Seqenv: linking sequences to environments through text mining.

Lucas Sinclair,Aaron Weimann,Ali Ijaz,Christopher Quince,Alexander Eiler,Evangelos Pafilis,Marco J.L Coolen,Christina Pavloudi,Julia Schnetzer,Anastasis Oulas,Lars Juhl Jensen,Alica Chroňáková,Umer Z Ijaz,Cecile Gubry-Rangin

doi:10.7717/peerj.2690

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.

Highlights

The annotation of DNA sequences, i.e., attaching meaningful labels to them, is key to the interpretation of genomics data
We apply this bioinformatics pipeline to two datasets of environmental marker genes derived from terrestrial archaeal ammonia oxidizers (AOA) (Gubry-Rangin et al, 2011) and the Black Sea plankton paleome (Coolen et al, 2013)
All sequences were from archaeal ammonia oxidizers (AOAs) as described previously (Gubry-Rangin et al, 2011)

Summary

Introduction

The annotation of DNA sequences, i.e., attaching meaningful labels to them, is key to the interpretation of genomics data. We introduce a new program titled ‘‘seqenv’’ which addresses this gap, automatically labeling sequences to the Environmental Ontology (EnvO) (Buttigieg et al, 2013) We apply this bioinformatics pipeline to two datasets of environmental marker genes derived from terrestrial archaeal ammonia oxidizers (AOA) (Gubry-Rangin et al, 2011) and the Black Sea plankton paleome (Coolen et al, 2013). This method reveals hitherto unknown patterns in AOA diversity, and adds to our understanding of the geological history of the Black Sea

Methods

Results

Discussion

Conclusion