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Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments - Administrative Supplement

数据发现：搜索短读测序实验的计算方法 - 行政补充

基本信息

批准号：
10393953
负责人：
Carleton Lee Kingsford
金额：
$ 0.82万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-01 至 2022-04-30
项目状态：
已结题

项目摘要

PROJECT SUMMARY / ABSTRACT This proposal aims to solve the sequencing experiment discovery problem. The data from hundreds of thou- sands of short-read sequencing experiments are now publicly available, and private collections of sequencing experiments are also growing rapidly. These experiments include hundreds of thousands of whole genome sequencing experiments, and tens of thousands of RNA-seq, metagenomic, and tumor sequencing samples. However, these experiments are vastly underused, with few analyses making use of more than a handful of ex- periments at a time and most analyses ignoring this collection of raw data entirely. One crucial reason for this is that merely ﬁnding the appropriate experiments is a signiﬁcant barrier to their use in downstream analyses. This is due to the lack of a computational platform that can search for relevant short-read sequencing data sets by the sequences they contain. It is not currently possible to ﬁnd all the metagenomic experiments in which the genes that form a particular pathway are present or to ﬁnd all experiments in which a novel lncRNA is observed. The experiment discovery problem is that of ﬁnding — on a global scale — those experiments that are relevant to an isoform, variant, or species under study. By building on our existing work in large-scale sequence search, we propose to develop a new distributed platform to index and search hundreds of thousands of raw short-read se- quencing data sets to enable researchers to quickly ﬁnd experiments that contain their query sequences. We will apply this system to searching RNA-seq, metagenomic, and cancer tumor samples. The research questions we will solve include how to improve the computational scaling, increase the types of biologically meaningful queries that can be answered, and increase our ability to ﬁnd relevant experiments in situations where muta- tions are common. We will produce a high-quality open-source implementation of the developed computational methods. The project will signiﬁcantly expand the usefulness of large repositories of raw sequencing reads and enabled new approaches for large-scale reanalysis and reuse of short-read experiments. The system will unlock a rich source of biological information for gene function prediction, for understanding microbial communities, and for connecting genetic variation with disease progression.

项目总结/摘要该方案旨在解决测序实验发现问题。数据来源于数十万- 大量的短读测序实验现在是公开的，实验也在迅速增长。这些实验包括数十万个全基因组测序实验，以及数以万计的RNA-seq、宏基因组和肿瘤测序样本。然而，这些实验被大大低估，很少有分析使用超过少数的前- 大多数分析完全忽略了原始数据的收集。其中一个关键原因是仅仅找到合适的实验是将其用于下游分析的重大障碍。这这是由于缺乏可以通过测序技术搜索相关短读测序数据集的计算平台。它们包含的序列。目前还不可能找到所有的宏基因组实验，其中基因形成一个特定的途径，或找到所有实验中观察到一种新的lncRNA。的实验发现问题是指在全球范围内发现那些与一个实验相关的实验。同种型、变体或研究中的种。通过建立在我们现有的大规模序列搜索工作，我们建议开发一个新的分布式平台来索引和搜索数十万个原始的短读序列，对数据集进行测序，使研究人员能够快速找到包含查询序列的实验。我们将将该系统应用于搜索RNA-seq、宏基因组和癌症肿瘤样本。研究问题我们将解决的问题包括如何提高计算规模，增加生物学意义的类型，可以回答的查询，并提高我们在穆塔的情况下找到相关实验的能力- 这些都是常见的。我们将产生一个高质量的开源实现开发的计算方法.该项目将显著扩大原始测序读数的大型储存库的有用性，为大规模再分析和重复使用短读实验提供了新的方法。系统会解锁一个丰富的生物信息来源，用于基因功能预测，用于了解微生物群落，将遗传变异与疾病进展联系起来