Transcriptomics.pdf - Transcriptomics/NGS in Protein...

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Transcriptomics/NGS in Protein Characterization Transcriptome: total pool ofall mRNA transcripts at a given time from a given cell, tissue, organism, culture, population, etc. NGS: Next GenerationSequencing, high-throughput sequencingtechniques, far outpacingtraditional sequencing(Sanger) Sanger vs ~ 0.1-2 Mb/day NGS ~ 100-600 Gb/day 3 years / human genome Human genome ~ 3Gb 100 human genomes / day
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mRNA structure DNA to RNA to Protein GENOME TRANSCRIPTOME PROTEOME
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Overview Traditional cDNA Approachs to Protein Sequencing and Characterization Benefits of NGS NGS Data Generation NGS Assembly NGS Data Mining Caveats of NGS Case Studies/Examples
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Traditional approaches to protein sequencing and characterization 1. Protein (typically band on gel) 2. Partial sequence information (MS/MS) 3. cDNA library generation (from mRNA) 4. Make degenerate primers 5. PCR 6. Clone PCR product into plasmid 7. Transform into E. coli 8. Sequence by Sanger
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Sanger Sequencing Overview
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NGS Sequencing Overview (mRNA = TRANSCRIPTOME) RNA Isolation Library Preparation Sequencing on Illumina platform Read Trimming Assembly (Trinity package) RSEM (expression mapping) Data Mining
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NGS Technologies X X
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Figure 1 Trends in Genetics 2008 24, 133-141DOI: (10.1016/j.tig.2007.12.007) 454 (Roche)
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Illumina Sequencing Technology
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ION Torrent (Life Technologies)
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Assembly of sequencing fragments (simplified) Isotigs: mRNA (contigs for DNA) Reads are assembled into groups of contiguoussequences Typical Raw data:10-100 million of “reads” that are 50-200 bp in length No information oftheir origin or how theyare related (except sequence overlaps)
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Bio-informatics Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011 May 15;29(7):644-52. doi: 10.1038/nbt.1883. PubMed PMID: 21572440. Groups reads into “isotigs” Trimmomatic Anthony M. Bolger, Marc Lohse, and Bjoern Usadel Trimmomatic: a flexible trimmer for Illumina sequence data Bioinformatics first published online April 1, 2014 doi:10.1093/bioinformatics/btu170 Flexible read trimmer for Illumina sequencingdata BMC Bioinformatics 2011, 12:323 doi:10.1186/1471-2105-12-323 RSEM (RNA-Seq by Expectation-Maximization) Maps read to isotigs to estimate expression level
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Example Transcriptome Data Phenol Gland Collagen Gland Accessory Gland RNA PURIFICATION RNAintegrity number 9.3 9.5 9.3 ILLUMINA SEQUENCING Number of untrimmed reads 56,650,426 43,293,488 87,720,502 Number of trimmed reads 54,764,254 40,746,558 60,426,226 ASSEMBLY Min isotig length: 201 201 201 Max isotig length: 9,916 8,373 10,084 Mean isotig length: 541.95 464.18 513.25 Standard deviation of isotig length: 562.28 439.1 522.61 Median isotig length: 339 314 337 N50 isotig length: 701 520 611 Number of isotigs: 44,774 39,426 59,030 Number of isotigs >=1kb: 5,162 3,020 5,728 Number of isotigs in N50: 8,804 8,895 12,369 Number of bases in all isotigs: 24,265,373 18,300,884 30,297,309 Number of bases in isotigs >=1kb: 9,104,355 5,044,646 10,047,098 GC Content of isotigs: 33.33% 34.00% 34.11% RSEM # transcripts withFPKM>500 174 187 240 % total transcripts with FPKM>500(#) 0.39% 0.47% 0.41% % total transcripts with FPKM>500(cumulativeFPKM) 89.80% 86.30% 76.30%
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  • Spring '09
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