MetaTISA: Metagenomic Translation Initiation Site Annotator for improving gene start prediction

Abstract

The C++ source code is freely available under the GNU GPL license via http://mech.ctb.pku.edu.cn/MetaTISA/.

Gene prediction in metagenomic fragments based on the SVM algorithm

“…The supervised TIS parameters used for the experiments including those trained on the RefSeq annotations and the TriTISA annotations, with Markov models ranging from 0-order to 4th-order. evaluated on simulated fragments [12][13][14][18][19][20][21]. However, two significant drawbacks of this methodology should be noted.…”

“…Accurate gene starts prediction is also a very important issue in metagenomic sequencing projects which is indispensable for experimental characterization of novel genes, however, has not been studied much in the literature [13,21]. TIS prediction for complete genomes has a long history and a number of tools have been developed [24,[36][37][38][39][40][41].…”

“…However, the short and anonymous nature of metagenomic fragments present more challenges. In one of our previous works, MetaTISA has been built to accomplish this problem and has greatly improved the TIS annotations for MetaGeneAnnotator [21]. Recently, two works have paid special attentions to the TIS prediction and have achieved substantial progresses [13,14].…”

Functional genomic analysis of Hawaii marine metagenomes

“…After that, MetaTISA [11] was applied to improve the predictions of translation initiation site (TIS) of all genes. MetaGUN not only predict genes efficiently, but also discover more potential novel genes [10].…”

“…Second, gene annotation by BLASTX limits the results only to the genes collected in the public database, the potential novel genes have been seriously ignored. With the recent development of bioinformatics on metagenomes, especially a series of metagenomic analysis tools designed by our lab [10][11][12], the latest methods can thus be applied to analyze the data from Hawaii sea microorganisms more accurately and deeply. Therefore, using series of metagenomic analysis methods and tools, we carried out gene prediction and function annotation on these seven datasets.…”