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The paralog-to-contig assignment problem: high quality gene models from fragmented assemblies

Overview of attention for article published in Algorithms for Molecular Biology, February 2016
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)

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Title
The paralog-to-contig assignment problem: high quality gene models from fragmented assemblies
Published in
Algorithms for Molecular Biology, February 2016
DOI 10.1186/s13015-016-0063-y
Pubmed ID
Authors

Henrike Indrischek, Nicolas Wieseke, Peter F. Stadler, Sonja J. Prohaska, Indrischek, Henrike, Wieseke, Nicolas, Stadler, Peter F, Prohaska, Sonja J

Abstract

The accurate annotation of genes in newly sequenced genomes remains a challenge. Although sophisticated comparative pipelines are available, computationally derived gene models are often less than perfect. This is particularly true when multiple similar paralogs are present. The issue is aggravated further when genomes are assembled only at a preliminary draft level to contigs or short scaffolds. However, these genomes deliver valuable information for studying gene families. High accuracy models of protein coding genes are needed in particular for phylogenetics and for the analysis of gene family histories. We present a pipeline, ExonMatchSolver, that is designed to help the user to produce and curate high quality models of the protein-coding part of genes. The tool in particular tackles the problem of identifying those coding exon groups that belong to the same paralogous genes in a fragmented genome assembly. This paralog-to-contig assignment problem is shown to be NP-complete. It is phrased and solved as an Integer Linear Programming problem. The ExonMatchSolver-pipeline can be employed to build highly accurate models of protein coding genes even when spanning several genomic fragments. This sets the stage for a better understanding of the evolutionary history within particular gene families which possess a large number of paralogs and in which frequent gene duplication events occurred.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Student > Master 3 13%
Lecturer 3 13%
Student > Bachelor 3 13%
Researcher 3 13%
Other 6 25%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 33%
Agricultural and Biological Sciences 7 29%
Computer Science 3 13%
Immunology and Microbiology 2 8%
Engineering 2 8%
Other 1 4%
Unknown 1 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 February 2016.
All research outputs
#14,451,101
of 25,139,853 outputs
Outputs from Algorithms for Molecular Biology
#92
of 262 outputs
Outputs of similar age
#141,843
of 304,903 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 2 outputs
Altmetric has tracked 25,139,853 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 262 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 304,903 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.