Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7319
Title: 2-D thresholding of the connectivity map following the multiple sequence alignments of diverse datasets
Authors: Doğan, Tunca
Karaçalı, Bilge
Keywords: Biomedical computing
Biostatistics
Sequence analysis
Biomedical engineering
Issue Date: 2013
Publisher: ACTA Press
Source: Doğan, T, and Karaçalı, B. (2013, February 13-15). 2-D thresholding of the connectivity map following the multiple sequence alignments of diverse datasets. Paper presented at the 10th IASTED International Conference on Biomedical Engineering, BioMed 2013. doi:10.2316/P.2013.791-092
Abstract: Multiple sequence alignment (MSA) is a widely used method to uncover the relationships between the biomolecular sequences. One essential prerequisite to apply this procedure is to have a considerable amount of similarity between the test sequences. It's usually not possible to obtain reliable results from the multiple alignments of large and diverse datasets. Here we propose a method to obtain sequence clusters of significant intragroup similarities and make sense out of the multiple alignments containing remote sequences. This is achieved by thresholding the pairwise connectivity map over 2 parameters. The first one is the inferred pairwise evolutionary distances and the second parameter is the number of gapless positions on the pairwise comparisons of the alignment. Threshold curves are generated regarding the statistical parameter values obtained from a shuffled dataset and probability distribution techniques are employed to select an optimum threshold curve that eliminate as much of the unreliable connectivities while keeping the reliable ones. We applied the method on a large and diverse dataset composed of nearly 18000 human proteins and measured the biological relevance of the recovered connectivities. Our precision measure (0.981) was nearly 20% higher than the one for the connectivities left after a classical thresholding procedure displaying a significant improvement. Finally we employed the method for the functional clustering of protein sequences in a gold standard dataset. We have also measured the performance, obtaining a higher F-measure (0.882) compared to a conventional clustering operation (0.827).
Description: 10th IASTED International Conference on Biomedical Engineering, BioMed 2013; Innsbruck; Austria; 13 February 2013 through 15 February 2013
URI: http://doi.org/10.2316/P.2013.791-092
https://hdl.handle.net/11147/7319
ISBN: 978-088986953-0
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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