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2013 pag-poultry-workshop
 

2013 pag-poultry-workshop

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    2013 pag-poultry-workshop 2013 pag-poultry-workshop Presentation Transcript

    • Evaluating and improving the chick genome & transcriptome C. Titus Brown Asst Prof, CSE and Microbiology; BEACON NSF STC Michigan State University ctb@msu.edu
    • AcknowledgementsThis is joint work with Hans Cheng (USDA ADOL), Jerry Dodgson (MSU).Likit Preeyanon (MSU) and Alexis Black Pyrkosz (ADOL) did the work.All of the software discussed in this talk is available. This work was primarily supported by the USDA NIFA through a grant to me.
    • Simulations show that incomplete gene reference=> inaccurate differential expression from mRNAseq Single End Reads Paired End Reads % Transcripts Expressed Inaccurately (2-fold Difference) % Transcripts Expressed Inaccurately (2-fold Difference) 100% 100% 10 10 0% 0% 90% 90% ex ex pr pr 80% e ss 80% es io sio 75 n 75 n 70% % 70% % ex ex pre pre ss s 60% ion 60% sio n 50% 50% 50% expr 50% ex p essio ress n ion 40% 40% 30% 25% expressi 30% 25% expre on ssion 20% 20% 10% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % Reference Completeness % Reference Completeness Alexis Black Pyrkosz
    • Existing chick gene models lack exons,isoforms Our data Models *This gene contains at least 4 isoforms. Likit Preeyanon
    • (Exon detection is pretty good.) Likit Preeyanon
    • Different approaches to gene set predictionyield distinct splice junction predictions > 95% of thee assembly-based splice junctions are supported by 4 or more independent reads. Likit Preeyanon
    • mRNAseq analysis with a combined de novoand genome-based approach. Likit Preeyanon
    • We can produce combined gene models. Cufflinks (ref based) + de novo assembly + known mRNA
    • Gene Model Summary (note: spleen mRNAseq) Method Gene TranscriptGlobal Assembly 14,832 32,311Local Assembly 15,297 23,028Global + Local Assembly 15,934 46,797 *Number of genes and transcripts might be overdue to incomplete assembly and spurious splice junctions.
    • Cross-validation with technical replicates Dataset Single-end Paired-end Mapped Unmapped Mapped UnmappedLine 6 uninfected 18,375,966 5,203,586 21,598,218 12,065,659 (77.93%) (22.07%) (64.16%) (35.84%)Line 6 infected 17,160,695 6,288,286 15,274,638 8633855 (73.18%) (26.82%) (63.89%) (36.11%)Line 7 uninfected 18,130,072 5,795,737 20,961,033 11,960,299 (75.77%) (24.22%) (63.67%) (36.33%)Line 7 infected 19,912,046 5,450,521 22,485,833 11,992,002 (78.51%) (21.49%) (65.22%) (34.78%) Single-ended reads were used to generate gene models; paired-end data was used as technical replicate cross-validation.
    • Gene Modeler Pipeline (“gimme”) Merge transcripts together based on transcript mapping to genome; can include existing gene predictions, & iteratively combine predictions. Construct gene models Remove redundant sequences Predict strands and ORFs Likit Preeyanon
    • Next problem: chick reference! We like using the reference genome to scaffold RNAseq contigs; purely de novo RNAseq assembly is messy. Genomes are also useful for other things, we hear.Problems: Poor sensitivity: the chick genome is missing a substantial number of genes from microchromosomes: 723 genes from HSA19q missing from chicken galGal4. ESTs and RNAseq transcripts for many or most. Gaps 9900 gaps on ordered chromosomes 21k gaps on chr-aligned but low-confidence/unaligned Over-collapsed tandem dups and under-collapsed het
    • Sensitivity – where is the problem?Are microchromosomes hard to sequence or is microchromosomal sequence hard to assemble?Sequences that simply don’t show up in the data are hard to include in the assembly… Unclonable (Sanger) Strong GC or AT biasSequences with biased (generally low) coverage are often discarded by assemblers.
    • Can we “even out” coverage?(Digital normalization) If you have two loci, or two mRNA species, with uneven coverage, can you remove the extra coverage?
    • Coverage before digital normalization: (MD amplified)
    • Coverage after digital normalization: Normalizes coverage Discards redundancy Eliminates majority of errors Scales assembly dramatically. Assembly is 98% identical.
    • Prelim results from digitalnormalizationReassembled chick genome contigs from 70x Illumina -> normalized reads in ~24 hours.Obtained 40 Mbp of assembled contigs that were not present in galGal4.Contig assembly contained partial or complete matches to 70% of previously unmappable transcripts assembled from chick spleen mRNAseq.⇒Bioinformatics remedies may help but are probably not sufficient. Likit Preeyanon
    • Can we improve the assembly? Read cleaning and improvement 1. Digital normalization evens out relative coverage, permitting recovery of difficult- to-sequence regions in assemblies. 2. Error correction and read-to-graph Selection of concordance editing collapses strategies and heterozygous regions. parameters 3. Paired-end de Bruijn graphs can be used to include long-distance constraints in primary contig assembly. 4. RNAseq data indicates contigs that can be combined into scaffolds. Assembly assessment 1. High-abundance k-mers present in the sequence data but missing from the assembly indicate poor sensitivity. 2. Discordant long-insert mate pairs Contig assembly indicate potentially erroneous contigs and and/or scaffolds. scaffolding 3. De novo RNAseq assembly can identify likely misassemblies and positively identify missing genomic sequence.
    • slides from http://slideshare.net/flxlex/ ; Lex NederbragtLonger reads! Repeat copy 1 Repeat copy 2 Long reads can span repeats and heterozygous regions Polymorphic contig 22 Polymorphic contig Contig 1 Contig 4 Polymorphic contig 33 Polymorphic contig
    • slides from http://slideshare.net/flxlex/ ; Lex NederbragtPacBio: first results (cod/salmon) Raw reads
    • Cod: PacBio results Mapping to the published genome 11.4 kbp subread 10.6 kbp subread 10.9 kbp subread slides from http://slideshare.net/flxlex/ ; Lex Nederbragt
    • Need to combine Illumina + PacBio still. P_errorCorrection pipeline from  93% of reads recovered 2.7x Alignments of at least 1kb to cod published assembly + Error-corrected reads 23x s + w rea d Ra 24 cpus 4.5 days 100 Gb RAMslides from http://slideshare.net/flxlex/ ; Lex
    • Concluding thoughts/commentsGene models and reference genome both need work.This is going to be a continuing process…Together with Wes Warren (WUSTL), Hans Cheng (USDA ADOL), Jerry Dodgson (MSU) proposing to apply PacBio sequencing and digital normalization to improve chick genome and regularly integrate community improvements; should be generalizable approach. Questions? Contact me at: ctb@msu.edu