Analysing re-sequencing samples. Anna Johansson WABI / SciLifeLab
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1 Analysing re-sequencing samples Anna Johansson 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 + B@CFFFFFHHHHHGJJJJJJJJJJJFHHIIIIJJ 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 + B@CFFFFFHHHHHGJJJJJJJJJJJFHHIIIIJJ JIHGIIJJJJIJIJIJJJJIIJJJJJIIEIHHIJ HGHHHHHDFFFEDDDDDCDDDCDDDDDDDCDC 5:1101:1160:2197 2:N:0:ATCACG CTTCGTCCACTTTCATTATTCCTTTCATACATG CTCTCCGGTTTAGGGTACTCTTGACCTGGCCTT TTTTCAAGACGTCCCTGACTTGATCTTGAAACG + CCCFFFFFHHHHHJJJJIJJJJJJJJJJJJJJJ JJJJJJJIJIJGIJHBGHHIIIJIJJJJJJJJI JJJHFFFFFFDDDDDDDDDDDDDDDEDCCDDDD
14 Mapping of pair-end reads Insert size
15 Adaptor trimming module add 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 add FastQC
17 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
18 GATK version
19 When in doubt, google it!
20 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
21 brute force TCGATCC x GACCTCATCGATCCCACTG
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 GACCTCATCGATCCCACTG
29 hash tables build an index of the reference sequence for fast access seed length 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
30 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
31 hash tables build an index of the reference sequence for fast access TCGATCC = chromosome 1, pos 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 Burroughs-Wheeler Aligner algorithm used in computer science for file compression original sequence can be reconstructed BWA (module add bwa) Burroughs-Wheeler Aligner
33 Input to mapping reference + raw reads Reference genome assembly Ind.fasta + fasta.fai R1.fastq R2.fastq >Potra CACGAGGTTTCATCATGGACTTGGCACCAT AAAAGTTCTCTTTCATTATATTCCCTTTAG GTAAAATGATTCTCGTTCATTTGATAATTT TGTAATAACCGGCCTCATTCAACCCATGAT CCGACTTGATGGTGAATACTTGTGTAATAA CTGATAATTTACTGTGATTTATATAACTAT CTCATAATGGTTCGTCAAAATCTTTTAAAA GATAAAAAAAACCTTTATCAATTATCTATA TAAATTCAAATTTGTACACATTTACTAGAA ATTACAACTCAGCAATAAAATTGACAAAAT ATAAAACAGAACCGTTAAATAAGCTATTAT TTATTTCATCACAAAACATCTAAGTCAAAA CAGTTGCGATGAGAGCGTTGAGAAGTATAAT ATAAGAAATAGTGAGATATGGAAACGTTGTG + B@CFFFFFHHHHHGJJJJJJJJJJJFHHIII NAGATTGTTTGTGTGCCTAAATAAATAAATA AAGGAATTTGAAATTAAGATTGAGATATTGA + #1=DDFFEHHDFHHJGGIJJJJGIHIGIJJJ NTATTTTTGCTATGTGTCTTTTCGTTTTAAG
34 Output from mapping - SAM format HEADER VN:1.0 SN:1 LN: AS:NCBI37 UR:file:/data/local/ref/GATK/human_g1k_v37.fasta SN:2 LN: AS:NCBI37 UR:file:/data/local/ref/GATK/human_g1k_v37.fasta SN:3 LN: AS:NCBI37 UR:file:/data/local/ref/GATK/human_g1k_v37.fasta ID:UM0098:1 PL:ILLUMINA PU:HWUSI-EAS LHAAXX-L001 LB:80 DT: T20:00: SM:SD37743 ID:UM0098:2 PL:ILLUMINA PU:HWUSI-EAS LHAAXX-L002 LB:80 DT: T20:00: SM:SD37743 ID:bwa VN:0.5.4 ALIGNMENT SECTION 8_96_444_ scaffold M * 0 0 ATGTAAAGTATTTCCATGGTACACAGCTTGGTCGTAATGTGATTGCTGAGCCAG C@B5)5CBBCCBCCCBC@@7C> 8_80_1315_ scaffold M = AGTACCTCCCTGGTACACAGCTTGGTAAAAATGTGATTGCTGAGCCAGACCTTC B?@?BA=>@>>7;ABA? 8_17_1222_ scaffold M1116N10M * 0 0 GGTAAAAATGTGATTGCTGAGCCAGACCTTCATCATGCAGTGAGAGACGC BB@BA??>CCBA2AAAB 8_43_1211_ scaffold M1116N27M * 0 0 TGAGCCAGACCTTCATCATGCAGTGAGAGACGCAAACATGCTGGTATTTG #>8<=<@6/:@9';@7A@ 8_32_1091_ scaffold M = CGCAAACATGCTGGTAGCTGTGACACCACATCAACAGCTTGACTATGTTTGTAA BBBBB@AABACBCA8B Start position Sequence Quality Read name
35 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
36 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
37 step 2: recalibration.bam 2.1 realign indels 2.2 remove duplicates 2.3 recalibrate base quality.realign.bam.realign.dedup.bam.realign.dedup.recal.bam
38 2.1 local realignment 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
39 2.1 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
40
41 2.2 PCR duplicates When two or more reads originate from same molecule (artificial duplicates) 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) mark or remove
42 Identify PCR duplicates Single or paired reads that map to identical positions Picard MarkDuplicates
43 2.3 base quality recalibration
44 Recalibration Method Bin each base by read group called quality position in read local dinucleotide context score observed quality per bin # of mismatches +1 / # of observed bases scale compared to reported quality
45 Reported vs empiral quality scores
46 Residual error by machine cycle
47 Residual error by dinucleotide
48 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
49 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
50 simple pileup methods acacagatagacatagacatagacagatgag acacagatagacatagacatagacagatgag acacacatagacatagacatagacagatgag acacagatagacatagacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatatacatagacagttgag acacagatagacatagacatagacagatgag acacagatagacatatacatagacagatgag acacagatagacatagacatagacagatgag Reference genome assembly
51 Baysian population-based calling
52 GATK haplotype caller
53 GATK best practice for cohorts
54 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
55 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,De 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="Qu ##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 0:48:8:51,51 1/ 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
56 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
57 Discovery of structural variants
58 1) Read depth analysis Depth of coverage can be used to estimate copy number Samples may exhibit variation in depth indicative of polymorphic copy number variants How many copies of a duplication in the reference? How similar are the copies? Difficult to distinguish homozygotes and heterozygotes.
59 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
60 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
61 4) De novo assembly to identify structural variants Assemble contigs Align to reference Look for insertions, deletions, rearrangements
62 Annotation of variants By comparing with existing annotation for the reference genome it is possible to gain information about localization and expected effect
63 Annotation of variants By comparing with existing annotation for the reference genome it is possible to gain information about localization and expected effect Most commonly used tools are Annovar and SNPEff
64 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
65 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
66 Steps in resequencing analysis 1) Setup programs, data 2,3,4) map reads to a reference bam file 5) recalibrate alignments find best placement of reads realign indels remove duplicates recalibrate base quality bam file 6) identify/call variants vcf file statistical algorithms to detect true variants
67 1) Access to data and programs 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 Tasks: align a couple of samples to reference, process, reacalibration, call and filter variants
68 1) Access to data and 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/
69 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
70 3) Merging alignments Combine alignments from paired ends into a SAM file bwa sampe <ref> <sai1> <sai2> <fq1> <fq2> > align.sam <ref> <sai1> <sai2> <fq1> <fq2> = reference sequence = alignment of seq 1 of pair = alignment of seq 2 of pair = fastq reads seq 1 of pair = fastq reads seq 2 of pair
71 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
72 5) Processing files 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>
73 5) Processing files 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>
74 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
75 Processing files NEXT: repeat steps 2-5 for at least another sample!
76 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>
77 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
78 7) Viewing data with IGV
79 X) Extra Extra 1: View data in UCSC-browser Extra 2: Select subset with BEDTools Extra 3: Annotate variants with annovar Extra 4: Make a script to run pipeline
80 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
81 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
82 2. raw reads: fastq (2 per sample) reference genome: fasta mapping mapped reads: 2 x sai per sample 3. merged SAM files processing 4. single BAM file per sample: indexed, sorted, +read groups 5. single BAM file: +realigned around indels +mark/remove duplicates +quality recalibrations variant calling 6. VCF file: +filtered variants
83 Naming conventions Initial file name according to information about the content NA06984.ILLUMINA.low_coverage.17q For each step of the pipeline, 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
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