RNA Seq: Methods and Applica6ons. Prat Thiru
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1 RNA Seq: Methods and Applica6ons Prat Thiru 1
2 Outline Intro to RNA Seq Biological Ques6ons Comparison with Other Methods RNA Seq Protocol RNA Seq Applica6ons Annota6on Quan6fica6on Other Applica6ons Expression Profiling Steps and SoGware Running TopHat and Cufflinks (Commands) 2
3 Goals of Sequencing the Transcriptome Annota6on Iden6fy genes, exons, splicing events, ncrnas, etc. Novel genes or transcripts Quan6fica6on Abundance of transcripts between different condi6ons 3
4 Transcriptome: RNA World hyp://finchtalk.geospiza.com/2009/05/small rnas get smaller.html 4
5 Transcriptome: Complexity hyp:// 5
6 Comparison of Methods for Studying the Transcriptome Technology Tiling microarray cdna or EST sequencing RNA Seq Technology specifica0ons Principle Hybridiza6on Sanger sequencing High throughput sequencing Resolu.on From several to 100 bp Single base Single base Throughput High Low High Reliance on genomic sequence Yes No In some cases Background noise High Low Low Applica0on Simultaneously map transcribed regions and gene expression Dynamic range to quan.fy gene expression level Ability to dis.nguish different isoforms Ability to dis.nguish allelic expression Prac0cal issues Yes Limited for gene expression Yes Up to a few hundredfold Not prac6cal >8,000 fold Limited Yes Yes Limited Yes Yes Required amount of RNA High High Low Cost for mapping transcriptomes of large genomes High High Rela6vely low Wang, Z. et al. RNA Seq: a revolu.onary tool for transcriptomics Nature Reviews Gene6cs (2009) 6
7 RNA Seq Experiment Wang, Z. et al. RNA Seq: a revolu.onary tool for transcriptomics Nature Reviews Gene6cs (2009) 7
8 Outline Intro to RNA Seq Biological Ques6ons Comparison with Other Methods RNA Seq Protocol RNA Seq Applica6ons Annota6on Quan6fica6on Other Applica6ons Expression Profiling Steps and SoGware Running TopHat and Cufflinks (Commands) 8
9 RNA Seq Applica6ons Annota6on: Alterna6ve Splicing Events Ozsolak, F. and Milos, P. RNA sequencing: advances, challenges and opportuni.es Nature Reviews Gene6cs (2011) 9
10 RNA Seq Applica6ons Annota6on: Iden6fy Known and Novel Transcripts Known exons/gene Mapped Reads: novel exon or gene? Unmapped Reads: novel splice junc6ons? GuYman, M. et al Ab ini.o reconstruc.on of cell type specific transcriptomes in mouse reveals the conserved mul. exonic structure of lincrnas Nature Biotechnology (2010) Trapnell, C. et al Transcript assembly and quan.fica.on by RNA Seq reveals unannotated transcripts and isoform switching during cell differen.a.on Nature Biotechnology (2010) 10
11 Assembly and Mapping RNA Seq Op6ons: Align and then assemble Assemble and then align Align to genome transcriptome Haas, B.J., and Zody, M.C. Advancing RNA Seq analysis Nature Biotechnology (2010) 11
12 RNA Seq Applica6ons Quan6fica6on: Expression Profiling Mortazavi A., et al. Mapping and quan.fying mammalian transcriptomes by RNA Seq Nature Methods (2008) 12
13 Need for Normaliza6on More reads mapped to a transcript if it is i) long ii) at higher depth of coverage Normalize such that i) features of different lengths ii) total sequence from different condi6ons can be compared 13
14 Quan6fying Expression: RPKM RPKM: Reads Per Kilobase per Million mapped reads RPKM = C : Number of mappable reads on a feature (eg. transcript, exon, etc.) L: Length of feature (in kb) N: Total number of mappable reads (in millions) Mortazavi A., et al. Mapping and quan.fying mammalian transcriptomes by RNA Seq Nature Methods (2008) 14
15 RPKM Example Gene A 600 bases Gene B 1100 bases Gene C 1400 bases RPKM = 12/(0.6*6) = 3.33 RPKM = 24/(1.1*6) =3.64 RPKM = 11/(1.4*6) = 1.31 Sample 1 C=12 C=24 C = 11 N = 6M Sample 2 C=19 C=28 C = 16 N = 8M RPKM = 19/(0.6*8) = 3.96 RPKM = 28/(1.1*8) =1.94 RPKM = 16/(1.4*8) =
16 Quan6fying Expression: FPKM FPKM: Fragments Per Kilobase of transcript per Million fragments mapped Analogous to RPKM but does not use read counts. the rela6ve abundances of transcripts are described in terms of the expected biological objects (fragments) observed from an RNA Seq experiment, which in the future may not be represented by single read Trapnell, C. et al Transcript assembly and quan.fica.on by RNA Seq reveals unannotated transcripts and isoform switching during cell differen.a.on Nature Biotechnology (2010) 16
17 Quan6fying Expression: Normaliza6on Methods Total count (eg. RPKM) Upper Quar6le (eg. 75 th percen6le):similar to Total count but per lane upperquar6le of counts for genes with reads in at least one lane. Quan6le: For each lane the distribu6on of read counts is matched to a reference distribu6on defined in terms of median counts Bullard, J., et al. Evalua.on of sta.s.cal methods for normaliza.on and differen.al expression in mrna Seq experiments BMC Bioinforma6cs (2010) 17
18 RNA Seq Applica6ons: Gene Fusion Ozsolak, F. and Milos, P. RNA sequencing: advances, challenges and opportuni.es Nature Reviews Gene6cs (2011) 18
19 Outline Intro to RNA Seq Biological Ques6ons Comparison with Other Methods RNA Seq Protocol RNA Seq Applica6ons Iden6fying Transcripts Quan6fica6on Other Applica6ons Expression Profiling Steps and SoGware Running TopHat and Cufflinks (Commands) 19
20 Expression Profiling Workflow QC: Filter Short Reads (See Hot Topics on Mapping NGS Reads) FASTX Toolkit FastQC R : ShortRead Align and Assemble or Assemble and Align Align with TopHat, assemble with Cufflinks Computa6onal Analysis: Quan6fy Expression, or other applica6ons Cuffcompare, Cuffdiff SAMtools, BEDtools R: edger, DESeq Visualize Data IGV (See Hot Topics on IGV) UCSC Genome Browser 20
21 The Tuxedo Tools hyp://mged12 deep sequencing analysis.wikispaces.com/file/view/cole_mged_tutorial_slides.pdf 21
22 TopHat Algorithm Trapnell, C., et al TopHat: discovering splice junc.ons with RNA Seq Bioinforma6cs (2009) 22
23 Cufflinks Algorithm Trapnell, C., et al Transcript assembly and quan.fica.on by RNA Seq reveals unannotated transcripts and isoform switching during cell differen.a.on Nature Biotechnology (2010) 23
24 Outline Intro to RNA Seq Biological Ques6ons Comparison with Other Methods RNA Seq Protocol RNA Seq Applica6ons Iden6fying Transcripts Quan6fica6on Other Applica6ons Expression Profiling Steps and SoGware Running TopHat and Cufflinks (Commands) 24
25 Running TopHat: Align Reads TopHat Manual: hyp://tophat.cbcb.umd.edu/ manual.html Running TopHat on Tak Usage: tophat [op6ons] <bow6e_index> <reads1[,reads2,...,readsn]> [reads1[,reads2,...,readsn]] eg. bsub tophat p 2 solexa1.3 quals max mul6hits 5 o s_1_tophat_out /nfs/genomes/ mouse_gp_jul_07_no_random/bow6e/mm9 s_1_sequence.txt Op6ons (See Manual for all available op6ons): o/ output dir Sets the name of the directory in which TopHat will write all of its output. solexa quals Use the Solexa scale for quality values in FASTQ files. solexa1.3 quals As of the Illumina GA pipeline version 1.3, quality scores are encoded in Phred scaled base 64. Use this op6on for FASTQ files from pipeline 1.3 or later. p/ num threads Use this many threads to align reads. The default is 1. g/ max mul6hits Instructs TopHat to allow up to this many alignments to the reference for a given read, and suppresses all alignments for reads with more than this many alignments. The default is
26 TopHat Output Output of TopHat is a bam file. Binary version of Sequence Alignment/Map (SAM) file Use Integra6ve Genomics Viewer (IGV) to view bam file or use SAMtools to analyze bam file eg. SAM File WICMT SOLEXA:1:20:670:1533# 137 chr M * 0 0 CTGGATCTGGACCTGGACCTGGATCTATAT :::::::::::::::: ::::::::::::: NM:i:1 NH:i:2 CC:Z:chr6 CP:i: WICMT SOLEXA:1:69:135:1285# 89 chr M * 0 0 TGCCTAAACTTATTAAGGCAGGCCATGGGC :((/+:::(+:+':/:+++&+//':++::: NM:i:2 NH:i:4 CC:Z:chr7 CP:i: WICMT SOLEXA:1:84:584:747# 153 chr M * 0 0 AGCAAGTTTTTTNTTAGCCCTAGATTCCAG ::::::::::::%::::::::::::::::: NM:i:1 NH:i:5 CC:Z:= CP:i: WICMT SOLEXA:1:75:1357:1675# 163 chr M = GTGGCTTTGTGGTCTTCACCAACCTTTCTC :::::::::::::::::::::::::::::: NM:i:1 NH:i:1 WICMT SOLEXA:1:75:1357:1675# 83 chr M = CTGTAGGTGTAATCCTAAATTCTTATTACG :::::::::::::::::::::::::::::: NM:i:0 NH:i:1 WICMT SOLEXA:1:8:59:283# 153 chr M * 0 0 TTTCTGCTTTGATTATGGTACTGATGTCTG :::::::::::4:::::::::::::::::: NM:i:2 NH:i:2 CC:Z:chr5 CP:i: WICMT SOLEXA:1:12:1161:945# 89 chr M * 0 0 TCTACATAGCCCAAACTGGCTTTGGACTCT :::::::::::::::::::::::::::::: NM:i:0 NH:i:3 CC:Z:chr10 CP:i: WICMT SOLEXA:1:45:1469:1826# 73 chr M * 0 0 CAAGTATTTAATGTTTTCATTAAATTGTTT ::::::::::::::::::::::::::4::: NM:i:0 NH:i:2 CC:Z:chr11 CP:i: WICMT SOLEXA:1:14:536:150# 73 chr M * 0 0 CTGGAAGACAATGTCCAAAAACTCTGAATC :::::::::::::::::::::::::%::&: NM:i:1 NH:i:2 CC:Z:chr11 CP:i: WICMT SOLEXA:1:66:646:1188# 137 chr M * 0 0 AAAAAAAAAACACCACCCCCAACAAAAAAA ''0++++:00::.&:::,:,: NM:i:2 NH:i:5 CC:Z:chr10 CP:i:
27 Cufflinks: Assemble and Quan6fy Reads Cufflinks Manual: hyp://cufflinks.cbcb.umd.edu/manual.html Running Cufflinks on Tak Op6onal: Supply annota6on in GTF format with G op6on Usage: cufflinks [op6ons] <hits.bam> eg. bsub cufflinks p 2 o s_1_cufflinks_out s_1_tophat_out/accepted_hits.bam eg. cufflinks will assemble and quan6fy using known transcripts using g~ file supplied bsub cufflinks p 2 G transcripts.g~ accepted_hits.bam 27
28 Cufflinks Output Output of Cufflinks is a GTF file with assembled isoforms eg. chr1 Cufflinks transcript gene_id "Neurl3"; transcript_id "NM_153408"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Neurl3"; transcript_id "NM_153408"; exon_number "1"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Neurl3"; transcript_id "NM_153408"; exon_number "2"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Neurl3"; transcript_id "NM_153408"; exon_number "3"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Neurl3"; transcript_id "NM_153408"; exon_number "4"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks transcript gene_id "Arid5a"; transcript_id "NM_145996"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Arid5a"; transcript_id "NM_145996"; exon_number "1"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Arid5a"; transcript_id "NM_145996"; exon_number "2"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Arid5a"; transcript_id "NM_145996"; exon_number "3"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Arid5a"; transcript_id "NM_145996"; exon_number "4"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; chr1 Cufflinks exon gene_id "Arid5a"; transcript_id "NM_145996"; exon_number "5"; FPKM " "; frac " "; conf_lo " "; conf_hi " "; cov " "; 28
29 Local Resources Descrip6on of available files, see /nfs/genomes/barc_genomes_readme.txt Bow6e index /nfs/genomes/<species>/bowtie eg. /nfs/genomes/mouse_gp_jul_07_no_random/bowtie GTF files /nfs/genomes/<species>/gtf eg. /nfs/genomes/mouse_gp_jul_07/gtf 29
30 Further Reading RNA Seq Mortazavi, A., et al. Mapping and quan.fying mammalian transcriptomes by RNA Seq Nature Methods 5(7): (2008) Wang, Z., at al. RNA Seq: a revolu.onary tool for transcriptomics Nature Reviews Gene6cs 10:57 63 (2009) Ozsolak, F. and Milos P.M. RNA sequencing: advances, challenges, and opportuni.es Nature Reviews Gene6cs 12:87 98 (2011) TopHat Trapnell, C., et al. TopHat: discovering splice junc.ons with RNA Seq Bioinforma6cs 25(9) (2009) Cufflinks Trapnell, C., et al. Transcript assembly and quan.fica.on by RNA Seq reveals unannotated transcripts and isoform switching during cell differen.a.on Nature Biotechnology 28(5) (2010) 30
31 Online Community Forum and Discussion hyp://seqanswers.com/ 31
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