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PeerJ. 2016 Dec 20;4:e2690. doi: 10.7717/peerj.2690. eCollection 2016.

Seqenv: linking sequences to environments through text mining.

Author information

1
Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden.
2
Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom.
3
The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
4
Western Australia Organic and Isotope Geochemistry Centre (WA-OIGC), Department of Chemistry, Curtin University of Technology, Bentley, WA, Australia.
5
Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom.
6
Institute of Soil Biology, Biology Centre, Czech Academy of Sciences, České Budějovice, Czech Republic.
7
Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
8
Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece.
9
Department of Molecular Ecology, Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.
10
Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
11
Hawkesbury Institute for the Environment, University of Western Sydney, Hawkesbury, Sydney, Australia.
12
Warwick Medical School, University of Warwick, Warwick, United Kingdom.
#
Contributed equally

Abstract

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.

KEYWORDS:

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

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