Gene identification in novel eukaryotic genomes by self-training algorithm

Author： Lomsadze Alexandre Ter-Hovhannisyan Vardges Chernoff Yury O. Borodovsky Mark

ISSN： 1362-4962

Source： Nucleic Acids Research, Vol.33, Iss.20, 2005-01, pp. : 6494-6506

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions

By Besemer J. Lomsadze A. Borodovsky M.

Nucleic Acids Research, Vol. 29, Iss. 12, 2001-06 ,pp. : 2607-2618

Oxford University Press

Access to resources Recommend Favorite

GS-Finder: a program to find bacterial gene start sites with a self-training method

By Ou H.-Y. Guo F.-B. Zhang C.-T.

The International Journal of Biochemistry and Cell Biology, Vol. 36, Iss. 3, 2004-03 ,pp. : 535-544

Elsevier

Access to resources Recommend Favorite

FrameD: a flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences

By Schiex Thomas

Nucleic Acids Research, Vol. 31, Iss. 13, 2003-07 ,pp. : 3738-3741

Oxford University Press

Access to resources Recommend Favorite

The excess of 5′ introns in eukaryotic genomes

By Lin Kui Zhang Da-Yong

Nucleic Acids Research, Vol. 33, Iss. 20, 2005-01 ,pp. : 6522-6527

Oxford University Press

Access to resources Recommend Favorite

A novel algorithm for computational identification of contaminated EST libraries

By Sorek Rotem Safer Hershel M.

Nucleic Acids Research, Vol. 31, Iss. 3, 2003-02 ,pp. : 1067-1074

Oxford University Press

Access to resources Recommend Favorite