PAG 2015-iPlant

Transcript

1. 2/3/2015 1 A Hybrid Approach to Assemble and Annotate the Brassica rapa Transcriptome in the Cloud through the iPlant Collaborative and XSEDE Upendra Kumar Devisetty Postdoctoral Researcher Maloof Lab, UC Davis R500 IMB211 • Reference Transcriptome • Genome annotation R500 (oil seed cultivar) IMB211 (rapid cycling cultivar) B. rapa mapping population Research in Maloof Lab Mainly relied on in silico gene models and EST’s data from datasets (Wang et al. 2011) – In silico gene models (GENSCAN, GlimmerHMM, Fgenesh) • short exons • very long exons • non-translated exons • genes that encode non-coding RNAs accurately – EST’s • miss 20-40% of novel transcripts • transcribed only under highly specific tissue, environmental or treatment conditions • 3’ biased • short length Original Why there is a need for accurate genome annotation? • Accurate and comprehensive genome annotation (e.g. gene models) is imperative for functional studies • Useful for accurate mRNA abundance and detection of eQTLs (expression QTLs) in mapping populations Objectives • To detect transcripts that are not present in the existing genome reference of B. rapa (novel transcripts) • To update the existing gene models of B. rapa genome UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Growth Chamber, Green House, Field apical meristem R500 Library construction  TRUSEQ RNA-SEQ kit (Illumina)  High throughput and easy to use Sequencing  128 RNA-Seq libraries  17 lanes  PE100 sequencing  Illumina GAIIx  3,354 million raw paired end reads Quality control o Atmosphere and iRODS o 2,550 million quality controlled paired end reads (888 GB) Servers (iPlant Atmosphere) XX-TB Storage (iPlant Data Store and EBS) Users Now everyone can share data without sharing resources!
2. 2/3/2015 2 B. rapa transcriptome assembly and genome reannotation pipeline UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Transcriptome assembly de novo vs Reference based assemblies Approach Advantages Disadvantages de novo -no reference needed -detection of non-collinear transcripts -lowly expressed genes -missassemblies due to repeats reference -alignment tolerates sequencing errors -repeats detected through alignment -reference is needed -assumes transcripts are collinear with the genome Transcriptome assembly UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics • XSEDE is the most powerful integrated advanced digital resources and services in the world funded by NSF • Scientists and Engineers around the world use XSEDE resources and services: supercomputers, collections of data, help services • Consists of supercomputers, high-end visualization, data analysis and storage around the country xsede.org xsede.org
3. 2/3/2015 3 Summary statistics for de novo assembly pipeline UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Assembly type Number of transcripts Average transcript length (bp) N50 Velvet-Oases 601,915 1553 2,218 Trinity 158,863 1112 1863 Transcriptome assembly TopHat-Cufflinks-Cuffcompare was run on Atmosphere Summary statistics for Reference assembly pipeline UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Do the assembly algorithms differ with respect to detection of novel transcripts? UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics RT-PCR validations of assembled novel transcripts Transcriptome annotation Entire Transcriptome annotation was run on Atmosphere Problem 1) Cap3 merged transcripts have multiple ORF's for the same contigs Challenges and problems during annotation Cap3 transdecoder cds to bam Merged final novel transcripts bam to bed Use blastx to NCBI nr database and chose appropriate filters Filter the transcripts using 10000bp cut-off
4. 2/3/2015 4 Problem 2) Overlapping transcripts in the bed file Use bedops merge and then select the long transcript transdecoder cds to bam Merged final novel transcripts bam to bed Cap3 Problem 3) Very long transcripts due to missassembly transdecoder cds to bam Merged final novel transcripts bam to bed Filter the transcripts After filtering Cap3 Problem 4) No lines connecting the exons in bed file Use a custom script and join the lines. Check UCSC bed file guideline cds to bam Merged final novel transcripts bam to bed Cap3 transdecode r UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Results for detection of novel transcripts Number of novel transcripts detected - 3,537 (v1.2) and 2,732 (v1.5) Original Novel Original Novel o Genome annotation pipeline from TIGR, used widely elsewhere o Uses EST spliced alignments to model genes o Gene structure consistent with experimental data o Identifies alternate splicing variations o Helps to correct gene structure Program to Assemble Spliced Alignments (PASA) UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics PASA was installed and run on Atmosphere Number of gene models updated – 28,139 (v1.2) & 28,112 (v1.5) UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Results for updating Gene models Original Novel Bra000108 Original Novel Bra022192
5. 2/3/2015 5 Genome Browser (http://tinyurl.com/BrapaGenome) Conclusions • Deep RNA-Seq provides enough coverage for the detection of a large number unknown transcripts and genome improved annotation • Neither de novo assembly nor reference-based category is the best choice and hybrid assembly can offer more accurate assembly and annotation • Problems during genome re-annotation needs to be addressed before a fully annotated genome is obtained • iPlant Collaborative and XSEDE provides the systems and people to facilitate transcriptome assembly and genome reannotation ACKNOWLEDGEMENTS • Julin Maloof • Mike Covington • Cody Markelz • An Tat • Kazu Nozue • Saradadevi Lekkala • Maloof lab • Harmer lab • Cynthia Weinig • Marc T. Brock • Matthew Rubin • Brian Haas • Andy Edmonds • Edwin Skidmore • Sangeeta Kuchimanchi • Matt Vaughan