20805240
This reference describes FragGeneScan.
| PMID | PMID 20805240 |
|---|---|
| Title | FragGeneScan: predicting genes in short and error-prone reads |
| Year | 2010 |
| Journal | Nucleic Acids Research |
| Author | Rho M, Tang H, Ye Y |
| Volume | |
| Start page |
Error: No contents found at URL http://www.ebi.ac.uk/europepmc/webservices/rest/MED/20805240/citations/4000.
According to Europe PubMed Central, this reference has Error: no local variable "citations" was set. " Error: no local variable "citations" was set. " is not a number. citations.
For reference, you can check Google Scholar, which lacks an API because Google ...
Error: Invalid JSON. According to Almetric, this reference has an Altmetric score of Error: no local variable "altscore" was set. " Error: no local variable "altscore" was set. " is not a number..
Full text description
The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved 62% for reads of 400 bases with 1% sequencing errors, and 18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.
