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Development of computational strategies for identification and characterisation of viruses in metagenomic samples

开发用于识别和表征宏基因组样本中病毒的计算策略

基本信息

批准号：
BB/M004805/1
负责人：
Richard Leggett
金额：
$ 39.17万
依托单位：
Earlham Institute
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FM004805%2F1
关键词：
Development computational strategies identification characterisation

项目摘要

Metagenomics is the study of the DNA of mixed environmental samples that include the genomes of many different organisms. We can sequence metagenomic samples using the same next generation sequencing technology that we use to sequence the genome of a single organism, but analysing the data is much more complicated because it is difficult to know in advance which organisms are present in a sample and therefore difficult to know which organism a particular fragment of DNA (a 'read') has come from.Assembly is the process of putting together short reads into contigs that represent a much longer fragment of DNA, enabling more useful analysis. Assembly is a difficult but relatively mature field when it involves DNA from a single organism. However, many of the simplifying assumptions made by assembly tools are invalid when dealing with metagenomic data, making the process of metagenomic assembly much harder and the field much less mature.The aim of this project is to develop computational algorithms for metagenomic assembly and to produce a tool that is sensitive and able to accurately differentiate between very similar species. We have targeted a particular type of metagenomic data involving viral detection because this is an important area and one that is particularly under-addressed with the small number of metagenomic assembly tools that already exist. Using such a tool enables scientists to gain vital information from metagenomic samples, including understanding the mechanisms of disease in animals and humans, detecting novel viruses and monitoring the spread of viruses in order to prevent and contain outbreaks.

宏基因组学是研究混合环境样本的DNA，其中包括许多不同生物体的基因组。我们可以用同样的下一代测序技术对宏基因组样本进行测序，但分析数据要复杂得多，因为很难事先知道样本中存在哪些生物体，因此也很难知道哪种生物体具有特定的DNA片段组装是将短的读段组合成代表更长DNA片段的重叠群的过程，从而实现更有用的分析。组装是一个困难但相对成熟的领域，当它涉及来自单个生物体的DNA时。然而，在处理宏基因组数据时，组装工具所做的许多简化假设都是无效的，这使得宏基因组组装的过程更加困难，该领域也更加不成熟。本项目的目的是开发用于宏基因组组装的计算算法，并产生一种灵敏且能够准确区分非常相似物种的工具。我们已经瞄准了涉及病毒检测的特定类型的宏基因组数据，因为这是一个重要的领域，并且是一个已经存在的少量宏基因组组装工具特别未解决的领域。使用这种工具使科学家能够从宏基因组样本中获得重要信息，包括了解动物和人类疾病的机制，检测新型病毒并监测病毒的传播，以预防和遏制疫情。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Capturing variation in metagenomic assembly graphs with MetaCortex.

DOI：
10.1093/bioinformatics/btad020
发表时间：
2023-01-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Capturing variation in metagenomic assembly graphs with MetaCortex

使用 MetaCortex 捕获宏基因组装配图的变化

DOI：
10.1101/2021.07.23.453484
发表时间：
2021
期刊：
影响因子：
0
作者：
Martin S
通讯作者：
Martin S

NextClip: an analysis and read preparation tool for Nextera Long Mate Pair libraries.

DOI：
10.1093/bioinformatics/btt702
发表时间：
2014-02-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
Leggett RM;Clavijo BJ;Clissold L;Clark MD;Caccamo M
通讯作者：
Caccamo M

Host Subtraction, Filtering and Assembly Validations for Novel Viral Discovery Using Next Generation Sequencing Data.

DOI：
10.1371/journal.pone.0129059
发表时间：
2015
期刊：
PloS one
影响因子：
3.7
作者：
Daly GM;Leggett RM;Rowe W;Stubbs S;Wilkinson M;Ramirez-Gonzalez RH;Caccamo M;Bernal W;Heeney JL
通讯作者：
Heeney JL

New approaches for assembly of short-read metagenomic data