Informatics Tools for High-Throughput Sequences Data Analysis

描述（由申请人提供）：基因组分析工具包（GATK）是一套一流的、广泛使用的、支持良好的开源工具，用于处理和分析下一代DNA测序（NGS）数据。这些工具目前包括多序列重配器、协变量校正碱基质量评分重校准器、多样本SNP、INDEL和CNV基因型、用于假阳性识别的机器学习算法、变异评估模块、体细胞SNP和indel调用器以及数百种其他工具。所有这些工具的基础是我们的结构化编程框架（GATK-Engine），它使用MapReduce的函数式编程理念，使编写功能丰富，高效和强大的分析工具变得容易。通过集中通用数据管理基础设施，所有基于GATK的工具都受益于引擎的正确性，CPU和内存效率，以及自动分布式和共享内存并行化，这些功能对于NGS数据集的庞大和不断增长的规模至关重要。GATK目前支持所有主要的测序技术，包括Illumina。生命科学454和ABI SOLID，从外显子组的混合捕获到1000个基因组计划中的1000个低通样本。我们对技术无关的处理工具的重视有助于推广现在分别用于表示NGS数据和变异调用的标准SAM/BAM和VCF格式。在本RFA中，我们建议继续开发GATK-Engine和数据处理工具，以（1）实现所有主要测序研究设计和NGS技术的完整和准确的变异发现和基因分型（2）优化GATK-Engine和流水线基础设施，以便在数万个样本规模的分布式数据集上有效运行（3）扩展GATK数据处理工具，以支持Complete Genomics、lon Torrent和Pacific Biosciences即将推出的测序技术以及我们目前的技术，（4）大幅扩展我们的教育和支持结构，以确保未来NGS的长尾用户能够从最好的-练习GATK中的数据处理和分析工具。

DESCRIPTION (provided by applicant): The Genome Analysis Toolkit (GATK) is a suite of best-in-class, widely-used, well-supported, open-source tools for processing and analysis of next-generation DNA sequencing (NGS) data. These tools currently include a multiple sequence realigner, a covariate-correcting base quality score recalibrator, multi-sample SNP, INDEL, and CNV genotypers, machine learning algorithms for false positive identification, variant evaluation modules, somatic SNP and indel callers, and hundreds of other tools. Underlying all of these tools is our structured programming framework (GATK-Engine) that uses the functional programming philosophy of MapReduce to make writing feature-rich, efficient and robust analysis tools easy. By centralizing common data management infrastructure, all GATK-based tools benefit from the engine's correctness, CPU and memory efficiency, as well as automatic distributed and shared memory parallelization, essential capabilities given the massive and growing size of NGS datasets. The GATK currently supports all of the major sequencing technologies including lllumina. Life Sciences 454, and ABI SOLID, from hybrid capture of exomes to 1000s of low-pass samples in the 1000 Genomes Project. Our emphasis on technology-agnostic processing tools has helped to popularize the now standard SAM/BAM and VCFs formats for representing NGS data and variation calls, respectively. In this RFA we propose to continue to develop the GATK-Engine and data processing tools to (1) achieve complete and accurate variation discovery and genotyping for all major sequencing study designs and NGS technologies (2) optimize the GATK-Engine and pipelining infrastructure to operate efficiently on distributed data sets at the scale of tens of thousands of samples (3) extend the GATK data processing tools to support the upcoming sequencing technologies of Complete Genomics, lon Torrent, and Pacific Biosciences as well as we do current technologies, (4) expand significantly our educational and support structures to ensure that the longtail of future NGS users can benefit from the best-practice data processing and analysis tools in the GATK.