PAG 2015-Brassica

Brassica genomics resources

Transcript

1. 2/3/2015 1 Development and Application of Genomic Resources in Brassica rapa Upendra Kumar Devisetty Postdoctoral Researcher Maloof Lab, UC Davis Outline of today’s talk • Introduction to Maloof Lab research • Current limitations of B. rapa genomic resources • Experimental design of deep RNA-Sequencing • Development of genomic resources/tools for Brassica rapa • Application of the developed genomic resources for B. rapa functional studies • Summary and Conclusions R500 IMB211 • High density Genetic map • Accurate Genome annotation R500 (oil seed cultivar) IMB211 (rapid cycling cultivar) B. rapa mapping population Research in Maloof Lab Current limitations for B. rapa genomic resources • Genome annotation (Wang et al., 2011) - In silico gene models (GENSCAN, Fganesh) - EST’s • Low resolution genetic map (Iniguez-Luy et al. 2009) - 224 molecular markers (RFLP) - 1125.3 cM total length - 5.7 cM/marker RNA-Seq Differential expression Novel transcripts Alternative splicing SNP detection Fusion transcripts RNA-Seq to the rescue Genome annotation Transcriptome assembly UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Growth Chamber, Green House, Field apical meristem Library construction  TruSeqTM v1 RNA sample Preparation 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 R500 IMB211 Quality control o 2,550 million quality controlled paired end reads
2. 2/3/2015 2 Objectives of deep RNA sequencing of B.rapa genotypes • SNP genotyping: To detect markers between the two B.rapa parents • Genome reannotation: To assemble transcriptome and improve the annotation of published B. rapa genome • Transcript profiling: Tissue/genome/condition specific expression profiling of genes • Community resource 1. SNP detection pipeline UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics (http://tinyurl.com/SNPdetect) UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Total number of SNPs SNP rate (No. of SNPs/100 bp of gene) R500 vs. IMB211 330,995 0.5 R500 vs. Chiifu* 639,788 0.83 IMB211 vs. Chiifu* 595,619 0.81 Summary of total number of SNPs detected (v1.2 B. rapa reference) * Chiifu-401 is cultivar that was used for Genome sequencing How does the SNP rate varies across the genome? UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics 2. B. rapa transcriptome assembly and genome reannotation pipeline UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Number of novel transcripts detected - 3,537 (v1.2) and 2,732 (v1.5) Original Novel Original Novel R500 De novo + Reference based assembly Blast to original transcriptome novel transcripts Accurate genome annotation: Detection of novel transcripts
3. 2/3/2015 3 Number of gene models updated – 28,139 (v1.2) & 28,112 (v1.5) UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics Original Novel Bra000108 Original Novel Bra022192 R500 B. rapa original transcripts Updated Gene models Accurate genome annotation: Updating Gene models PASA 1. Genotyping of B. rapa RIL population • 131 RILs along with parents were grown in five replications across two treatments (Total of 133 x 5 x 2 =1330 RNAseq libraries were made) IMB211 R500 Old B. rapa genetic map (RFLP based) B. rapa genetic map (v1.5) 2. Construction of B. rapa Genetic map Total number of markers Maximum spacing Marker density New map (v1.5) 1451 36.7 1 marker/0.7cM Old map 225 34.3 1 marker/5.2cM 3. eQTL mapping for shade avoidance traits P0817 Systems Genetics of Crowding Tolerance in Brassica rapa Date: Monday, January 12, 2015 Cody Markelz , University of California, Davis, CA Upendra Kumar Devisetty , University of California, Davis, CA Summary of Brassica rapa genome resources 1. B. rapa genome annotation files for mapping (v1.2 and v1.5) - Updated PASA Gene models (fasta file + bed files) - Novel transcript Gene models (fasta file + bed files) - Final transcript Gene models (fasta file + bed files) 3. Functional annotations for data analysis - Genome Ontology annotations (wego file) - SNP annotation (vcf file) 4. QTL and eQTL analysis - Molecular markers information - Genetic/Recombination map 2. SNPs between Chiifu, R500 and IMB211 (vcf files) B. rapa Genome Browser (http://tinyurl.com/BrapaGenome)
4. 2/3/2015 4 Conclusions • Deep RNA-Seq provides enough coverage for the detection of a large number of polymorphisms, discovery of unknown transcripts, and improved annotation • Neither de novo assembly nor reference-based category is the best choice and hybrid assembly can offer more accurate assembly and annotation • SNPs and improved genome annotation in this study will help researchers for accurate mRNA abundance and detection of expression QTL (eQTLs) ACKNOWLEDGEMENTS • Julin Maloof • Mike Covington • Cody Markelz • An Tat • Kazu Nozue • Saradadevi Lekkala • Hannah, Mary, Shweta • Maloof lab • Harmer lab • Cynthia Weinig • Marc T. Brock • Matthew Rubin • Brian Haas • Andy Edmonds • Edwin Skidmore • Sangeeta Kuchimanchi • Matt Vaughan