MOTIVATION: As a starting point in annotation of bacterial genomes, gene finding programs are used for the prediction of functional elements in the DNA sequence. Due to the faster pace and increasing number of genome projects currently underway, it is becoming especially important to have performant methods for this task. RESULTS: This study describes the development of joint application strategies that combine the strengths of two microbial gene finders to improve the overall gene finding performance. Critica is very specific in the detection of similarity-supported genes as it uses a comparative sequence analysis-based approach. Glimmer employs a very sophisticated model of genomic sequence properties and is sensitive also in the detection of organism-specific genes. Based on a data set of 113 microbial genome sequences, we optimized a combined application approach using different parameters with relevance to the gene finding problem. This results in a significant improvement in specificity while there is similarity in sensitivity to Glimmer. The improvement is especially pronounced for GC rich genomes. The method is currently being applied for the annotation of several microbial genomes. AVAILABILITY: The methods described have been implemented within the gene prediction component of the GenDB genome annotation system.
MOTIVATION: As a starting point in annotation of bacterial genomes, gene finding programs are used for the prediction of functional elements in the DNA sequence. Due to the faster pace and increasing number of genome projects currently underway, it is becoming especially important to have performant methods for this task. RESULTS: This study describes the development of joint application strategies that combine the strengths of two microbial gene finders to improve the overall gene finding performance. Critica is very specific in the detection of similarity-supported genes as it uses a comparative sequence analysis-based approach. Glimmer employs a very sophisticated model of genomic sequence properties and is sensitive also in the detection of organism-specific genes. Based on a data set of 113 microbial genome sequences, we optimized a combined application approach using different parameters with relevance to the gene finding problem. This results in a significant improvement in specificity while there is similarity in sensitivity to Glimmer. The improvement is especially pronounced for GC rich genomes. The method is currently being applied for the annotation of several microbial genomes. AVAILABILITY: The methods described have been implemented within the gene prediction component of the GenDB genome annotation system.
Authors: Jong-In Han; Hong-Kyu Choi; Seung-Won Lee; Paul M Orwin; Jina Kim; Sarah L Laroe; Tae-Gyu Kim; Jennifer O'Neil; Jared R Leadbetter; Sang Yup Lee; Cheol-Goo Hur; Jim C Spain; Galina Ovchinnikova; Lynne Goodwin; Cliff Han Journal: J Bacteriol Date: 2010-12-23 Impact factor: 3.490
Authors: Pieter De Maayer; Wai Yin Chan; Fabio Rezzonico; Andreas Bühlmann; Stephanus N Venter; Jochen Blom; Alexander Goesmann; Jürg E Frey; Theo H M Smits; Brion Duffy; Teresa A Coutinho Journal: J Bacteriol Date: 2012-03 Impact factor: 3.490
Authors: Axel W Strittmatter; Heiko Liesegang; Ralf Rabus; Iwona Decker; Judith Amann; Sönke Andres; Anke Henne; Wolfgang Florian Fricke; Rosa Martinez-Arias; Daniela Bartels; Alexander Goesmann; Lutz Krause; Alfred Pühler; Hans-Peter Klenk; Michael Richter; Margarete Schüler; Frank Oliver Glöckner; Anke Meyerdierks; Gerhard Gottschalk; Rudolf Amann Journal: Environ Microbiol Date: 2009-01-14 Impact factor: 5.491
Authors: Theo H M Smits; Sebastian Jaenicke; Fabio Rezzonico; Tim Kamber; Alexander Goesmann; Jürg E Frey; Brion Duffy Journal: BMC Genomics Date: 2010-01-04 Impact factor: 3.969