Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.

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Circos: An information aesthetic for comparative genomics

All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

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Signatures of mutational processes in human cancer

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

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An integrated map of genetic variation from 1,092 human genomes

Abstract 3284: COSMIC: Integrating and interpreting the world's knowledge of somatic mutations in cancer

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk) is the world9s largest and most comprehensive online resource for exploring the impact of somatic mutations in human cancer. Live since 2004, the 71st release (Nov 2014) describes over 2 million mutations in more than 1 million tumour samples across most human genes. To emphasise depth of knowledge on known cancer genes, mutation information is curated manually from the scientific literature, allowing very precise definitions of disease types and clinically relevant patient details. Combination of over 20,000 published studies gives substantial resolution of how mutations and phenotypes relate in human cancer, providing insights into the stratification of populations and new diseases behind known biomarkers. Conversely, our curation of over 15,000 cancer genome studies emphasises knowledge breadth, driving discovery of new unrecognised cancer-driving hotspots and molecular targets. Our high-resolution curation approach is globally unique, giving substantial insight into molecular biomarkers in human oncology. For example, BRAF is well characterized in skin melanoma, transiently treatable with inhibitors such as Vemurafenib. It is also well known in colorectal cancer, which is largely non-responsive to BRAF inhibitors. COSMIC9s unique approach demonstrates the impact of BRAF mutations in much less well-known cancers, for instance, Hairy Cell Leukaemia (89% of samples mutated) and Langerhans Cell Histiocytosis (49%), both of which respond remarkably well to BRAF inhibitors. Converse to skin melanoma, our curations suggest BRAF has a minimal role in Uveal melanoma (6% of Uveal tumors mutated for BRAF), with higher mutation rates in other genes (particularly GNA11, BAP1 and GNAQ), suggesting different mechanisms behind this disease. In addition to describing over two million coding point mutations across cancer, COSMIC also details more than six million non-coding mutations, 10,567 gene fusions, 61,232 genome rearrangements, 702,652 abnormal copy number segments, and more than 6 million abnormal expression variants. All these types of somatic mutation are annotated to both the human genome and each affected coding gene, then correlated across disease and mutation types. As increasing amounts of genetic data are gathered into COSMIC across human cancer, our annotations are beginning to emphasise events with a higher impact in cancer, highlighting the more functional coding mutations and major amplifications and deletions. This concept of high-impact data is being extended across the entire COSMIC system, much more strongly defining genes and mutations which drive oncogenesis. Citation Format: Simon A. Forbes, Dave Beare, Prasad Gunasekaran, Kenric Leung, Charambulos Boutselakis, Minjie Ding, Mingming Jia, Tisham De, Nidhi Bindal, Chai Yin Kok, Sally Bamford, Sari Ward, Charlotte Cole, Jon Teague, Michael R. Stratton, Peter J. Campbell. COSMIC: Combining the world9s knowledge of somatic mutation in human cancer. [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 62. doi:10.1158/1538-7445.AM2015-62

Abstract 62: COSMIC: Combining the world's knowledge of somatic mutation in human cancer

Abstract 2601: COSMIC-3D: exploring cancer mutations in three dimensions for drug design and discovery

Background. COSMIC (http://www.sanger.ac.uk/cosmic) is a system designed to curate the world's literature on somatic mutations in known cancer genes. Initially conceived to capture the mutation spread in point-mutated genes, COSMIC has now grown to encompass gene fusion products of genome rearrangement events which generate completely novel transcripts, together with all the somatic mutation data from candidate gene screens at the Cancer Genome Project, UK (CGP), covering almost 5000 genes of potential interest in cancer genetics. Results. The latest release of COSMIC (version 37; July 2008) now holds full and up-to-date curation of over 5,900 scientific papers, examining over 268,000 tumours, in which over 59,000 mutations are detailed through 60 pointmutated genes. Fusion gene products have been curated for 16 pairs of genes, described through over 4200 tumours. 2246 papers were rejected during manual curation, usually due to significant inconsistencies in the publication. A relational database holds the captured information, which is warehoused for each release. The information is presented on the internet with a series of graphical and tabulated views aiding navigation and interpretation. Conclusions. The current version of COSMIC is close to fulfilling its original intentions, with curation of most pointmutated genes in cancer complete. However, new challenges are emerging with the need to calculate the effect of high numbers of observed sequence changes to identify those driving tumour formation, and the need to meaningfully handle the increasing quantities of data from high-throughput screens and next-generation sequencing technologies.

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COSMIC, curating the cancer variome


 Somatic mutations accumulate in cells throughout their life. Most of them do not bring any negative effect. However, certain mutations change protein behaviour, structure, or level of expression. More importantly, some mutations are known to initiate and drive oncogenic transformation. These mutations often make good therapeutic targets but recognising this small subset in a cancer sample is a major challenge. The average cancer cell carries a life-long baggage of somatic mutations, and the mutational process is sped up in these cells through genomic instability (one of the hallmarks of cancer). As a result, there are hundreds of thousands of variants of unknown significance identified through sequencing of cancer DNA. COSMIC Cancer Mutation Census (CMC) answers this challenge by identifying coding mutations with a potential to drive cancer. This is achieved by combining manually curated information regarding cancer genes and genetic variants with data on variant frequencies in cancer and non-cancer populations, and algorithmic evaluation of variant significance. It applies a simple and transparent set of rules to the whole set of coding mutations in COSMIC to identify variants with the highest potential of clinical relevance. In current version (v95, November 2021) the CMC describes 4.7 million somatic variants and segregates them into four tiers. Tier 1 is the highest confidence set. This set includes 1558 mutations that are found in Cancer Gene Census genes and are also described as pathogenic in cancer by ClinVar. Tiers 2 and 3 contain variants with less extensive evidence of involvement in carcinogenesis. The dN/dS algorithm is used to include variants that are under positive selection in cancer cells. Finally, mutations without evidence for driving cancer are classified as Tier 4. In addition to this classification, CMC integrates and presents the information used to prioritise variants, including their frequencies in various cancer types (COSMIC), germline frequencies (gnomAD), ClinVar annotations, dN/dS analysis results, and nucleotide and amino acid conservation. Data can be accessed and scrutinised through a dedicated website at https://cancer.sanger.ac.uk/cmc.
 Citation Format: Zbyslaw Sondka, Bhavana Harsha, Helder Pedro, Nidhi Bindal Dhir, Charlie Hathaway, Sumodh Nair, Doron Sondheimer, Simon A. Forbes. COSMIC cancer mutation census: Classifying somatic coding variants by their potential to drive cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1200.

Simon A. Forbes

Papers

Abstract 3284: COSMIC: Integrating and interpreting the world's knowledge of somatic mutations in cancer

Abstract 62: COSMIC: Combining the world's knowledge of somatic mutation in human cancer

Abstract 2601: COSMIC-3D: exploring cancer mutations in three dimensions for drug design and discovery

COSMIC, curating the cancer variome

Abstract 1200: COSMIC cancer mutation census: Classifying somatic coding variants by their potential to drive cancer