Back

Genome ARTIST_v2 software - a support for annotation of class II natural transposons in new sequenced genomes

Ecovoiu, A. A.; Ghita, I. C.; Chifiriuc, D. I. M.; Ghionoiu, I. C.; Ciuca, A. M.; Bologa, A. M.; Ratiu, A. C.

2020-11-01 bioinformatics
10.1101/2020.10.30.360610 bioRxiv
Show abstract

Transposon annotation is a very dynamic field of genomics and various tools assigned to support this bioinformatics endeavor were reported. Genome ARTIST (GA) software was initially developed for mapping artificial transposons mobilized during insertional mutagenesis projects. Now, the new functions of GA_v2 qualify it as an effective companion for mapping and annotation of class II natural transposons in assembled genomes, contigs or sequencing reads. Tabular export of mapping and annotation data for subsequent high-throughput data analysis, the export of a list of flanking sequences around either the coordinates of insertion or around the target site duplications (TSDs) and generation of a consensus sequence for the respective flanking sequences are all key assets of GA_v2. Additionally, we developed two accompanying short scripts that enable the user to annotate transposons existent in assembled genomes and to use various annotation offered by FlyBase for Drosophila melanogaster genome. Herein, we present the applicability of GA_v2 for a preliminary annotation of the class II transposon P-element in the genome of D. melanogaster strain Horezu, Romania, which was sequenced with Nanopore technology in our laboratory. Our results point that GA_v2 is a reliable tool to be integrated in pipelines designed to perform transposon annotation in new sequenced genomes. GA_v2 is open source software compatible with Ubuntu, Mac OS and Windows and is available at https://github.com/genomeartist/genomeartist and at www.genomeartist.ro.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.