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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 13

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 12

Issue 6, November 2015

Issue 5, September 2015

Issue 4, July/August 2015

Issue 3, May/June 2015

Issue 2, March/April 2015

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Editorial from the editor-in-chief

Guest editors' introduction: selected papers from ACM-BCB 2013

MRFy: remote homology detection for beta-structural proteins using Markov random fields and stochastic search

RLIMS-P 2.0: a generalizable rule-based information extraction system for literature mining of protein phosphorylation information

Phenotype-dependent coexpression gene clusters: application to normal and premature ageing

Global network alignment in the context of aging

Reachability analysis in probabilistic biological networks

GLProbs: aligning multiple sequences adaptively

Prediction and informative risk factor selection of bone diseases

Capturing uncertainty by modeling local transposon insertion frequencies improves discrimination of essential genes

A deep learning network approach to ab initio protein secondary structure prediction

A hierarchical clustering method of selecting kernel SNP to unify informative SNP and tag SNP

A maximum a posteriori probability and time-varying approach for inferring gene regulatory networks from time course gene microarray data

An algorithm for motif discovery with iteration on lengths of motifs

Discovering binding cores in protein-DNA binding using association rule mining with statistical measures

Gene tree diameter for deep coalescence

Heterogeneous cloud framework for big data genome sequencing

Identification of protein complexes using weighted pagerank-nibble algorithm and core-attachment structure

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Parallel implementation of MAFFT on CUDA-enabled graphics hardware

Predicting protein function using multiple kernels

Tractable cases of (*,2)-bounded parsimony haplotyping

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 11

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 10

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 9

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 8

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 7

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 6

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 5

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 4

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 3

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 2

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 1

GLProbs: aligning multiple sequences adaptively

Content Provider	ACM Digital Library
Author	Ye, Yongtao Ting, Hing-Fung Wang, Yadong Zhang, Qing Yiu, Siu-Ming Lam, Tak-Wah Cheung, David Wai-lok
Copyright Year	2015
Abstract	This paper introduces a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align them locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool called GLProbs and compared its performance with about one dozen leading alignment tools on three benchmark alignment databases, and GLProbs's alignments have the best scores in almost all testings. We have also evaluated the practicability of the alignments of GLProbs by applying the tool to three biological applications, namely phylogenetic trees construction, protein secondary structure prediction and the detection of high risk members for cervical cancer in the HPV-E6 family, and the results are very encouraging.
Starting Page	67
Ending Page	78
Page Count	12
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2316820
Volume Number	12
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hidden Markov model Multiple sequence alignment Phylogenetic analysis Progressive alignment Secondary structure prediction
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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GLProbs: Aligning multiple sequences adaptively

GLProbs: Aligning Multiple Sequences Adaptively

GLProbs: Aligning multiple sequences adaptively

Title GLProbs : Aligning multiple sequences adaptively

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction

A Novel Heuristic for Local Multiple Alignment of Interspersed DNA Repeats

Multiple sequence alignment with hidden Markov models learned by random drift particle swarm optimization

Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction

Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization

GLProbs: aligning multiple sequences adaptively

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GLProbs: Aligning multiple sequences adaptively

GLProbs: Aligning Multiple Sequences Adaptively

GLProbs: Aligning multiple sequences adaptively

Title GLProbs : Aligning multiple sequences adaptively

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction

A Novel Heuristic for Local Multiple Alignment of Interspersed DNA Repeats

Multiple sequence alignment with hidden Markov models learned by random drift particle swarm optimization

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GLProbs: aligning multiple sequences adaptively