TIP_finder: An HPC Software to Detect Transposable Element Insertion Polymorphisms in Large Genomic Datasets

Abstract

Transposable elements (TEs) are non-static genomic units capable of moving indistinctly from one chromosomal location to another. Their insertion polymorphisms may cause beneficial mutations, such as the creation of new gene function, or deleterious in eukaryotes, e.g., different types of cancer in humans. A particular type of TE called LTR-retrotransposons comprises almost 8% of the human genome. Among LTR retrotransposons, human endogenous retroviruses (HERVs) bear structural and functional similarities to retroviruses. Several tools allow the detection of transposon insertion polymorphisms (TIPs) but fail to efficiently analyze large genomes or large datasets. Here, we developed a computational tool, named TIP_finder, able to detect mobile element insertions in very large genomes, through high-performance computing (HPC) and parallel programming, using the inference of discordant read pair analysis. TIP_finder inputs are (i) short pair reads such as those obtained by Illumina, (ii) a chromosome-level reference genome sequence, and (iii) a database of consensus TE sequences. The HPC strategy we propose adds scalability and provides a useful tool to analyze huge genomic datasets in a decent running time. TIP_finder accelerates the detection of transposon insertion polymorphisms (TIPs) by up to 55 times in breast cancer datasets and 46 times in cancer-free datasets compared to the fastest available algorithms. TIP_finder applies a validated strategy to find TIPs, accelerates the process through HPC, and addresses the issues of runtime for large-scale analyses in the post-genomic era. TIP_finder version 1.0 is available at https://github.com/simonorozcoarias/TIP_finder.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era

“…Software like D-GENIES report execution times shorter than G-SAIP, but we present a software that is easily integrated into pipelines, also, G-SAIP is easier configurable than D-GENIES which is a web application useful for making dot-plots within an interactive interface, and the default parameters like maximum RAM memory, maximum file size are not intuitive modifiable. In addition, the application of HPC has demonstrated high performances for several bioinformatics tasks such as multiple sequence alignment, [41][42][43][44][45] sequence mapping, [45][46][47][48]81 analyzing transposable elements, [82][83][84] and identification of transposon insertion polymorphisms, 83 among others. Nevertheless, HPC software has not been deployed for graphical alignment.…”

“…Where s is the serial time execution of G-SAIP, p is the proportion of execution times and N is the number of processors. In weak scaling, Gustafson’s law 84 provides the formula for scaled speedup:…”