| Version | MAJ | GenePRIMP | | |
| 0.3 | 2013-04-19 | Download | Doc |
Identification of anomalous gene calls
The GenePRIMP pipeline consists of a series of computational units that identify erroneous gene calls and missed genes, and then correct a subset of the identified anomalous features. The data input to GenePRIMP needs to be a file of gene calls in GenBank or EMBL format. As its output, GenePRIMP generates reports of identified anomalies, plus a corrected EMBL file.
Remarque | Run Unix # geneprimp | Run Web # |
Remarque | Run Unix # genscan | Run Web # |
| Version | MAJ | glimmer | | |
| glimmer-3.02 | 2008-12-12 | Download | Doc |
Glimmer (Gene Locator and Interpolated Markov ModelER) prédit la position des gènes dans une séquence d'ADN (bactérie, archae, virus) en s'appuyant sur des modèles de Markov.
Remarque | Run Unix # glimmer3 | Run Web # |
| Version | MAJ | gmorse | | |
| 1.0 | 2009-08-05 | Download | Doc |
G-Mo.R-Se is a method aimed at using RNA-Seq short reads to build de novo gene models. First, candidate exons are built directly from the positions of the reads mapped on the genome (without any ab initio assembly of the reads), and all the possible splice junctions between those exons are tested against unmapped reads : the testing of junctions is directed by the information available in the RNA-Seq dataset rather than a priori knowledge about the genome. Exons can thus be chained into stranded gene models.
Remarque | Run Unix # gmorse -h | Run Web # |
Gene Finding Program for Metagenomics MetaGene predicts prokaryotic genes on anonymous genomic sequences. Fragmented sequences (longer than 100 bp) can be accepted.
Remarque | Run Unix # metagene [multi-fasta] | Run Web # |
Version améliorée du programe d'annotation de données métagénomiques Metagene. Prediction de genes procaryotes à partir d'un génome ou d'un set de génomes anonymes. Particulierement adapté aux analyses métagénomiques.
Remarque | Run Unix # metageneannotator | Run Web # |
| Version | MAJ | prodigal
| | |
| 2.60 | 2013-03-26 | Download | Doc |
Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:
Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
Remarque Prodigal Reference: Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010 Mar 8;11(1):119. (Highly Accessed) | Run Unix # prodigal -h
| Run Web # |
RATT is software to transfer annotation from a reference (annotated) genome to an unannotated query genome.
Remarque | Run Unix # start.ratt.sh | Run Web # |
| Version | MAJ | SHOW | | |
| 20111109 | 2011-11-11 | Download | Doc |
SHOW (Structured HOMgeneity Watcher) permet une utilisation souple des modeles de chaines de Markov cachees. L'utilisateur peut construire son propre modele dont les parametres peuvent ensuite etre estimes par maximum de vraisemblance avec l'algorithme EM. Le modele peut alors servir a faire des predictions avec l'algorithme forward-backward (posterior decoding) ou avec l'algorithme de Viterbi. Il peut aussi servir a simuler des sequences. SHOW implemente aussi un detecteur de genes bacteriens. L'utilisateur n'a alors pas a se soucier du modele ni des parametres. SHOW a deja servi a annoter des genomes complets publies.
Remarque | Run Unix # show_viterbi # show2mugen.pl | Run Web # |
