry and Sustainable development.pptx
Green chemistry and Sustainable development.pptx
Β 
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...
Discovery of an Accretion Streamer and a Slow Wide-angle Outflow around FUOri...
Β 
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
Β 
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow πŸ’‹ Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Β 

exceRpt: A computational exRNA-seq analysis pipeline

  • 1. exceRpt
 a computational exRNA-seq analysis pipeline Rob Kitchen Yale 2015 - 04 - 23 1
  • 2. Extracellular RNA Communication NIH Common Fund > data management
 > reference profiles
 > exRNA biogenesis
 > exRNA biomarker
 > exRNA therapy currently 30 funded projects exRNA.org 2
  • 3. exRNA-seq analysis software 3 small RNA-seq
 analysis toolkit exceRpt quantification (RPM) of
 miRNA, tRNA, piRNA, 
 snoRNA, circularRNA,
 & long transcript fragments long RNA-seq
 analysis toolkit RSEQTools quantification (RPKM) of 
 [multi-exon] transcripts
  • 4. agenda 1. exceRpt smallRNA-seq analysis suite
 - filtering, QC, and alignment
 - quantification & normalisation
 - visualisations
 - differential expression (soon) 2. use-case involving 345 samples 3. downstream analysis
 - miRNA-mRNA targets from miRTarBase
 - network analysis
 - enrichment analysis
 - pathway analysis
  • 5. exRNA-seq analysis software 5 small RNA-seq
 analysis toolkit exceRpt quantification (RPM) of
 miRNA, tRNA, piRNA, 
 snoRNA, circularRNA,
 & long transcript fragments long RNA-seq
 analysis toolkit RSEQTools quantification (RPKM) of 
 [multi-exon] transcripts
  • 6. for a typical cellular sample… 99% pass adapter removal 1% map to calibrator oligos 1% fail adapter removal 3% rRNA contamination 88% map to host genome 75% map to known miRNAs 7% taken on to exogenous smRNA mapping 12% novel smRNAs? contaminants? all smRNA-seq reads 1. remove 3’adapters 2. map to calibrator oligos 3. map to 45S & 5S rRNA 4. map to host genome smRNA-seq analysis workflow 1% tRNA, snoRNA piRNA, Rfam β€’ exRNA samples typically much noisier β€’ cascade of read-alignment steps mitigates contamination 6
  • 7. .fq.gz.fq .sra sequenced read (50nt) smallRNA (~20-30nt) adapter (~20-30nt) AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC optional: map to spike-in library map to UniVec contaminants read length analysisfastQC map to 
 tRNAs, piRNAs, & gencode map to host genome
 (hg19 hg38 mm10) sRNABench map to 
 endogenous miRNAs map to 45S, 5S, & mitochondrial rRNAs unmapped reads unmapped reads unmapped reads preprocessing filtering QC input endogenous
 alignment unmapped reads map to 
 exogenous miRNAs exogenous
 alignment map to all genomes in ensembl & NCBI β€’ automatic pre- processing and QC of sequence reads β€’ absolute quantitation by quantification of exogenous spike-in sequences β€’ explicit rRNA filtering & QC β€’ quantify many different smallRNA types β€’ choice of 3 end-points exceRpt unmapped reads unmapped reads standard formats remove adapters
 + primers for reads >15nt
 
 map using bowtie2 hierarchical mapping using bowtie choreographed by sRNABench map using STAR read-quality filter 1 2 3 1 2 3
  • 8. effect of filtering β€’ what happens to alignments when we individually remove upstream libraries? β€’ gencode appears to be a subset of the repetitive elements β€’ quality filter, UniVec, and repetitive elements have the largest impact 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 0.0% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 4.2% 3.8% 0.0% 3.8% 3.8% 3.8% 3.8% 3.8% 3.8% 88.8% 85.8% 83.3% 79.5% 79.5% 79.5% 79.5% 79.5% 79.5% 65.2% 62.3% 62.5% 58.8% 58.8% 58.8% 58.8% 58.8% 58.8% 52.3% 47.9% 47.9% 48.5% 48.5% 48.5% 48.5% 48.5% 48.5% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.4% 0.4% 0.4% 0.0% 0.4% 0.4% 0.4% 0.4% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 4.6% 4.8% 4.5% 3.9% 3.8% 0.0% 3.7% 3.7% 3.7% 3.9% 5.8% 4.6% 3.5% 3.5% 0.0% 3.4% 3.4% 3.4% 1.5% 1.8% 2.3% 1.3% 1.2% 4.7% 0.0% 1.2% 1.2% 1.2% 1.2% 1.3% 1.0% 1.0% 4.2% 0.0% 1.0% 1.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.7% 19.4% 18.4% 18.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.6% 19.4% 18.4% 18.4% 1.4% 2.0% 1.3% 1.3% 1.3% 1.4% 1.7% 1.3% 1.3%exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter successfully_clipped input SRR822433_1_noQualFilter SRR822433_2_noUniVec SRR822433_3_noRiboRNA SRR822433_4_notRNA SRR822433_5_nopiRNA SRR822433_6_noGencode SRR822433_7_noRE SRR822433_8_noCirc SRR822433_FullPipeline Sample Stage Read pancreas placenta skin temporal neocortex 0.00 0.25 0.50 0.75 1.00 ReadFraction
  • 9. effect of filtering β€’ what happens to alignments when we individually remove upstream libraries? β€’ gencode appears to be a subset of the repetitive elements β€’ quality filter, UniVec, and repetitive elements have the largest impact 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 10.3% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 0.0% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 6.4% 4.2% 3.8% 0.0% 3.8% 3.8% 3.8% 3.8% 3.8% 3.8% 88.8% 85.8% 83.3% 79.5% 79.5% 79.5% 79.5% 79.5% 79.5% 65.2% 62.3% 62.5% 58.8% 58.8% 58.8% 58.8% 58.8% 58.8% 52.3% 47.9% 47.9% 48.5% 48.5% 48.5% 48.5% 48.5% 48.5% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.4% 0.4% 0.4% 0.0% 0.4% 0.4% 0.4% 0.4% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 4.6% 4.8% 4.5% 3.9% 3.8% 0.0% 3.7% 3.7% 3.7% 3.9% 5.8% 4.6% 3.5% 3.5% 0.0% 3.4% 3.4% 3.4% 1.5% 1.8% 2.3% 1.3% 1.2% 4.7% 0.0% 1.2% 1.2% 1.2% 1.2% 1.3% 1.0% 1.0% 4.2% 0.0% 1.0% 1.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.7% 19.4% 18.4% 18.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 19.8% 18.4% 18.4% 18.4% 18.6% 19.4% 18.4% 18.4% 1.4% 2.0% 1.3% 1.3% 1.3% 1.4% 1.7% 1.3% 1.3%exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter successfully_clipped input SRR822433_1_noQualFilter SRR822433_2_noUniVec SRR822433_3_noRiboRNA SRR822433_4_notRNA SRR822433_5_nopiRNA SRR822433_6_noGencode SRR822433_7_noRE SRR822433_8_noCirc SRR822433_FullPipeline Sample Stage Read pancreas placenta skin temporal neocortex 0.00 0.25 0.50 0.75 1.00 ReadFraction
  • 10. β€’ what happens if we run the smallRNA libraries through the pipeline as if they were reads? β€’ miRNAs include all species, but most are highly conserved β€’ 1/20 piRNAs are rRNA mappability 100.0% 100.0% 100.0% 100.0% 100 100.0% 100.0% 100.0% 100.0% 100 0.0% 0.0% 0.0% 0.0% 0.0 0.3% 0.3% 0.3% 0.0% 0.0 NA% NA% NA% NA% NA 0.0% 0.0% 0.0% 0.0% 0.0 0.1% 0.2% 0.1% 5.0% 0.0 99.6% 99.5% 99.6% 95.0% 100 75.5% 75.5% 73.9% 94.9% 84. 32.5% 32.4% 27.7% 0.0% 0.0 0.6% 0.6% 0.3% 0.0% 0.0 0.0% 0.0% 0.0% 0.2% 84. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 94.6% 0.0 0.0% 0.0% 0.0% 0.1% 0.0 7.8% 7.7% 8.3% 0.0% 12. 4.9% 4.9% 5.8% 0.0% 3.2 8.1% 7.9% 7.5% 0.0% 0.5 8.9% 9.1% 7.4% 0.0% 25. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 0.0% 0.0 18.7% 18.7% 20.8% 0.0% 0.0 13.6% 13.6% 14.9% 0.0% 0.0 1.4% 1.4% 1.4% 0.0% 0.0 1.2% 1.2% 1.4% 0.0% 0.0exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter clipped input miRBase21_hg19_FullPipeline miRBase21_hg38_FullPipeline miRBase21_mm10_FullPipeline piRNAs_hg38_FullPipeline Sample Stage
  • 11. 100.0% 100.0% 100.0% 100.0% 100 100.0% 100.0% 100.0% 100.0% 100 0.0% 0.0% 0.0% 0.0% 0.0 0.3% 0.3% 0.3% 0.0% 0.0 NA% NA% NA% NA% NA 0.0% 0.0% 0.0% 0.0% 0.0 0.1% 0.2% 0.1% 5.0% 0.0 99.6% 99.5% 99.6% 95.0% 100 75.5% 75.5% 73.9% 94.9% 84. 32.5% 32.4% 27.7% 0.0% 0.0 0.6% 0.6% 0.3% 0.0% 0.0 0.0% 0.0% 0.0% 0.2% 84. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 94.6% 0.0 0.0% 0.0% 0.0% 0.1% 0.0 7.8% 7.7% 8.3% 0.0% 12. 4.9% 4.9% 5.8% 0.0% 3.2 8.1% 7.9% 7.5% 0.0% 0.5 8.9% 9.1% 7.4% 0.0% 25. 0.0% 0.0% 0.0% 0.0% 0.0 0.0% 0.0% 0.0% 0.0% 0.0 18.7% 18.7% 20.8% 0.0% 0.0 13.6% 13.6% 14.9% 0.0% 0.0 1.4% 1.4% 1.4% 0.0% 0.0 1.2% 1.2% 1.4% 0.0% 0.0exogenous_genomes input_to_exogenous_genomes miRNA_exogenous_sense input_to_miRNA_exogenous circularRNA_antisense circularRNA_sense repetitiveElement_antisense repetitiveElement_sense gencode_antisense gencode_sense piRNA_antisense piRNA_sense tRNA_antisense tRNA_sense miRNA_antisense miRNA_sense genome reads_used_for_alignment rRNA UniVec_contaminants calibrator failed_homopolymer_filter failed_quality_filter clipped input miRBase21_hg19_FullPipeline miRBase21_hg38_FullPipeline miRBase21_mm10_FullPipeline piRNAs_hg38_FullPipeline Sample Stage mappability β€’ what happens if we run the smallRNA libraries through the pipeline as if they were reads? β€’ miRNAs include all species, but most are highly conserved β€’ 1/20 piRNAs are rRNA
  • 12. total reads by biotype 12 ● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● IG_D_gene IG_J_gene IG_J_pseudogene TR_J_pseudogene circularRNA transcribed_unitary_pseudogene translated_unprocessed_pseudogene TR_J_gene IG_C_pseudogene TR_C_gene translated_processed_pseudogene TR_V_pseudogene IG_C_gene IG_V_pseudogene pseudogene IG_V_gene 3prime_overlapping_ncrna non_coding non_stop_decay polymorphic_pseudogene Mt_rRNA TR_V_gene rRNA transcribed_processed_pseudogene unitary_pseudogene known_ncrna tRNA transcribed_unprocessed_pseudogene exogenous_miRNA sense_overlapping unprocessed_pseudogene TEC sense_intronic Mt_tRNA processed_pseudogene misc_RNA snRNA snoRNA nonsense_mediated_decay exogenous_genomes antisense processed_transcript piRNA retained_intron lincRNA RE protein_coding miRNA 1eβˆ’02 1eβˆ’01 1e+00 1e+01 1e+02 1e+03 1e+04 1e+05 1e+06 1e+07 readCount biotype
  • 14. exceRpt @ Genboree β€’ extremely simple to use (1 input, 1 output) β€’ can process multiple samples in parallel β€’ very customisable (choice of smRNA libs, calibrators, etc) 14 input output
  • 15. β€’ exceRpt output from multiple samples can be combined, compared, and tested for differential abundance β€’ features:
 - intuitive QC visualisations
 - filtering & normalisation
 - differential expression β€’ processed expression data 
 are excel, R, matlab, & 
 geneSpring compatible downstream analysis 15 0e+00 1e+07 2e+07 3e+07 4e+07 0 50 100 150 read length (nt) #reads readβˆ’length distributions read length ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● 1eβˆ’03 1e+00 1e+03 1e+06 sample Readspermillion(RPM) miRNA abundance distributions (RPM) miRNARPM ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● 1eβˆ’02 1e+01 1e+04 1e+07 sample Readcount miRNA abundance distributions (raw counts) miRNAcount
  • 16. agenda 1. exceRpt smallRNA-seq analysis suite
 - filtering, QC, and alignment
 - quantification & normalisation
 - visualisations
 - differential expression (soon) 2. use-case involving 345 samples 3. downstream analysis
 - miRNA-mRNA targets from miRTarBase
 - network analysis
 - enrichment analysis
 - pathway analysis
  • 17. 17 Downstream Analyses: Overview PathwaysGenboree Focused Β  set Β of Β  Β  interactions All Β known Β  interactions PD Β vs Β PDD Β diff Β exp Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
  • 19. 19 2. Network Analysis: Cytoscape Upregulated Β and Β  downregulated Β in Β PD Β  with Β dementia Β relative Β  to Β PD Β alone. Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
  • 21. 55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 21 2. Network Analysis: Cytoscape colour or filter miRNA-mRNA connections based on type of experimental support e.g. luciferase, CLIP, etc.
  • 22. 55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 22 3. Enrichment Analysis: BiNGO
  • 23. 23 4. Pathway Analysis: WikiPathways wikipathways.org
  • 24. 55207 6632 8382 11080 1647 23390 3162 5294 23266 4148 11215 23396 64781 80204 163081 3976 10019 55809 221035 54726 57038 5305 9945 26258 57608 5048 5324 4170 65059 285672 5743 4839 1993 6938 595 51727 7431 54332 10160 4211 3720 599596 3308 60436 3491 55591 26523 23670 110044306 7832 80223 56144 23554 65987 10159 3725 4001 57532 22908 1843 57142 23224 6097 51167 4038 54877 3821 23219 23345 3598 57502 248 5459 3785 323 23657 1871 5451 2146 4204 64418 146691 6674 116984 1635 8726 4613 857 7422 1021 3855 4609 9134 7535 4602 6427 1019 6657 7465 898 4233 79923 166968 2099 6651 23405 84612 23054 377677 6856 9972 51809 4193 26071 3205 3298 3690 5054 860 3589 26273 253461 632 6659 3381 55432 5868 23212 27125 10966 7048 1490 7430 4089 472 64864 10672 28962 25843 10983 51715 2033 138151 hsa-miR-101-3p hsa-miR-26b-5p hsa-miR-30c-5p hsa-miR-26a-5p hsa-miR-135a-5p hsa-miR-30a-3p hsa-miR-148b-3p hsa-miR-195-5p hsa-miR-503-5p hsa-miR-34c-5p hsa-miR-34b-3p hsa-miR-34b-5p hsa-miR-18b-5p hsa-miR-30b-5p hsa-miR-374a-5p hsa-miR-211-5p hsa-miR-374b-5p hsa-miR-18a-5p hsa-miR-135b-5p hsa-miR-204-5p 24 Downstream Analyses: Summary PathwaysGenboree Focused Β  set Β of Β  Β  interactions All Β known Β  interactions PD Β vs Β PDD Β diff Β exp Β  Burgos Β K., Β et Β al. Β  Β (2014) Β PLoS Β ONE
  • 25. summary β€’ trivial to use exceRpt thanks to:
 - automated, linear workflow
 - GUI, processing, & data/result storage on Genboree β€’ average runtime for high quality RNA-seq sample:
 - 1hr for endogenous-only analysis
 - 2hr 30mins for endogenous+exogenous alignments β€’ downstream network analysis tools directly integrated in genboree and automatically accept exceRpt results
 
 25 exRNA.orggenboree.org
  • 26. Acknowledgements Data Integration and Analysis Component (DIAC) members of the Data Management Resource and Repository (DMRR) Mark Gerstein Aleks Milosavljevic Joel Rozowsky Matt Roth Sai Lakshmi
 Subramanian William
 Thistlethwaite Alex PicoFabio
 Navarro