DeePromoter: Robust Promoter Predictor Using Deep Learning

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm

From protein-protein to isoform-isoform interactions: the toolkit to map alternative splicing to interactome

Alternative splicing (AS) can impact protein structure and lead to protein-protein interaction (PPI) rewiring. Available PPI networks neglect alternative splicing isoforms: as interactions might happen only between a subset of isoforms, the PPI network contains both false-positive and false-negative interactions. Since it is not feasible to validate all isoform-isoform interactions experimentally, we present a set of tools to investigate AS on a network level: DIGGER to map splicing to the PPI network, as well as NEASE and Spycone to evaluate the functional consequences of network rewiring. DIGGER (https://exbio.wzw.tum.de/digger) integrates PPIs, domain-domain, and residuelevel interactions - the structures that might be spliced in or out and result in interaction gain or loss. Users can explore possible rewiring for an isoform or exon of interest and extract relevant subnetworks. NEASE (https://github.com/louadi/NEASE) identifies pathways that are significantly affected by network rewiring. NEASE extends classic gene set enrichment analysis by considering isoform-specific interactions affecting pathways. Spycone (https://github.com/yollct/spycone) addresses the time-course changes in AS. It searches for isoforms that demonstrate similar temporal splicing patterns and reflect the splicing co-regulation. Spycone further integrates gene set, network, and splicing-aware NEASE enrichment. Overall, we offer a splicing-focused network analysis toolkit that allows for studying the mechanistic consequences of AS.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

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Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning

Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as “Splicing Codes”. The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence. Here, we develop a deep learning approach, called Deep Splicing Code (DSC), for categorizing the well-studied classes of AS namely alternatively skipped exons, alternative 5’ss, alternative 3’ss, and constitutively spliced exons based only on the sequence of the exon junctions. The proposed approach significantly improves the prediction and the obtained results reveal that constitutive exons have distinguishable local characteristics from alternatively spliced exons. Using the motif visualization technique, we show that the trained models learned to search for competitive alternative splice sites as well as motifs of important splicing factors with high precision. Thus, the proposed approach greatly expands the opportunities to improve alternative splicing modeling. In addition, a web-server for AS events prediction has been developed based on the proposed method

DIGGER: exploring the functional role of alternative splicing in protein interactions.

Alternative splicing plays a major role in regulating the functional repertoire of the proteome. However, isoform-specific effects to protein-protein interactions (PPIs) are usually overlooked, making it impossible to judge the functional role of individual exons on a systems biology level. We overcome this barrier by integrating protein-protein interactions, domain-domain interactions and residue-level interactions information to lift exon expression analysis to a network level. Our user-friendly database DIGGER is available at https://exbio.wzw.tum.de/digger and allows users to seamlessly switch between isoform and exon-centric views of the interactome and to extract sub-networks of relevant isoforms, making it an essential resource for studying mechanistic consequences of alternative splicing