CNV and variant detection for human genome resequencing data - for biomedical researchers (II)

Transcription

1 CNV and variant detection for human genome resequencing data - for biomedical researchers (II) Chuan-Kun Liu 劉傳崑 Senior Maneger National Center for Genome Medican bioit@ncgm.sinica.edu.tw

2 Abstract Common NGS Data Analysis Pipelines Data Output of NGS Platforms Quality Check and Read Trimming Sequence Alignment Variant Detection HiPipe Project

3 Common NGS Data Analysis Pipelines

4 Common NGS data analysis pipelines (DNA) Format Conversion and Demultiplexing (CASAVA) Quality Check (FastX / FastQC) Sequence Alignment (BWA) de novo Assembly (Velvet) Sequence Markduplicate (Sambamba) Variant calling (Freebayes) Sequence Realignment (GATK) Sequence Markduplicate (GATK) Variants Annotation (VarioWatch) Multi-sample Variant calling (GATK) LOH Detection (ExomeCNV or NCGM) CNV Detection (ExomeCNV) Translocation (SVDetect) Variants Annotation (VarioWatch) Visualization (Circos)

5 Common NGS data analysis pipelines (RNA) Format Conversion and Demultiplexing (CASAVA) Quality Check (FastX / FastQC) Sequence Alignment (Bowtie2) Sequence Alignment (Bowtie) de novo assembly (Trinity) Differential expression (Cufflinks) Sequence Realignment (GATK) Gene-fusion Detection (TopHat-fusion) mirna expression analysis (mirdeep2) Visualization Variant calling Gene Annotation (CummeRbund) (GATK) (VarioWatch) Gene Annotation (VarioWatch) Variants Annotation (VarioWatch)

6 NGS data analysis pipelines (Others) ChIP RNA IP Methylation Aptamer Virus integration Metagenomics

7 Data Output of NGS Platforms

8 Data output of NGS platforms Illumina HiSeq Plaform (BCL files) Roche GS FLX+ (Standard Flowgram Format (SFF) files) Thermo Fisher Ion Proton (FASTQ files) FASTQ PacBio RS II (FASTQ files) 8

9 Format Conversion and 2:N:0:GCCAAT ATGCAAATAAACTAGAAAATCTAGAAGAAATGGAGAAATTCCTGGACACAC + CCCFFFFFHHHGHJIIJJIJJJIIJJIJJIJJJFIIJAJJIJIJJJJI??? Illumina HiSeq 2000 (multiplexing BCL files) FASTQ by samples Phred Quality Score Probability of incorrect base call Base call accuracy 10 1 in % 20 1 in % 30 1 in % 40 1 in % 50 1 in %

10 ASCII code

11 Three described FASTQ variants Description, OBF name ASCII characters Quality score Range Offset Type Range Sanger standard* fastq-sanger PHRED 0 to 93 Solexa/early Illumina* fastq-solexa Solexa -5 to 62 Illumina 1.3+* fastq-illumina PHRED 0 to 62 Illumina 1.8+ fastq-sanger PHRED 0 to 93 *Nucleic Acids Res April; 38(6):

12 Quality Check and Read Trimming

13 FastQC Project Home Quality Check Merge fastq files into one file Java environment Interactive UI

14 Trimming (When) When to trim reads Format conversion Before alignment Upon alignment Trimming Trimming by lane Trimming by reads

15 How to trim reads Trimming (How) Specify the bases to be trimmed (eg. n5y*n5) Specify the lower bound threshold of base quality and trim 2:N:0:GCCAAT ATGCAAATAAACTAGAAAATCTAGAAGAAATGGAGAAATTCCTGGACACAC + CCCFFFFFHHHGHJIIJJIJJJIIJJIJJIJJJFIIJAJJIJIJJJJI???

16 A tool for read trimming Trimmomatic : A flexible read trimming tool for Illumina NGS data Project Home Adaptor trimming Quality trimming Using Sliding Window strategy Cuts a read when the average base quality within a sliding window drops below the lower bound threshold

17 Trimming Threshold Bases above Q30 85% (2 x 50 bp) 80% (2 x 100 bp) Q30 or Q24 or Q5? Is it necessary to trim reads?

18 Sequence Alignment

19 Tools for Sequence Alignment Open source BWA aln, BWA mem Bowtie, Bowtie2 STAR Commercial CASAVA, Isaac (Illumina) CLC Genomics Workbench

20 Human reference sequence NCBI36 (Aug, 2005) the last assembly produced by Human Genome Project (HGP) hg18, Ensembl release 54 GRCh37 (Feb, 2009) The first assembly submitted by Genome Reference Consortium (GRC) hg19, Ensembl release 55~75 (v75 Feb, 2014) GRCh38 (Dec, 2013) hg38, Ensembl release 76+ (v76 Aug, 2014)

21 GRCh37 Primary assembly Chromosome assembly (chr1-22, X, Y, Mt) Unlocalized sequence Unplaced sequence Alternate loci A sequence that provides an alternate representation of a locus found in a largely haploid assembly. (MHC region, UGT2B17, MAPT) Patches A contig sequence that is released outside of the full assembly release cycle.

22 Choose your reference genome Human_g1k_v37 The reference sequence provided by the 1000 Genomes Project* Unlocalized and Unplaced sequence are included (Full primary assembly) Mitochondrial sequence was replaced with the revised Cambridge Reference Sequences (rcrs; AC:NC_012920) (APR, 30, 2010)** Male or female *ftp://ftp.ncbi.nih.gov/1000genomes/ftp/technical/reference/readme.human_g1k_v37.fasta.txt **

23 SAM/BAM format Sequence Alignment/Map (SAM) format text file BAM format (binary file) to reduce the file size

24 Coverage Whole genome sequencing Mean coverage Whole exome sequencing Over 90% of target region were covered with 0.2x mean coverage Custom panel PCR based (~ Mean coverage) Capture-based (~ whole exome sequencing)

25 TruSeq Exome Enrichment Kit

26 Variant Detection

27 DNA Variant Detection Variant Detection Variants Alignment Result Sequencing Reads Reference Sequnece (GRCh37) A C G T

28 Input - bam files Output - vcf files Tools Variant Detection Germline mutation Genome Analysis Toolkit (GATK) (MAF > 5%) FreeBayes Somatic mutation MuTect Somatic Variant Caller (MAF > 1%)

29 Genome Analysis Toolkit (GATK) Project Home Maintenance - Broad Institute License Version before Free for all users (the MIT license) Version after 2.4 Free for academics Fee for commercial use

30 GATK Best Practices 1. Pre-processing Mark duplicates Realign indels Recalibrate Bases 2. Variant discovery Call Varinats Filter Varinats 3. Callset refinement Refine Genotypes Annotate variants Evaluate variants

31 Mark Duplicates AGGGAAACCACACAGGCTTCTTAGGCCATTGGAAT GGAAACCACACAGGCTT---AGGCCATTGGAA GAAACCACACAGGCTT---AGGCCATTGGAAT AAACCACACAGGCTT---AGGCCATTGG AAACCACACAGGCTT---AGGCCATTGG CCACACAGGCTT---AGGCCATTGGAA CACACAGGCTT---AGGCCATTGGAAT

32 Mark Duplicates The same DNA fragments may be sequenced several times The resulting duplicate reads are not informative and should not be counted as additional evidence for or against a putative variant The process mark the reads only, does not remove them NOT be applied to amplicon sequencing data

33 Realign Indels (Before) AGGGAAACCACACAGGCTTCTTAGGCCATTGGAAT GGAAACCACACAGGCTT---AGGCCATTGGAA GAAACCACACAGGC---TTAGGCCATTGGAAT AAACCACACAGGCT---TAGGCCATTGG AAACCACACAGGCTT---AGGCCATTGG CCACACAGGC---TTAGGCCATTGGAA CACACAGG---CTTAGGCCATTGGAAT

34 Realign Indels (After) AGGGAAACCACACAGGCTTCTTAGGCCATTGGAAT GGAAACCACACAGGCTT---AGGCCATTGGAA GAAACCACACAGGCTT---AGGCCATTGGAAT AAACCACACAGGCTT---AGGCCATTGG AAACCACACAGGCTT---AGGCCATTGG CCACACAGGCTT---AGGCCATTGGAA CACACAGGCTT---AGGCCATTGGAAT

35 Realignment reads that align on the edges of indels often get mapped with mismatching bases that might look like evidence for SNPs, but are actually mapping artifacts.

36 Project Home FreeBayes Maintenance Erik Garrison, Gabor Marth License Free for all users (the MIT license) Pipeline using FreeBayes SpeedSeq ( Varpipe (

37 (1) Summary and description of fields VCF format (2) Basic info of variants (3) Variants info by individual

38 Output File Size Human whole genome 30X Raw reads (FASTQ) 100GB Alignment results (BAM) 100GB Variant detection results (VCF) 2GB Functional analysis of variants (CSV) 1GB Human whole exome 100X Raw reads(fastq) 20GB Alignment results(bam) 20GB Variant detection results(vcf) 0.1GB Functional analysis of variants(csv) 0.1GB

39 HiPipe Project

40 Why HiPipe? It is hard to analyze NGS data for researchers with biology background: Most analysis tools run only in the Linux environment Few websites provide proper analysis Many tools are only applied for small genome analysis It takes a long time to get a result Transferring large amount of data between websites is required to complete an analysis It is error-prone to orchestrate different analysis tools

41 User Expectations for NGS Easy to learn and use analysis tools Researchers without computer science background can complete an analysis themselves Doing analysis anywhere, anytime Suitable for any genome size Without file size limitation Upload sequence data upon read sequencing Get an analysis result rapidly Combine different experiment data in one analysis

42 Challenges familiar with bioinformatics tools ( 行中 亭均 ) Job scheduling and pipeline developing (Louis) Cluster and distributed system management ( 方智 ) Performance tuning Software ( 耀德 仁屏 ) Hardware (MIS Team) Bandwidth Disk I/O User Interface ( 爾瞻 萬嘉 中饋 ) Team coordinate ( 傳崑 ) Adam Yao

43 Architecture

44 HiPipe (Basic)

45 HiPipe (Basic) Online since Jun, 2013 Provide 7 popular NGS data analysis pipelines: Whole Genome & Exome Variant Detection Differential Expression Analysis Gene Fusion Detection mirna Analysis RNA Variant Detection De novo Assembly Exome CNV Detection

46 HiPipe Professional Authentication / Authorization Web-based interface supporting unlimited upload file size and resumable file upload Multi-sample indel realignment and variant detection Cloud / Onsite Providing integrated NGS data analysis and data store service

47 HiPipe Basic vs HiPipe Professional HiPipe HiPipe Professional HiPipe Basic HiPipe Cloud HiPipe Cluster HiPipe Quad HiPipe Duo HiPipe Uno Single sample analysis Multi-sample analysis Data storage location Cloud Cloud Local Local Local Local Nodes CPU cores X human whole genome variant detection 56 mins 56 mins 56 mins 7 hrs ~12 hrs ~36 hrs Expected ship date 2013 Q Q

48 Authentication / Authorization 1. Select a project 2. Add or remove members of the project

49 Upload files through a browser Unlimited upload file size and resumable file upload

50 Create Project / Analysis

51 Configure settings

52 Choose samples

53 Save settings

54 Confirm settings

55 Launch analysis

56 Multi-sample analysis result Multi-sample indel realignment

57 Integrative Genomics Viewer 1. Integrate and visualize NGS, microarray and annotation in genomics 2. Load data only in specified regions to reduce physical memory usage 3. Customize the attribute of samples for further filtering 4. Browse multiple region at the same time 5. It s FREE

58 Browse VCF and BAM in IGV

59 VCF Track I The allele fraction A C G T

60 VCF Track II Heterozygous Homozygous Reference A C G T

61 Zoom in to see alignment results Thorvaldsdóttir H et al. Brief Bioinform 2013;14: The Author(s) Published by Oxford University Press.

62 Browse alignment result in larger region Set to 200kb for browsing most of genes Status Memory usage

63 VarioWatch Functional analysis of Variants

64 VarioWatch Visualized result

65 Performance (Human) Estimated Analysis Time Whole Genome 56 minutes Note 40x Coverage Exome 10 minutes 100x Coverage Transcriptome 1-3 days 12 * 2 Gb (Paired Sample) de novo 24 hours 100 Gb K-mer = 73

66 NGS data analysis service Analysis Pipeline Samples DNA Whole Genome Variant Detection 135 Whole Exome Variant Detection 1241 CNV Decection 68 LOH Detection 47 de novo assembly 3 RNA Differential Expression Analysis 153 Gene-fusion Detection 32 mirna expression analysis 14 Variant Detection 43 de novo assembly 4 Other ChIP 25 Aptamer 25 RNA IP 15 Virus integration 15 Total (2011/10 ~ 2015/06) 1820

67 Applying for NGS data analysis service Contact us and discuss detail information in Pre-Service Meeting Create account for LIMS system and issue tracking system (JIRA) Create issue for data analysis on issue tracking system (JIRA) Confirm the analysis pipeline and the estimated price Transfer the raw data ( raw Reads) NGS data analysis Provide NGS data analysis results (preserve 6 month) Confirm the final price Case closed and charge from LIMS system Discuss results in Post-Service Meeting

68 HiPipe Basic (for free) HiPipe Cloudbeta apply for trial account Thanks for your attention!