Literature DB >> 2720468

Methods for calculating the probabilities of finding patterns in sequences.

R Staden1.   

Abstract

This paper describes the use of probability-generating functions for calculating the probabilities of finding motifs in nucleic acid and protein sequences. Equations and algorithms are given for calculating the probabilities associated with nine different ways of defining motifs. Comparisons are made with searches of random sequences. A higher level structure--the pattern--is defined as a list of motifs. A pattern also specifies the permitted ranges of spacing allowed between its constituent motifs. Equations for calculating the expected numbers of matches to patterns are given.

Mesh:

Year:  1989        PMID: 2720468     DOI: 10.1093/bioinformatics/5.2.89

Source DB:  PubMed          Journal:  Comput Appl Biosci        ISSN: 0266-7061


  58 in total

1.  Statistical significance of clusters of motifs represented by position specific scoring matrices in nucleotide sequences.

Authors:  Martin C Frith; John L Spouge; Ulla Hansen; Zhiping Weng
Journal:  Nucleic Acids Res       Date:  2002-07-15       Impact factor: 16.971

2.  Gibbs Recursive Sampler: finding transcription factor binding sites.

Authors:  William Thompson; Eric C Rouchka; Charles E Lawrence
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

3.  Bipartite pattern discovery by entropy minimization-based multiple local alignment.

Authors:  Chengpeng Bi; Peter K Rogan
Journal:  Nucleic Acids Res       Date:  2004-09-23       Impact factor: 16.971

4.  Novel transcription regulatory elements in Caenorhabditis elegans muscle genes.

Authors:  Debraj GuhaThakurta; Lawrence A Schriefer; Robert H Waterston; Gary D Stormo
Journal:  Genome Res       Date:  2004-12       Impact factor: 9.043

Review 5.  Databases, models, and algorithms for functional genomics: a bioinformatics perspective.

Authors:  Gautam B Singh; Harkirat Singh
Journal:  Mol Biotechnol       Date:  2005-02       Impact factor: 2.695

6.  Alignments anchored on genomic landmarks can aid in the identification of regulatory elements.

Authors:  Kannan Tharakaraman; Leonardo Mariño-Ramírez; Sergey Sheetlin; David Landsman; John L Spouge
Journal:  Bioinformatics       Date:  2005-06       Impact factor: 6.937

Review 7.  Comparative genomic reconstruction of transcriptional regulatory networks in bacteria.

Authors:  Dmitry A Rodionov
Journal:  Chem Rev       Date:  2007-07-18       Impact factor: 60.622

8.  Identification of muscle-specific regulatory modules in Caenorhabditis elegans.

Authors:  Guoyan Zhao; Lawrence A Schriefer; Gary D Stormo
Journal:  Genome Res       Date:  2007-02-06       Impact factor: 9.043

Review 9.  The Staden sequence analysis package.

Authors:  R Staden
Journal:  Mol Biotechnol       Date:  1996-06       Impact factor: 2.695

10.  Contribution of ultra-short invasive elements to the evolution of the mitochondrial genome in the genus Podospora.

Authors:  F Koll; J Boulay; L Belcour; Y d'Aubenton-Carafa
Journal:  Nucleic Acids Res       Date:  1996-05-01       Impact factor: 16.971

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