Supplementary Information. Detecting and annotating genetic variations using the HugeSeq pipeline

Transcription

1 Supplementary Information Detecting and annotating genetic variations using the HugeSeq pipeline Hugo Y. K. Lam 1,#, Cuiping Pan 1, Michael J. Clark 1, Phil Lacroute 1, Rui Chen 1, Rajini Haraksingh 1, Maeve O Huallachain 1, Mark B. Gerstein 2,3,4, Jeffrey M. Kidd 1, Carlos D. Bustamante 1 and Michael Snyder 1 1. Department of Genetics, Stanford University, Stanford, California, USA 2. Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA, 3. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA 4. Department of Computer Science, Yale University, New Haven, Connecticut, USA # Present address: Personalis, Inc., Palo Alto, California, USA

2 Table of Contents METHODS 3 COMPUTATIONAL PIPELINE 3 SEQUENCE ALIGNMENT 3 SNP AND INDEL DETECTION 3 SV AND CNV DETECTION 3 FUNCTIONAL ANNOTATION 4 GENOME SEQUENCING 4 BENCHMARKING 4 SENSITIVITY TEST 4 TABLES 5 TABLE S1 5 TABLE S2 5 FIGURES 6 FIGURE S1 6 FIGURE S2 7 FIGURE S3 8 2

3 Methods Computational pipeline HugeSeq is implemented in the Python programming language and bash shell scripts. It was designed to run on Unix- based system and was tested on the Red Hat Enterprise Linux (RHEL) server v5.6. It uses the Modules package to provide dynamic modification (e.g. changing the path and version of Python) of a user's environment via module files. Its MapReduce approach was implemented mainly based on a custom Simple Job Management framework, SJM, which currently supports Sun Grid Engine but can be easily extended to support other batch systems such as PBS. Each step in the pipeline was implemented in a separate shell script and the job description file generated for SJM is in a human- readable format. Sequence alignment BWA version was used for sequence alignment against the human reference genome HG19. Illumina s quality score was converted into Sanger s quality score by BWA. The multithreading option was enabled with two concurrent threads for generating the SA coordinates in mapping. The original alignment output, which was in a SAM format, was converted into BAM using SAMtools version Sorting of the BAMs was done by the Picard tool ( version 1.32 and binning the BAMs by chromosome was performed using SAMtools. Picard was used to remove duplicates in alignments, whereas GATK version was used for local realignment and base quality recalibration. SNP and Indel detection The UnifiedGenotyper in GATK version was used for SNP and indel detection with call confidence set to 30.0 and emit confidence set to Dindel model was enabled in indel calling. Filter label was applied using the VariantFiltration program in GATK for allele balance (AB) greater than 0.75, quality score (QUAL) less than 50.0, depth of coverage (DP) greater than 360, strand bias (SB) greater than or mapping- quality zero reads (MQ0) greater than or equal to 4. The mpileup function in SAMtools/BCFtools version was also used for SNP and indel detection. The generated VCFs were concatenated and merged using VCFtools version and indexed using Tabix version SV and CNV detection BreakDancer version 1.1 was used for paired- end mapping. Pindel version was used for split- read analysis. Calls from BreakDancer were used in Pindel to increase sensitivity and specificity. CNVnator version was used for read- depth analysis. BreakSeq Lite version 1.0 was used for junction mapping. Only SV and CNV calls greater than or equal to 50bp were selected for final output. Outputs from different 3

4 callers were converted into GFF using custom scripts and merged using BEDtools version Functional annotation ANNOVAR version was used for functional annotation of variants. The UCSC known genes and repeat masker databases were used for gene and repeat annotations respectively. SIFT scores were based on the SIFT database. SNP annotation was based on dbsnp version 132. Genome sequencing Peripheral Blood Mononuclear Cells (PBMCs) were isolated from whole blood sample of an individual by density gradient centrifugation at 400 x g for 25 minutes using the Lymphocyte Separation Media (MP Biomedicals). 20 ml of saliva sample was also collected from the same individual and processed immediately. DNA was isolated from both blood and saliva using the AllPrep DNA/RNA/Protein Mini Kit (QIAGEN). Paired- end sequencing was performed using Illumina HiSeq 2000 with an average read length of 101bp. Benchmarking Benchmarking was performed at sequencing coverage 6X, 12X, 24X, 48X and 96X with sequenced reads randomly selected from both the blood and saliva sequencing data. The pipeline was installed and executed in a computer cluster at the Stanford Center for Genomics and Personalized Medicine. There were 48 Intel Xeon 3.00GHz CPU cores assigned for the performance test and up to 12GB of physical memory allocated for each computer job. The reads were divided into subsets each has about 6X sequencing coverage and was aligned independently. The processing time and memory usage for each job were recorded by SJM. The non- parallel processing time was estimated by the individual run time of the parallel jobs in the pipeline. Sensitivity test Illumina s HumanOmni1- Quad genotyping array with 1M markers was used on the blood sample of the sequenced individual. SNP calls were generated by the Illumina GenomeStudio version CNV calls were generated by the CNVPartition module version of GenomeStudio and by CNVision version 1.0 requiring two or more algorithms. Heterozygous SNP calls and CNV deletion calls were selected for testing the sensitivity of SNP and CNV detection of the pipeline, respectively. 4

5 Tables Table S1 Comparison of various platforms for genome data analysis. Alignment SNP Calling Indel Calling SV Calling Availability Data Size Limit License Functional Annotation HugeSeq yes yes yes yes downloadable no SOAP yes yes yes yes downloadable no GATK partial yes yes no downloadable no public; open- source public; open- source public; open- source yes no yes Galaxy yes yes yes no web- based; downloadable yes in online version public; open- source yes GenePattern no no no no web- based; downloadable yes in online version public yes GenomeQuest yes yes yes no web- based; downloadable N.A. commercial yes DNA Nexus yes yes yes no web- based N.A. commercial yes Table S2 Breakdown of SV events of the detected SVs. SV Event All Merged Concordant Deletion 24,024 19,809 1,594 Duplication 1,077 1,077 0 Insertion Inversion Total 25,698 21,381 1,639 5

6 Figures Figure S1 Variant size distribution. a) Indel size distribution of the merged call set. b) Indel size distribution of the high- confidence call set. c) SV deletion size distribution of the merged call set. d) SV deletion size distribution of the high- confidence call set. The blue line indicates the typical size of an L1 element whereas the red line indicates that of an Alu element. 6

7 Figure S2 Benchmark of the overall performance of HugeSeq. a) Run time of different processes at different sequencing coverage in a non- parallel computation mode. b) Run time of HugeSeq for different processes at different sequencing coverage. c) Comparing the overall run time of a non- parallel computation mode and HugeSeq. d) Memory usage of different processes. 7

8 Figure S3 Benchmark of the variant detection performance of HugeSeq. a) Run time of different variant detection processes at different sequencing coverage in a non- parallel computation mode. b) Run time of HugeSeq for different variant detection processes at different sequencing coverage. c) Comparing the overall run time of variant detection between a non- parallel computation mode and HugeSeq. d) Memory usage of different variant detection processes. 8