Centre for Bioinformatics

AbstractMotivationThe identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of research across diverse disciplines. However, current bioinformatic tools do not support covariance matrices in DEG modeling. Here, we introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling including covariates, weights, random effects, covariance matrices, and fit metrics.ResultsIn simulated datasets, kimma detects DEGs with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like AIC. Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity.AvailabilityKimma is freely available on GitHub https://github.com/BIGslu/kimma with an instructional vignette at https://bigslu.github.io/kimma_vignette/kimma_vignette.htmlSupplementary informationScripts related to this manuscript can be found at https://github.com/BIGslu/kimma_MS_public

AbstractSummaryPascalX is a Python library providing fast and accurate tools for mapping SNP-wise GWAS summary statistics. Specifically, it allows for scoring genes and annotated gene sets for enrichment signals based on data from, both, single GWAS and pairs of GWAS. The gene scores take into account the correlation pattern between SNPs. They are based on the cumulative density function of a linear combination of χ2 distributed random variables, which can be calculated either approximately or exactly to high precision. Acceleration via multi-threading and GPU is supported. The code of PascalX is fully open source and well suited as a base for method development in the GWAS enrichment test context.AvailabilityThe source code is available at https://github.com/BergmannLab/PascalX and archived under doi://10.5281/zenodo.4429922. A user manual with usage examples is available at https://bergmannlab.github.io/PascalX/.Supplementary informationSupplementary materialSupplementary material is available at Bioinformatics online.

AbstractMotivationWe present pyGOMoDo, a Python library to perform homology modeling and docking, specifically designed for human GPCRs. pyGOMoDo is a python wrap-up of the updated functionalities of GOMoDo web server (https://gomodo.grs.kfa-juelich.de). It was developed having in mind its usage through Jupyter notebooks, where users can create their own protocols of modeling and docking of GPCRs. In this article, we focus on the internal structure and general capabilities of pyGOMoDO and on how it can be useful for carrying out structural biology studies of GPCRs.AvailabilityThe source code is freely available at https://github.com/rribeiro-sci/pygomodo under the Apache 2.0 license. Tutorial notebooks containing minimal working examples can be found at https://github.com/rribeiro-sci/pygomodo/tree/main/examples.

AbstractMotivationThere is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation.ResultsWe present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI-platform for calculation of distance correlation in omics data, measuring linear and non-linear dependences between variables, as well as correlation between vectors of different lengths, e.g., different sample sizes. By combining the sign of the overall trend from Pearson’s correlation with distance correlation values, we further provide a novel signed distance correlation of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement partial distance correlation, calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset.AvailabilityThe SiDCo software application is freely available at https://complimet.ca/sidco.Supplementary informationSupplementary help pages are provided at https://complimet.ca/sidco. Supplementary MaterialSupplementary Material shows an example of an application of SiDCo in metabolomics.

AbstractMotivationInferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, correct taxonomic inference is crucial when identifying different viral strains with high sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of SARS-CoV-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples.ResultsWe present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores.Availability and ImplementationPepGM is written in Python and embedded into a Snakemake workflow. It is available on https://github.com/BAMeScience/PepGM.

AbstractSummaryWe describe a compression scheme for BUS files and an implementation of the algorithm in the BUStools software. Our compression algorithm yields smaller file sizes than gzip, at significantly faster compression and decompression speeds. We evaluated our algorithm on 533 BUS files from scRNA-seq experiments with a total size of 1TB. Our compression is 2.2x faster than the fastest gzip option 35% slower than the fastest zstd option and results in 1.5x smaller files than both methods. This amounts to an 8.3x reduction in the file size, resulting in a compressed size of 122GB for the dataset.Availability and ImplementationA complete description of the format is available at https://github.com/BUStools/BUSZ-format and an implementation at https://github.com/BUStools/bustools. The code to reproduce the results of this paper is available at https://github.com/pmelsted/BUSZ_paper.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractSummaryDetection and analysis of viral genomes with Nanopore sequencing has shown great promise in the surveillance of pathogen outbreaks. However, the number of virus detection pipelines supporting Nanopore sequencing is very limited. Here, we present VirPipe, a new pipeline for the detection of viral genomes from Nanopore or Illumina sequencing input featuring streamlined installation and customization.AvailabilityVirPipe Source code and documentation are freely available for download at https://github.com/KijinKims/VirPipe, implemented in Python and Nextflow.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationAllostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.ResultsOut of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein’s global dynamics.AvailabilityAPOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationPathogenic copy number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts.ResultsHere, we introduce the CNV-ClinViewer, an open-source web-application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators patient care and for basic scientists translational genomic research.AvailabilityThe web-application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationTesting association between multiple phenotypes with a set of genetic variants simultaneously, rather than analyzing one trait at a time, is receiving an increasing attention for its high statistical power and easy explanation on pleiotropic effects. The kernel-based association test (KAT), being free of data dimensions and structures, has proven to be a good alternative method for genetic association analysis with multiple phenotypes. However, KAT suffers from substantial power loss when multiple phenotypes have moderate to strong correlations. To handle this issue, we propose a maximum kernel-based association test (MaxKAT) and suggest using the generalized extreme value distribution to calculate its statistical significance under the null hypothesis.ResultsWe show that MaxKAT reduces computational intensity greatly while maintaining high accuracy. Extensive simulations demonstrate that MaxKAT can properly control type I error rates and obtain remarkably higher power than KAT under most of the considered scenarios. Application to a porcine dataset used in biomedical experiments of human disease further illustrates its practical utility.Availability and implementationThe R package MaxKAT that implements the proposed method is available on Github https://github.com/WangJJ-xrk/MaxKAT.

AbstractSummaryGenome-wide association studies (GWASs) have identified numerous genetic variants associated with complex disease risk; however, most of these associations are non-coding, complicating identifying their proximal target gene. Transcriptome-wide association studies (TWASs) have been proposed to mitigate this gap by integrating expression quantitative trait loci (eQTL) data with GWAS data. Numerous methodological advancements have been made for TWAS, yet each approach requires ad-hoc simulations to demonstrate feasibility. Here, we present twas_sim, a computationally scalable and easily extendable tool for simplified performance evaluation and power analysis for TWAS methods.AvailabilitySoftware and documentation are available at https://github.com/mancusolab/twas_simSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationThe human microbiome which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared to traditional imputation methods.ResultsThis work proposes DeepMicroGen, a bidirectional recurrent neural network based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier.AvailabilityDeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationWhile conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python.ResultspyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs.AvailabilitypyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at (https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts), along with the raw flow cytometry input data.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationMolecular complexes play a major role in the regulation of biological pathways. The Biological Pathway Exchange format (BioPAX) facilitates the integration of data sources describing interactions some of which involving complexes. The BioPAX specification explicitly prevents complexes to have any component that is another complex (unless this component is a black-box complex whose composition is unknown). However, we observed that the well-curated Reactome pathway database contains such recursive complexes of complexes. We propose reproductible and semantically-rich SPARQL queries for identifying and fixing invalid complexes in BioPAX databases, and evaluate the consequences of fixing these non-conformities in the Reactome database.ResultsFor the Homo sapiens version of Reactome, we identify 5,833 recursively defined complexes out of the 14,987 complexes (39%). This situation is not specific to the human dataset, as all tested species of Reactome exhibit between 30% (Plasmodium falciparum) and 40% (Sus scrofa, Bos taurus, Canis familiaris, Gallus gallus) of recursive complexes. As an additional consequence, the procedure also allows the detection of complex redundancies. Overall, this method improves the conformity and the automated analysis of the graph by repairing the topology of the complexes in the graph. This will allow to apply further reasoning methods on better consistent data.AvailabilityWe provide a jupyter notebook detailing the analysis https://github.com/cjuigne/non_conformities_detection_biopax.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationPredicting the binding between T-cell receptor (TCR) and peptide presented by HLA molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed.ResultsWe collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of > 3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with AUC (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy.AvailabilityepiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractSummarySynthetic small RNAs are gaining increasing attention in the field of synthetic biology and bioengineering for efficient post-transcriptional regulation of gene expression. However, the optimal design of synthetic small RNAs is challenging because alterations may impair functions or off-target effects can arise. Here, we introduce DIGGER-Bac, a toolbox for Design and Identification of seed regions for Golden Gate assembly and Expression of synthetic small RNAs in Bacteria. The SEEDling tool predicts optimal small RNA seed regions in combination with user-defined small RNA scaffolds for efficient regulation of specified mRNA targets. Results are passed on to the G-GArden tool, which assists with primer design for high-fidelity Golden Gate assembly of the desired synthetic small RNA constructs.Availability and implementationBoth SEEDling and G-GArden are freely available at https://github.com/DIGGER-Bac under the CC BY-NC-SA 4.0 license.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationAs prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts’ evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs.ResultsIn this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node’s taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods.AvailabilityThe source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOTSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

Abstract•Digital PCR (dPCR) is an emerging technology that enables accurate and sensitive quantification of nucleic acids. Most available dPCR systems have two channel optics, with ad hoc software limited to the analysis of single- and duplex assays. Although multiplexing strategies were developed, variable assay designs, dPCR systems, and the analysis of low DNA input data restricted the ability for a universal automated clustering approach. To overcome these issues, we developed digital PCR Cluster Predictor (dPCP), an R package and a Shiny app for automated analysis of up to 4-plex dPCR data. dPCP can analyse and visualize data generated by multiple dPCR systems carrying out accurate and fast clustering not influenced by the amount and integrity of input of nucleic acids. With the companion Shiny app, the functionalities of dPCP can be accessed through a web-browser.AvailabilityR package: https://cran.r-project.org/web/packages/dPCP/index.html; https://github.com/alfodefalco/dPCP; Web: https://dpcp.lns.luSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.

AbstractMotivationThe detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here we present the novel, unsupervised algorithm ICAT that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions.ResultsUsing simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared to current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in single-cell RNA sequencing data.Availability and implementationhttps://github.com/BradhamLab/icatSupplementary informationSupplemental Methods, Tables and Figures are available at Bioinformatics online.

AbstractSummaryUnderstanding the binding site of the target protein is essential for rational drug design. Pocket detection software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated in comparison with the actual volume of the bound ligands. This study proposes a refinement tool for the pockets predicted by an alpha sphere-based approach, Pocket to Concavity (P2C). P2C is divided into two modes: Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shape of the deep and druggable concavity where the core scaffold can bind. The LB mode searches the deep concavity around the bound ligand. Thus, P2C is useful for identifying and designing desirable compounds in Structure-Based Drug Design (SBDD).Availability and implementationPocket to Concavity is freely available at https://github.com/genki-kudo/Pocket-to-Concavity. This tool is implemented in Python3 and Fpocket2.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.