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. They have many important functions involving gene regulation and the rise of new genes and pseudo genes in response to severe stress. Additionally, LTR-retrotransposons have several applications in biotechnology. Due to the abundance and the importance of LTR-retrotransposons, multiple computational tools have been developed for their detection. However, none of these tools take advantages of the availability of related genomes; they process one chromosome at a time. Further, recently nested LTR-retrotransposons (multiple elements of the same family are inserted into each other) cannot be annotated accurately - or cannot be annotated at all - by the currently available tools. Motivated to overcome these two limitations, we built Look4LTRs, which can annotate LTR-retrotransposons in multiple related genomes simultaneously and discover recently nested elements. The methodology of Look4LTRs depends on techniques imported from the signal-processing field, graph algorithms, and machine learning with a minimal use of alignment algorithms. Four plant genomes were used in developing Look4LTRs and eight plant genomes for evaluating it in contrast to three related tools. Look4LTRs is the fastest while maintaining better or comparable F1 scores (the harmonic average of recall and precision) to those obtained by the other tools. Our results demonstrate the added benefit of annotating LTR-retrotransposons in multiple related genomes simultaneously and the ability to discover recently nested elements. Expert human manual examination of six elements - not included in the ground truth - revealed that three elements belong to known families and two elements are likely from new families. With respect to examining recently nested LTR-retrotransposons, three out of five were confirmed to be valid elements. Look4LTRs - with its speed, accuracy, and novel features - represents a true advancement in the annotation of LTR-retrotransposons, opening the door to many studies focused on understanding their functions in plants.

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. We evaluated variational autoencoders, Siamese networks, triplet networks, and variational auto-encoders combined with Siamese or triplet networks. These networks were compared to a baseline network consisting of multiple separable convolutional layers. Our study revealed the following: (i) the triplet architecture proved the most effective one due to learning a relative distance - not an absolute distance; (ii) combining auto-encoders with networks that learn metrics (e.g., Siamese or triplet networks) is unwarranted; and (iii) an architecture based on separable convolutional layers is a reasonable simple alternative to triplet networks. These results can potentially impact our field by encouraging architects to develop advanced networks that take advantage of separable convolution and relative distance.

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 EnhancerTracker , 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. EnhancerTracker is trained, validated, and tested on 52,789 putative enhancers obtained from the FANTOM5 Project and control sequences derived from the human genome. Unlike available tools, which accept one sequence at a time, the input to our tool is three sequences; the first two are enhancers active in the same cell type. EnhancerTracker outputs 1 if the third sequence is an enhancer active in the same cell type(s) where the first two enhancers are active. It outputs 0 otherwise. On a held-out set (15%), EnhancerTracker achieved an accuracy of 64%, a specificity of 93%, a recall of 35%, a precision of 84%, and an F1 score of 49%. Availability and implementation: https://github.com/BioinformaticsToolsmith/EnhancerTracker. Contact: hani.girgis@tamuk.edu.