Download as PDF, PPTX
![SAMPLE QUALITY SCORE
Usage)
$ TBI-toolkit-qscore [FASTQ]
Sanger(Phred33) or Illumina 1.8+
0 to 93 using ASCII 33 to 126
Run)
$ cd /KOGO/RNA-seq/inputs
$ TBI-toolkit-qscore S01_1.fq.gz
Sanger(Phred33) or Illumina 1.8+
0 to 93 using ASCII 33 to 126
0:1, 1:”, 2:#, 3:$, 4:%, 5:&, ......](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-50-320.jpg)
![REFERENCE INDEX
Index for bowtie2 mapper
Usage)
$ bowtie2-build [options] <reference_in> <bt2_base>
Run)
$ cd /KOGO/RNA-seq/ref
$ bowtie2-build chr.fa chr.fa
$ ls
chr.fa.1.bt2 chr.fa.2.bt2 ......
Fasta index
Usage)
$ samtools faidx <ref.fasta>
Run)
$ cd /KOGO/RNA-seq/ref
$ samtools faidx chr.fa
$ ls
chr.fa.fai](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-51-320.jpg)
![MASK GENESET
...... We recommend including any annotated rRNA, mitochondrial transcripts other
abundant transcripts you wish to ignore in your analysis in this file. Due to variable
efficiency of mRNA enrichment methods and rRNA depletion kits, masking these
transcripts often improves the overall robustness of transcript abundance estimates.
cufflinks manuals (http://cufflinks.cbcb.umd.edu/manual.html)
Usage)
$ TBI-toolkit-gtf_selector [IN GTF] [OUT GTF] [Source 1] [Source 2] ......
Run)
$ cd /KOGO/RNA-seq/ref
$ TBI-toolkit-gtf_selector ens.gtf mask.gtf tRNA rRNA Mt_tRNA Mt_rRNA](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-52-320.jpg)
![FASTQC
Usages)
$ fastqc seqfile1 seqfile2 .. seqfileN
$ fastqc [-o output dir] [--(no)extract] [-f fastq|bam|sam] [-c contaminant file] seqfile1 .. seqfileN
Arguments
-f format bam,sam,bam_mapped,sam_mapped and fastq
-t threads
Run)
$ cd /KOGO/RNA-seq/inputs
$ fastqc -f fastq -t 2 S01_1.fq.gz S01_2.fq.gz
Output)
$ firefox R01_1.fq_fastqc/fastqc_report.html
$ firefox R01_2.fq_fastqc/fastqc_report.html](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-57-320.jpg)
![FILTERING
Usages)
$ TBI-toolkit filter [option*] seqfile_1 seqfile_2 output_1 output_2
Option)
-n N_ratio
-a integer : Average QV of read
-m NT_ratio < QV
Run)
$ cd /KOGO/RNA-seq/inputs
$ TBI-toolkit-fq_filter -n 0.1 -m 0.4 -a 20 S01_1.fq.gz S01_2.fq.gz S01_Q20_1.fq.gz S01_Q20_2.fq.gz
$ ls
S01_Q20_1.fq.gz S01_Q20_2.fq.gz S01_Q20.log S01_Q20.err
$ cat S01_Q20.log
$ less S01_Q20.err](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-61-320.jpg)
![USAGE
Usage
$ tophat [options] <bowtie_index_base> <reads1_1> <reads1_2>
Option Value Description
-o/--output-dir string The default is "./tophat_out".
-p/--num-threads int Use this many threads to align reads. The default is 1.
-r/--mate-inner-dist int This is the expected (mean) inner distance between mate pairs.The default is 50bp
The standard deviation for the distribution on inner distances between mate pairs.
--mate-std-dev int
The default is 20bp.
fr-unstranded fr-unstranded : Standard Illumina
--library-type fr-firststrand fr-firststrand : dUTP, NSR, NNSR
fr-secondstrand fr-secondstrand : Ligation, Standard Solid
--solexa-quals - Use the Solexa scale for quality values in FASTQ files.
--solexa1.3-quals - Phred64/Illumina 1.3~1.5
-G/--GTF Geneset Geneset (GTF 2.2 or GFF3 formatted file)
--rg-id string Read group ID
--rg-sample string Sample ID](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-66-320.jpg)
![USAGE
$ cufflinks [options] <aligned_reads.(sam/bam)>
Option Value Description
Sets the name of the directory in which Cufflinks will write all of its output.
-o/--output-dir String
The default is "./".
Quantification
-p/--num-threads int Use this many threads to align reads. The default is 1.
Use the supplied reference annotation (a GFF file) to estimate isoform
-G/--GTF geneset
expression. It will not assemble novel transcripts. Novel Isoforms
Use the supplied reference annotation (GFF) to guide RABT assembly.
-g/--GTF-guide geneset Output will include all reference transcripts as well as any novel genes and
isoforms that are assembled.
Improving
Ignore all reads that could have come from transcripts in this GTF file. We
-M/--mask-file mask geneset accuracy
recommend including any annotated rRNA, mitochondrial transcripts other
abundant transcripts you wish to ignore in your analysis in this file.
fr-unstranded fr-unstranded : Standard Illumina
--library-type fr-firststrand fr-firststrand : dUTP, NSR, NNSR /
fr-secondstrand fr-secondstrand : Ligation, Standard Solid](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-83-320.jpg)
![SIMPLE STATISTICS
$ cd /KOGO/RNA-seq/outputs/S01
# Check higest expressed genes
$ sort -r -g -k 10 genes.fpkm_tracking | head -n 30
# Select FPKM S
$ cut -f 1,10 genes.fpkm_traking > gene_fpkm_s
#
$R
> data <- read.table(“gene_fpkm_s”, header=TRUE)
> fpkm_s <- as.numeric(data[,2])
>
> mean(fpkm_s)
> sd(fpkm_s)
>
> fpkm_s.log10 <- log(fpkm_s+1,10)
> bin_seq = seq(min(fpkm_s.log10-0.1),max(fpkm_s.log10+0.1),by=0.1)
> hist(fpkm_s.log10, breaks=bin_seq, xlab=‘log10(x+1)’, ylab=‘Number of genes’, axes=TRUE)
>
> boxplot(fpkm_s.log10)](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-91-320.jpg)
![USAGE
$ cuffmerge [options] <assembly_GTF_list.txt>
Option Value Description
-o <outprefix> Write the summary stats into the text output file <outprefix>(instead of stdout)
An optional "reference" annotation GTF. The input assemblies are merged together with
-g/--ref-gtf geneset
the reference GTF and included in the final output.
-p/--num-threads <int> Use this many threads to align reads. The default is 1.
This argument should point to the genomic DNA sequences for the reference. If a
directory, it should contain one fasta file per contig. If a multifasta file, all contigs should
be present. The merge script will pass this option to cuffcompare, which will use the
<seq_dir>/
-s/--ref-sequence sequences to assist in classifying transfrags and excluding artifacts (e.g. repeats). For
<seq_fasta>
example, Cufflinks transcripts consisting mostly of lower-case bases are classified as
repeats. Note that <seq_dir> must contain one fasta file per reference chromosome,
and each file must be named after the chromosome, and have a .fa or .fasta extension.](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-95-320.jpg)
![CUFFDIFF
• Use to find significant changes in transcript expression,
splicing, and promoter use.
Usage)
$ cuffdiff [options]* <transcripts.gtf> <sample1_replicate1.sam[,...,sample1_replicateM]>
<sample2_replicate1.sam[,...,sample2_replicateM.sam]>
Option Value Description
-o / Sets the name of the directory in which Cuffdiff will write all
<string>
--output-dir of its output. The default is "./".
-L / Specify a label for each sample, which will be included in
<label1,label2,...,labelN>
--labels various output files produced by Cuffdiff.
-p /
<int> Use this many threads to align reads. The default is 1.
--num-threads](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-104-320.jpg)
![OUTPUT
Type Files Description
genes.fpkm_tracking Gene [FPKMs, counts, read group tracking]. Tracks the summed
Genes genes.count_tracking [FPKMs, counts, read group tracking] of transcripts sharing each
genes.read_group_tracking gene_id
isoforms.fpkm_tracking
Isoforms isoforms.count_tracking Transcript [FPKMs, counts, read group tracking]
isoforms.read_group_tracking
cds.fpkm_tracking Coding sequence [FPKMs, counts, read group tracking]. Tracks
CDS cds.count_tracking the summed [FPKMs, counts, read group tracking] of transcripts
cds.read_group_tracking sharing each p_id, independent of tss_id
tss_groups.fpkm_tracking Primary transcript [FPKMs, counts, read group tracking]. Tracks
Primary
tss_groups.count_tracking the summed [FPKMs, counts, read group tracking] of transcripts
Transcripts
tss_groups.read_group_tracking sharing each tss_id](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-106-320.jpg)
![HTSEQ-COUNT
If you have paired-end data, you have to sort the SAM file by read name first
Usage)
$ htseq-count [options] <sam_file> [gff_file, ensembl gtf]
Options)
-m [union,intersection-strict,intersection-nonempty]
-s.--stranded=<yes, no, or reverse>
whether the data is from a strand-specific assay (default: yes)
Run)
$ cd /KOGO/RNA-seq/outputs/S01
$ samtools sort -n accepted_hits.bam accepted_hits.nameSorted
$ samtools view accepted_hits.nameSorted.bam | htseq-count
-m union -s no - ../merged_asm/merged.gtf > accepted_hits.count
$ less accepted_hits.count
# ..... for (S02, S03, S04)](https://image.slidesharecdn.com/kogo2013workshopv3wh-130215022246-phpapp01/85/Kogo-2013-RNA-seq-analysis-113-320.jpg)
Uploaded byJunsu Ko
Kogo 2013 RNA-seq analysis
The document describes an RNA-seq analysis workflow that includes: 1. Preprocessing raw reads including quality control, filtering, and alignment to a reference genome using tools like FastQC, Bowtie2, and TopHat. 2. Assembling transcripts and estimating abundance using Cufflinks and HTseq-count. 3. Identifying differentially expressed genes between samples using DESeq and Cuffdiff. 4. Providing gene annotations and visualizing results using tools like GO, KEGG, and CummeRbund. The workflow follows a typical reference-based analysis approach and uses various open source tools for read mapping, assembly, quantification, and differential expression.
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- 1. RNA-SEQ ANALYSIS 고준수, 송상훈, 김현민 테라젠 바이오 연구소 2012. 2. 5
- 2. CONTENTS • NGS • Mapping • RNA-seq • PCR Duplication • File Forat • Expression • Workflow • DEG • Preparation • Report • Filtering & QC
- 3. TODAY’S KEYWORDS NGS File Format Illumina, Paired-End Fastq, BAM DEG Cuffdiff, DESeq RNA-seq mRNA, Reference-based Expression Design Cufflinks, Cuffmerge Mapping Replicates TopHat
- 4.
- 5.
- 6. NEXT-GENERATION SEQUENCING 2nd Generation 3rd Generation Nat Rev Genet. 2010 Jan;11(1):31-46. doi: 10.1038/ nrg2626. Epub 2009 Dec 8. Sequencing technologies - the next generation. Metzker ML.
- 7. NGS WEAKNESS ANDOVERCOMING Sanger 0.001% Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, Bertoni A, Swerdlow HP, Gu Y. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics. 2012 Jul 24;13:341. Nature Biotechnology 26, 1135 - 1145 (2008), Next-generation DNA sequencing, Shendure J. and Ji H.
- 8. NGS Library Construction Sequencing Raw Reads http://users.ugent.be/~avierstr/nextgen/nextgen.html
- 9. GENERAL NGS ANALYSISPROCESS Speed 3 1 WGS Low depth < NT < High depth Mapping 2 Depth (Coverage) Coverage Shearer AE, Hildebrand MS, Sloan CM, Smith RJ. Deafness in the genomics era. Hear Res. 2011 Dec;282(1-2):1-9. doi: 10.1016/j.heares.2011.10.001. Epub 2011 Oct 8.
- 10. MAPPING TOOLS BWA • Mapper Type for • DNA • RNA WGS • miRNA • bisulphite TopHat for RNA Fonseca NA, Rung J, Brazma A, Marioni JC. Tools for mapping high- throughput sequencing data. Bioinformatics. 2012 Dec 1;28(24):3169-77.
- 11. PCR DUPLICATION http://www.clcbio.com/clc-plugin/duplicate-reads- removal-plugin/ remove
- 12. ILLUMINA PAIRED-END mate-pair inner distnace http://vallandingham.me/ RNA_seq_differential_expr ession.html Quinlan AR, Boland MJ, Leibowitz ML, Shumilina S, Pehrson SM, Baldwin KK, Hall IM. Genome sequencing of mouse induced pluripotent stem cells reveals retroelement stability and infrequent DNA rearrangement during reprogramming. Cell Stem Cell. 2011 Oct 4;9(4):366-73. doi: 10.1016/j.stem.2011.07.018. Haas BJ, Zody MC. fastq_1 Advancing RNA-Seq analysis. Nat Biotechnol. 2010 May;28(5):421-3. doi: 10.1038/nbt0510-421. http://users.ugent.be/~avierstr/ fastq_2 nextgen/nextgen.html
- 13. SUMMARY • NGS platform : Short Reads, Depth, Coverage • Sequencing Protocol • Analysis Protocol • Mapping • PCR duplication • Illumina Paired-end
- 14. TRANSCRIPTOME RNA-SEQ
- 15. TRANSCRIPTOME • The complete set of transcripts in a cell, and their quantity • The key aims of transcriptomics are: • to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs • to determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications • to quantify the changing expression levels of each transcript during development and under different conditions. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/ nrg2484.
- 16. ADVANTAGES OF RNA-SEQ WangZ, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484.
- 17. RNA-SEQ & MICROARRAY WangZ, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/ nrg2484.
- 18. RNA-SEQ • Gene expression level • Relative expression level in sample • Differentially expressed gene • Identification of alternative spliced transcripts • Prediction of novel transcripts • Gene Fusion Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484.
- 19. RNA-SEQ VS. DNA-SEQ RNA-seq DNA-seq Reference-based, WES, Methods de novo assembly WGS re-sequencing, WGS de novo Expression, Differentially Expressed Genes, Goal Novel transcript, SNPs, Indels, SV Alternative splicing form, Gene fusion Measure Mapped Read Count Base accuracy
- 20. OVERVIEW OF ATYPICAL RNA-SEQ
- 21. RNA MAPPING Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol. 2010;11(12):220. Trapnell C, Salzberg SL., How to map billions of short reads onto genomes. Nat Biotechnol. 2009 May;27(5):455-7.
- 22. MAPPER Mapper Data Seq.Plat. Input Output Cit. Cit/years Reference MapSplice RNA I FASTA/Q SAM, BED 50 28.17 Wang et al. (2010) MicroRazerS miRNA N FASTA/Q SAM, TSV 7 2.75 Emde et al. (2010) mrFAST miRNA I FASTA/Q SAM 158 58.34 Alkan et al. (2009) mrsFAST miRNA I,So FASTA/Q SAM 32 18.03 Hach et al. (2010) Passion RNA I,4,Sa,P FASTA/Q BED - - Zhang et al. (2012) PatMaN miRNA N FASTA TSV 38 9.36 Prufer et al. (2008) QPALMA RNA I,4 Specific TSV 75 21.11 De Bona et al. (2008) RNA-Mate RNA So CFASTA BED, Counts 28 10.04 Cloonan et al. (2009) RUM RNA I,4 FASTA/Q SAM,TSV,BED 2 2.36 Grant et al. (2011) SOAPSplice RNA I,4 FASTA/Q TSV 3 3.54 Huang et al. (2011) SpliceMap RNA I FASTA/Q SAM, BED 63 29.80 Au et al. (2010) Supersplat RNA N FASTA TSV 21 9.93 Bryant Jr et al. (2010) TopHat RNA I FASTA/Q, GFF BAM 389 121.04 Trapnell et al. (2009) The number of citations (Cit.) was obtained from Google Scholar on April 14, 2012 Fonseca NA, Rung J, Brazma A, Marioni JC. Tools for mapping high-throughput sequencing data. Bioinformatics. 2012 Dec 1;28(24):3169-77.
- 23. ANALYSIS STRATEGIES Reference-based de novo •Using a reference genome •not use a reference genome Method •The transcriptome assembly can be built upon it • Contamination or sequencing • Not depend on a reference genome artefacts are not a major concern • Not depend on the correct alignment • Very sensitive and can assemble of reads to known splice sites or the Adv. transcripts of low abundance prediction of novel splicing sites • To discover novel transcripts that • Trans-spliced transcripts can be are not present in the current assembled annotation Disadv. • Depends on the quality of the • Computing resources reference genome being used. • Senstive to sequencing errors Depth ~ 10x > 30x Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82. doi: 10.1038/ nrg3068.
- 24. REFERENCE-BASED Martin JA, WangZ. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82. doi: 10.1038/nrg3068.
- 25. REFERENCE-BASED Martin JA, WangZ. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82. doi: 10.1038/nrg3068.
- 26. SUMMARY • Transcriptome • RNA-seq advantages • Process • Analysis strategies • Reference-based method
- 27.
- 28. FILE FORMAT • NGS • Fastq • SAM/BAM • VCF • Reference • Fasta • GTF / GFF
- 29. Sequencer FASTQ FORMAT Fastq • de factor standard file format for raw reads • fq, fastq, fq.gz, fastq.gz 1: @title identifier description S01_1.fq 2: Sequence 3: + description 4: Quality values Paired-end S01_2.fq
- 30. QUALITY SCORE • The base-calling error probabilities. • Types • Pred33 / Illumina 1.8+ • Score 0~60 • ASCII 33 ~ 126 • Solexa / Illumina 1.0 • -5~62 • ASCII 56 ~ 126 • Pred64 / Illumina 1.3 ~ 1.5 • 0 ~ 62 • ASCII 64 ~126 http://www.asciitable.com
- 31. SAM / BAMFORMAT Sequencer • SAM stands for Sequence Alignment/Map format. • TAB-delimited text format Fastq • 11 mandatory fields Mapper SAM/ BAM Read Name Flag Quality Reference Length Pos. of Position Mate
- 32. SAM / BAM Flag SAM CIGAR
- 33. TOOLS FOR SAM/BAM •Samtools • tview • index • mpileup • view • Picard • sort • SortSam • faidx • MarkDuplicates • flagstat • ......
- 34. GTF (ENSEMBL) Gene ID Transcript ID protein_coding, mtRNA, miRNA, lincRNA, pseudogene......
- 35. SUMMARY • Fastq format • de facto standard • Quality Score • Pred33/Illumina 1.8+, Illumina 1.0, Pred64/Illumina 1.3~1.5 • SAM/BAM format • GTF
- 36.
- 37.
- 38. REFERENCE WORKFLOW Mapped Assembled Sample reads transcripts 1 Final TopHat Cufflinks Cuffmerge transcriptome Cuffdiff assembly Differential Sample Mapped Assembled expression 2 reads transcripts results Expression CummeRbund plots
- 39. OUR WORKFLOW Samples Reference Geneset DEG analysis Read Expression Gene Cuffdiff Filtering DEGseq Report Mapping Level Structure Duplication DESeq TopHat Picard RUM Samtools Cufflinks cummeRbund TBI-toolkit Cuffmerge GO BWA HTseq-count Annotation Bowtie2 UniProt FastQC RSeQC GO KEGG
- 40.
- 41. DIRECTORY /KOGO/RNA-seq ref chr.fa, ens.gtf, mask.gtf inputs S01.fq.gz, S02.fq.gz outputs S01 accepted_hits.bam, transcripts.gtf ...... accepted_hits.bam, transcripts.gtf merged_asm merged.gtf, transcripts.gtf scripts Diff-S01-S02 gene_exp.diff, isoforms_exp.diff Tools
- 42. SAMPLES 운동전 운동후 Horse 1 S01 S02 Horse I1 S03 S04
- 43. TOOLS Category Programs Version Homepage QC FastQC 0.10.1 http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Bowtie2 2.0.5 http://bowtie-bio.sourceforge.net/bowtie2/index.shtml Mapper TopHat 2.0.7 http://tophat.cbcb.umd.edu Cufflinks 2.0.2 http://cufflinks.cbcb.umd.edu Abundance HTseq-count - http://www-huber.embl.de/users/anders/HTSeq/doc/count.html DESeq 1.10.1 http://bioconductor.org/packages/release/bioc/html/DESeq.html Annotation goseq 1.10.0 http://www.bioconductor.org/packages/2.11/bioc/html/goseq.html samtools 0.1.18 http://samtools.sourceforge.net picard 1.83 http://picard.sourceforge.net Tools TBI-toolkit 0.1 http://dev.totalomics.kr/ R 2.15.0 http://www.r-project.org Gnuplot - http://www.gnuplot.info
- 44. TBI-TOOLKIT • TBI NGS Toolkit • http://dev.totalomics.kr • Application • TBI-toolkit-qscore • TBI-toolkit-fq_filter • TBI-toolkit-gtf_selector • TBI-toolkit-fa_spliter • TBI-toolkit-make_matrix
- 45. REFERENCE • Reference-based strategy Name FileType Description Reference fasta Genome Sequence Geneset GTF2.2/GFF3 Reference Geneset Name Source Description Geneset that has ncRNA information. Mask Geneset Geneset (rRNA, tRNA, and other ncRNA) Bowtie2 Index Reference Index files for running bowtie2 Optional GO information GO Gene ontology information for GO enrichment
- 46. REFERENCE SOURCE • Ensembl (http://www.ensembl.org) • General file format for all species • Geneset (GTF format) • Constant Database schema for all species • Comprehensive Annotation (GO, InterPro, Pfam, Prosite Smart, ...... ) • Automated Update • UCSC (http://genome.ucsc.edu) • Semi general file format for all species • Semi constant Database schea for all species • Gene table dump (BED format compatible) • Annotation (Pfam, Kegg) • Comparative Analysis • NCBI • Raw data bank • GFF type geneset file
- 47. ENSEMBL ensembl.org plants.ensembl.org fungi.ensembl.org metazoa.ensembl.org protists.ensembl.org bacteria.ensembl.org
- 48. ENSEMBL • Homo Sapiens ( ftp://ftp.ensembl.org/pub/release-69 ) • fasta/homo_sapiens/ chr.fa • dna/Homo_sapiens.GRCh37.69.dna.toplevel.fa.gz • dna/Homo_sapiens.GRCh37.69.dna.chromosome.1.fa.gz • cdna/Homo_sapiens.GRCh37.69.cdna.all.fa.gz ens.gtf • gtf/homo_sapiens/Homo_sapiens.GRCh37.69.gtf.gz • mysql/homo_sapiens_core_69_37/ • Arabidopsis thaliana ( ftp://ftp.ensemblgenomes.org/pub/release-16/plants ) • fasta/arabidopsis_thaliana • dna/Arabidopsis_thaliana.TAIR10.16.dna.toplevel.fa.gz • cdna/Arabidopsis_thaliana.TAIR10.16.cdna.all.fa.gz • gtf/arabidopsis_thaliana/Arabidopsis_thaliana.TAIR10.16.gtf.gz • mysql/arabidopsis_thaliana_core_16_69_10/
- 49. PRE-PROCESSING • Check quality score type of input file • Reference files • Reference index • Mask geneset
- 50. SAMPLE QUALITY SCORE Usage) $TBI-toolkit-qscore [FASTQ] Sanger(Phred33) or Illumina 1.8+ 0 to 93 using ASCII 33 to 126 Run) $ cd /KOGO/RNA-seq/inputs $ TBI-toolkit-qscore S01_1.fq.gz Sanger(Phred33) or Illumina 1.8+ 0 to 93 using ASCII 33 to 126 0:1, 1:”, 2:#, 3:$, 4:%, 5:&, ......
- 51. REFERENCE INDEX Index forbowtie2 mapper Usage) $ bowtie2-build [options] <reference_in> <bt2_base> Run) $ cd /KOGO/RNA-seq/ref $ bowtie2-build chr.fa chr.fa $ ls chr.fa.1.bt2 chr.fa.2.bt2 ...... Fasta index Usage) $ samtools faidx <ref.fasta> Run) $ cd /KOGO/RNA-seq/ref $ samtools faidx chr.fa $ ls chr.fa.fai
- 52. MASK GENESET ...... Werecommend including any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. Due to variable efficiency of mRNA enrichment methods and rRNA depletion kits, masking these transcripts often improves the overall robustness of transcript abundance estimates. cufflinks manuals (http://cufflinks.cbcb.umd.edu/manual.html) Usage) $ TBI-toolkit-gtf_selector [IN GTF] [OUT GTF] [Source 1] [Source 2] ...... Run) $ cd /KOGO/RNA-seq/ref $ TBI-toolkit-gtf_selector ens.gtf mask.gtf tRNA rRNA Mt_tRNA Mt_rRNA
- 53. SUMMARY • Directory • /KOGO/RNA-seq • Tools • Reference • Pre-processing
- 54. FILTERING & QC DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 55. Filtering FILTERING & QC Mapping Duplication • Improving assembly accuracy Expression • Removing artifacts Gene • Sequencing adaptor Structure • Low quality reads DEG Annotation • Near-identical reads Report • PCR amplification • rRNA and other RNA • Applications • Filtering - TBI-toolkit, fastx-toolkit • QC - FastQC, SolexaQC, RSeQC
- 56. QUALITY CONTROL • FastQC ( v0.10.1 ) • A quality control tool for high throughput sequence data. • Java • http://www.bioinformatics.babraham.ac.uk/ projects/fastqc/ • RSeQC • RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data • http://code.google.com/p/rseqc/
- 57. FASTQC Usages) $ fastqc seqfile1seqfile2 .. seqfileN $ fastqc [-o output dir] [--(no)extract] [-f fastq|bam|sam] [-c contaminant file] seqfile1 .. seqfileN Arguments -f format bam,sam,bam_mapped,sam_mapped and fastq -t threads Run) $ cd /KOGO/RNA-seq/inputs $ fastqc -f fastq -t 2 S01_1.fq.gz S01_2.fq.gz Output) $ firefox R01_1.fq_fastqc/fastqc_report.html $ firefox R01_2.fq_fastqc/fastqc_report.html
- 58. FASTQC Per Base SequenceQuality Per Sequence Quality Scores Per Base Sequence Content Per Base GC Content Per Sequence GC Content Per Base N Content Sequence Length Distribution Duplicate Sequences
- 59.
- 60. READ FILTERING (CUTOFF) RNA-seq DNA-seq N > 10% N > 10% Low Average QV < Q20 Average AV < Q20 Quality NT (<Q20) > 40% NT (<Q20) > 5% No trimming Trimming or Trimming Trimming
- 61. FILTERING Usages) $ TBI-toolkit filter[option*] seqfile_1 seqfile_2 output_1 output_2 Option) -n N_ratio -a integer : Average QV of read -m NT_ratio < QV Run) $ cd /KOGO/RNA-seq/inputs $ TBI-toolkit-fq_filter -n 0.1 -m 0.4 -a 20 S01_1.fq.gz S01_2.fq.gz S01_Q20_1.fq.gz S01_Q20_2.fq.gz $ ls S01_Q20_1.fq.gz S01_Q20_2.fq.gz S01_Q20.log S01_Q20.err $ cat S01_Q20.log $ less S01_Q20.err
- 62. FASTQC Run) $ cd /KOGO/RNA-seq/inputs $fastqc -f fastq -t 2 S01_Q20_1.fq.gz S01_Q20_2.fq.gz
- 63. SUMMARY • Read Quality • FastQC • RSeQC • Filter
- 64. MAPPING READS (TOPHAT) DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 65. Filtering TOPHAT Mapping Duplication • TopHat is a fast splice junction mapper for RNA- Expression Seq reads. Gene Structure • It aligns RNA-Seq reads to mammalian-sized genomes using the ultra high-throughput short DEG Annotation read aligner Bowtie, and then analyzes the mapping results to identify splice junctions between exons. Report Trapnell C, Salzberg SL. How to map billions of short reads onto genomes. Nat Biotechnol. 2009 May;27(5):455-7.
- 66. USAGE Usage $ tophat [options]<bowtie_index_base> <reads1_1> <reads1_2> Option Value Description -o/--output-dir string The default is "./tophat_out". -p/--num-threads int Use this many threads to align reads. The default is 1. -r/--mate-inner-dist int This is the expected (mean) inner distance between mate pairs.The default is 50bp The standard deviation for the distribution on inner distances between mate pairs. --mate-std-dev int The default is 20bp. fr-unstranded fr-unstranded : Standard Illumina --library-type fr-firststrand fr-firststrand : dUTP, NSR, NNSR fr-secondstrand fr-secondstrand : Ligation, Standard Solid --solexa-quals - Use the Solexa scale for quality values in FASTQ files. --solexa1.3-quals - Phred64/Illumina 1.3~1.5 -G/--GTF Geneset Geneset (GTF 2.2 or GFF3 formatted file) --rg-id string Read group ID --rg-sample string Sample ID
- 67. RUN $ cd /KOGO/RNA-seq/outputs $tophat -o S01 -p 1 -r 170 --library-type fr-unstranded -G ../ref/ens.gtf --rg-id S01_Q20 --rg-sample S01_Q20 ../ref/chr.fa ../inputs/S01_Q20_1.fq.gz ../inputs/S01_Q20_2.fq.gz Category Option Value Output -o/--output-dir /KOGO/RNA-seq/outputs/S01 Thread -p/--num-threads 1 Inner Distance Mean -r/--mate-inner-dist 170 check Inner distance SD. --mate-std-dev 20 (default) Library Type --library-type fr-unstranded (Standard Illumina) Quality Score Phred33 (default) Geneset -G/--GTF /KOGO/RNA-seq/ref/ens_69.gtf --rg-id Read Group S01_Q20 --rg-sample
- 68. ALGORITHM Trapnell C, PachterL, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009 May 1;25(9):1105-11.
- 69. TOPHAT • Two step method • Extracting the transcript sequences and using Bowtie to align reads to this virtual transcriptome first. • Only the reads that do not fully map to the transcriptome will then be mapped on the genome. • Optimized for reads >= 75bp • The values in the first column of the provided GTF/GFF file must match the name of the reference sequence in the Bowtie index you are using with TopHat.
- 70. OUTPUT Filename Types Description A list of read alignments in SAM format. accepted_hits.bam BAM Coordinate-sorted unmapped.bam BAM A list of unmapped read in SAM format. junctions.bed UCSC BED A track of junctions reported by TopHat chromLeft referes to the last genomic base insertions.bed UCSC BED before the insertion chromLeft referes to the first genomic base deletions.bed UCSC BED before the insertion
- 71. SIMPLE ALIGNMENT VIEW Usage $cd /KOGO/RNA-seq/output/S01 $ samtools index accepted_hits.bam 25:413751 $ samtools tview accepted_hits.bam ../../ref/chr.fa Key Desc ? This window Arrows Small scroll movement H,J,K,L Large scroll movement space Scroll one screen backspace Scroll back one screen g Go to specific location m Color for mapping qual n Color for nucleotide b Color for base quality . Toggle on/off dot view q Exit
- 72. MAPPING STATISTICS Run) Run) $ cd /KOGO/RNA-seq/outputs/S01 $ cd /KOGO/RNA-seq/outputs/S01 $ samtools flagstat accepted_hits.bam $ bam_stat.py -i accepted_hits.bam 45338688 + 0 in total (QC-passed reads + QC-failed Total Reads (Records): 45338688 reads) QC failed: 0 0 + 0 duplicates Optical/PCR duplicate: 0 45338688 + 0 mapped (100.00%:-nan%) Non Primary Hits 1861695 45338688 + 0 paired in sequencing Unmapped reads: 0 22757885 + 0 read1 Multiple mapped reads: 586067 22580803 + 0 read2 39796048 + 0 properly paired (87.78%:-nan%) Uniquely mapped: 42890926 42308960 + 0 with itself and mate mapped Read-1: 21527100 Read-2: 21363826 3029728 + 0 singletons (6.68%:-nan%) Reads map to '+': 21457407 705846 + 0 with mate mapped to a different chr Reads map to '-': 21433519 92166 + 0 with mate mapped to a different chr Non-splice reads: 32872272 (mapQ>=5) Splice reads: 10018654 Reads mapped in proper pairs: 38402964
- 73. SUMMARY • TopHat •Splice junction • Geneset • Two step method • accepted_hits.bam
- 74. PCR DUPLICATES (OPTIONAL) DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 75. PCR DUPLICATION Filtering Mapping Duplication • Removing reads that have same mapping coordinates. Expression • Tools Gene • samtools - rmdup Structure • Picard - MarkDuplicates DEG Annotation Run) Report $ cd /KOGO/RNA-seq/outputs/S01/ $ samtools rmdup accepted_hits.bam accepted_hits.rmdup.bam Run) $ cd /KOGO/RNA-seq/outputs/S01/ $ java -jar /KOGO/RNA-seq/Tools/Picard/MarkDuplicates.jar INPUT=accepted_hits.bam OUTPUT=accpted_hits.mark_dup.bam ASSUME_SORTED=true REMOVE_DUPLICATES=true METRICS_FILE=accpeted_hits.metric
- 76. PCR DUPLICATION accepted_hits.bam samtools Picard (Mark) Picard (Remove) 45338688 + 0 in total 29259330 + 0 in total 45338688 + 0 in total 27621444 + 0 in total 0 + 0 duplicates 0 + 0 duplicates 17717244 + 0 duplicates 0 + 0 duplicates 45338688 + 0 mapped 29259330 + 0 mapped 45338688 + 0 mapped 27621444 + 0 mapped 45338688 + 0 paired 29259330 + 0 paired 45338688 + 0 paired 27621444 + 0 paired 22757885 + 0 read1 14717809 + 0 read1 22757885 + 0 read1 13820471 + 0 read1 22580803 + 0 read2 14541521 + 0 read2 22580803 + 0 read2 13800973 + 0 read2 39796048 + 0 properly 24471885 + 0 properly 39796048 + 0 properly 24945306 + 0 properly paired (87.78%:-nan%) paired (83.64%:-nan%) paired (87.78%:-nan%) paired (90.31%:-nan%) 42308960 + 0 with itself and 26229602 + 0 with itself and 42308960 + 0 with itself and 26660814 + 0 with itself and mate mapped mate mapped mate mapped mate mapped 3029728 + 0 singletons 3029728 + 0 singletons 3029728 + 0 singletons 960630 + 0 singletons (6.68%:-nan%) (10.35%:-nan%) (6.68%:-nan%) (3.48%:-nan%) 705846 + 0 with mate 705846 + 0 with mate 705846 + 0 with mate 655922 + 0 with mate mapped to a different chr mapped to a different chr mapped to a different chr mapped to a different chr 92166 + 0 with mate 92166 + 0 with mate 92166 + 0 with mate 52614 + 0 with mate mapped to a different chr mapped to a different chr mapped to a different chr mapped to a different chr (mapQ>=5) (mapQ>=5) (mapQ>=5) (mapQ>=5)
- 77. EXPRESSION (CUFFLINKS) DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 78. EXPRESSINO & MODELING Adam Roberts et al., Iden%fica%on of novel transcripts in annotated genomes using RNA-‐Seq. Bioinforma4cs, 2011, 27:2325–2329
- 79. NORMALIZATION • Read counts need to be properly normalized to extract meaningful expression estimates • First, RNA fragmentation during library construction causes longer transcripts to generate more reads compared to shorter transcripts present at the same abundance in the sample • Second, the variability in the number of reads produced for each run causes fluctuations in the number of fragments mapped across samples Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011 Jun;8(6):469-77.
- 80. RPKM the reads per kilobase of transcript per million mapped reads Relative Expression Level in Sample • C : the number of mappable reads that fell onto the gene’s exons • N : the total number of mappable reads in the experiment • L : the sum of the exons in base pairs Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., and Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by rna-seq. Nat Methods, 5(7):621-628.
- 81. Filtering CUFFLINKS Mapping Duplication • Cufflinks assembles transcripts, estimates their Expression abundances, and tests for differential expression and regulation in RNA-Seq Gene Structure samples DEG Annotation • Cufflinks constructs a parsimonious set of Report transcripts that "explain" the reads observed in an RNA-Seq experiment http://cufflinks.cbcb.umd.edu/index.html
- 82. CUFFLINKS PACKAGE • cufflinks • assembles transcripts • estimates their abundances • cuffmerge • a script called cuffmerge that you can use to merge together several Cufflinks assemblies. • cuffdiff • tests for differential expression
- 83. USAGE $ cufflinks [options]<aligned_reads.(sam/bam)> Option Value Description Sets the name of the directory in which Cufflinks will write all of its output. -o/--output-dir String The default is "./". Quantification -p/--num-threads int Use this many threads to align reads. The default is 1. Use the supplied reference annotation (a GFF file) to estimate isoform -G/--GTF geneset expression. It will not assemble novel transcripts. Novel Isoforms Use the supplied reference annotation (GFF) to guide RABT assembly. -g/--GTF-guide geneset Output will include all reference transcripts as well as any novel genes and isoforms that are assembled. Improving Ignore all reads that could have come from transcripts in this GTF file. We -M/--mask-file mask geneset accuracy recommend including any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. fr-unstranded fr-unstranded : Standard Illumina --library-type fr-firststrand fr-firststrand : dUTP, NSR, NNSR / fr-secondstrand fr-secondstrand : Ligation, Standard Solid
- 84. RUN $ cd /KOGO/RNA-seq/outputs $cufflinks -o S01 -p 1 --library-type fr-unstranded -g ../ref/ens.gtf -M ../ref/mask.gtf S01/accepted_hits.bam Category Option Value Output -o/--output-dir /KOGO/RNA-seq/outputs/S01 Thread -p/--num-threads 1 Guide Geneset -g/--GTF-guide /KOGO/RNA-seq/ref/ens.gtf Mask Geneset -M/--mask-file /KOGO/RNA-seq/ref/mask.gtf Library Type --library-type fr-unstranded
- 85. ALGORITHM Trapnell C, WilliamsBA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010 May;28(5):511-5.
- 86. CUFFLINKS EXPRESSION • FPKM • Fragments Per Kilobase of exon per Million fragments mapped • analogous to single-read “RPKM” • Isoform expression estimation • maximum likelihood estimation • Normalization • by total number of mapped reads • by upper quantile method
- 87. OUTPUT File Description transcripts.gtf The GTF file contains Cufflinks ‘ assembled isoforms The estimated isoform-level expression values in the isoforms.fpkm_tracking generic FPKM Tracking Format. The estimated gene-level expression values in the genes.fpkm_tracking generic FPKM Tracking Format.
- 88. TRANSCRIPTS.GTF Col. Name Example Description 1 seqname chrX Chromosome or contig name 2 source Cufflinks The name of the program that generated this file (always 'Cufflinks') 3 feature exon The type of record (always either "transcript" or "exon". 4 start 77696957 The leftmost coordinate of this record (where 1 is the leftmost possible coordinate) 5 end 77712009 The rightmost coordinate of this record, inclusive. The most abundant isoform for each gene is assigned a score of 1000. Minor 6 score 1000 isoforms are scored by the ratio (minor FPKM/major FPKM) 7 strand + Cufflinks' guess for which strand the isoform came from. Always one of "+", "-", "." Cufflinks does not predict where the start and stop codons (if any) are located 7 frame . within each transcript, so this field is not used. 8 attributes ... See below.
- 89. TRANSCRIPTS.GTF Attribute Example Description gene_id CUFF.1 Cufflinks gene id transcript_id CUFF.1.1 Cufflinks transcript id Isoform-level relative abundance in Fragments Per Kilobase of exon model per FPKM 101.267 Million mapped fragments frac 0.7647 Reserved. Please ignore, as this attribute may be deprecated in the future Lower bound of the 95% confidence interval of the abundance of this isoform, as a conf_lo 0.07 fraction of the isoform abundance. That is, lower bound = FPKM * (1.0 - conf_lo) Upper bound of the 95% confidence interval of the abundance of this isoform, as a conf_hi 0.1102 fraction of the isoform abundance. That is, upper bound = FPKM * (1.0 + conf_lo) cov 100.765 Estimate for the absolute depth of read coverage across the whole transcript When RABT assembly is used, this attribute reports whether or not all introns and full_read_support yes internal exons were fully covered by reads from the data.
- 90. FPKM TRACKING FILES Col. name Example Description 1 tracking_id TCONS_00000001 A unique identifier describing the object (gene, transcript, CDS, primary transcript) The class_code attribute for the object, or "-" if not a transcript, or if class_code isn't 2 class_code = present 3 nearest_ref_id NM_008866.1 The reference transcript to which the class code refers, if any 4 gene_id NM_008866 The gene_id(s) associated with the object 5 gene_short_name Lypla1 The gene_short_name(s) associated with the object The tss_id associated with the object, or "-" if not a transcript/primary transcript, or if 6 tss_id TSS1 tss_id isn't present 7 locus chr1:4797771-4835363 Genomic coordinates for easy browsing to the object 8 length 2447 The number of base pairs in the transcript, or '-' if not a transcript/primary transcript 9 coverage 43.4279 Estimate for the absolute depth of read coverage across the object 10 FPKM 8.01089 FPKM of the object in sample 11 FPKM_lo 7.03583 the lower bound of the 95% confidence interval on the FPKM of the object in sample 12 FPKM_hi 8.98595 the upper bound of the 95% confidence interval on the FPKM of the object in sample OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), 13 status OK HIDATA (too many fragments in locus), or FAIL
- 91. SIMPLE STATISTICS $ cd/KOGO/RNA-seq/outputs/S01 # Check higest expressed genes $ sort -r -g -k 10 genes.fpkm_tracking | head -n 30 # Select FPKM S $ cut -f 1,10 genes.fpkm_traking > gene_fpkm_s # $R > data <- read.table(“gene_fpkm_s”, header=TRUE) > fpkm_s <- as.numeric(data[,2]) > > mean(fpkm_s) > sd(fpkm_s) > > fpkm_s.log10 <- log(fpkm_s+1,10) > bin_seq = seq(min(fpkm_s.log10-0.1),max(fpkm_s.log10+0.1),by=0.1) > hist(fpkm_s.log10, breaks=bin_seq, xlab=‘log10(x+1)’, ylab=‘Number of genes’, axes=TRUE) > > boxplot(fpkm_s.log10)
- 92. SUMMARY • Expression Level • Normalization • RPKM (FPKM) • Length Bias • Cufflinks • Isoforms • maximum likelihood estimation
- 93. CUFFMERGE DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 94. Filtering CUFFMERGE Mapping Duplication Expression • Use to merge together several Cufflinks assemblies Gene Structure • Automatically filters a number of transfrags that are probably artfifacts DEG Annotation • The main purpose of this script is Report to make it easier to make an assembly GTF file suitable for use with Cuffdiff Trapnell C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012 Mar 1;7(3):562-78. doi: 10.1038/nprot.2012.016.
- 95. USAGE $ cuffmerge [options]<assembly_GTF_list.txt> Option Value Description -o <outprefix> Write the summary stats into the text output file <outprefix>(instead of stdout) An optional "reference" annotation GTF. The input assemblies are merged together with -g/--ref-gtf geneset the reference GTF and included in the final output. -p/--num-threads <int> Use this many threads to align reads. The default is 1. This argument should point to the genomic DNA sequences for the reference. If a directory, it should contain one fasta file per contig. If a multifasta file, all contigs should be present. The merge script will pass this option to cuffcompare, which will use the <seq_dir>/ -s/--ref-sequence sequences to assist in classifying transfrags and excluding artifacts (e.g. repeats). For <seq_fasta> example, Cufflinks transcripts consisting mostly of lower-case bases are classified as repeats. Note that <seq_dir> must contain one fasta file per reference chromosome, and each file must be named after the chromosome, and have a .fa or .fasta extension.
- 96. RUN $ cd /KOGO/RNA-seq/outputs $find ./ -iname transcripts.gtf > gtf_list.txt $ cuffmerge -p 1 -g ../ref/ens.gtf -s ../ref/chr.fa gtf_list.txt Category Option Value Outputprefix -o /KOGO/RNA-seq/outputs Geneset -g/--ref-gtf /KOGO/RNA-seq/ref/ens.gtf Thread -p/--num-threads 1 Reference -s/--ref-sequence /KOGO/RNA-seq/ref/chr.fa
- 97. RUN $ cd /KOGO/RNA-seq/outputs/merged_asm $less transcripts.gtf $ less merged.gtf $ gffread -g /KOGO/ref/chr.fa -w transcripts.fa transcripts.gtf $ head transcripts.fa >CUFF.11.1 gene=CUFF.11 GTGCATGTAACCCAAGAAGGGTTTGGCTGGGGGCTGTGGCAGCGCCAGAGTTCT GTTCGAATCCCAATTG GGTTCTGGTCACAGATTTGGCATGGAGCAGAAGAGAGATACAGCATGGTTGAAAA GCAGTTATTGGCTAC $ grep '>' transcripts.fa | head -n 30 >CUFF.2.1 gene=CUFF.2 >CUFF.11.1 gene=CUFF.11 >ENSGALT00000015891 gene=CUFF.11 >CUFF.12.1 gene=CUFF.12
- 98. DEG ANALYSIS DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 99. Filtering DIFFERENTIALLY EXPRESSEDGENE Mapping Duplication • Abundance of transcripts between Expression different conditions Gene Structure DEG Annotation Report Robinson MD, Oshlack A. A scaling normalization method for Zhang et al., Mol Cancer Res differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. doi: 10.1186/gb-2010-11-3- June 2006 4; 401 r25. Epub 2010 Mar 2.
- 100. LENGTH BIAS Oshlack A,Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009 Apr 16;4:14.
- 101. BIAS Robinson MD, OshlackA. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25.
- 102. REPLICATES More variance, More useful Technical Biological Replicates Replicates Source Same samples Different samples A quantity from the reproducibility of difference sources Purpose the results under the same conditions. what is similar in your The differences are replicates and how they based only on Issue are different from a technical issues in different set of the measurement conditions Taylor S, Wakem M, Dijkman G, Alsarraj M, Nguyen M. A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods. 2010 Apr;50(4):S1-5. doi: 10.1016/j.ymeth. 2010.01.005. http://wiki.answers.com/Q/ What_is_defference_between_Biological_replicates_a nd_technical_replicates
- 103. DEG METHODS Cuffdiff DEGseq DESeq - Poisson Negative binomial Isoform Gene Gene geneset Raw Read Count Raw Read Count BAM files Technical Technical Biological Replicates Replicates Replicates
- 104. CUFFDIFF • Use to find significant changes in transcript expression, splicing, and promoter use. Usage) $ cuffdiff [options]* <transcripts.gtf> <sample1_replicate1.sam[,...,sample1_replicateM]> <sample2_replicate1.sam[,...,sample2_replicateM.sam]> Option Value Description -o / Sets the name of the directory in which Cuffdiff will write all <string> --output-dir of its output. The default is "./". -L / Specify a label for each sample, which will be included in <label1,label2,...,labelN> --labels various output files produced by Cuffdiff. -p / <int> Use this many threads to align reads. The default is 1. --num-threads
- 105. RUN $ cd /KOGO/RNA-seq/outputs $cuffdiff -o Diff-S01-S02 -L S01,S02 -p 1 merged_asm/merged.gtf S01/ accepted_hits.bam S02/accepted_hits.bam Category Option Value Output -o/--output-dir /KOGO/RNA-seq/outputs/Diff-S01-S02 Label -L / --labels S01,S02 Thread -p / --num-threads 1
- 106. OUTPUT Type Files Description genes.fpkm_tracking Gene [FPKMs, counts, read group tracking]. Tracks the summed Genes genes.count_tracking [FPKMs, counts, read group tracking] of transcripts sharing each genes.read_group_tracking gene_id isoforms.fpkm_tracking Isoforms isoforms.count_tracking Transcript [FPKMs, counts, read group tracking] isoforms.read_group_tracking cds.fpkm_tracking Coding sequence [FPKMs, counts, read group tracking]. Tracks CDS cds.count_tracking the summed [FPKMs, counts, read group tracking] of transcripts cds.read_group_tracking sharing each p_id, independent of tss_id tss_groups.fpkm_tracking Primary transcript [FPKMs, counts, read group tracking]. Tracks Primary tss_groups.count_tracking the summed [FPKMs, counts, read group tracking] of transcripts Transcripts tss_groups.read_group_tracking sharing each tss_id
- 107. FPKM TRACKING FILES $cd /KOGO/RNA-seq/outputs/Diff-S01-S02 $ cut -f 1,3,4,5,9,10,13,14,17 genes.fpkm_tracking | head Col. Column name Example Description A unique identifier describing the object (gene, transcript, CDS, primary 1 tracking_id TCONS_00000001 transcript) 3 nearest_ref_id NM_008866.1 The reference transcript to which the class code refers, if any 4 gene_id NM_008866 The gene_id(s) associated with the object 5 gene_short_name Lypla1 The gene_short_name(s) associated with the object 9 coverage 43.4279 Estimate for the absolute depth of read coverage across the object 10 q0_FPKM 8.01089 FPKM of the object in sample 0 OK (deconvolution successful), LOWDATA (too complex or shallowly 13 q0_status OK sequenced), HIDATA (too many fragments in locus), or FAIL 14 q1_FPKM 8.55155 FPKM of the object in sample 1 OK (deconvolution successful), LOWDATA (too complex or shallowly 17 q1_status OK sequenced), HIDATA (too many fragments in locus), or FAIL
- 108. OUTPUT Type Files Description Gene differential FPKM. Tests difference sin the summed FPKM of Genes gene_exp.diff transcripts sharing each gene_id Isoforms isoform_exp.diff Transcript differential FPKM. Coding sequence differential FPKM. Tests differences in the summed FPKM CDS cds_exp.diff of transcripts sharing each p_id independent of tss_id Primary Primary transcript differential FPKM. Tests differences in the summed FPKM tss_group_exp.diff Transcripts of transcripts sharing each tss_id how much differential splicing exists between isoforms processed from a Splicing splicing.diff single primary transcript the amount of overloading detected among its coding sequences, i.e. how CDS cds.diff much differential CDS output exists between samples the amount of overloading detected among its primary transcripts, i.e. how Promoter promoter.diff much differential promoter use exists between samples.
- 109. GENE_EXP.DIFF $cd /KOGO/RNA-seq/outputs/Diff-S01-S02 $ cut -f 1,2,7,8,9,10,11,12,13,14 gene_exp.diff | head Col. Name Example Description 1 Tested id XLOC_000001 A unique identifier 2 gene Lypla1 The gene_name(s) or gene_id(s) being tested OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too 6 Test status NOTEST complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL 7 FPKMx 8.01089 FPKM of the gene in sample x 8 FPKMy 8.551545 FPKM of the gene in sample y 9 log2(FPKMy/FPKMx) 0.06531 The (base 2) log of the fold change y/x The value of the test statistic used to compute significance of the observed change in 10 test stat 0.860902 FPKM 11 p value 0.389292 The uncorrected p-value of the test statistic 12 q value 0.985216 The FDR-adjusted p-value of the test statistic Can be either "yes" or "no", depending on whether p is greater then the FDR after 13 significant no Benjamini-Hochberg correction for multiple-testing
- 110. SIMPLE STATISTICS $ cd/KOGO/RNA-seq/outputs/Diff-S01-S02 $ gnuplot gnuplot> set grid gnuplot> set zeroaxis -1 gnuplot> set xlabel ‘log(FPKMs of S01)’ gnuplot> set ylabel ‘log(FPKMs of S02)’ gnuplot> pl ‘genes.fpkm_tracking’ u (log($10)):(log($14)) w points notitle, x notitle gnuplot> exit $ cd /KOGO/RNA-seq/outputs/Diff-S01-S02 $ grep yes gene_exp.diff > gene_exp.diff.yes $ less gene_exp.diff.yes $ grep no gene_exp.diff > gene_exp.diff.no $ gnuplot gnuplot> set grid gnuplot> set zeroaxis lt 2 gnuplot> set xlabel ‘log2foldchange’ gnuplot> set ylabel ‘-log(p-value)’ gnuplot> pl ‘gene_exp.diff.no’ u 10:(-log($12)) lt 0 no title, ‘gene_exp.diff.yes’ u 10:(-log($12)) lt 1 pt 6 ps 2 t ‘DE’ gnuplot> exit
- 111. DESEQ • Differential gene expression analysis based on the negative binomial distribution • R • raw count • biological replicates • http://bioconductor.org/packages/release/ bioc/html/DESeq.html http://jura.wi.mit.edu/bio/education/hot_topics/RNAseq/ RNAseqDE_Dec2011.pdf
- 112. HTSEQ-COUNT • To count how many reads map to each feature • Not counted for any feature for various reasons, namely: • no_feature: reads which could not be assigned to any feature • ambiguous: reads which could have been assigned to more than one feature and hence were not counted for any of these • too_low_aQual: reads which were not counted due to the -a option • not_aligned: reads in the SAM file without alignment • alignment_not_unique: reads with more than one reported alignment. These reads are recognized from the NH optional SAM field tag. • If you have paired-end data, you have to sort the SAM file by read name first http://www-huber.embl.de/users/anders/HTSeq/doc/count.html
- 113. HTSEQ-COUNT If you havepaired-end data, you have to sort the SAM file by read name first Usage) $ htseq-count [options] <sam_file> [gff_file, ensembl gtf] Options) -m [union,intersection-strict,intersection-nonempty] -s.--stranded=<yes, no, or reverse> whether the data is from a strand-specific assay (default: yes) Run) $ cd /KOGO/RNA-seq/outputs/S01 $ samtools sort -n accepted_hits.bam accepted_hits.nameSorted $ samtools view accepted_hits.nameSorted.bam | htseq-count -m union -s no - ../merged_asm/merged.gtf > accepted_hits.count $ less accepted_hits.count # ..... for (S02, S03, S04)
- 114. RUN Run) $ cd /KOGO/RNA-seq/outputs $mkdir DESeq $ TBI-toolkit-make_matrix S01/hits.count 2 S02/hits.count 2 S03/hits.count 2 S04/hits.count 2 > DESeq/hits.mtx $ cd DESeq $ less hits.mtx $ cp /KOGO/RNA-seq/scripts/DESeq.4samples.R . $ R CMD BATCH DESeq.R DESeq.2samples for 2 samples
- 115. DEG METHODS • Cuffdiff, baySeq, DESeq, edgeR and NOISeq generated consistent results • edgeR identified more DGE than the other methods at the same cut- off, which might infer less control of type 1 error with this method Nookaew I, et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012 Nov 1;40(20):10084-97.
- 116. SUMMARY • DEG • Replicate • Technical replicates • Biological replicates • Cuffdiff • HTSeq-count • DESeq
- 117. REPORT (CUMMERBUND) DEG Read analysis Filtering Expression Gene Mapping Report Duplication Level Structure FastQC RSeQC Annotation
- 118. CUMMERBUND • anR packagethat is designed to aid and simplify the task of analyzing Cufflinks RNA-Seq output. •R • using SQLite • cuffData.db
- 119.
- 120. RUN Run) # Pairwise Scatterplots $ cd /KOGO/outputs/Diff-S01-S02 > s<-csScatter(genes(cuff),"S01","S02",smooth=T) $R >s > library(cummeRbund) # Geneset level plots > cuff <- readCufflinks() > cuff > data(sampleData) # Global statistics and Quality Control > myGeneIds <- sampleIDs > disp<-dispersionPlot(genes(cuff)) > myGenes <- getGenes(cuff,myGeneIds) > disp > h<-csHeatmap(myGenes,cluster='both') # Density >h > dens<-csDensity(genes(cuff)) # Barplot > dens > b <- expressionBarplot(myGenes) # Boxplot >b > b<-csBoxplot(genes(cuff)) # Cluster >b # Volcano > ic <-csCluster(myGenes,k=4) > v<-csVolcanoMatrix(genes(cuff)) > icp <- csClusterPlot(ic) >v > icp > v<-csVolcano(genes(cuff),"S01","S02")
- 121.
- 122. VIEWER • IGV Run) Generate BAM index $ cd /KOGO/RNA-seq/outputs/S01 • Integrative Genomics Viewer $ samtools index accepted_hits.bam $ ls • http://www.broadinstitute.org/igv/ accepted_hits.bai
- 123. GO ENRICHMENT • GO annotation • GO Enrichment • Using SwissProt • GOseq • Blastx • Fisher's exact test • Blast2Go • DAVID • InterProScan
- 124. CONCLUSION • (m)RNA-seq analysis • Mapping • Reference-based method • RNA mapper • NGS data analysis • Gene Expression • RNA-seq vs. DNA-seq • Normalization • Filtering • DEG Analysis • Low Quality • RPKM • PCR Duplication • Replicates
- 125.