Exact mapping of Illumina blind spots in the Mycobacterium tuberculosis genome reveals platform-wide and workflow-specific biases

Whole-genome sequencing (WGS) is fundamental to Mycobacterium tuberculosis basic research and many clinical applications. Coverage across Illumina-sequenced M. tuberculosis genomes is known to vary with sequence context, but this bias is poorly characterized. Here, through a novel application of phylogenomics that distinguishes genuine coverage bias from deletions, we discern Illumina ‘blind spots’ in the M. tuberculosis reference genome for seven sequencing workflows. We find blind spots to be widespread, affecting 529 genes, and provide their exact coordinates, enabling salvage of unaffected regions. Fifty-seven pe/ppe genes (the primary families assumed to exhibit Illumina bias) lack blind spots entirely, while the remaining pe/ppe genes account for 55.1 % of blind spots. Surprisingly, we find coverage bias persists in homopolymers as short as 6 bp, shorter tracts than previously reported. While G+C-rich regions challenge all Illumina sequencing workflows, a modified Nextera library preparation that amplifies DNA with a high-fidelity polymerase markedly attenuates coverage bias in G+C-rich and homopolymeric sequences, expanding the ‘Illumina-sequenceable’ genome. Through these findings, and by defining workflow-specific exclusion criteria, we spotlight effective strategies for handling bias in M. tuberculosis Illumina WGS. This empirical analysis framework may be used to systematically evaluate coverage bias in other species using existing sequencing data.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Abstract LB-308: A novel data safe haven approach to bring analyses to the International Cancer Genome Consortium data

To target and personalize cancer therapies to the genomic aberrations present in a particular patient's tumor, researchers need to identify the genes that drive the progression of malignant tumors. This requires analysis of somatic mutations from large samples of patients to identify driver mutations up to the “tail end” of the frequency distribution. Community genomics data sets from the TCGA and ICGC projects represent a valuable resource to which researchers can add their own data to gain statistical power in their analyzes. The current issue to this methodology is the highly fragmented storage of public and private data and the inefficient access to public data. Researchers spend weeks to months downloading hundreds of terabytes of data from central repositories before computations can begin. What is needed is a data “safe haven” where researchers can bring compute to the reference data without the need to incur in bulky data transfers or duplicative storage costs, in an environment that protects the privacy of the patients’ data. In collaboration with the International Cancer genome Consortium, we developed ShareSeq, a genomic data safe haven platform that provides an informatics solution for storing, handling and analyzing protected identifiable genomic data. This resource leverages Annai-GNOS, the technology which we developed to create and manage the CGHub TCGA repository together with UCSC, and that is being used in the ICGC Pan Cancer Analysis of Whole Genomes project, and combines it with a high-performance compute environment and an array of tools to process and analyze genomic data. Built using a walled garden approach, where the data is stored, processed and managed within the security of the system, ShareSeq avoids the complexity of assured end point encryption. GeneTorrent, our fast and secure file transfer mechanism, enables researchers’ private information to be transferred into the walled garden simply and securely to combine it with the public datasets. ShareSeq differs dramatically from the traditional cloud in two features: (i) formal mechanisms and a service level agreement to store protected identifiable genomic data securely and safely, built into the system from the ground up; (ii) the system is specifically designed for genomic computing over large shared data sets supporting common bioinformatics workflow tools; (iii) Fast download and access to raw genomic information and its metadata; and (iv) access controls leveraging federated authentication systems that Data Access Committees utilize to authorize access to the restricted data. ShareSeq is initially hosting raw, normalized, and processed data from the ICGC, but we envision that over time it will host an increasing number of high value reference genomic public datasets and add standards-based interfaces promoted by the Global Alliance of Genomes and Health to allow broader data discovery and sharing.

Citation Format: Francisco M. De La Vega, Ying Wu, Tal Shmaya, Thomas Schlumpberger, James Wiley, Akshay Patel, Raja Hayek. A novel data safe haven approach to bring analyses to the International Cancer Genome Consortium data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-308. doi:10.1158/1538-7445.AM2015-LB-308