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      Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance

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          Abstract

          Esophageal adenocarcinoma (EAC) has a poor outcome, and targeted therapy trials have thus far been disappointing due to a lack of robust stratification methods. Whole-genome sequencing (WGS) analysis of 129 cases demonstrates that this is a heterogeneous cancer dominated by copy number alterations with frequent large scale rearrangements. Co-amplification of receptor tyrosine kinases (RTKs) and/or downstream mitogenic activation is almost ubiquitous; thus tailored combination RTKi therapy might be required, as we demonstrate in vitro. However, mutational signatures reveal three distinct molecular subtypes with potential therapeutic relevance, which we verify in an independent cohort (n=87): i) enriched for BRCA signature with prevalent defects in the homologous recombination pathway; ii) dominant T>G mutational pattern associated with a high mutational load and neoantigen burden; iii) C>A/T mutational pattern with evidence of an ageing imprint. These subtypes could be ascertained using a clinically applicable sequencing strategy (low coverage) as a basis for therapy selection.

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          Most cited references86

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          Is Open Access

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            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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              Is Open Access

              A global reference for human genetic variation

              The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Nature Genetics
                Nat Genet
                Springer Science and Business Media LLC
                1061-4036
                1546-1718
                October 2016
                September 05 2016
                October 2016
                : 48
                : 10
                : 1131-1141
                Article
                10.1038/ng.3659
                f139583e-59af-41e1-836b-9818db0e5278
                © 2016

                http://www.springer.com/tdm

                http://www.springer.com/tdm

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