Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Quantitative synteny scoring improves homology inference and partitioning of gene families
Stockholms universitet, Naturvetenskapliga fakulteten, Numerisk analys och datalogi (NADA). Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center, Sweden .ORCID-id: 0000-0001-5341-1733
2013 (Engelska)Ingår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, nr Suppl,15, s. S12-Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
BioMed Central, 2013. Vol. 14, nr Suppl,15, s. S12-
Nationell ämneskategori
Bioinformatik (beräkningsbiologi) Biokemi och molekylärbiologi Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)
Identifikatorer
URN: urn:nbn:se:su:diva-97258DOI: 10.1186/1471-2105-14-S15-S12ISI: 000328316700012OAI: oai:DiVA.org:su-97258DiVA, id: diva2:676306
Konferens
11th Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative GenomicsLyon, FRANCE, OCT 17-19, 2013
Forskningsfinansiär
Swedish e‐Science Research Center
Anmärkning

AuthorCount: 4;

Tillgänglig från: 2013-12-05 Skapad: 2013-12-05 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Arvestad, Lars
Av organisationen
Numerisk analys och datalogi (NADA)Science for Life Laboratory (SciLifeLab)
I samma tidskrift
BMC Bioinformatics
Bioinformatik (beräkningsbiologi)Biokemi och molekylärbiologiMedicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 932 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf