Literature DB >> 7800709

Statistical studies of biomolecular sequences: score-based methods.

S Karlin1.   

Abstract

The massive accumulation of DNA and protein sequence data poses challenges and opportunities in terms of interpretation and analysis. This presentation reviews the method of score-based sequence analysis with the objectives of discerning distinctive segments in single sequences and identifying significant common segments in sequence comparisons. A number of new results are described here for both the theory and its applications. These include distributional theory involving several high scoring segments in single sequences, distribution formulas for general scoring regimes in multiple sequence comparisons, bounds for periodic scoring assignments, sensitivity analysis of genome composition and refinements on predicting exons and genes in DNA sequences.

Mesh:

Substances:

Year:  1994        PMID: 7800709     DOI: 10.1098/rstb.1994.0078

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  5 in total

Review 1.  Statistical signals in bioinformatics.

Authors:  Samuel Karlin
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-12       Impact factor: 11.205

2.  Characterizations of diverse residue clusters in protein three-dimensional structures.

Authors:  S Karlin; Z Y Zhu
Journal:  Proc Natl Acad Sci U S A       Date:  1996-08-06       Impact factor: 11.205

3.  Modeling horizontal gene transfer (HGT) in the gut of the Chagas disease vector Rhodnius prolixus.

Authors:  Scott Matthews; Vadrevu SreeHari Rao; Ravi V Durvasula
Journal:  Parasit Vectors       Date:  2011-05-14       Impact factor: 3.876

4.  Quod erat demonstrandum? The mystery of experimental validation of apparently erroneous computational analyses of protein sequences.

Authors:  L M Iyer; L Aravind; P Bork; K Hofmann; A R Mushegian; I B Zhulin; E V Koonin
Journal:  Genome Biol       Date:  2001-11-13       Impact factor: 13.583

5.  AT excursion: a new approach to predict replication origins in viral genomes by locating AT-rich regions.

Authors:  David S H Chew; Ming-Ying Leung; Kwok Pui Choi
Journal:  BMC Bioinformatics       Date:  2007-05-21       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.