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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

WeavePop: A bioinformatics workflow to explore and analyze genomic variants of eukaryotic populations

Analyzing genomic variants in large datasets composed of short-read sequencing data is a process that requires multiple steps and computational tools, which makes it a complicated task that is difficult to reproduce across projects and laboratories. To address this need, we developed a reproducible and scalable Snakemake workflow called WeavePop, which aligns samples to selected references, obtains reference-based assemblies, annotations, and sequences, and identifies small variants and copy-number variants in eukaryotic haploid organisms. All the results are integrated into a database that can be easily shared and explored through a graphical web interface provided alongside the workflow, making the discovery of variants in a population of study very simple. WeavePop is available from GitHub (https://github.com/magwenelab/WeavePop) for Linux operating systems. Here we exemplify the use of WeavePop in a large collection of isolates of the pathogenic fungus Cryptococcus neoformans. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/670593v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1b1bf6eorg.highwire.dtl.DTLVardef@8edae9org.highwire.dtl.DTLVardef@c60290org.highwire.dtl.DTLVardef@1d27bef_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

TAFFISH: A lightweight, modular, and containerized workflow framework for reproducible bioinformatics analyses

SummaryBioinformatics analyses often rely on shell commands and small shell scripts whose executable context is difficult to preserve, inspect and reuse. TAFFISH addresses this gap by packaging command-line tool calls and lightweight shell flows as installable, versioned and inspectable executable units. Through a curated public Hub, TAFFISH indexes command interfaces, execution backends, platform constraints, release metadata and smoke-test/validation records. Together, these components provide a command-level reproducibility layer that works directly in ordinary shells and can also be invoked from existing workflow systems. Availability and ImplementationTAFFISH is implemented in Common Lisp and released under Apache-2.0. TAFFISH version 0.10.1, together with the source code, documentation and public Hub resources, is available at https://github.com/taffish/taffish, https://taffish.com, https://taffish.github.io and https://github.com/taffish/. The submitted software release, frozen Hub snapshot, test data, command logs, checksums and reproducibility package are archived at https://doi.org/10.5281/zenodo.21054185. The authors will maintain the software and public resources for at least two years after publication. ContactHao Lyu: hao.lyu@uestc.edu.cn; Hao Lin: hlin@uestc.edu.cn; Kejun Deng: dengkj@uestc.edu.cn Supplementary informationSupplementary data are available online.

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bioRxiv2025

CluSeek: Bioinformatics Tool to Identify and Analyze Gene Clusters

Gene clusters are key structural and functional units that encode diverse phenotypes in genomes, from metabolism to pathogenesis. As genome sequencing expands, tools for systematic exploration of this growing data are increasingly needed. We present CluSeek, an open-source, Python-based platform for discovering, visualizing, and analyzing gene clusters across all GenBank data. Unlike existing tools, CluSeek does not rely on predefined cluster types or reference libraries but enables mining of any gene neighborhoods containing colocalized homologs of user-specified genes. It features an intuitive graphical interface suitable for non-bioinformaticians and is freely available at https://cluseek.com. We demonstrate the versatility of Cluseek in two distinct case studies: (i) mining of specialized metabolites, where CluSeek uncovered over 16 new classes containing the bioactivity-enhancing 4-alkyl-L-proline moiety, previously known in only three Golden Era antibiotic classes; and (ii) analysis of type III secretion systems present in Bordetella species, revealing previously unrecognized taxonomic distribution, and genetic variants, including gene multiplications and novel components with potential functional significance. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/676505v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1767e2eorg.highwire.dtl.DTLVardef@5603e3org.highwire.dtl.DTLVardef@1193475org.highwire.dtl.DTLVardef@1c32e83_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

omicML: An Integrative Bioinformatics and Machine Learning Framework for Transcriptomic Biomarker Identification

IntroductionTranscriptomic biomarker discovery has been a challenge due to variation in datasets and platforms, complexity in statistical and computational methods, integration of multiple programming languages, and intricacy of ML workflow to evaluate biomarkers. Standard workflows necessitate several stages (quality control, normalization, differential expression), typically executed in R or Python, resulting in bottlenecks for non-experts. Existing platforms have alleviated certain challenges by offering graphical interfaces for data loading, normalization, differential gene expression analysis, and functional analysis; nevertheless, they typically do not incorporate integrated machine learning procedures for biomarker selection. MethodIn this regard, we present omicML, an intuitive graphical user interface (GUI) that combines transcriptomic data analysis with machine learning (ML)-based classification via integrating R and Python packages/libraries. It supports both RNA-Seq and microarray data, automating preprocessing (data import, quality control, and normalization) and differential expression analysis. The tool annotates differentially expressed genes (DEGs) with descriptions, gene ontology, and pathway information and incorporates comparative analysis. Our extensive ML pipeline enables both supervised and unsupervised learning, integrates various datasets based on candidate gene signatures, standardizes and eliminates less significant features, benchmarks multiple ML classifiers with robust performance metrics (e.g., AUROC, AUPRC), assesses feature importance, develops single-gene and multi-gene predictive models, and systematically finalizes the biomarker algorithm. All functionalities are available in omicML, hence reducing the barrier for biologists without computational proficiency. ResultIn a case study, omicML identified a six-gene diagnostic model that distinguishes Mpox (monkeypox virus) infections from those caused by other viruses, including SARS-CoV-2, HIV, Ebola, and varicella-zoster. These results illustrate omicMLs capacity to discern clinically relevant biomarkers from complex transcriptome data. ConclusionThrough the unified system, omicML (https://omicml.org), integrating data preprocessing, differential gene expression analysis, annotation, heatmap analysis, dataset integration, batch effect correction, machine learning approach, and functional analysis can diminish technical barriers and accelerates the conversion of expression data into diagnostic insights for clinicians and bench scientists.

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bioRxiv2017

Hot-starting software containers for bioinformatics analyses

Using software containers has become standard practice to reproducibly deploy and execute biomedical workflows on the cloud. We demonstrate that hot-starting, from containers that have been frozen after the application has already begun execution, reduces the costs of cloud computing by avoiding repetitive initialization steps. The method is widely applicable and can provide substantial savings both for small jobs and for large-scale deployments using automated schedulers.

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bioRxiv2017

Bioinformatics Workflow Management With The Wobidisco Ecosystem

ReferencesTo conduct our computational experiments, our team developed a set of workflow-management-related projects: Ketrew, Biokepi, and Coclobas. The family of tools and libraries are designed with reliability and flexibility as main guiding principles. We describe the components of the software stack and explain the choices we made. Every piece of software is free and open-source; the umbrella documentation project is available at https://github.com/hammerlab/wobidisco.

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bioRxiv2017

Bioinformatics analysis quantifies neighborhood preferences of cancer cells in Hodgkin lymphoma.

MotivationHodgkin lymphoma is a tumor of the lymphatic system and represents one of the most frequent lymphoma in the Western world. It is characterized by Hodgkin cells and Reed-Sternberg cells, which exhibit a broad morphological spectrum. The cells are visualized by immunohistochemical staining of tissue sections. In pathology, tissue images are mainly manually evaluated, relying on the expertise and experience of pathologists. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of cancer cells is of great interest.\n\nResultsHere, we systematically quantified and investigated cancer cell properties and their spatial neighborhood relations by applying statistical analyses to whole slide images of Hodgkin lymphoma and lymphadenitis, which describes a non-cancerous inflammation of the lymph node. We differentiated cells by their morphology and studied the spatial neighborhood relation of more than 400,000 immunohistochemically stained cells. We found that, according to their morphological features, the cells exhibited significant preferences for and aversions to cells of specific profiles as nearest neighbor. We quantified differences between Hodgkin lymphoma and lymphadenitis concerning the neighborhood relations of cells and the sizes of cells. The approach can easily be applied to other cancer types.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

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bioRxiv2018

Rapid Identification and Validation of Novel Rheumatoid Arthritis Drug Treatments using an Integrative Bioinformatics Platform

The majority of drugs currently used to treat rheumatoid arthritis (RA) act on a small number of immunomodulatory targets. We applied an integrative biomedical-informatics-based approach and in vivo testing to identify new drug candidates and potential therapeutic targets that could form the basis for future drug development in RA. A computational model of RA was constructed by integrating patient gene expression data, molecular interactions, and clinical drug-disease associations. Drug candidates were scored based on their predicted efficacy across these data types. Ten high-scoring candidates were subsequently screened in a collagen-induced arthritis model of RA. Treatment with exenatide, olopatadine, and TXR-112 significantly improved multiple preclinical endpoints, including animal mobility which was measured using a novel digital platform. These three drug candidates do not act on common RA therapeutic targets; however, links between known candidate pharmacology and pathological processes involved in RA suggest hypothetical mechanisms contributing to the observed efficacy.

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bioRxiv2018

Bioinformatic analysis of endogenous and exogenous small RNAs on lipoproteins

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, \"Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)\". To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine, and liver samples. A key advance for the TIGER pipeline is the ability to analyze both host and non-host sRNAs at genomic, parent RNA, and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins, and >85% of sRNAs in liver, bile, and urine, a significant advance compared to existing software. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

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bioRxiv2018

HPCI: A Perl module for writing cluster-portable bioinformatics pipelines

BackgroundMost biocomputing pipelines are run on clusters of computers. Each type of cluster has its own API (application programming interface). That API defines how a program that is to run on the cluster must request the submission, content and monitoring of jobs to be run on the cluster. Sometimes, it is desirable to run the same pipeline on different types of cluster. This can happen in situations including when:\n\nO_LIdifferent labs are collaborating, but they do not use the same type of cluster\nC_LIO_LIa pipeline is released to other labs as open source or commercial software\nC_LIO_LIa lab has access to multiple types of cluster, and wants to choose between them for scaling, cost or other purposes\nC_LIO_LIa lab is migrating their infrastructure from one cluster type to another\nC_LIO_LIduring testing or travelling, it is often desired to run on a single computer\nC_LI\n\nHowever, since each type of cluster has its own API, code that runs jobs on one type of cluster needs to be re-written if it is desired to run that application on a different type of cluster. To resolve this problem, we created a software module to generalize the submission of pipelines across computing environments, including local compute, clouds and clusters.\n\nResultsHPCI (High Performance Computing Interface) is a Perl module that provides the interface to a standardized generic cluster.\n\nWhen the HPCI module is used, it accepts a parameter to specify the cluster type. The HPCI module uses this to load a driver HPCD:: . This is used to translate the abstract HPCI interface to the specific software interface.\n\nSimply by changing the cluster parameter, the same pipeline can be run on a different type of cluster with no other changes.\n\nConclusionThe HPCI module assists in writing Perl programs that can be run in different lab environments, with different site configuration requirements and different types of hardware clusters. Rather than having to re-write portions of the program, it is only necessary to change a configuration file.\n\nUsing HPCI, an application can manage collections of jobs to be runs, specify ordering dependencies, detect success or failure of jobs run and allow automatic retry of failed jobs (allowing for the possibility of a changed configuration such as when the original attempt specified an inadequate memory allotment).

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bioRxiv2018

Identification of potential microRNAs associated with Herpesvirus family based on bioinformatic analysis

MicroRNAs (miRNAs) are known key regulators of gene expression on posttranscriptional level in many organisms encoded in mammals, plants and also several viral families. To date, no homologous gene of a virus-originated miRNA is known in other organisms. To date, only a few homologous miRNA between two different viruses are known, however, no gene of a virus-originated miRNA is known in any organism of other kingdoms. This can be attributed to the fact that classical miRNA detection approaches such as homology-based predictions fail at viruses due to their highly diverse genomes and their high mutation rate.\n\nHere, we applied the virus-derived precursor miRNA (pre-miRNA) prediction pipeline ViMiFi, which combines information about sequence conservation and machine learning-based approaches, on Human Herpesvirus 7 (HHV7) and Epstein-Barr virus (EBV). ViMiFi was able to predict 61 candidates in EBV, which has 25 known pre-miRNAs. From these 25, ViMiFi identified 20. It was further able to predict 18 candidates in the HHV7 genome, in which no miRNA had been described yet. We also studied the undescribed candidates of both viruses for potential functions and found similarities with human snRNAs and miRNAs from mammals and plants.

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bioRxiv2018

Single nucleotide polymorphisms of the c-MYC gene’s relationship with formation of Burkitt’s lymphoma using bioinformatics analysis

Burkitts lymphoma (BL) is an aggressive form of non-Hodgkin lymphoma, originates from germinal center B cells, MYC gene (MIM ID 190080) is an important proto-oncogene transcriptional factor encoding a nuclear phosphoprotein for central cellular processes. Dysregulated expression or function of c-MYC is one of the most common abnormalities in BL. This study focused on the investigation of the possible role of single nucleotide polymorphisms (SNPs) in MYC gene associated with formation of BL.\n\nMYC SNPs were obtained from NCBI database. SNPs in the coding region that are non-synonymous (nsSNPs) were analysed by multiple programs such as SIFT, Polyphen2, SNPs&GO, PHD-SNP and I-mutant. In this study, a total of 286 Homo sapiens SNPs were found. Roughly, forty-eight of them were deleterious and were furtherly investigated.\n\nEight SNPs were considered most disease causing [rs4645959 (N26S), rs4645959 (N25S), rs141095253 (P396L), rs141095253 (P397L), rs150308400 (C233Y), rs150308400 (C147Y), rs150308400 (C147Y), rs150308400 (C148Y)] according to the four softwares used. Two of which have not been reported previously [rs4645959 (N25S), rs141095253 (P396L)]. SNPs analysis helps is a diagnostic marker which helps in diagnosing and consequently, finding therapeutics for clinical diseases. This is through SNPs genotyping arrays and other techniques. Thus, it is highly recommended to confirm the findings in this study in vivo and in vitro.

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bioRxiv2018

Bioinformatics Approach to Identify Diseasome and Comorbidities Effect of Mitochondrial Dysfunctions on the Progression of Neurological Disorders

Mitochondrial dysfunction can cause various neurological diseases. We therefore developed a quantitative framework to explore how mitochondrial dysfunction may influence the progression of Alzheimers, Parkinsons, Huntingtons and Lou Gehrigs diseases and cerebral palsy through analysis of genes showing altered expression in these conditions. We sought insights about the gene profiles of mitochondrial and associated neurological diseases by investigating gene-disease networks, KEGG pathways, gene ontologies and protein-protein interaction network. Gene disease networks were constructed to connect shared genes which are commonly found between the neurological diseases and Mitochondrial Dysfunction. We also generated KEGG pathways and gene ontologies to explore functional enrichment among them, and protein-protein interaction networks to identify the shared protein groups of these diseases. Finally, we verified our biomarkers using gold benchmark databases (e.g., OMIM and dbGaP) which identified effective reasons of it. Our network-based methodologies are useful to investigate disease mechanisms, predictions for comorbidities and identified distinct similarities among different neurological disorders for mitochondrial dysfunction.

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bioRxiv2018

Rare variants imputation in admixed populations: Comparison across reference panels and bioinformatics tools.

BackgroundImputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants imputation in an admixed Caribbean Hispanic population (CH). MethodsWe evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% [≤]minor allele frequency (MAF) [≤] 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: a) high-quality variant counts according to imputation tools internal indexes (e.g. IMPUTE2 "Info"[≥]80%). b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; c) Cohens kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion. ResultsSHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs 0.75% for high-quality rare and ultra-rare variants, respectively; Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g. rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05). ConclusionReference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

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bioRxiv2019

multiPhATE: bioinformatics pipeline for functional annotation of phage isolates

SummaryTo address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene-calling algorithm and assigns putative functions to gene calls using protein-, virus-, and phage-centric databases. multiPhATEs modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. Availability and implementationmultiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. Contactzhou4@llnl.gov or carol.zhou@comcast.net. Supplementary informationData generated during the current study are included as supplementary files available for download at https://github.com/carolzhou/PhATE_docs.

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bioRxiv2019

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the effcacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

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bioRxiv2019

A Theory of Intrinsic Bias in Biology and its Application in Machine Learning and Bioinformatics

It is common to consider a data-intensive strategy to be an appropriate way to develop systemic analyses in biology and physiology. Therefore, options for data collection, sampling, standardization, visualization, and interpretation determine how causes are identified in time series to build mathematical models. However, there are often biases in the collected data that can affect the validity of the model: while collecting enough large datasets seems to be a good strategy for reducing the bias of the collected data, persistent and dynamical anomalies in the data structure can affect the overall validity of the model. In this work we present a methodology based on the definition of homological groups to evaluate persistent anomalies in the structure of the sampled time series. In this evaluation relevant patterns in the combination of different time series are clustered and grouped to customize the identification of causal relationships between parameters. We test this methodology on data collected from patients using mobile sensors to test the response to physical exercise in real-world conditions and outside the lab. With this methodology we plan to obtain a patient stratification of the time series to customize models in medicine.

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bioRxiv2019

Identification of novel key biomarkers in Simpson-Golabi-Behmel Syndrome: Evidence from bioinformatics analysis

BackgroundThe Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is a rare inherited X-linked condition characterized by pre- and postnatal overgrowth. The aim of the present study is to identify functional non-synonymous SNPs of GPC3 gene using various in silico approaches. These SNPs are supposed to have a direct effect on protein stability through conformation changes.\n\nMaterial and methodsThe SNPs were retrieved from the Single Nucleotide Polymorphism database (dbSNP) and further used to investigate a damaging effect using SIFT, PolyPhen, PROVEAN, SNAP2, SNPs&GO, PHD-SNP and P-mut, While we used I-mutant and MUPro to study the effect of SNPs on GPC3 protein structure. The 3D structure of human GPC3 protein is not available in the Protein Data Bank, so we used RaptorX to generate a 3D structural model for wild-type GPC3 to visualize the amino acids changes by UCSF Chimera. For biophysical validation we used project HOPE. Lastly we run conservational analysis by BioEdit and Consurf web server respectively.\n\nResultsour results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are found to be the most deleterious which effect on the GPC3 structure and function.\n\nConclusionThis present study could provide a novel insight into the molecular basis of overgrowth Syndrome.

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