Literature DB >> 8006999

Genomic alignment.

J Hein1, J Støvlbaek.   

Abstract

As sequencing techniques become increasingly efficient, the average length of a sequence is bound to grow. Traditional sequence-comparison algorithms can either compare DNA or protein, but not a mixture, which is actually a common situation. Most obtained DNA sequences contain coding regions, and it is more reliable to compare the coding regions as protein than just as DNA. A heuristic algorithm is presented that can compare DNA with both coding and noncoding regions, but that also can compare multiple reading frames and determine which exons are homologous. A program, GenA1 (Genomic Alignment), was developed that implements the algorithm. Its use is demonstrated on two retroviruses.

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Year:  1994        PMID: 8006999     DOI: 10.1007/BF00176094

Source DB:  PubMed          Journal:  J Mol Evol        ISSN: 0022-2844            Impact factor:   2.395


  4 in total

1.  Matching sequences under deletion-insertion constraints.

Authors:  D Sankoff
Journal:  Proc Natl Acad Sci U S A       Date:  1972-01       Impact factor: 11.205

2.  A general method applicable to the search for similarities in the amino acid sequence of two proteins.

Authors:  S B Needleman; C D Wunsch
Journal:  J Mol Biol       Date:  1970-03       Impact factor: 5.469

3.  An algorithm combining DNA and protein alignment.

Authors:  J Hein
Journal:  J Theor Biol       Date:  1994-03-21       Impact factor: 2.691

4.  An improved algorithm for matching biological sequences.

Authors:  O Gotoh
Journal:  J Mol Biol       Date:  1982-12-15       Impact factor: 5.469

  4 in total
  3 in total

1.  RevTrans: Multiple alignment of coding DNA from aligned amino acid sequences.

Authors:  Rasmus Wernersson; Anders Gorm Pedersen
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  A maximum-likelihood approach to analyzing nonoverlapping and overlapping reading frames.

Authors:  J Hein; J Støvlbaek
Journal:  J Mol Evol       Date:  1995-02       Impact factor: 2.395

3.  Composition-based statistics and translated nucleotide searches: improving the TBLASTN module of BLAST.

Authors:  E Michael Gertz; Yi-Kuo Yu; Richa Agarwala; Alejandro A Schäffer; Stephen F Altschul
Journal:  BMC Biol       Date:  2006-12-07       Impact factor: 7.431

  3 in total

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