ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life

Pafilis, Evangelos; Frankild, Sune; Schnetzer, Julia; Fanini, Lucia; Faulwetter, Sarah; Pavloudi, Christina; Vasileiadou, Katerina; Leary, Patrick; Hammock, Jennifer; Schulz, Katja; Parr, Cynthia; Arvanitidis, Christos; Jensen, Lars Juhl

doi:10.1101/011403

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…In particular, the frequency of occurrence of each word is noted. Concretely, this is done offline by using a named entity recognition (NER) system [8] and placing results into an SQLite3 database that is automatically downloaded on the first run of seqenv.…”

Section: Methodsmentioning

confidence: 99%

“…In general, we have found the isolation source metadata to be the most dependable source of environmental information and the results presented here are restricted to that field. A custom named entity recognition (NER) system based on [8] is then used to label the resulting text with terms from the EnvO ontology [3]. An ontology is a formal specifications of the terms in a particular knowledge domain and the relations among them.…”

Section: Introductionmentioning

confidence: 99%

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Seqenv: linking sequences to environments through text mining

Sinclair

Ijaz

Jensen

et al. 2016

Preprint

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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, go to: https://github.com/xapple/seqenv

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seqenv: linking sequences to environments through text mining

Sinclair

Ijaz

Jensen

et al. 2016

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Concretely, this is done offline by using a named entity recognition (NER) system. The NER algorithm is an optimized dictionary-based tagger, it searches for keywords associated with each ENVO term but also using a stop-list of problematic words [8].…”

Section: Methodsmentioning

confidence: 99%

“…The ability of the NER engine to tag text with ENVO terms was evaluated in [8] through comparison to a manually curated corpus this resulted in 87.8% precision and 77.0% recall, corresponding to an F1 score of 82.0%. The results were placed into an SQLite3 database that is automatically downloaded on the first run of seqenv.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Peer Review #3 of " Seqenv: linking sequences to environments through text mining (v0.1)"

2016

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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

show abstract

ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life

Cited by 2 publications

References 7 publications

Seqenv: linking sequences to environments through text mining

Seqenv: linking sequences to environments through text mining

Peer Review #3 of " Seqenv: linking sequences to environments through text mining (v0.1)"

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