TRAL 2.0: Tandem Repeat Detection With Circular Profile Hidden Markov Models and Evolutionary Aligner

The Tandem Repeat Annotation Library (TRAL) focuses on analyzing tandem repeat units in genomic sequences. TRAL can integrate and harmonize tandem repeat annotations from a large number of external tools, and provides a statistical model for evaluating and filtering the detected repeats. TRAL version 2.0 includes new features such as a module for identifying repeats from circular profile hidden Markov models, a new repeat alignment method based on the progressive Poisson Indel Process, an improved installation procedure and a docker container. TRAL is an open-source Python 3 library and is available, together with documentation and tutorials viavital-it.ch/software/tral.

digIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes

Background The insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements. Results In this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes. Conclusion We provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS.