Look4LTRs: a Long terminal repeat retrotransposon detection tool capable of cross species studies and discovering recently nested repeats.

Plant genomes include large numbers of transposable elements. One particular type of these elements is flanked by two Long Terminal Repeats (LTRs) and can translocate using RNA. Such elements are known as LTR-retrotransposons; they are the most abundant type of transposons in plant genomes.

EnhancerTracker: Comparing cell-type-specific enhancer activity of DNA sequence triplets via an ensemble of deep convolutional neural networks.

Motivation: Transcriptional enhancers - unlike promoters - are unrestrained by distance or strand orientation with respect to their target genes, making their computational identification a challenge. Further, there are insufficient numbers of confirmed enhancers for many cell types, preventing robust training of machine-learning-based models for enhancer prediction for such cell types.

Results: We present , a novel tool that leverages an ensemble of deep separable convolutional neural networks to identify cell-type-specific enhancers with the need of only two confirmed enhancers.

Evaluation of metric and representation learning approaches: Effects of representations driven by relative distance on the performance.

Several deep neural network architectures have emerged recently for metric learning. We asked which architecture is the most effective in measuring the similarity or dissimilarity among images. To this end, we evaluated six networks on a standard image set.

MeShClust v3.0: high-quality clustering of DNA sequences using the mean shift algorithm and alignment-free identity scores.

Background: Tools for accurately clustering biological sequences are among the most important tools in computational biology. Two pioneering tools for clustering sequences are CD-HIT and UCLUST, both of which are fast and consume reasonable amounts of memory; however, there is a big room for improvement in terms of cluster quality. Motivated by this opportunity for improving cluster quality, we applied the mean shift algorithm in MeShClust v1.

: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models.

Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic-slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment-including gaps-of two sequences); there is no need for visualizing how every two sequences are aligned.

Benchmarking of alignment-free sequence comparison methods.

Background: Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment.

Results: Here, we present a community resource (http://afproject.

Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov models.

Motivation: Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases and applications in biotechnology. There is an immediate need for an accurate tool for detecting microsatellites in newly sequenced genomes. The current available tools are either sensitive or specific but not both; some tools require adjusting parameters manually.

LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo.

Background: Long terminal repeat retrotransposons are the most abundant transposons in plants. They play important roles in alternative splicing, recombination, gene regulation, and defense mechanisms. Large-scale sequencing projects for plant genomes are currently underway.

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures.

Background: Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

MeShClust: an intelligent tool for clustering DNA sequences.

Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster.

A survey and evaluations of histogram-based statistics in alignment-free sequence comparison.

Motivation: Since the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences.

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

Background: With 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.

MsDetector: toward a standard computational tool for DNA microsatellites detection.

Microsatellites (MSs) are DNA regions consisting of repeated short motif(s). MSs are linked to several diseases and have important biomedical applications. Thus, researchers have developed several computational tools to detect MSs.

Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs.

Background: Researchers seeking to unlock the genetic basis of human physiology and diseases have been studying gene transcription regulation. The temporal and spatial patterns of gene expression are controlled by mainly non-coding elements known as cis-regulatory modules (CRMs) and epigenetic factors. CRMs modulating related genes share the regulatory signature which consists of transcription factor (TF) binding sites (TFBSs).

On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures.

To predict the three dimensional structure of proteins, many computational methods sample the conformational space, generating a large number of candidate structures. Subsequently, such methods rank the generated structures using a variety of model quality assessment programs in order to obtain a small set of structures that are most likely to resemble the unknown experimentally determined structure. Model quality assessment programs suffer from two main limitations: (i) the rank-one structure is not always the best predicted structure; in other words, the best predicted structure could be ranked as the 10th structure (ii) no single assessment method can correctly rank the predicted structures for all target proteins.