Mobile DNAEvolutionary BiologyPeer-reviewed journal article
FishPi: a bioinformatic prediction tool to link piRNA and transposable elements in zebrafish
Abstract
BackgroundPiwi-interacting RNAs (piRNA)s are non-coding small RNAs that post-transcriptionally affect gene expression and regulation. Through complementary seed region binding with transposable elements (TEs), piRNAs protect the genome from transposition. A tool to link piRNAs with complementary TE targets will improve our understanding of the role of piRNAs in genome maintenance and gene regulation. Existing tools such as TEsmall can process sRNA-seq datasets to produce differentially expressed piRNAs, and piRScan developed for nematodes can link piRNAs and TEs but it requires knowledge about the target region of interest and works backwards. ResultsWe have therefore developed FishPi to predict the pairings between piRNA and TEs for available genomes from zebrafish, medaka and tilapia, with full user customisation of parameters including orientation of piRNA, mismatches in the piRNA seed binding to TE and scored output lists of piRNA-TE matches. FishPi works with individual piRNAs or a list of piRNA sequences in fasta format. The software focuses on the piRNA-TE seed region and analyses reference TEs for piRNA complementarity. TE type is examined, counted and stored to a dictionary, with genomic loci recorded. Any updates to piRNA-TE binding rules can easily be incorporated by changing the seed-region options in the graphic user-interface. FishPi provides a graphic interface using tkinter for the user to input piRNA sequences to generate comprehensive reports on piRNA-TE interactions. FishPi can easily be adapted to genomes from other species and taxa opening the interpretation of piRNA functionality to a wide community. ConclusionsUsers will gain insight into genome mobility and FishPi will help further our understanding of the biological role of piRNAs and their interaction with TEs in a similar way that public databases have improved the access to and the understanding of the role of small RNAs.
- organism: zebrafish
- assay: bulk RNA-seq
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 0.89
- Field percentile
- top 35%
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