MOTIVATION: Transposable elements (TEs) and repetitive DNA make up a sizable fraction of Eukaryotic genomes, and their annotation is crucial to the study of the structure, organization, and evolution of any newly sequenced genome. Although RepeatMasker and nHMMER are useful for identifying these repeats, they require a pre-compiled repeat library-which is not always available. De novo identification tools such as Recon, RepeatScout or RepeatGluer serve to identify TEs purely from sequence content, but are either limited by runtimes that prohibit whole-genome use or degrade in quality in the presence of substitutions that disrupt the sequence patterns. RESULTS: phRAIDER is a de novo TE identification tool that address the issues of excessive runtime without sacrificing sensitivity as compared to competing tools. The underlying model is a new definition of elementary repeats that incorporates the PatternHunter spaced seed model, allowing for greater sensitivity in the presence of genomic substitutions. As compared with the premier tool in the literature, RepeatScout, phRAIDER shows an average 10× speedup on any single human chromosome and has the ability to process the whole human genome in just over three hours. Here we discuss the tool, the theoretical model underlying the tool, and the results demonstrating its effectiveness. AVAILABILITY AND IMPLEMENTATION: phRAIDER is an open source tool available from https://github.com/karroje/phRAIDER CONTACT: : [email protected] or SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Palindromic sequences, or inverted repeats (IRs), in DNA sequences involve important biological processes such as DNA-protein binding, DNA replication and DNA transposition. Development of bioinformatics tools that are capable of accurately detecting perfect IRs can enable genome-wide studies of IR patterns in both prokaryotes and eukaryotes. Different from conventional string-comparison approaches, we propose a novel algorithm that uses a cumulative score system based on a prime number representation of nucleotide bases. We then implemented this algorithm as a MATLAB-based program for perfect IR detection. In comparison with other existing tools, our program demonstrates a high accuracy in detecting nested and overlapping IRs. AVAILABILITY AND IMPLEMENTATION: The source code is freely available on (http://bioinfolab.miamioh.edu/bioinfolab/palindrome.php) CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In this article we build a model of the CpG dinucleotide substitution rate and use it to challenge the claim that, that rate underwent a sudden mammalian-specific increase approximately 90 million years ago. The evidence supporting this hypothesis comes from the application of a model of neutral substitution rates able to account for elevated CpG dinucleotide substitution rates. With the initial goal of improving that model's accuracy, we introduced a modification enabling us to account for boundary effects arising by the truncation of the Markov field, as well as improving the optimization procedure required for estimating the substitution rates. RESULTS: When using this modified method to reproduce the supporting analysis, the evidence of the rate shift vanished. Our analysis suggests that the CpG-specific rate has been constant over the relevant time period and that the asserted acceleration of the CpG rate is likely an artifact of the original model.

MOTIVATION: Mammalian genomes contain many 'genomic fossils' i.e. pseudogenes. These are disabled copies of functional genes that have been retained in the genome by gene duplication or retrotransposition events. Pseudogenes are important resources in understanding the evolutionary history of genes and genomes. RESULTS: We have developed a homology-based computational pipeline ('PseudoPipe') that can search a mammalian genome and identify pseudogene sequences in a comprehensive and consistent manner. The key steps in the pipeline involve using BLAST to rapidly cross-reference potential "parent" proteins against the intergenic regions of the genome and then processing the resulting "raw hits" -- i.e. eliminating redundant ones, clustering together neighbors, and associating and aligning clusters with a unique parent. Finally, pseudogenes are classified based on a combination of criteria including homology, intron-exon structure, and existence of stop codons and frameshifts.