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Summary Multiple sequence alignment is an initial step in many bioinformatics pipelines, including phylogeny estimation, protein structure prediction and taxonomic identification of reads produced in amplicon or metagenomic datasets, etc. Yet, alignment estimation is challenging on datasets that exhibit substantial sequence length heterogeneity, and especially when the datasets have fragmentary sequences as a result of including reads or contigs generated by next-generation sequencing technologies. Here, we examine techniques that have been developed to improve alignment estimation when datasets contain substantial numbers of fragmentary sequences. We find that MAGUS, a recently developed MSA method, is fairly robust to fragmentary sequences under many conditions, and that using a two-stage approach where MAGUS is used to align selected ‘backbone sequences’ and the remaining sequences are added into the alignment using ensembles of Hidden Markov Models further improves alignment accuracy. The combination of MAGUS with the ensemble of eHMMs (i.e. MAGUS+eHMMs) clearly improves on UPP, the previous leading method for aligning datasets with high levels of fragmentation. Availability and implementation UPP is available on https://github.com/smirarab/sepp, and MAGUS is available on https://github.com/vlasmirnov/MAGUS. MAGUS+eHMMs can be performed by running MAGUS to obtain the backbone alignment, and then using the backbone alignment as an input to UPP. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. Results We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. Availability and implementation The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. Supplementary information Supplementary data are available at Bioinformatics online.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein–protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Motivation The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures. Results We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity. Availability and implementation The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Summary Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here, we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or—more flexibly—as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. Availability and implementation AlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. Results Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. Availability and implementation The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Minimizers are efficient methods to sample k-mers from genomic sequences that unconditionally preserve sufficiently long matches between sequences. Well-established methods to construct efficient minimizers focus on sampling fewer k-mers on a random sequence and use universal hitting sets (sets of k-mers that appear frequently enough) to upper bound the sketch size. In contrast, the problem of sequence-specific minimizers, which is to construct efficient minimizers to sample fewer k-mers on a specific sequence such as the reference genome, is less studied. Currently, the theoretical understanding of this problem is lacking, and existing methods do not specialize well to sketch specific sequences. Results We propose the concept of polar sets, complementary to the existing idea of universal hitting sets. Polar sets are k-mer sets that are spread out enough on the reference, and provably specialize well to specific sequences. Link energy measures how well spread out a polar set is, and with it, the sketch size can be bounded from above and below in a theoretically sound way. This allows for direct optimization of sketch size. We propose efficient heuristics to construct polar sets, and via experiments on the human reference genome, show their practical superiority in designing efficient sequence-specific minimizers. Availability and implementation A reference implementation and code for analyses under an open-source license are at https://github.com/kingsford-group/polarset. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression. Results In this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time. Availability and implementation Source code and data are freely available at https://github.com/csclab/CGA-BNI. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. Results Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. Availability and implementation The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations. Results We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement. Availability and implementation https://github.com/yourh/DeepGraphGO. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood–brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased. Results The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules’ synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry. Availability and implementation All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The availability of human genomic data, together with the enhanced capacity to process them, is leading to transformative technological advances in biomedical science and engineering. However, the public dissemination of such data has been difficult due to privacy concerns. Specifically, it has been shown that the presence of a human subject in a case group can be inferred from the shared summary statistics of the group, e.g. the allele frequencies, or even the presence/absence of genetic variants (e.g. shared by the Beacon project) in the group. These methods rely on the availability of the target’s genome, i.e. the DNA profile of a target human subject, and thus are often referred to as the membership inference method. Results In this article, we demonstrate the haplotypes, i.e. the sequence of single nucleotide variations (SNVs) showing strong genetic linkages in human genome databases, may be inferred from the summary of genomic data without using a target’s genome. Furthermore, novel haplotypes that did not appear in the database may be reconstructed solely from the allele frequencies from genomic datasets. These reconstructed haplotypes can be used for a haplotype-based membership inference algorithm to identify target subjects in a case group with greater power than existing methods based on SNVs. Availability and implementation The implementation of the membership inference algorithms is available at https://github.com/diybu/Haplotype-based-membership-inferences.

Motivation Protein domain duplications are a major contributor to the functional diversification of protein families. These duplications can occur one at a time through single domain duplications, or as tandem duplications where several consecutive domains are duplicated together as part of a single evolutionary event. Existing methods for inferring domain-level evolutionary events are based on reconciling domain trees with gene trees. While some formulations consider multiple domain duplications, they do not explicitly model tandem duplications; this leads to inaccurate inference of which domains duplicated together over the course of evolution. Results Here, we introduce a reconciliation-based framework that considers the relative positions of domains within extant sequences. We use this information to uncover tandem domain duplications within the evolutionary history of these genes. We devise an integer linear programming approach that solves our problem exactly, and a heuristic approach that works well in practice. We perform extensive simulation studies to demonstrate that our approaches can accurately uncover single and tandem domain duplications, and additionally test our approach on a well-studied orthogroup where lineage-specific domain expansions exhibit varying and complex domain duplication patterns. Availability and implementation Code is available on github at https://github.com/Singh-Lab/TandemDuplications. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.

MotivationSingle-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of tran-scriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling and spatial organization mapping of cells simultaneously. Among the tech-nologies, imaging-based methods can offer higher spatial resolutions, while they are limited by either the small number of genes imaged or the low gene detection sensitivity. Although several methods have been proposed for enhancing spatially resolved transcriptomics, inadequate accuracy of gene expression prediction and in-sufficient ability of cell-population identification still impede the applications of these methods.ResultsWe propose stPlus, a reference-based method that leverages information in scRNA-seq data to enhance spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-NN. stPlus outperforms baseline meth-ods with higher gene-wise and cell-wise Spearman correlation coefficients. We also introduce a clustering-based approach to assess the enhancement performance systematically. Using the data enhanced by stPlus, cell populations can be better identified than using the measured data. The predicted expression of genes unique to scRNA-seq data can also well characterize spatial cell heterogeneity. Besides, stPlus is robust and scalable to datasets of diverse gene detection sensitivity levels, sample sizes, and number of spatially meas-ured genes. We anticipate stPlus will facilitate the analysis of spatial transcriptomics.AvailabilitystPlus with detailed documents is freely accessible at http://health.tsinghua.edu.cn/software/stPlus/ and the source code is openly available on https://github.com/xy-chen16/stPlus.Contactruijiang@tsinghua.edu.cnSupplementary informationSupplementary data are available at Bioinformatics online.

Motivation Large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). Results We describe a novel multi-task unsupervised DA method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared feature representations. TUGDA’s ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of NT for in vitro models (94% overall) compared to state-of-the-art methods. For DA to in vivo settings, TUGDA improved over previous methods for patient-derived xenografts (9 out of 14 drugs) as well as patient datasets (significant associations in 9 out of 22 drugs). TUGDA’s ability to avoid NT thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availabilityand implementation https://github.com/CSB5/TUGDA. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.ResultsIn this study, we developed unsupervised learning based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.AvailabilityThe C++ and python source codes of the Umibato software are available at https://github.com/shion-h/UmibatoContactshion_hosoda@asagi.waseda.jp, mhamada@waseda.jp

Motivation It is largely established that all extant mitochondria originated from a unique endosymbiotic event integrating an α−proteobacterial genome into an eukaryotic cell. Subsequently, eukaryote evolution has been marked by episodes of gene transfer, mainly from the mitochondria to the nucleus, resulting in a significant reduction of the mitochondrial genome, eventually completely disappearing in some lineages. However, in other lineages such as in land plants, a high variability in gene repertoire distribution, including genes encoded in both the nuclear and mitochondrial genome, is an indication of an ongoing process of Endosymbiotic Gene Transfer (EGT). Understanding how both nuclear and mitochondrial genomes have been shaped by gene loss, duplication and transfer is expected to shed light on a number of open questions regarding the evolution of eukaryotes, including rooting of the eukaryotic tree. Results We address the problem of inferring the evolution of a gene family through duplication, loss and EGT events, the latter considered as a special case of horizontal gene transfer occurring between the mitochondrial and nuclear genomes of the same species (in one direction or the other). We consider both EGT events resulting in maintaining (EGTcopy) or removing (EGTcut) the gene copy in the source genome. We present a linear-time algorithm for computing the DLE (Duplication, Loss and EGT) distance, as well as an optimal reconciled tree, for the unitary cost, and a dynamic programming algorithm allowing to output all optimal reconciliations for an arbitrary cost of operations. We illustrate the application of our EndoRex software and analyze different costs settings parameters on a plant dataset and discuss the resulting reconciled trees. Availability and implementation EndoRex implementation and supporting data are available on the GitHub repository via https://github.com/AEVO-lab/EndoRex.

Motivation The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem. Results In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions. Availability and implementation EnHiC is publicly available at https://github.com/wmalab/EnHiC. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Oxford Nanopore Technologies sequencing devices support adaptive sequencing, in which undesired reads can be ejected from a pore in real time. This feature allows targeted sequencing aided by computational methods for mapping partial reads, rather than complex library preparation protocols. However, existing mapping methods either require a computationally expensive base-calling procedure before using aligners to map partial reads or work well only on small genomes. Results In this work, we present a new streaming method that can map nanopore raw signals for real-time selective sequencing. Rather than converting read signals to bases, we propose to convert reference genomes to signals and fully operate in the signal space. Our method features a new way to index reference genomes using k-d trees, a novel seed selection strategy and a seed chaining algorithm tailored toward the current signal characteristics. We implemented the method as a tool Sigmap. Then we evaluated it on both simulated and real data and compared it to the state-of-the-art nanopore raw signal mapper Uncalled. Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4× speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49% more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). Availability and implementation Sigmap code is accessible at https://github.com/haowenz/sigmap. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationThe construction of the compacted de Bruijn graph from collections of reference genomes is a task of increasing interest in genomic analyses. These graphs are increasingly used as sequence indices for short and long read alignment. Also, as we sequence and assemble a greater diversity of genomes, the colored compacted de Bruijn graph is being used as the basis for efficient methods to perform comparative genomic analyses on these genomes. Therefore, designing time and memory efficient algorithms for the construction of this graph from reference sequences is an important problem.ResultsWe introduce a new algorithm, implemented in the toolCuttlefish, to construct the (colored) compacted de Bruijn graph from a collection of one or more genome references. Cuttlefish introduces a novel approach of modeling de Bruijn graph vertices as finite-state automata; it constrains these automata’s state-space to enable tracking their transitioning states with very low memory usage. Cuttlefish is fast and highly parallelizable. Experimental results demonstrate that it scales much better than existing approaches, especially as the number and the scale of the input references grow. On our test hardware, Cuttlefish constructed the graph for 100 human genomes in under 9 hours, using ~29 GB of memory while no other tested tool completed this task. On 11 diverse conifer genomes, the compacted graph was constructed by Cuttlefish in under 9 hours, using ~84 GB of memory, while the only other tested tool that completed this construction on our hardware took over 16 hours and ~289 GB of memory.AvailabilityCuttlefish is written inC++14, and is available under an open source license athttps://github.com/COMBINE-lab/cuttlefish.Contactrob@cs.umd.eduSupplementary informationSupplementary text are available atBioinformaticsonline.

Motivation The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. Results Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. Availability and implementation The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package=sivs. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. Results Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. Availability and implementation DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance, and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data. Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network (CNN) and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network (GCN) to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools., Comment: 15 pages, 10 figures

Motivation Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning. Results We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case–control datasets, with median r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. Availability and implementation Our method is available with a GPL license as an R package from CRAN and GitHub. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control. Result We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems theory. In the embedded space, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes, number of replicates and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method. Availabilityand implementation A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The analysis of longitudinal datasets and construction of gene regulatory networks (GRNs) provide a valuable means to disentangle the complexity of microRNA (miRNA)–mRNA interactions. However, there are no computational tools that can integrate, conduct functional analysis and generate detailed networks from longitudinal miRNA–mRNA datasets. Results We present TimiRGeN, an R package that uses time point-based differential expression results to identify miRNA–mRNA interactions influencing signaling pathways of interest. miRNA–mRNA interactions can be visualized in R or exported to PathVisio or Cytoscape. The output can be used for hypothesis generation and directing in vitro or further in silico work such as GRN construction. Availability and implementation TimiRGeN is available for download on Bioconductor (https://bioconductor.org/packages/TimiRGeN) and requires R v4.0.2 or newer and BiocManager v3.12 or newer. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. Results Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. Availability and implementation Code is freely available at https://github.com/sysbio-bioinf/BNStatic. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge. Results To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis. Availability and implementation The Vetra is available at https://github.com/wgzgithub/VeTra. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step toward deriving mechanistic insights into complex biological phenomena. Here, we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. Results We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. Availability and implementation omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field. Results We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies. Availability and implementation Code and data are available at https://github.com/LichtargeLab/BioVNN Supplementary information Supplementary data are available at Bioinformatics online.

Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify ‘subspecies’, which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling., Bioinformatics, 38 (4), ISSN:1367-4803, ISSN:1460-2059

Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of enthusiasm for AI followed by periods of disillusionment, reduced investments, and progress, known as “AI Winters.” We are now at risk of another AI Winter in health/healthcare due to increasing publicity of AI solutions that are not representing touted breakthroughs, and thereby decreasing trust of users in AI. In this article, we first highlight recently published literature on AI risks and mitigation strategies that would be relevant for groups considering designing, implementing, and promoting self-governance. We then describe a process for how a diverse group of stakeholders could develop and define standards for promoting trust, as well as AI risk-mitigating practices through greater industry self-governance. We also describe how adherence to such standards could be verified, specifically through certification/accreditation. Self-governance could be encouraged by governments to complement existing regulatory schema or legislative efforts to mitigate AI risks. Greater adoption of industry self-governance could fill a critical gap to construct a more comprehensive approach to the governance of AI solutions than US legislation/regulations currently encompass. In this more comprehensive approach, AI developers, AI users, and government/legislators all have critical roles to play to advance practices that maintain trust in AI and prevent another AI Winter.

Background Infectious Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was used in the validation of methods for detection of SARS-CoV-2 on stainless-steel surfaces in the AOAC Research Institute Emergency Response Validation project. Handling infectious virus requires Biosafety Level (BSL)-3 facilities. Objective To compare the recovery and detection of infectious and heat-inactivated (HI; 65°C for 30 min) SARS-CoV-2 from stainless steel by the modified US Centers for Disease Control and Prevention (CDC) 2019-Novel Coronavirus (2019-nCoV) Real Time Reverse Transcription Polymerase Chain Reaction (RT-PCR) Diagnostic Panel. Method Viral stocks were diluted in viral transport medium (VTM) and deposited onto stainless-steel test areas at 2 × 103 and 2 × 104 genomic copies for low and high, respectively. Test areas were sampled and aliquots of the resulting test solutions analyzed by RT-qPCR according to the CDC method. Results were analyzed by probability of detection (POD) statistics. Results The low level, where fractional positive results (25–75%) are expected, yielded PODI = 0.80 (0.58, 0.92) for the infectious virus and PODHI = 0.15 (0.05, 0.36) for the HI virus. The bias, dPODHI = −0.65 (−0.80, −0.35), demonstrated a statistical difference between infectious and HI virus detection. No difference was observed at the high inoculation level. Conclusions Despite the statistical difference observed, the use of the HI virus is a viable alternative for matrix extension studies using a method comparison study design. Highlights: The use of HI SARS-CoV-2 can mitigate the need for a BSL-3 facility for matrix extension validation of alternative methods in SARS-CoV-2 studies. Highlights The use of HI SARS-CoV-2 can mitigate the need for a BSL-3 facility for matrix extension validation of alternative methods in SARS-CoV-2 studies.

Motivation Marker genes, defined as genes that are expressed primarily in a single-cell type, can be identified from the single-cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Microbial gene catalogs are data structures that organize genes found in microbial communities, providing a reference for standardized analysis of the microbes across samples and studies. Although gene catalogs are commonly used, they have not been critically evaluated for their effectiveness as a basis for metagenomic analyses. Results As a case study, we investigate one such catalog, the Integrated Gene Catalog (IGC), however, our observations apply broadly to most gene catalogs constructed to date. We focus on both the approach used to construct this catalog and on its effectiveness when used as a reference for microbiome studies. Our results highlight important limitations of the approach used to construct the IGC and call into question the broad usefulness of gene catalogs more generally. We also recommend best practices for the construction and use of gene catalogs in microbiome studies and highlight opportunities for future research. Availability and implementation All supporting scripts for our analyses can be found on GitHub: https://github.com/SethCommichaux/IGC.git. The supporting data can be downloaded from: https://obj.umiacs.umd.edu/igc-analysis/IGC_analysis_data.tar.gz. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. Results In this article, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies show that when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. Availability and implementation The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Do machine learning methods improve standard deconvolution techniques for gene expression data? This article uses a unique new dataset combined with an open innovation competition to evaluate a wide range of approaches developed by 294 competitors from 20 countries. The competition’s objective was to address a deconvolution problem critical to analyzing genetic perturbations from the Connectivity Map. The issue consists of separating gene expression of individual genes from raw measurements obtained from gene pairs. We evaluated the outcomes using ground-truth data (direct measurements for single genes) obtained from the same samples. Results We find that the top-ranked algorithm, based on random forest regression, beat the other methods in accuracy and reproducibility; more traditional gaussian-mixture methods performed well and tended to be faster, and the best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a resource useful for multiple applications. Availability and implementation The data is freely available at clue.io/data (section Contests) and the software is on GitHub at https://github.com/cmap/gene_deconvolution_challenge Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug–drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availabilityand implementation The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Breast cancer is a very heterogeneous disease and there is an urgent need to design computational methods that can accurately predict the prognosis of breast cancer for appropriate therapeutic regime. Recently, deep learning-based methods have achieved great success in prognosis prediction, but many of them directly combine features from different modalities that may ignore the complex inter-modality relations. In addition, existing deep learning-based methods do not take intra-modality relations into consideration that are also beneficial to prognosis prediction. Therefore, it is of great importance to develop a deep learning-based method that can take advantage of the complementary information between intra-modality and inter-modality by integrating data from different modalities for more accurate prognosis prediction of breast cancer. Results We present a novel unified framework named genomic and pathological deep bilinear network (GPDBN) for prognosis prediction of breast cancer by effectively integrating both genomic data and pathological images. In GPDBN, an inter-modality bilinear feature encoding module is proposed to model complex inter-modality relations for fully exploiting intrinsic relationship of the features across different modalities. Meanwhile, intra-modality relations that are also beneficial to prognosis prediction, are captured by two intra-modality bilinear feature encoding modules. Moreover, to take advantage of the complementary information between inter-modality and intra-modality relations, GPDBN further combines the inter- and intra-modality bilinear features by using a multi-layer deep neural network for final prognosis prediction. Comprehensive experiment results demonstrate that the proposed GPDBN significantly improves the performance of breast cancer prognosis prediction and compares favorably with existing methods. Availabilityand implementation GPDBN is freely available at https://github.com/isfj/GPDBN. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. Results To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. Availabilityand implementation https://bitbucket.org/anthakki/prism. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a ‘one-size-fits-all’ approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability and implementation Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR. Supplementary information Supplementary data are available at Bioinformatics online.

Summary Peptide microarrays have emerged as a powerful technology in immunoproteomics as they provide a tool to measure the abundance of different antibodies in patient serum samples. The high dimensionality and small sample size of many experiments challenge conventional statistical approaches, including those aiming to control the false discovery rate (FDR). Motivated by limitations in reproducibility and power of current methods, we advance an empirical Bayesian tool that computes local FDR statistics and local false sign rate statistics when provided with data on estimated effects and estimated standard errors from all the measured peptides. As the name suggests, the MixTwice tool involves the estimation of two mixing distributions, one on underlying effects and one on underlying variance parameters. Constrained optimization techniques provide for model fitting of mixing distributions under weak shape constraints (unimodality of the effect distribution). Numerical experiments show that MixTwice can accurately estimate generative parameters and powerfully identify non-null peptides. In a peptide array study of rheumatoid arthritis, MixTwice recovers meaningful peptide markers in one case where the signal is weak, and has strong reproducibility properties in one case where the signal is strong. Availabilityand implementation MixTwice is available as an R software package https://cran.r-project.org/web/packages/MixTwice/. Supplementary information Supplementary data are available at Bioinformatics online.

Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K

Motivation Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal. Results This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model’s dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling. Availability and implementation The method was developed with the R statistical software and is made available through the R package ‘irg’ hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e. signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. Results We developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Availability and implementation Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824. Supplementary information Supplementary data are available at Bioinformatics online.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative agent of the coronavirus disease 2019 (COVID-19) pandemic, is thought to release its RNA genome at either the cell surface or within endosomes, the balance being dependent on spike protein stability, and the complement of receptors, co-receptors and proteases. To investigate possible mediators of pH-dependence, pKa calculations have been made on a set of structures for spike protein ectodomain and fragments from SARS-CoV-2 and other coronaviruses. Dominating a heat map of the aggregated predictions, three histidine residues in S2 are consistently predicted as destabilizing in pre-fusion (all three) and post-fusion (two of the three) structures. Other predicted features include the more moderate energetics of surface salt–bridge interactions and sidechain–mainchain interactions. Two aspartic acid residues in partially buried salt-bridges (D290–R273 and R355–D398) have pKas that are calculated to be elevated and destabilizing in more open forms of the spike trimer. These aspartic acids are most stabilized in a tightly closed conformation that has been observed when linoleic acid is bound, and which also affects the interactions of D614. The D614G mutation is known to modulate the balance of closed to open trimer. It is suggested that D398 in particular contributes to a pH-dependence of the open/closed equilibrium, potentially coupled to the effects of linoleic acid binding and D614G mutation, and possibly also A570D mutation. These observations are discussed in the context of SARS-CoV-2 infection, mutagenesis studies, and other human coronaviruses.

Motivation We previously described the construction and characterization of fixed reusable blood transcriptional module repertoires. More recently we released a third iteration (‘BloodGen3’ module repertoire) that comprises 382 functionally annotated modules and encompasses 14 168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here an R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability and implementation The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module. Supplementary information Supplementary data are available at Bioinformatics online.

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared a global pandemic by the World Health Organization, and the situation worsens daily, associated with acute increases in case fatality rates. The main protease (Mpro) enzyme produced by SARS-CoV-2 was recently demonstrated to be responsible for not only viral reproduction but also impeding host immune responses. The element selenium (Se) plays a vital role in immune functions, both directly and indirectly. Thus, we hypothesised that Se-containing heterocyclic compounds might curb the activity of SARS-CoV-2 Mpro. We performed a molecular docking analysis and found that several of the selected selenocompounds showed potential binding affinities for SARS-CoV-2 Mpro, especially ethaselen (49), which exhibited a docking score of −6.7 kcal/mol compared with the −6.5 kcal/mol score for GC376 (positive control). Drug-likeness calculations suggested that these compounds are biologically active and possess the characteristics of ideal drug candidates. Based on the binding affinity and drug-likeness results, we selected the 16 most effective selenocompounds as potential anti-COVID-19 drug candidates. We also validated the structural integrity and stability of the drug candidate through molecular dynamics simulation. Using further in vitro and in vivo experiments, we believe that the targeted compound identified in this study (ethaselen) could pave the way for the development of prospective drugs to combat SARS-CoV-2 infections and trigger specific host immune responses., Graphical Abstract

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.