Počet záznamů: 1  

Seqenv: linking sequences to environments through text mining

  1. 1.
    0469017 - BC 2017 RIV US eng J - Článek v odborném periodiku
    Sinclair, L. - Ijaz, U.Z. - Jensen, L.J. - Coolen, M.J.L. - Gubry-Rangin, C. - Chroňáková, Alica - Oulas, A. - Pavloudi, Ch. - Schnetzer, J. - Weimann, A. - Ijaz, A. - Eiler, A. - Quince, Ch. - Pafilis, E.
    Seqenv: linking sequences to environments through text mining.
    PeerJ. Roč. 4, December (2016), č. článku e2690. ISSN 2167-8359. E-ISSN 2167-8359
    Institucionální podpora: RVO:60077344
    Klíčová slova: bioinformatics * ecology * microbiology * genomics * sequence analysis * text processing
    Kód oboru RIV: EH - Ekologie - společenstva
    Impakt faktor: 2.177, rok: 2016

    Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the "nt" nucleotide database provided by NCBI and, out of every hit, extracts if it is available the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.
    Trvalý link: http://hdl.handle.net/11104/0267037

     
     
Počet záznamů: 1  

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