Analysing re-sequencing samples. Malin Larsson WABI / SciLifeLab
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- Alberta Skinner
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1 Analysing re-sequencing samples Malin Larsson WABI / SciLifeLab
2 Re-sequencing Reference genome assembly...gtgcgtagactgctagatcgaaga...!
3 Re-sequencing IND 1! GTAGACT! AGATCGG! GCGTAGT IND 2! TGCGTAG! ATCGAAG! AGACTGC IND 3! TAGACTG! GATCGAA! GACTGCT IND 4! AGATCGA! GTAGACT! GATCGAA Reference genome assembly!...gtgcgtagactgctagatcgaaga...!
4 Re-sequencing IND 1! GTAGACT! AGATCGG! GCGTAGT IND 2! TGCGTAG! ATCGAAG! AGACTGC IND 3! TAGACTG! GATCGAA! GACTGCT IND 4! AGATCGA! GTAGACT! GATCGAA Reference genome assembly!...gtgcgtagactgctagatcgaaga...!
5 Re-sequencing IND 1! GTAGACT! AGATCGG! GCGTAGT IND 2! TGCGTAG! ATCGAAG! AGACTGC IND 3! TAGACTG! GATCGAA! GACTGCT IND 4! AGATCGA! GTAGACT! GATCGAA Reference genome assembly!...gtgcgtagactgctagatcgaaga...! IND 1! 5 GTAGACT! 12 AGTTCGG! 3 GCGTAGT IND 2! 16 TGCGTAG! 6 ATCGAAG! 7 AAACTGC IND 3! 24 AGTTCGA! 5 GTAGACT! 18 GATCGAA IND 4! 8 AGATCGA! 19 GTAGGCT! 2 GATCGAA
6 Rare variants in human
7 Exome sequencing in trios to detect de novo coding variants
8 Population genetics speciation, adaptive evolution Darwin Finches
9 Population genetics speciation, adaptive evolution Darwin Finches Heliconius Butterflies
10 Population genetics speciation, adaptive evolution Darwin Finches Heliconius Butterflies Lake Victoria cechlid fishes
11 Paired end sequencing
12 Pair-end reads Two.fastq files containing the reads are created The order in the files are identical and naming of reads are the same with the exception of the end The naming of reads is changing and depends on software version 5:1101:1160:2197 1:N:0:ATCACG! CAGTTGCGATGAGAGCGTTGAGAAGTATAATAGG AGTTAAACTGAGTAACAGGATAAGAAATAGTGAG ATATGGAAACGTTGTGGTCTGAAAGAAGATGT! +! JIHGIIJJJJIJIJIJJJJIIJJJJJIIEIHHIJ HGHHHHHDFFFEDDDDDCDDDCDDDDDDDCDC! 5:1101:1160:2197 2:N:0:ATCACG! CTTCGTCCACTTTCATTATTCCTTTCATACATG CTCTCCGGTTTAGGGTACTCTTGACCTGGCCTT TTTTCAAGACGTCCCTGACTTGATCTTGAAACG! +! CCCFFFFFHHHHHJJJJIJJJJJJJJJJJJJJJ JJJJJJJIJIJGIJHBGHHIIIJIJJJJJJJJI JJJHFFFFFFDDDDDDDDDDDDDDDEDCCDDDD!
13 Pair-end reads Two.fastq files containing the reads are created The order in the files are identical and naming of reads are the same with the exception of the end The naming of reads is changing and depends on software version 5:1101:1160:2197 1:N:0:ATCACG! CAGTTGCGATGAGAGCGTTGAGAAGTATAATAGG AGTTAAACTGAGTAACAGGATAAGAAATAGTGAG ATATGGAAACGTTGTGGTCTGAAAGAAGATGT! +! JIHGIIJJJJIJIJIJJJJIIJJJJJIIEIHHIJ HGHHHHHDFFFEDDDDDCDDDCDDDDDDDCDC! 5:1101:1160:2197 2:N:0:ATCACG! CTTCGTCCACTTTCATTATTCCTTTCATACATG CTCTCCGGTTTAGGGTACTCTTGACCTGGCCTT TTTTCAAGACGTCCCTGACTTGATCTTGAAACG! +! CCCFFFFFHHHHHJJJJIJJJJJJJJJJJJJJJ JJJJJJJIJIJGIJHBGHHIIIJIJJJJJJJJI JJJHFFFFFFDDDDDDDDDDDDDDDEDCCDDDD!
14 Paired end sequencing Read 1 Unknown sequence Read 2 Insert size
15 Adapter trimming Module load cutadapt! 3' Adapter or When the adaptor has been read in sequencing, it is present in reads and needs to be removed prior to mapping 5' Adapter or Read Anchored 5' adapter Adapter Removed sequence
16 Basic quality control - FASTQC Module load FastQC!
17 Genome Analysis Tool Kit (GATK) Mapping Alignment refinement Variant discovery Callset refinement 17
18 GATK
19 When in doubt, google it!
20 Steps in resequencing analysis Setup programs, data map reads to a reference bam file process alignments bam file find best placement of reads realign indels remove duplicates recalibrate base quality identify/call variants vcf file statistical algorithms to detect true variants
21 Mapping to reference genome 21
22 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
23 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
24 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
25 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
26 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
27 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
28 brute force TCGATCC! x! GACCTCATCGATCCCACTG!
29 brute force TCGATCC!! GACCTCATCGATCCCACTG!
30 hash tables build an index of the reference sequence for fast access seed length !! GACCTCATCGATCCCACTG! GACCTCA!à chromosome 1, pos 0! ACCTCAT!à chromosome 1, pos 1! CCTCATC!à chromosome 1, pos 2! CTCATCG!!!à chromosome 1, pos 3! TCATCGA!!!à chromosome 1, pos 4! CATCGAT!!à chromosome 1, pos 5! ATCGATC!!à chromosome 1, pos 6! TCGATCC!!à chromosome 1, pos 7! CGATCCC!à chromosome 1, pos 8! GATCCCA!à chromosome 1, pos 9!
31 hash tables build an index of the reference sequence for fast access TCGATCC?! !! GACCTCATCGATCCCACTG! GACCTCA!!à chromosome 1, pos 0! ACCTCAT!!à chromosome 1, pos 1! CCTCATC!!à chromosome 1, pos 2! CTCATCG!!!à chromosome 1, pos 3! TCATCGA!!!à chromosome 1, pos 4! CATCGAT!!à chromosome 1, pos 5! ATCGATC!!à chromosome 1, pos 6! TCGATCC!!à chromosome 1, pos 7! CGATCCC!à chromosome 1, pos 8! GATCCCA!à chromosome 1, pos 9!
32 hash tables build an index of the reference sequence for fast access TCGATCC = chromosome 1, pos 7! !! GACCTCATCGATCCCACTG! GACCTCA!!à chromosome 1, pos 0! ACCTCAT!!à chromosome 1, pos 1! CCTCATC!!à chromosome 1, pos 2! CTCATCG!!!à chromosome 1, pos 3! TCATCGA!!!à chromosome 1, pos 4! CATCGAT!!à chromosome 1, pos 5! ATCGATC!!à chromosome 1, pos 6! TCGATCC!!à chromosome 1, pos 7! CGATCCC!à chromosome 1, pos 8! GATCCCA!à chromosome 1, pos 9!
33 Burroughs-Wheeler Aligner algorithm used in computer science for file compression original sequence can be reconstructed BWA (module add bwa) Burroughs-Wheeler Aligner
34 Input to mapping Reference genome Sample data Reference.fasta Reference.fai R1.fastq R2.fastq >Potra000002! CACGAGGTTTCATCATGGACTTGGCACCATAAAA GTTCTCTTTCATTATATTCCCTTTAGGTAAAATG ATTCTCGTTCATTTGATAATTTTGTAATAACCGG CCTCATTCAACCCATGATCCGACTTGATGGTGAA TACTTGTGTAATAACTGATAATTTACTGTGATTT ATATAACTATCTCATAATGGTTCGTCAAAATCTT TTAAAAGATAAAAAAAACCTTTATCAATTATCTA TATAAATTCAAATTTGTACACATTTACTAGAAAT TACAACTCAGCAATAAAATTGACAAAATATAAAA CAGAACCGTTAAATAAGCTATTATTTATTTCATC ACAAAACATCTAAGTCAAAAATTTGACATAAGTT TCATCAATTTACAAACAAACACAATTTTACAAAA TCTCAACCAAACCATAACATGTACAAATTATAAA TATCAACAATATTGTTTGAGAAAAAAACTATAAC ACAAGTAAATACCAAAAAAAATACATATACTACA AAACAATATATAAAAAATTAACATTTTAAAATTG 5:1101:1160:2197 1:N:0:ATCACG! CAGTTGCGATGAGAGCGTTGAGAAGTATAATAGG AGTTAAACTGAGTAACAGGATAAGAAATAGTGAG ATATGGAAACGTTGTGGTCTGAAAGAAGATGT! +! JIHGIIJJJJIJIJIJJJJIIJJJJJIIEIHHIJ 5:1101:1448:2164 1:N:0:ATCACG! NAGATTGTTTGTGTGCCTAAATAAATAAATAAAT AAAAATGATGATGGTCTTAAAGGAATTTGAAATT AAGATTGAGATATTGAAAAAGCAGATGTGGTC! +! #1=DDFFEHHDFHHJGGIJJJJGIHIGIJJJJJI IJJJJIJJJFIJJF? FHHHIIJJIIJJIGIIJJJIJIGHGHIIJJIHGH
35 Output from mapping
36 Output - SAM format HEADER SN:17 ID:bwa PN:bwa VN: r1126 CL:bwa sampe human_17_v37.fasta NA06984.ILLUMINA.low_coverage.17_1.sai NA06984.ILL! UMINA.low_coverage.17_2.sai /proj/g /labs/gatk/fastq/wgs/na06984.illumina.low_coverage.17q_1.fq /proj/g /labs/! gatk/fastq/wgs/na06984.illumina.low_coverage.17q_2.fq!! ALIGNMENT SECTION SRR M = CATCTCTATCAGAATTAG! AGTAAAAGACCCCTGCCCCCAAGCAAAGGATACAAAGGAAATGAAAGTTTGAATAATA A?@=:@?@BB@@B@@AA@ 76M,0;!?@@?;@@ABAB8@@<?B@B;A@@@B@@A>A@>>:<8A@@B@@@@B@@AAA@@@B@@=@! XT:A:R NM:i:0 SM:i:0 AM:i:0 X0:i:2 X1:i:0 XM:i:0 XO:i:0 XG:i:0 MD:Z:76 XA:Z:17, ,! SRR M = AATGTGAGAGGAAGGTTT! AACATAACACATCTCTATCAGAATTAGAGTAAAAGACCCCTGCCCCCAAGCAAAGGAT A@>@@@############ 76M,1;! >BA@>=@?<@@AA@A?@@@@;@AAB;A?AA@A<A<A<@?>A@@A@>?3=>A;?@0>>@! XT:A:U NM:i:0 SM:i:23 AM:i:0 X0:i:1 X1:i:1 XM:i:0 XO:i:0 XG:i:0 MD:Z:76 XA:Z:17, ,! SRR M = TAAAGAGGGACACCACGT! AATGATAGAAAAGCACAATTTGTAACGAAAGAACGCTCGAAATCTGCATCCTCCTGAC BAA>B@>BA??A?@A?A> XT:A:U NM:i:0 SM:i:37 AM:i:37 X0:i:1 X1:i:0 XM:i:0 XO:i:0 XG:i:0 MD:Z:76! Read name Chr Start position Sequence Quality
37 Steps in resequencing analysis Setup programs, data map reads to a reference bam file process alignments bam file find best placement of reads realign indels remove duplicates recalibrate base quality identify/call variants vcf file statistical algorithms to detect true variants
38 Processing BAM files.bam Realign around indels.realign.bam Remove duplicates.realign.dedup.bam Recalibrate base quality.realign.dedup.recal.bam
39 Realign around indels 39
40 Realign around indels mapping is done one read at a time single variants may be split into multiple variants solution: realign these regions taking all reads into account
41 Local realignment ----! ----! ----! ----! A A A A A T T T T T A A A A A or? can be performed using GATK commands: RealignerTargetCreator followed by IndelRealigner ----! ----! ----! ----! ----! ----! ----! ----! ----! A A A T T T A A A A T A A A T T A A A T A A T A A T A
42
43 Remove duplicates 43
44 PCR duplicates The same DNA fragment sequenced multiple times not independent observations skew allele frequency and read depth errors double counted PCR duplicates occur during library prep, or optical duplicates (one cluster read as two) Reading:
45 Paired end sequencing
46 Identify PCR duplicates Single or paired reads that map to identical positions Mark and/or remove them! Picard MarkDuplicates
47 Base quality score recalibration Base quality scores are per-base estimates of error emitted by the sequencing machines (i.e. probability that the called base is wrong). Scores produced by the machines are subject to various sources of systematic technical error, leading to over- or under-estimated base quality scores in the data.
48 Base quality score recalibration 1. Empirically models errors in the quality scores using a machine learning process 2. Adjusts the quality scores to minimize errors Empirical modeling of error in quality score At a given position in the genome: Compare RMSE = ( Qualityscore EmpiricalScore)2 The average base quality scores over all reads With Observed error rate, i.e. fraction of reads that differ from the reference genome sequence at non-polymorphic sites RMSE = Root mean square error Measure of the difference between predicted values and the values actually observed i.e. base qualities vs fraction of reads that differ from reference
49 Base quality score recalibration After recalibration, the quality scores in the QUAL field in the output BAM are more accurate in that the reported quality score is closer to its actual probability of mismatching the reference genome. 49
50 Results from BQSR
51 Residual error by machine cycle
52 Residual error by dinucleotide
53 Steps in resequencing analysis Setup programs, data map reads to a reference bam file process alignments bam file find best placement of reads realign indels remove duplicates recalibrate base quality identify/call variants vcf file statistical algorithms to detect true variants
54 Variant calling 54
55 simple pileup methods Reference: acacagatagacatagacatagacagatgag!! acacagatagacatagacatagacagatgag acacacatagacatagacatagacagatgag acacagatagacatagacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatatacatagacagttgag acacagatagacatagacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatagacatagacagatgag!
56 Baeysian population-based calling
57 GATK haplotype caller
58 GATK best practice for cohorts
59 VCF format ##fileformat=vcfv4.0 ##filedate= ##source=myimputationprogramv3.1 ##reference=1000genomespilot-ncbi36 ##phasing=partial ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency"> ##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129"> ##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership"> ##FILTER=<ID=q10,Description="Quality below 10"> ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA rs G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0 0:48:1:51,51 1 0:48:8:51,51 1/1:43:5:., T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0 0:49:3:58,50 0 1:3:5:65,3 0/0:41: rs A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1 2:21:6:23,27 2 1:2:0:18,2 2/2:35: T. 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0 0:54:7:56,60 0 0:48:4:51,51 0/0:61: microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3
60 VCF format ##fileformat=vcfv4.0 ##filedate= ##source=myimputationprogramv3.1 ##reference=1000genomespilot-ncbi36 ##phasing=partial ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=.,Type=Float,D Frequency"> ##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129"> ##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership"> ##FILTER=<ID=q10,Description="Q ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA rs G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0 0:48:1:51,51 1 0:48:8:51, T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0 0:49:3:58,50 0 1:3:5:65,3 0/0:41: rs A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1 2:21:6:23, T. 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0 0:54:7:56,60 0 0:48:4:51,51 0/0:61: microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3
61 gvcf format ##fileformat=vcfv4.0 ##filedate= ##source=myimputationprogramv3.1 ##reference=1000genomespilot-ncbi36 ##phasing=partial ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency"> ##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129"> ##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership"> ##FILTER=<ID=q10,Description="Quality below 10"> ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> ##GVCFBlock=minGQ=0(inclusive),maxGQ=5(exclusive) ##GVCFBlock=minGQ=20(inclusive),maxGQ=60(exclusive) ##GVCFBlock=minGQ=5(inclusive),maxGQ=20(exclusive) #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA rs G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0 0:48:1:51,51 1 0:48:8:51,51 1/1:43:5:., T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0 0:49:3:58,50 0 1:3:5:65,3 0/0:41: rs A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1 2:21:6:23,27 2 1:2:0:18,2 2/2:35: T. 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0 0:54:7:56,60 0 0:48:4:51,51 0/0:61: microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3
62 Discovery of structural variants
63 1) Read depth analysis Depth of coverage can be used to estimate copy number variation in depth indicate copy number variants Difficult to distinguish homozygotes and heterozygotes
64 2) Paired end analysis Paired ends have a fixed length between them Genomic rearrangements cause them to vary Deletion: reads will map too far apart Insertion: reads will map too close Inversion: reads in wrong orientation more reliable with long pairs
65 3) Split-read alignments Base-level breakpoint resolution Only works with long reads short reads have many spurious splits Caveat: breakpoints may be duplicated reads won't split if single alignment is good
66 4) De novo assembly to identify structural variants Assemble contigs Align to reference Look for insertions, deletions, rearrangements
67 Annotation of variants Compare variants with annotation of the reference genome -protein coding exon -untranslated exon -regulatory region Gives clues to expected effect of variant
68 Annotation of variants Compare variants with annotation of the reference genome -protein coding exon -untranslated exon -regulatory region Gives clues to expected effect of variant Most commonly used tools are Annovar and SNPEff
69 Downstream analysis Software for file handling BEDTools enables genome arithmetics (module add BEDTools) Vcftools for manipulations of vcf-files - (module add vcftools) bcftools for manipulations of bcf-files - (module add bcftools) bamtools for manipulations of bam-files - (module add bamtools) Annotations to compare with can be extracted from e.g the UCSC browser, ensemble database, etc Scripting yourself with.. Perl / python / bash / awk
70 Excercise Setup programs, data map reads to a reference bam file process alignments bam file find best placement of reads realign indels remove duplicates recalibrate base quality identify/call variants vcf file statistical algorithms to detect true variants
71 Overview of excercise 1. Access to data and programs 2. Mapping (BWA) 3. Merging alignments (BWA) 4. Creating BAM files (Picard) 5. Processing files (GATK) 6. Variant calling and filtering (GATK) 7. Viewing data (IGV) X. Optional extras
72 1) Access to data Data comes from 1000 genomes pilot project 81 low coverage (2-4 x) Illumina WGS samples 63 Illumina exomes 15 low coverage 454 ~ 1 Mb from chromosome 17 Fastq files located in /sw/courses/ngsintro/gatk this folder is read only
73 1) Access to programs BWA and samtools modules can be loaded: module load bioinfo-tools!!!module load bwa!!!module load samtools! picard and GATK are are set of java programs: /bubo/sw/apps/bioinfo/gatk/3.4-46/!!!/bubo/sw/apps/bioinfo/picard/1.69/kalkyl/!
74 Index reference genome Sample1: NA06984 read1.fq read2.fq read1.fq Sample2 read2.fq Sample3 Mapping bwa aln bwa aln bwa aln bwa aln Process alignments bwa sampe AddOrReplaceReadGroups BuildBamIndex RealignerTargetCreator IndelRealigner bwa sampe AddOrReplaceReadGroups BuildBamIndex RealignerTargetCreator IndelRealigner Note: Reference genome only needs to be indexed once MarkDuplicates MarkDuplicates BuildBamIndex BuildBamIndex BaseRecalibrator BaseRecalibrator PrintReads PrintReads Genotyping NA06984.realign.dedup.recal.bam HaplotypeCaller NA06984.realign.dedup.recal.bam HaplotypeCaller NA06984.g.vcf Sample2.g.vcf Sample3.g.vcf Joint genotyping Filtering GenotypeGVCFs AllSamples.vcf VariantFiltration AllSamples.filtered.vcf View in IGV
75 Naming conventions Initial file name according to information about the content NA06984.ILLUMINA.low_coverage.17q For each step of the exercise, create a new file NA06984.ILLUMINA.low_coverage.17q.merge.bam NA06984.ILLUMINA.low_coverage.17q.merge.realign.bam NA06984.ILLUMINA.low_coverage.17q.merge.realign.dedup.bam NA06984.ILLUMINA.low_coverage.17q.merge.realign.dedup.recal.bam
76 Regarding index files Many steps in the exercise require that certain input files are indexed. For example the reference genome and the bam file. Index files are usually NOT given as direct input to programs. The programs assume that index files are located in the same folder as the indexed input file. Example: bwa sampe <ref> <sai1> <sai2> <fq1> <fq2> > align.sam! If you give the following file as reference: ~/glob/gatk/human_17_v37.fasta BWA requires that index files exist in the folder ~/glob/gatk/
77 Viewing data with IGV
78 GATK Support Forum
79 Index reference genome Sample1: NA06984 read1.fq read2.fq read1.fq Sample2 read2.fq Sample3 Mapping bwa aln bwa aln bwa aln bwa aln Process alignments bwa sampe AddOrReplaceReadGroups BuildBamIndex RealignerTargetCreator IndelRealigner bwa sampe AddOrReplaceReadGroups BuildBamIndex RealignerTargetCreator IndelRealigner Note: Reference genome only needs to be indexed once MarkDuplicates MarkDuplicates BuildBamIndex BuildBamIndex BaseRecalibrator BaseRecalibrator PrintReads PrintReads Genotyping NA06984.realign.dedup.recal.bam HaplotypeCaller NA06984.realign.dedup.recal.bam HaplotypeCaller NA06984.g.vcf Sample2.g.vcf Sample3.g.vcf Joint genotyping Filtering GenotypeGVCFs AllSamples.vcf VariantFiltration AllSamples.filtered.vcf View in IGV
80 2) Align each paired end separately!!! bwa aln <ref> <fq1> > <sai1>! bwa aln <ref> <fq2> > <sai2>! <ref>!= reference sequence <fq1>!= fastq reads seq 1 of pair <fq2>!= fastq reads seq 2 of pair <sai1>!= alignment of seq 1 of pair <sai2>!= alignment of seq 2 of pair!
81 3) Merging alignments Combine alignments from paired ends into a SAM file bwa sampe <ref> <sai1> <sai2> <fq1> <fq2> > align.sam!! <ref>!= reference sequence <sai1>!= alignment of seq 1 of pair <sai2>!= alignment of seq 2 of pair <fq1>!= fastq reads seq 1 of pair <fq2>!= fastq reads seq 2 of pair
82 4) Creating and editing BAM files Create.bam and add read groups (picard) java -Xmx2g jar /path/addorreplacereadgroups.jar! INPUT=<sam file>! OUTPUT=<bam file>!... more options! index new BAM file (picard) java -Xmx2g jar /path/buildbamindex.jar! INPUT=<bam file>!... more options!
83 5) Process BAM mark problematic indels (GATK) java -Xmx2g -jar /path/genomeanalysistk.jar! -I <bam file>! -R <ref file>! -T RealignerTargetCreator! -o <intervals file>!! realign around indels (GATK) java -Xmx2g -jar /path/genomeanalysistk.jar! -I <bam file>! -R <ref file>! -T IndelRealigner! -o <realigned bam>! -targetintervals <intervals file>!
84 5) Process BAM cont. mark duplicates (picard) java -Xmx2g -jar /path/markduplicates.jar! INPUT=<input bam>! OUTPUT=<marked bam>! METRICS_FILE=<metrics file>! quality recalibration - compute covariation (GATK) java -Xmx2g -jar /path/genomeanalysistk.jar! -T BaseRecalibrator! -I <input bam>! -R <ref file>! -knownsites <vcf file>! -recalfile <calibration table>! Second step quality recalibration - compute covariation (GATK) java -Xmx2g -jar /path/genomeanalysistk.jar! -T PrintReads -BQSR <calibration table>! -I <input bam>! -R <ref file>! -o <recalibrated bam>!
85 6) Variant calling HaplotypeCaller (GATK) java -Xmx2g! -jar /path/genomeanalysistk.jar! -T HaplotypeCaller! -R <ref file>! -I <bam>! -o <filename.g.vcf>! -emitrefconfidence GVCF! -variant_index_type LINEAR! -variant_index_parameter !
86 Processing files NEXT: repeat steps 2-5 for at least another sample!
87 6) Genotyping gvcf Assigning genotypes based on joint analysis of multiple samples java -Xmx2g -jar /path/genomeanalysistk.jar! -T GenotypeGVCFs! -R <ref file>! --variant <sample1>.g.vcf! --variant <sample2>.g.vcf!...! -o <output vcf>!
88 6) Filtering variants variant filtering java -Xmx2g -jar /path/genomeanalysistk.jar! -T VariantFiltration! -R <reference>! -V <input vcf>! -o <output vcf>! --filterexpression "QD<2.0" --filtername QDfilter! --filterexpression "MQ<40.0" --filtername MQfilter! --filterexpression "FS>60.0" --filtername FSfilter! --filterexpression "HaplotypeScore>13.0" --filtername HSfilter! --filterexpression "MQRankSum<-12.5" --filtername MQRSfilter! --filterexpression "ReadPosRankSum<-8.0" --filtername RPRSfilter!
89 7) Viewing data with IGV
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91 pipeline (1) 2. Mapping bwa index samtools faidx! bwa aln 3. Merging alignments bwa sampe 4. Creating BAM files picard AddOrReplaceReadGroups! picard BuildBamIndex! raw reads: fastq (2 per sample) reference genome: fasta mapped reads: 2 x sai merged SAM files single BAM file per sample: indexed, sorted, +read groups
92 pipeline (2) single BAM file per sample: indexed, sorted, +read groups 5. Processing files (GATK) GATK RealignerTargetCreator! GATK IndelRealigner! picard MarkDuplicates! GATK CountCovariates! picard MergeSamFiles 6. Variant calling and filtering (GATK) GATK UnifiedGenotyper! GATK VariantFiltration 7. Viewing data (IGV) merged BAM file: +realigned around indels +mark/remove duplicates +quality recalibrations VCF file: +filtered variants
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