Literature DB >> 16632492

Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments.

Vladimir Vacic1, Lilia M Iakoucheva, Predrag Radivojac.   

Abstract

SUMMARY: Two Sample Logo is a web-based tool that detects and displays statistically significant differences in position-specific symbol compositions between two sets of multiple sequence alignments. In a typical scenario, two groups of aligned sequences will share a common motif but will differ in their functional annotation. The inclusion of the background alignment provides an appropriate underlying amino acid or nucleotide distribution and addresses intersite symbol correlations. In addition, the difference detection process is sensitive to the sizes of the aligned groups. Two Sample Logo extends WebLogo, a widely-used sequence logo generator. The source code is distributed under the MIT Open Source license agreement and is available for download free of charge.

Mesh:

Year:  2006        PMID: 16632492     DOI: 10.1093/bioinformatics/btl151

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  182 in total

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6.  A computational algorithm to predict shRNA potency.

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7.  Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

Authors:  Yanju Zhang; Ruopeng Xie; Jiawei Wang; André Leier; Tatiana T Marquez-Lago; Tatsuya Akutsu; Geoffrey I Webb; Kuo-Chen Chou; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

8.  webPRC: the Profile Comparer for alignment-based searching of public domain databases.

Authors:  Bernd W Brandt; Jaap Heringa
Journal:  Nucleic Acids Res       Date:  2009-05-06       Impact factor: 16.971

9.  Spial: analysis of subtype-specific features in multiple sequence alignments of proteins.

Authors:  Arthur Wuster; A J Venkatakrishnan; Gebhard F X Schertler; M Madan Babu
Journal:  Bioinformatics       Date:  2010-09-29       Impact factor: 6.937

10.  Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy.

Authors:  Zheng Rong Yang
Journal:  BMC Bioinformatics       Date:  2009-10-29       Impact factor: 3.169

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