ODGI: understanding pangenome graphs.
ODGI: understanding pangenome graphs.
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DOI：
10.1093/bioinformatics/btac308
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发表时间：
2022-06-27
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Pangenome图提供了基因组集合相互比对的完整表示。这些模型提供了研究种群的整个基因组多样性的机会，包括结构复杂的区域。然而，使用庞格诺姆图分析数百亿数据库规模的基因组是困难的，因为它没有得到现有工具的很好支持。因此，需要快速、通用的软件来高效地向这些数据提出高级问题。我们编写了优化的动态基因组/图实现(ODGI)，这是一套新的工具，实现了可伸缩的算法，并以变异图的形式在内存中高效地表示DNA泛基因组图。ODGI支持图形片段汇编格式的预建图形。ODGI包括用于检测复杂区域、提取泛基因组基因座、去除伪影、探索性分析、操作、验证和可视化的工具。它的快速并行执行方便了常规的泛基因组任务，以及可以快速回答千兆基规模的泛基因组图的复杂生物学问题的流水线。ODGI在麻省理工学院开放源码许可下作为自由软件发布。源代码可从https://github.com/pangenome/odgi下载，文档可从https://odgi.readthedocs.io.下载ODGI可以通过Bioconda https://bioconda.github.io/recipes/odgi/README.html或GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm.安装补充数据可在生物信息学在线上获得。
Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way. We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs. ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm. Supplementary data are available at Bioinformatics online.
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影响因子：
12.3
作者：
Li H;Feng X;Chu C
通讯作者：
Chu C
影响因子：
64.8
作者：
Armstrong J;Hickey G;Diekhans M;Fiddes IT;Novak AM;Deran A;Fang Q;Xie D;Feng S;Stiller J;Genereux D;Johnson J;Marinescu VD;Alföldi J;Harris RS;Lindblad-Toh K;Haussler D;Karlsson E;Jarvis ED;Zhang G;Paten B
通讯作者：
Paten B
影响因子：
3
作者：
Kehr B;Trappe K;Holtgrewe M;Reinert K
通讯作者：
Reinert K
DOI：
10.1146/annurev-genom-120219-080406
发表时间：
2020-08-31
影响因子：
8.7
作者：
Eizenga JM;Novak AM;Sibbesen JA;Heumos S;Ghaffaari A;Hickey G;Chang X;Seaman JD;Rounthwaite R;Ebler J;Rautiainen M;Garg S;Paten B;Marschall T;Sirén J;Garrison E
通讯作者：
Garrison E
影响因子：
12.3
作者：
Ballouz, Sara;Dobin, Alexander;Gillis, Jesse A.
通讯作者：
Gillis, Jesse A.