Routine processing of large scale human whole genome sequencing data.

Transcription

1 Routine processing of large scale human whole genome sequencing data. Ies Nijman, UMCU, CPCT, Hartwig Medical Foundation Compute Resources for Life Science Research

2 Center for Personalized Cancer Treatment Bottom-up initiative founded in 2010 UMCU, EUR, NKI UMC Groningen AMC Amsterdam VuMC Amsterdam LUMC Leiden Meander Radboud Nijmegen MUMC Maastricht

3 Personalized treatment

4 Two Weeks Center for Personalized Cancer Treatment Patient with Metastatic Disease 2-4 Biopsies (fresh frozen) Pathological Analysis ng DNA Isolation ng Patient Stratification Research IonTorrent PGM MiSeq Illumina HiSeq X ten or Actionable Mutations & Amplification >50 genes + Biomarker Discovery Profiling Cancer Pathways and Processes Start Targeted Therapy Allocation Fase1 Clinical Trial Systems Biology Whole Genome Sequencing Response monitoring Bioinformatic analysis Resistance / Progression Remission / Cure Databanking Mutations, INDELs, Copy Number Variations in vitro / in vivo Modeling of Hypotheses Response monitoring in ecrf

5 National scaling: Hartwig Medical Foundation Value chain Including patients Collecting clinical data Taking biopsies 1 Preprocessing biopsies Sequencing DNA of biopsies Value chain Managing sequence and clinical data database, and reporting Analyzing database for new treatment and non-treatment options Conducting clinical trials Storage of tissue 1 (biobank) Centralized Facility in a Foundation setup - Made possible through philanthropy (2 to 3 years) - Whole genome sequencing using Illumina Xten setup - Integrate clinical and genetic data - Provide input for individual patient reporting - Provide access to cohort information for research to benefit future patient care Location: Matrix VI, Amsterdam Science Park Start: April 2015 Operational: Summer 2015

6 HUB organoids Test specific drugs on tumor organoids to confirm sensitivity Treat patient with selected drug(s) until disease progression Bioinformatics and Systems Biology to identify pathways Obtain patient biopsy

7 Targets: cpct: 2500 patients/yr Reference 30x; tumor 90x (= 4 genome eq) clinical genetics labs: 7500 samples/yr Total/max ~ genomes/yr

8 Compute Xten generates on avg 50 genomes/day (16 genomes/machine/3 days) Processing target: 16 genomes or 4 T-N pairs in 3 days Storage Raw data (BCL.gz>Fastq.gz): ~ 100 Gb/sample = ~ 5 TB/day Processed data (BAM, gvcf): ~ 100 Gb/sample = ~ 5 TB/day Temp data: 2-3 fold increase during processing Store what for how long? Datasharing Centrale storage / archive? How to get data to customers How to visualize/use centrally stored data.

9 BLCs bcl2fastq Fastq s BWA-MEM DeDuplication IndelRealign BaseRecalibration? BAM conversion report Read QC report Bam QC report bioinformatic NGS dataprocessing - Perl wrapper, logging & control backbone - Submits to grid engine - Runs with standardized.ini files to configure each module GATK Haplotypecaller gvcf GenotypeVCF VarScan Strelka Freebayes Mutect Somatic VCF Contra (exome only) FreeC CNVs Delly DEL, DUP, INV, TRA Filter & Annotate Filter & Annotate Filter & Annotate Filter & Annotate VCF Somatic VCF CNVs SVs

10 3 sequencers active Each linked to processing server (36 cores, 256Gb RAM, 10TB SSD) Overflow/extra runs in UMCU HPC. Processing still limiting factor! Merge runs / redos etc killing! Not the intention to expand local hardware Optimalisations in hardware & pipeline

11 single sample WGS 60x; Real time (hrs): total 58 Flagstat; 0,28 single sample WGS 60x; Core time (hrs): total 308 Flagstat; 2,28 variant_caller; 11,05 Sorting; 1,63 Mapping; 18,56 variant_caller; 60,00 indelrealign; 10,15 indelrealign; 3,48 Prestats; 6,70 Sorting; 16,65 Mapping; 186,89 Merge; 3,72 Poststats; 6,00 Merge; 24,91 Prestats; 6,70 Poststats; 6,00

12 7 sample set, 30x WGS;Real time (hrs): total 212 CNV_freec; 7,69 Flagstat; 0,96 7 sample set, 30x WGS; Core time (hrs): total 1058 CNV_freec; 11,15 Flagstat; 7,76 Variantcaller; 42,70 Mapping; 52,66 Sorting; 4,98 Variantcaller; 247,05 Merge; 11,81 Sorting; 52,05 Mapping; 522,38 Indelrealignment; 60,64 Prestats; 24,59 Poststats; 4,00 Indelrealignment; 84,00 Prestats; 24,57 Merge; 86,70 Poststats; 16,00

13 Tumor 120x- Normal 30x pair: Real time (hrs): total 1083 Indexing; 1,66 flagstat; 6,48 Tumor 120x- Normal 30x pair: Core time (hrs): total 2245 Indexing; 4,45 flagstat; 17,04 Mapping; 154,68 Merge&dedup; 22,21 Poststats; 0,00 PreStats; 55,23 Somatic; 1.077,28 Mapping; 581,72 Merge&dedup; 65,78 Indelrealignment; 57,42 Poststats; 0,00 PreStats; 60,61 Somatic; 705,37 Sorting; 17,59 VariantCalling; 45,04 CNV; 18,15 CNV; 25,56 VariantCalling; 272,04 Indelrealignment; 86,14 Sorting; 55,17

14 Pilots with various partners with central goal: Each sample runs with predictable and constant turn around time (<=3 days) and for the same price. Q: scalability; min and max sample flow Q: price curve days?

15 Partners: Curoverse (Arvados) on Azure Cloud BlueBee (IBM/TU Delft) on local power hardware Schuberg-Phillis on local, private cloud Genalice on simple hardware with proprietary software Surf-Sara Vancis

16 Genalice: insanely fast (mapping, variant calling): ~ 45 Results differ; still to figure out FP/FN Still need for additional hardware to complete CNV, somatic and other analyses. Surf Sara / Vancis: pilots not actively started yet

17 Pipeline runs; small optimalisations to match infrastructure Test set is Tumor 90x Normal 30x pair Wall time: 53 hrs Tumor 90x - Normal 30x pair: CPU hrs total 1128 QC stats; 16 Somatics; 192 Realignment & Variant calling; 312 Mapping, sorting, dedup; 608

18 tumor 90x - Normal 30x pair: Real Time (hrs): total 143 flagstat; 7,37 tumor 90x - Normal 30x pair: Core Time (hrs): total flagstat; 117,92 Mapping&markdup; 1.380,27 Mapping&markdup; 21,57 Somatic; 4.350,89 merge; 772,00 Somatic; 67,23 merge; 12,06 Indelrealignment; 2.074,14 Indelrealignment; 29,72 VariantAnnotation; 63,20 VariantCalling; 1.997,60 VariantAnnotation; 3,95 VariantCalling; 9,73

19 The type of data and analyses are difficult to optimize large number of parallel chunks suffer from slow/io merge steps. Some tools are just slow.. Until now we have no numbers on scalability from partners and/or concrete pricelevels..