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Quantitative synteny scoring improves homology inference and partitioning of gene families
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center, Sweden .ORCID iD: 0000-0001-5341-1733
2013 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, no Suppl,15, S12- p.Article in journal (Refereed) Published
Abstract [en]

Background

Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential.

Results

Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data.

Conclusions

The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.

Place, publisher, year, edition, pages
BioMed Central, 2013. Vol. 14, no Suppl,15, S12- p.
National Category
Bioinformatics (Computational Biology) Biochemistry and Molecular Biology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:su:diva-97258DOI: 10.1186/1471-2105-14-S15-S12ISI: 000328316700012OAI: oai:DiVA.org:su-97258DiVA: diva2:676306
Conference
11th Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative GenomicsLyon, FRANCE, OCT 17-19, 2013
Funder
Swedish e‐Science Research Center
Note

AuthorCount: 4;

Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2017-12-06Bibliographically approved

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Arvestad, Lars
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Bioinformatics (Computational Biology)Biochemistry and Molecular BiologyMedical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)

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