Red: an intelligent, rapid, accurate tool for detecting repeats de-novo on the genomic scale

Abstract

BackgroundWith rapid advancements in technology, the sequences of thousands of species’ genomes are becoming available. Within the sequences are repeats that comprise significant portions of genomes. Successful annotations thus require accurate discovery of repeats. As species-specific elements, repeats in newly sequenced genomes are likely to be unknown. Therefore, annotating newly sequenced genomes requires tools to discover repeats de-novo. However, the currently available de-novo tools have limitations concerning the size of the input sequence, ease of use, sensitivities to major types of repeats, consistency of performance, speed, and false positive rate.ResultsTo address these limitations, I designed and developed Red, applying Machine Learning. Red is the first repeat-detection tool capable of labeling its training data and training itself automatically on an entire genome. Red is easy to install and use. It is sensitive to both transposons and simple repeats; in contrast, available tools such as RepeatScout and ReCon are sensitive to transposons, and WindowMasker to simple repeats. Red performed consistently well on seven genomes; the other tools performed well only on some genomes. Red is much faster than RepeatScout and ReCon and has a much lower false positive rate than WindowMasker. On human genes with five or more copies, Red was more specific than RepeatScout by a wide margin. When tested on genomes of unusual nucleotide compositions, Red located repeats with high sensitivities and maintained moderate false positive rates. Red outperformed the related tools on a bacterial genome. Red identified 46,405 novel repetitive segments in the human genome. Finally, Red is capable of processing assembled and unassembled genomes.ConclusionsRed’s innovative methodology and its excellent performance on seven different genomes represent a valuable advancement in the field of repeats discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0654-5) contains supplementary material, which is available to authorized users.

Analysis of the spotted gar genome suggests absence of causative link between ancestral genome duplication and transposable element diversification in teleost fish

“…Accordingly, if we assume that most superfamilies have been transmitted vertically from parents to offspring, we can propose that the ancestral mobilome before the split between the gar and teleost lineages was constituted by 11 superfamilies of DNA transposons and 14 superfamilies of retroelements (Figure 4) in the last common ancestor of ray-finned fish and sarcopterygians/tetrapods. Indeed, most of the superfamilies present in the rayfinned fish mobilome were detected in at least one sarcopterygian lineage (Chalopin et al, 2013;2015). While TE diversity was maintained in non-teleost and teleost fish species as well as in coelacanth, several superfamilies were lost in most tetrapod lineages including mammals and birds (Volff et al, 2003;Chalopin et al, 2015).…”

“…() combining automatic annotations with RepeatScout (Price, Jones, & Pevzner, ) and RepeatModeler (A.F.A Smit, R. Hubley; RepeatModeler Open‐1.0 http://www.repeatmasker.org) together with manual reannotation was used. Repeat content was also estimated using Red (Girgis, ), a de novo tool more sensitive to detect simple repeats. TEs were mapped along the spotted gar linkage groups (LG) using DensityMap (Guizard, Piégu, & Bigot, ) with options –c 1 –ty fused.…”

“…Repeat content was also estimated using Red (Girgis, 2015), a de novo tool more sensitive to detect simple repeats. TEs were mapped along the spotted gar linkage groups (LG) using DensityMap (Guizard, Piégu, & Bigot, 2016) with options -c 1 -ty fused.…”

“…A first analysis using RepeatMasker and a gar-specific repeat library (Braasch et al, 2016) estimated that repeated DNA (TEs and other repeats) constituted about 20% of the spotted gar genome. A second estimation using Red (Girgis, 2015) indicated that 38% of the spotted gar assembly may actually be repetitive, with TEs making up about 19.7% of the gar genome assembly.…”

FASTCAR: Rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models

“…This design was inspired by our earlier research. We have successfully implemented adaptive software tools using self-supervised learning algorithms for locating cis-regulatory modules (Girgis and Ovcharenko, 2012), identifying DNA repeats (Girgis, 2015;Velasco II et al, 2018), and for clustering DNA sequences . Hierarchical models were reported to perform very well in ranking the quality of predicted protein structures (Girgis and Corso, 2008;Girgis, 2008;Girgis et al, 2009).…”