Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Citation - Scopus: 1Doğrusal Olmayan Gömme Teknikleri Altında Gen Dizilerinin Evrimsel İ̇lişkileri(IEEE, 2010) Doğan, Tunca; Karaçalı, BilgeWe present an error analysis on the application of non-linear embedding on pairwise evolutionary distances inferred over a collection of genetic sequences following multiple sequence alignment. To this end, we have generated gene sequences evolved by random substitutions along three different evolutionary pathways with known evolutionary distances between every sequence pair. We have compared the discrepancy between the inferred evolutionary distances to the true distances before and after non-linear embedding into a low dimensional vector space. The results indicate that non-linear embedding achieves significant reduction in error in the estimated evolutionary distances. Consequently, nonlinear embedding of evolutionary distances can provide more reliable inferences on the evolutionary relationships between genetic sequences. ©2010 IEEE.Conference Object 2-D Thresholding of the Connectivity Map Following the Multiple Sequence Alignments of Diverse Datasets(ACTA Press, 2013) Doğan, Tunca; Karaçalı, BilgeMultiple 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).