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Motivation: Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites., Results: We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES' usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones., Availability and Implementation: PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes)., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types., Results: Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains., Availability and Implementation: https://github.com/BlanchetteLab/RefHiC., (© The Author(s) 2023. Published by Oxford University Press.)

Motivation: The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA-protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods., Results: In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA-RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA-RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results., Availability and Implementation: The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Motivation: The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent and identically distributed (iid), in which case trained models are vulnerable to out-of-distribution generalization problems. Of particular interest are problems where data correspond to observations made on phylogenetically related samples (e.g. antibiotic resistance data)., Results: We introduce DendroNet, a new approach to train neural networks in the context of evolutionary data. DendroNet explicitly accounts for the relatedness of the training/testing data, while allowing the model to evolve along the branches of the phylogenetic tree, hence accommodating potential changes in the rules that relate genotypes to phenotypes. Using simulated data, we demonstrate that DendroNet produces models that can be significantly better than non-phylogenetically aware approaches. DendroNet also outperforms other approaches at two biological tasks of significant practical importance: antiobiotic resistance prediction in bacteria and trophic level prediction in fungi., Availability and Implementation: https://github.com/BlanchetteLab/DendroNet., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood., Results: We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes., Availability and Implementation: Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor., Results: In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs., Availability and Implementation: Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on., Results: In this article, we introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Those features are then used as input to a Random Forests classifier. The classification accuracy was assessed on multiple published empirical SNP, microsatellite or consensus sequence datasets with wide ranges of size, geographical distribution and population structure and on simulated datasets. It compared favorably against widely used assessment tests or mixture analysis methods such as STRUCTURE and Admixture, and against another machine-learning based approach using principal component analysis for dimensionality reduction. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium. Moreover, the phylogenetic network approach estimates mixture proportions with good accuracy., Availability and Implementation: Mycorrhiza is released as an easy to use open-source python package at github.com/jgeofil/mycorrhiza., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood., Results: We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker's strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts., Availability and Implementation: Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Motivation: Genome rearrangements drastically change gene order along great stretches of a chromosome. There has been initial evidence that these apparently non-local events in the 1D sense may have breakpoints that are close in the 3D sense. We harness the power of the Double Cut and Join model of genome rearrangement, along with Hi-C chromosome conformation capture data to test this hypothesis between human and mouse., Results: We devise novel statistical tests that show that indeed, rearrangement scenarios that transform the human into the mouse gene order are enriched for pairs of breakpoints that have frequent chromosome interactions. This is observed for both intra-chromosomal breakpoint pairs, as well as for inter-chromosomal pairs. For intra-chromosomal rearrangements, the enrichment exists from close (<20 Mb) to very distant (100 Mb) pairs. Further, the pattern exists across multiple cell lines in Hi-C data produced by different laboratories and at different stages of the cell cycle. We show that similarities in the contact frequencies between these many experiments contribute to the enrichment. We conclude that either (i) rearrangements usually involve breakpoints that are spatially close or (ii) there is selection against rearrangements that act on spatially distant breakpoints., Availability and Implementation: Our pipeline is freely available at https://bitbucket.org/thekswenson/locality., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Motivation: Codon reassignments have been reported across all domains of life. With the increasing number of sequenced genomes, the development of systematic approaches for genetic code detection is essential for accurate downstream analyses. Three automated prediction tools exist so far: FACIL, GenDecoder and Bagheera; the last two respectively restricted to metazoan mitochondrial genomes and CUG reassignments in yeast nuclear genomes. These tools can only analyze a single genome at a time and are often not followed by a validation procedure, resulting in a high rate of false positives., Results: We present CoreTracker, a new algorithm for the inference of sense-to-sense codon reassignments. CoreTracker identifies potential codon reassignments in a set of related genomes, then uses statistical evaluations and a random forest classifier to predict those that are the most likely to be correct. Predicted reassignments are then validated through a phylogeny-aware step that evaluates the impact of the new genetic code on the protein alignment. Handling simultaneously a set of genomes in a phylogenetic framework, allows tracing back the evolution of each reassignment, which provides information on its underlying mechanism. Applied to metazoan and yeast genomes, CoreTracker significantly outperforms existing methods on both precision and sensitivity., Availability and Implementation: CoreTracker is written in Python and available at https://github.com/UdeM-LBIT/CoreTracker., Contact: mabrouk@iro.umontreal.ca., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Motivation: The analysis of large biological datasets often requires complex processing pipelines that run for a long time on large computational infrastructures. We designed and implemented a simple script-like programming language with a clean and minimalist syntax to develop and manage pipeline execution and provide robustness to various types of software and hardware failures as well as portability., Results: We introduce the BigDataScript (BDS) programming language for data processing pipelines, which improves abstraction from hardware resources and assists with robustness. Hardware abstraction allows BDS pipelines to run without modification on a wide range of computer architectures, from a small laptop to multi-core servers, server farms, clusters and clouds. BDS achieves robustness by incorporating the concepts of absolute serialization and lazy processing, thus allowing pipelines to recover from errors. By abstracting pipeline concepts at programming language level, BDS simplifies implementation, execution and management of complex bioinformatics pipelines, resulting in reduced development and debugging cycles as well as cleaner code., Availability and Implementation: BigDataScript is available under open-source license at http://pcingola.github.io/BigDataScript., (© The Author 2014. Published by Oxford University Press.)

Motivation: The identification of non-coding functional regions of the human genome remains one of the main challenges of genomics. By observing how a given region evolved over time, one can detect signs of negative or positive selection hinting that the region may be functional. With the quickly increasing number of vertebrate genomes to compare with our own, this type of approach is set to become extremely powerful, provided the right analytical tools are available., Results: A large number of approaches have been proposed to measure signs of past selective pressure, usually in the form of reduced mutation rate. Here, we propose a radically different approach to the detection of non-coding functional region: instead of measuring past evolutionary rates, we build a machine learning classifier to predict current substitution rates in human based on the inferred evolutionary events that affected the region during vertebrate evolution. We show that different types of evolutionary events, occurring along different branches of the phylogenetic tree, bring very different amounts of information. We propose a number of simple machine learning classifiers and show that a Support-Vector Machine (SVM) predictor clearly outperforms existing tools at predicting human non-coding functional sites. Comparison to external evidences of selection and regulatory function confirms that these SVM predictions are more accurate than those of other approaches., Availability: The predictor and predictions made are available at http://www.mcb.mcgill.ca/~blanchem/sadri., Contact: blanchem@mcb.mcgill.ca.

Summary: The computational inference of ancestral genomes consists of five difficult steps: identifying syntenic regions, inferring ancestral arrangement of syntenic regions, aligning multiple sequences, reconstructing the insertion and deletion history and finally inferring substitutions. Each of these steps have received lot of attention in the past years. However, there currently exists no framework that integrates all of the different steps in an easy workflow. Here, we introduce Ancestors 1.0, a web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure. It implements several alignment algorithms, an indel maximum likelihood solver and a context-dependent maximum likelihood substitution inference algorithm. The results presented by the server include the posterior probabilities for the last two steps of the ancestral genome reconstruction and the expected error rate of each ancestral base prediction., Availability: The Ancestors 1.0 is available at http://ancestors.bioinfo.uqam.ca/ancestorWeb/.

Motivation: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches., Results: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements.

Motivation: It is currently believed that the human genome contains about twice as much non-coding functional regions as it does protein-coding genes, yet our understanding of these regions is very limited., Results: We examine the intersection between syntenically conserved sequences in the human, mouse and rat genomes, and sequence similarities within the human genome itself, in search of families of non-protein-coding elements. For this purpose we develop a graph theoretic clustering algorithm, akin to the highly successful methods used in elucidating protein sequence family relationships. The algorithm is applied to a highly filtered set of about 700 000 human-rodent evolutionarily conserved regions, not resembling any known coding sequence, which encompasses 3.7% of the human genome. From these, we obtain roughly 12 000 non-singleton clusters, dense in significant sequence similarities. Further analysis of genomic location, evidence of transcription and RNA secondary structure reveals many clusters to be significantly homogeneous in one or more characteristics. This subset of the highly conserved non-protein-coding elements in the human genome thus contains rich family-like structures, which merit in-depth analysis., Availability: Supplementary material to this work is available at http://www.soe.ucsc.edu/~jill/dark.html