Data structures based on k-mers for querying large collections of sequencing data sets.
Data structures based on k-mers for querying large collections of sequencing data sets.
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DOI：
10.1101/gr.260604.119
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发表时间：
2021-01
期刊：
影响因子：
7
通讯作者：
Chikhi R
中科院分区：
文献类型：
--
作者：
Marchet C;Boucher C;Puglisi SJ;Medvedev P;Salson M;Chikhi R
高通量测序数据集通常存放在公共储存库(例如，欧洲核苷酸档案库)中，以确保重复性。由于数据量已经达到PB级，存储库不允许执行在线序列搜索，但这样的功能将对调查人员非常有用。为了实现这一目标，在过去的几年里，已经引入了几种计算方法来索引和查询大型数据集集合。在这里，我们建议对这些方法进行可访问的调查，这些方法通常基于将数据集表示为k-MERS集。我们回顾了它们的性质，介绍了它们的分类，并给出了它们的一般直觉。我们总结了他们的表现，并强调了他们目前的优势和局限性。
High-throughput sequencing data sets are usually deposited in public repositories (e.g., the European Nucleotide Archive) to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.
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DOI：
10.1093/bioinformatics/btw279
发表时间：
2016-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Chikhi R;Limasset A;Medvedev P
通讯作者：
Medvedev P
影响因子：
46.9
作者：
Bray, Nicolas L.;Pimentel, Harold;Pachter, Lior
通讯作者：
Pachter, Lior
影响因子：
14.9
作者：
Cook, Charles E.;Lopez, Rodrigo;Apweiler, Rolf
通讯作者：
Apweiler, Rolf
影响因子：
3.7
作者：
Crainiceanu, Adina;Lemire, Daniel
通讯作者：
Lemire, Daniel
影响因子：
5.8
作者：
Cox, Anthony J.;Bauer, Markus J.;Rosone, Giovanna
通讯作者：
Rosone, Giovanna