Seqenv : linking sequences to environments through text mining

PeerJ

article

Lucas Sinclair¹  Umer Z. Ijaz²  Lars Juhl Jensen³  Marco J.L. Coolen⁴  Cecile Gubry-Rangin⁵  Alica Chroňáková⁶  Anastasis Oulas⁷  Christina Pavloudi⁸  Julia Schnetzer⁹  Aaron Weimann^1,10  Ali Ijaz^1,11  Alexander Eiler¹  Christopher Quince^1,12  Evangelos Pafilis⁸ 

[1] Department of Ecology and Genetics, Limnology, Uppsala University;Infrastructure and Environment Research Division, School of Engineering, University of Glasgow;The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen;Western Australia Organic and Isotope Geochemistry Centre ,(WA-OIGC), Department of Chemistry, Curtin University of Technology;Institute of Biological & Environmental Sciences, University of Aberdeen;Institute of Soil Biology, Biology Centre, Czech Academy of Sciences;Bioinformatics Group, The Cyprus Institute of Neurology and Genetics;Institute of Marine Biology Biotechnology and Aquaculture ,(IMBBC), Hellenic Centre for Marine Research;Department of Molecular Ecology, Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology;Computational Biology of Infection Research, Helmholtz Centre for Infection Research;Hawkesbury Institute for the Environment, University of Western Sydney;Warwick Medical School, University of Warwick

关键词: Bioinformatics;    Ecology;    Microbiology;    Genomics;    Sequence analysis;    Text processing;    Statistics;    Pipeline;    Open source software;   

DOI  :  10.7717/peerj.2690

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY