Gene Expression analysis with RNA-Seq data

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1 Gene Expression analysis with RNA-Seq data C3BI Hands-on NGS course November 24th 2016 Frédéric Lemoine

2 Plan Quality Control 3. Read Mapping 4. Gene Expression Analysis 5. Splicing/Transcript Analysis 6. Other Analyses 7. Visualization 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 2

3 Sequencing : Reminder High throupghput sequencing (HTS pour high-throughput sequencing), or NGS is a set of methods developped in 2005, that produce millions of sequences in a run, at a low cost. Example: Genome «reads» Coverage : 10 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 3

Run time 1 -> 3,5 D 12 genomes per run Max Output 1800 Gb Max Read Number 6 B Max Read Length 2x150 bp

4 Sequencers (Illumina) NextSeq 500 HiSeq 4000 X Ten Max Output 120 Gb Max Read Number 800 M Max Read Length 2x150 bp Run time 29 h 9 exomes per run Output 1500 Gb Read Number 4->5 B Read Length 2x150 bp Run time 1 -> 3,5 D 12 genomes per run Max Output 1800 Gb Max Read Number 6 B Max Read Length 2x150 bp Run time < 3 D > human genomes per year 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 4

5 Sequencing: Applications DNA-Seq: DNA Sequencing CHIP-Seq: Study of protein/dna interaction CLIP-Seq: Study Protein/RNA interaction RNA-Seq : RNA Sequencing 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 5

6 Sequencing: Applications DNA-Seq: DNA Sequencing CHIP-Seq: Study of protein/dna interaction CLIP-Seq: Study Protein/RNA interaction RNA-Seq : RNA Sequencing 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 6

7 RNA-Seq : Definition RNA-Seq allows to reveal the presence and quantity of RNA in a genome at a given moment in time «reads» Chromosome Genes Junction reads Exonic reads Coverage Qualitative + Quantitative! 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 7

8 RNA-Seq : Data types reads Illumina flowcell reads per lane sequencing lanes 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 8

9 RNA-Seq : Data types A little modification of library preparation allows to read both ends (forward and reverse) of the fragments. FastQ File 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 9

10 RNA-Seq : Data Types 1 length=76 GTCGATGATGCCTGCTAAACTGCAGCTTGACGTACTGCGGACCCTGCAGTCCAGCGCTCGTCATGGAACGCAAACG + HHHHHHHHHHHHFGHHHHHHFHHGHHHGHGHEEHHHHHEFFHHHFHHHHBHHHEHFHAH?CEDCBFEFFFFAFDF9 FASTA format >SRR length=76 GTCGATGATGCCTGCTAAACTGCAGCTTGACGTACTGCGGACCCTGCAGTCCAGCGCTCGTCATGGAACGCAAACG SFF - Standard Flowgram Format - binary format for 454 reads Colorspace (SOLiD) - 2_34_121_F3 T ;;9:;>+0*&:*.*1-.5($2$3&$570*$575&$9966$5835'665 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 10

11 RNA-Seq : FASTQ Read ID Read sequence Per base quality 1:N:0:GCCAAT GGAAAACATATTCACCCAAGACCTGT Read ID Details Column Descrip.on HWI-ST946 Unique iden8fier of sequencer 381 Project (run) iden8fier C2ABHACXX Flowcell Iden8fier 1 Lane number into flowcell 1101 Tile number in Lane 1154 X Coordinate of the read cluster in the Tile 2156 Y Coordinate of the read cluster in the Tile 1 Read number in the pair (1 or 2). Only if paired-end/mate-pair sequencing N Pass read filter :«Y» or «N».«N» indicates a bad read. 0 0 when no control bit is ac8vated GCCAAT Index of the sequence: When several samples are mul8pexed 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 11

12 RNA-Seq : FASTQ Scores 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 12

RNA-Seq : FASTQ Scores Phred Quality Score Incorrect iden.fica.on probability Base iden.fica.on precision 10 1/10 90% 20 1/100 99% 30 1/1000 99.

13 RNA-Seq : FASTQ Scores Phred Quality Score Incorrect iden.fica.on probability Base iden.fica.on precision 10 1/10 90% 20 1/100 99% 30 1/ % 40 1/ % 50 1/ % 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 13

14 RNA-Seq : Applications Measure gene expression Measure alternative splicing Detect expressed mutations Gene annotation (new exons) Detect fusion transcripts 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 14

15 RNA-Seq : Whole pipeline Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 15

16 RNA-Seq : Whole pipeline Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 16

17 RNA-Seq : Quality control Sequence quality controls Mapping quality controls We use FastQC & PicardTools 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 17

Sequence quality: FastQC Documentation http://www.bioinformatics.babraham.ac.

18 Sequence quality: FastQC Documentation 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 18

19 Sequence quality: Per base A warning will be issued if the lower quar.le for any base is less than 10, or if the median for any base is less than 25. A failure is raised if the lower quar.le for any base is less than 5 or if the median for any base is less than /12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 19

An error is raised if the most frequently observed mean quality is below 20 -

20 Sequence quality: Per sequence S1 S2 A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. An error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 20

This module will indicate a failure if the sum of the devia.ons from the normal distribu.

21 Sequence quality: GC content per sequence S1 S2 A warning is raised if the sum of the devia.ons from the normal distribu.on represents more than 15% of the reads. This module will indicate a failure if the sum of the devia.ons from the normal distribu.on represents more than 30% of the reads. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 21

22 Sequence quality: N content S2 S3 This module raises a warning if any posi.on shows an N content of >5%. This module will raise an error if any posi.on shows an N content of >20%. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 22

This module will issue an error if any sequence is found to represent more

23 Sequence quality: Overrepresented sequences S1 S2 This module will issue a warning if any sequence is found to represent more than 0.1% of the total. This module will issue an error if any sequence is found to represent more than 1% of the total. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 23

This module will fail if the difference between A and T, or G and C is greater

24 Sequence quality: Nucleotide content S1 S2 This module issues a warning if the difference between A and T, or G and C is greater than 10% in any posi.on. This module will fail if the difference between A and T, or G and C is greater than 20% in any posi.on. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 24

25 Quality control: Mapping Sample Uniquely mapped reads Mapped to too many loci Mapped to mul.ple loci Unmapped reads: too short Unmapped reads: other s % 4.93% 2.54% 11.97% 0.64% s % 6.73% 3.38% 5.29% 1.03% s % 4.10% 2.32% 6.38% 0.52% s % 5.21% 2.84% 4.29% 0.79% s % 2.92% 2.08% 8.98% 0.44% s % 5.82% 3.45% 7.36% 1.02% s % 3.28% 2.04% 6.27% 0.42% s % 3.80% 2.24% 11.67% 0.48% s % 4.17% 2.43% 10.32% 0.56% s % 4.31% 2.58% 4.52% 0.70% s % 4.96% 2.41% 14.16% 0.70% s % 11.45% 3.85% 4.65% 2.16% s % 4.13% 2.42% 7.22% 0.56% s % 7.21% 3.68% 6.54% 1.28% s % 5.39% 2.92% 6.11% 0.82% s % 3.57% 2.67% 24.05% 0.34% s % 2.88% 2.12% 1.90% 0.58% STAR 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 25

26 Quality control: Transcript coverage S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 Picard Tools 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 26

27 Quality control: Read localization Genes Exons Introns UTRs Intergenic regions rrnas 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 27

28 Quality control: Read localization % BASES Sample RIBOSOMAL CODING UTR INTRONIC INTERGENIC MRNA USABLE s1 0% 79% 15% 2% 3% 95% 76% s2 0% 76% 18% 2% 4% 93% 80% s3 0% 83% 13% 2% 2% 96% 84% s4 0% 80% 15% 2% 3% 95% 84% s5 0% 84% 13% 2% 2% 97% 82% s6 0% 76% 17% 2% 4% 93% 78% s7 0% 84% 12% 2% 2% 97% 85% s8 0% 83% 13% 2% 2% 96% 78% s9 0% 81% 14% 2% 2% 96% 79% s10 0% 83% 13% 2% 3% 96% 85% s11 0% 83% 13% 2% 3% 95% 74% s12 0% 73% 19% 3% 5% 92% 74% s13 0% 82% 14% 2% 2% 96% 82% s14 0% 74% 19% 3% 5% 93% 77% s15 0% 78% 16% 2% 3% 95% 81% s16 0% 75% 19% 2% 4% 94% 62% s17 0% 87% 10% 1% 2% 97% 91% Picard Tools 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 28

29 Quality control: Read localization Picard Tools 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 29

30 RNA-Seq : Whole pipeline Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 30

31 Mapping RNA-Seq 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 31

32 Mapping RNA-Seq: Difficult Splice Junctions! 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 32

33 Mapping RNA-Seq: Tophat Tophat pipeline Trapnell et. al. Bioinforma0cs, /12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 33

34 Mapping RNA-Seq: STAR 1) Search MMP : SA 2) Alignment clustering Dobin et. al. Bioinforma0cs, /12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 34

35 Format SAM SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*) Sequence ID Flag Chr Position Map Qual Cigar Paired end info HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M1S = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 35

36 Format SAM SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*)... Sequence Base qualities Optional tags AGAGAATCGACAAAAGGCTCTGGCCCG CCCFFFFFHHHHHJJJIJIJJJJJJJIJJJB NH:i:1 HI:i:1 AS:i:197 nm:i:0 TCTGGCCCGCAGAGCTGAGAAGTTATT DDDDDBDBDCDDDDDEDDDEDDCCAACDEEE NH:i:1 HI:i:1 AS:i:197 nm:i:0 AACGAATGTAACTTTAAGGCAGGAAAG CCCFFFFFHHHHHJJJJJJJJJJIJJJIIII NH:i:1 HI:i:1 AS:i:198 nm:i:0 ATAGAGGCCCTCTAAATAAGGAATAAA DDDDDDDFFFDDHHHHHJIIGJJJIJIGGCJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 CCTGAGATGTGCGTAGCCTCCGTGTAA CCCFFFFFHHHHHJJJJJJJJJIJIJJJJJJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 ACCCAGCCTTTACCAGCAGCGTACGGC ADDDDDDCDDDCDDDDDDDDDDDFFFFHHHH NH:i:1 HI:i:1 AS:i:198 nm:i:0 GCTGGCATGGTGGTGGGCACCCATAAT CCCFFFFDHHFHHHGIJIJJJJJJJJJJIJJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 GGGCACCCATAATCCTAGCTGCTCAGG DDDBCDCDDDDDCDDDDDDEEECCCFFFEHH NH:i:1 HI:i:1 AS:i:198 nm:i:0 GCCCTTTCAACTTTCCCTCTGGTCCTT CCCFFFFFHHHHHJJIJJIJJJGIJJJJJJJ NH:i:1 HI:i:1 AS:i:196 nm:i:1 CACATCCCCATCTGGGCCCTCTCCTTT DDDDDDDDDCBDDDDDDDDCDEFFFFFHHHH NH:i:1 HI:i:1 AS:i:196 nm:i:1 (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 36

37 Format SAM SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*) Sequence ID Flag Chr Position Map Qual Cigar Paired end info HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M1S = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 37

SAM Fields : FLAG FLAG : Combination of bitwise FLAGs Example: Decimal Flag Value 83 Binary Flag Value 20481024512256 128 64

38 SAM Fields : FLAG FLAG : Combination of bitwise FLAGs Example: Decimal Flag Value 83 Binary Flag Value To each bit corresponds a meaning 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 38

39 SAM Fields : «Explain FLAG» tool h0ps://broadins6tute.github.io/picard/explain-flags.html 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 39

40 SAM Format SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*) Sequence ID Flag Chr Position Map Qual Cigar Paired end info HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M1S = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 40

41 SAM Fields : Mapping quality Higher mapping quality = more unique = 10 log10 ( ) With p : Estimate of the probability that the alignment does not correspond to the read s true point of origin A mapping quality of < 10 indicates that there is > a 1 in 10 chances that the read truly originated elsewhere 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 41

42 SAM Format SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*) Sequence ID Flag Chr Position Map Qual Cigar Paired end info HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M1S = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 42

43 SAM Fields : CIGAR String representation of the alignment Example: 52M36890N45M3S REF : chr20 READ 3689N 52 M 45M 3S All Cigar operations 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 43

44 SAM Format SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*) Sequence ID Flag Chr Position Map Qual Cigar Paired end info HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M1S = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4320: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4274: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4333: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = HWI-ST1136:196:HS113:4:1101:4353: chr M = (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 44

45 SAM Fields : Paired-end Information REF : chr20 READ Mate read First read 3 fields: 1) Chromosome «=» if the paired read maps on the same chromosome The other chromosome otherwise 2) Position Mapping position of the paired read 3) Template Length Number of bases from the leftmost mapped base to the rightmost mapped base 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 45

46 Format SAM SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. (*)... Sequence Base qualities Optional tags AGAGAATCGACAAAAGGCTCTGGCCCG CCCFFFFFHHHHHJJJIJIJJJJJJJIJJJB NH:i:1 HI:i:1 AS:i:197 nm:i:0 TCTGGCCCGCAGAGCTGAGAAGTTATT DDDDDBDBDCDDDDDEDDDEDDCCAACDEEE NH:i:1 HI:i:1 AS:i:197 nm:i:0 AACGAATGTAACTTTAAGGCAGGAAAG CCCFFFFFHHHHHJJJJJJJJJJIJJJIIII NH:i:1 HI:i:1 AS:i:198 nm:i:0 ATAGAGGCCCTCTAAATAAGGAATAAA DDDDDDDFFFDDHHHHHJIIGJJJIJIGGCJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 CCTGAGATGTGCGTAGCCTCCGTGTAA CCCFFFFFHHHHHJJJJJJJJJIJIJJJJJJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 ACCCAGCCTTTACCAGCAGCGTACGGC ADDDDDDCDDDCDDDDDDDDDDDFFFFHHHH NH:i:1 HI:i:1 AS:i:198 nm:i:0 GCTGGCATGGTGGTGGGCACCCATAAT CCCFFFFDHHFHHHGIJIJJJJJJJJJJIJJ NH:i:1 HI:i:1 AS:i:198 nm:i:0 GGGCACCCATAATCCTAGCTGCTCAGG DDDBCDCDDDDDCDDDDDDEEECCCFFFEHH NH:i:1 HI:i:1 AS:i:198 nm:i:0 GCCCTTTCAACTTTCCCTCTGGTCCTT CCCFFFFFHHHHHJJIJJIJJJGIJJJJJJJ NH:i:1 HI:i:1 AS:i:196 nm:i:1 CACATCCCCATCTGGGCCCTCTCCTTT DDDDDDDDDCBDDDDDDDDCDEFFFFFHHHH NH:i:1 HI:i:1 AS:i:196 nm:i:1 (*) h?ps://samtools.github.io/hts-specs/samv1.pdf 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 46

47 SAM Fields : Optional TAGS Example of optional tags NM:i : Edit distance between read and reference (number of mismatches fo example) RG:id : Read group. When several samples are merged in one bam file for example NH:i : number of reported alignments for that read XS:A:+/- : if rnaseq stranded library AS:i : Alignment Score 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 47

48 SAM Fields : Optional TAGS Alignment Score AS:i (Ex Bowtie) Higher the score and the more similar the read sequence is to the reference sequence aligned to. A score is calculated by subtracting penalties for each difference (mismatch, gap, etc) and, in local alignment mode, adding bonuses for each match. Example: ACGCGATCGGACTACCATCTAGCATCGACTGCGCATAC ACGCGATCGGACTAGCATCTAGCAT--ACTGCGCATAC Type # Score Matches Mismatches 1-5 GAP 2-11 (=-5-3-3) Total 81 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 48

49 BAM Format BAM : Binary SAM (BGZF compressed) BGZF is block compression implemented on top of the standard gzip file format.13 The goal of BGZF is to provide good compression while allowing efficient random access to the BAM file for indexed queries. The BGZF format is gunzip compatible, in the sense that a compliant gunzip utility can decompress a BGZF compressed file (*) Can be: 1) Sorted by coordinates (or read names) 2) Indexed (bai file) 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 49

50 Converting SAM To BAM SAMTOOLS (*) : Samtools is a suite of programs for interacting with highthroughput sequencing data. Sam to Bam conversion > samtools view Shb o sample.bam sample.sam Sorting Bam file > samtools sort o sample_sorted.bam sample.bam (*) h?p:// 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 50

51 Indexing BAM Indexing aims to achieve fast retrieval of alignments overlapping a specified region without going through the whole alignments. BAM must be sorted by the reference ID and then the leftmost coordinate before indexing. Indexation: Indexing creates a.bai file, next to each indexed bam file Each index uses virtual file offsets into the BGZF file And after? Using a sorted bam file and an index file allows to pose queries like: What reads overlap chrx:start-end? Without decompressing and traversing the whole bam file Indexing Bam file > samtools index sample_sorted.bam 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 51

52 RNA-Seq : Whole Pipeline Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 52

53 RNA-Seq : Differential expression Two main steps: 1) Counting reads on genes 2) Statistical analysis 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 53

54 RNA-Seq : Differential expression Counting reads on genes Gene model Exons Introns Junctions 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 54

55 RNA-Seq : Differential expression Counting reads on genes Gene model Exons Introns Junctions 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 55

56 GFF Format GFF (General Feature Format) lines are based on the Sanger GFF2 specification. GFF lines have nine required fields that must be tab-separated (*) It s a text file with the following fields: 1. seqname - The name of the sequence (chromosome/scaffold) 2. source - The program that generated this feature 3. feature - Type of feature ("CDS", "start_codon", "stop_codon", "exon ) 4. start - Starting position of the feature in the sequence (starts at 1) 5. end - Ending position of the feature (inclusive). 6. score - Score between 0 and 1000 (or. if no value) 7. strand - '+', '-', or '.' 8. frame - If coding exon, frame should be 0-2: reading frame of the first base. 9. group - All lines with the same group are linked together into a single item. GTF format: Refinement of GFF format (*) 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 56

57 GTF Format Example GTF (Gene Transfer Format) is a refinement to GFF that tightens the specification. The first eight GTF fields are the same as GFF. The group field has been expanded into a list of attributes. Each attribute consists of a type/value pair. chr9 hg38_refgene stop_codon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene CDS gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene start_codon gene_id NM_020469; transcript_id NM_020469; chr9 hg38_refgene exon gene_id NM_020469; transcript_id NM_020469; (*) 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 57

net/ Modèle de gène GTF File BAM File Feature Count G1 count1 G2

58 RNA-Seq : Differential expression Subread FeatureCount : Modèle de gène GTF File BAM File Feature Count G1 count1 G2 count2 Gn countn hip:// 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 58

59 RNA-Seq : Differential expression Subread FeatureCount : Options: -t gene : feature type to count -s 1 : stranded -a gtf file : annotation file -o output file : count file «A read is said to overlap a feature if at least one read base is found to overlap the feature. For paired-end data, a fragment (or template) is said to overlap a feature if any of the two reads from that fragment is found to overlap the feature.» 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 59

60 RNA-Seq : Differential expression Statistical Analysis: DESeq2 Next presentation 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 60

61 RNA-Seq : Pipeline complet Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 61

62 Splicing analysis 3 levels of analysis: Exon Patterns Full transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 62

63 Splicing analysis: Exon level We search for exons that are differentially included in the genes 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 63

64 Splicing analysis: Exon level We search for exons that are differentially included in the genes DEXSeq (*) (*) 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 64

65 Splicing Analysis: DEXSeq Counts per exons Counts are modeled by a negative binomial distribution: One model per gene 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 65

66 Splicing Analysis: DEXSeq 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 66

67 Splicing analysis: Pattern level Différent kind of splicing patterns 1 er alternative exons; Last alternative exons; PA PA Cassette exons; Mutually exclusive exons; Intron retentions; Alternative acceptor splice sites; Alternative donor splice sites. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 67

68 Splicing analysis: Pattern level Assign each read to a pattern: Comparing counts between samples: Sort and list regulated patterns 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 68

69 Splicing analysis: Pattern level Griffith et. al., Nature Methods, /12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 69

70 Splicing analysis: Pattern level Griffith et. al., Nature Methods, /12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 70

71 Splicing analysis: Transcript level Transcript assembly Cufflinks Trinity Kissplice Etc. Analysis Cuffdiff RSEM EBSeq BitSeq Etc. 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 71

72 Splicing analysis: Cufflinks Assembly with Cufflinks Output transcripts.gtf isoforms.fpkm_tracking genes.fpkm_tracking 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 72

73 Splicing analysis: Cufflinks Cuffcompare : Compare assembled transcripts with reference annotations Track transcripts between different replicates Output output.stats output.combined.gtf output.tracking Cuffdiff Find significant differences between transcript expression, splicing, and promotor usage Output FPKM tracking files Count tracking files Read group tracking files Differential expression tests Differential splicing tests - splicing.diff 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 73

74 RNA-Seq : Whole Pipeline Raw Data Preprocessing Mapping Quality control Expression Splicing Fusion transcripts SNPs Visualization Exon Splicing Patterns Transcript 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 74

75 Integrated Genome Viewer The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations. Helga ThorvaldsdóQr, James T. Robinson, Jill P. Mesirov. Integra0ve Genomics Viewer (IGV): highperformance genomics data visualiza0on and explora0on. Briefings in Bioinforma0cs 14, (2013). 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 75

76 Integrated Genome Viewer 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 76

77 Integrated Genome Viewer Organism chromosome Genome Positions Basic usage Data Tracks 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 77

78 Integrated Genome Viewer Basic usage Gene annotations Read coverage Read alignments 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 78

79 Integrated Genome Viewer Basic usage Gene annotations SNV C->A: coverage : 73 %: 96% 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 79

80 Integrated Genome Viewer Basic usage: Mapping information 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 80

81 Integrated Genome Viewer Basic usage: Annotations 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 81

82 Integrated Genome Viewer Advanced usage : Importing a new genome 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 82

83 Integrated Genome Viewer Advanced usage : Importing datasets 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 83

84 Integrated Genome Viewer Advanced usage : Importing datasets 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 84

85 Integrated Genome Viewer Advanced usage : Igv Tools - Computing coverage data 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 85

86 Integrated Genome Viewer Advanced usage : Igv Tools - Computing coverage data 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 86

87 Integrated Genome Viewer : RNA-Seq 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 87

88 Integrated Genome Viewer : RNA-Seq 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 88

89 RNA-Seq Lots of applications Variant detection Gene Expression Splicing analysis Fusion transcript Difficult analysis: No fully detailed recommendations for all analyses 10/12/15 Institut Pasteur - C3BI Hands-on NGS course - RNA-Seq 89

10/06/2014. RNA-Seq analysis. With reference assembly. Cormier Alexandre, PhD student UMR8227, Algal Genetics Group

10/06/2014. RNA-Seq analysis. With reference assembly. Cormier Alexandre, PhD student UMR8227, Algal Genetics Group RNA-Seq analysis With reference assembly Cormier Alexandre, PhD student UMR8227, Algal Genetics Group Summary 2 Typical RNA-seq workflow Introduction Reference genome Reference transcriptome Reference