Grouping sequences into similar clusters is an important part of sequence analysis. Widely used clustering tools sacrifice quality for speed. Previously, we developed MeShClust, which utilizes k-mer counts in an alignment-assisted classifier and the mean-shift algorithm for clustering DNA sequences. Although MeShClust outperformed related tools in terms of cluster quality, the alignment algorithm used for generating training data for the classifier was not scalable to longer sequences. In contrast, MeShClust 2 generates semi-synthetic sequence pairs with known mutation rates, avoiding alignment algorithms. MeShClust 2 clustered 3600 bacterial genomes, providing a utility for clustering long sequences using identity scores for the first time. 4/1310/13

Long terminal repeat retrotransposons are the most abundant transposons in plants. They play important roles in alternative splicing, recombination, gene regulation, and genomic evolution. Large-scale sequencing projects for plant genomes are currently underway. Software tools are important for annotating long terminal repeat retrotransposons in these newly available genomes. However, the available tools are not very sensitive to known elements and perform inconsistently on different genomes. Some are hard to install or obsolete. They may struggle to process large plant genomes. None are concurrent or have features to support manual review of new elements. To overcome these limitations, we developed LtrDetector, which uses signal-processing techniques. LtrDetector is easy to install and use. It is not species specific. It utilizes multi-core processors available in personal computers. It is more sensitive than other tools by 14.4%-50.8% while maintaining a low false positive rate on six plant genomes.

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