Basic Info

The IS (Insertion Sequence) was analyzed and genotyped based on the bacterial genetic information, the similarity among transposases, and the relationship between repetitive sequences

More Details

The IS sequence is a relatively short and compact mobile element within the bacterial genome, with a length approximately ranging from 0.7 to 2.5 kilobase pairs (kb). It does not carry any genes unrelated to the transposition function. Most IS sequences are flanked by short inverted repeat sequences at both ends, which serve as recognition sites for recombinases. The central sequence encodes transposases and regulatory proteins associated with transcription. When IS sequences undergo transposition within the genome, they facilitate genomic rearrangements, altering the order of genes and subsequently leading to changes in function and phenotype. Various types of IS sequences or multiple copies of IS sequences can exist within bacterial chromosomes and plasmids.

What's New

ISEScan is a python pipeline to identify IS elements in genome. It includes an option to report either complete IS elements or both complete and partial IS elements. ISEScan reports both complete and partial IS elements by default.

ISEScan was developed using Python3. It 1) scans genome (or metagenome) in fasta format; 2) predicts/translates (using FragGeneScan) genome into proteome; 3) searches the pre-built pHMMs (profile Hidden Markov Models) of transposases (two files shipped with ISEScan; clusters.faa.hmm and clusters.single.faa) against the proteome and identifies the transposase gene in genome; 4) then extends the identified transposase gene into the complete IS elements based on the common characteristics shared by the known IS elements reported by literatures and database; 5) finally reports the identified IS elements in result files.

Additional Information

Related links:

https://github.com/xiezhq/ISEScan

Literature

Xie Z, Tang H. ISEScan: automated identification of insertion sequence elements in prokaryotic genomes. Bioinformatics. 2017;33(21):3340-3347. doi:10.1093/bioinformatics/btx433