Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5277
Title: Automatic identification of highly conserved family regions and relationships in genome wide datasets including remote protein sequences
Authors: Doğan, Tunca
Karaçalı, Bilge
Keywords: Sequence analysis
Proteins
Genome analysis
Genetic database
Receiver operating characteristic
Issue Date: Sep-2013
Publisher: Public Library of Science
Source: Doğan, T., and Karaçalı, B. (2013). Automatic identification of highly conserved family regions and relationships in genome wide datasets including remote protein sequences. PLoS One, 8(9). doi:10.1371/journal.pone.0075458
Abstract: Identifying shared sequence segments along amino acid sequences generally requires a collection of closely related proteins, most often curated manually from the sequence datasets to suit the purpose at hand. Currently developed statistical methods are strained, however, when the collection contains remote sequences with poor alignment to the rest, or sequences containing multiple domains. In this paper, we propose a completely unsupervised and automated method to identify the shared sequence segments observed in a diverse collection of protein sequences including those present in a smaller fraction of the sequences in the collection, using a combination of sequence alignment, residue conservation scoring and graph-theoretical approaches. Since shared sequence fragments often imply conserved functional or structural attributes, the method produces a table of associations between the sequences and the identified conserved regions that can reveal previously unknown protein families as well as new members to existing ones. We evaluated the biological relevance of the method by clustering the proteins in gold standard datasets and assessing the clustering performance in comparison with previous methods from the literature. We have then applied the proposed method to a genome wide dataset of 17793 human proteins and generated a global association map to each of the 4753 identified conserved regions. Investigations on the major conserved regions revealed that they corresponded strongly to annotated structural domains. This suggests that the method can be useful in predicting novel domains on protein sequences.
URI: http://doi.org/10.1371/journal.pone.0075458
http://hdl.handle.net/11147/5277
ISSN: 1932-6203
1932-6203
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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