Bioinformatics Research and Applications: 10th International by Mitra Basu, Yi Pan, Jianxin Wang

By Mitra Basu, Yi Pan, Jianxin Wang

This publication constitutes the refereed complaints of the tenth overseas Symposium on Bioinformatics examine and functions, ISBRA 2014, held in Zhangjiajie, China, in June 2014. The 33 revised complete papers and 31 one-page abstracts incorporated during this quantity have been conscientiously reviewed and chosen from 119 submissions. The papers conceal a variety of themes in bioinformatics and computational biology and their purposes together with the improvement of experimental or advertisement structures.

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Extra info for Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014, Proceedings (Lecture Notes in Computer Science)

Example text

We note once again that the applicability EDAlignres is limited to equal length proteins. In EDAlignsse , we overcome this limitation by using matrix eigendecomposition to obtain SSE-pairs and subsequently residue-pairs from these. The details are as follows. Identifying SEEs: In this step, we map the residues to secondary structure elements(SSEs) which are limited to α-helices and β-sheets. 42Ao . To identify such structures we have used the DSSP program that implements these inequalities [36, 37].

Ch. Panigrahi and A. Mukhopadhyay to an SSE into a single entity and create a modified cost matrix, M , each entry P being the cost of the correspondence of a pair of SSEs, one in Si,j and the other Q in Si ,j . When both the SSEs are α-helices the cost is: α M [SSEPα , SSEQ ]= α , C ∈SSE α Ca ∈SSEP b Q M (Ca , Cb ) , 49 (17) while if both are β-sheets the cost is: β M [SSEPβ , SSEQ ]= β β Ca ∈SSEP , Cb ∈SSEQ M (Ca , Cb ) . (18) 16 To avoid a correspondence between an α-helix and a β-sheet, we set the cost of such a correspondence to zero.

L4(T) = 1 Fig. 2. Likelihood computation with the pruning algorithm [14, pp. 253-255] Phylogenetic Bias in the Likelihood Method Caused by Missing Data 15 We first define an array for each of the nodes including the leaf nodes. The array contains four elements for nucleotide sequences and 20 for amino acid sequences. For a leaf node i with a resolved nucleotide S, Li(S) = 1, and Li(not S) = 0. For an unknown or missing nucleotide, Li(1) = Li(2)= Li(3)= Li(4) = 1. For an internal node i with two offspring (o1 and o2), Li is recursively defined as  3  3  Li ( s ) =   Psk (bi,o1 ) Lo1 (k )    Psk (bi ,o2 ) Lo2 (k )   k =0   k =0  (2) where bi,o1 means the branch length between internal node i and its offspring o1, and Psk is the transition probability from state s to state k.

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