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ParsEval: parallel comparison and analysis of gene structure annotations

Overview of attention for article published in BMC Bioinformatics, August 2012
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Title
ParsEval: parallel comparison and analysis of gene structure annotations
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-187
Pubmed ID
Authors

Daniel S Standage, Volker P Brendel

Abstract

Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation. In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows. Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources. We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations. ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided. These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlapping, gene-model-centric genomic loci. Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations. Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Sweden 2 3%
Germany 1 1%
Netherlands 1 1%
Brazil 1 1%
Australia 1 1%
France 1 1%
United Kingdom 1 1%
Czechia 1 1%
Other 2 3%
Unknown 56 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 26%
Student > Ph. D. Student 17 25%
Student > Master 8 12%
Professor > Associate Professor 7 10%
Student > Bachelor 7 10%
Other 10 14%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 62%
Biochemistry, Genetics and Molecular Biology 10 14%
Computer Science 7 10%
Engineering 2 3%
Immunology and Microbiology 1 1%
Other 2 3%
Unknown 4 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 August 2012.
All research outputs
#18,312,024
of 22,673,450 outputs
Outputs from BMC Bioinformatics
#6,285
of 7,247 outputs
Outputs of similar age
#126,171
of 164,713 outputs
Outputs of similar age from BMC Bioinformatics
#84
of 107 outputs
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