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Bioinformatics

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bioRxiv2025

Network pharmacology, bioinformatics, molecular docking and experimental verification of the mechanism of HXZTS treatment on osteoarthritis synovium

BackgroundOsteoarthritis (OA) is a prevalent joint disease characterized by synovial inflammation and synovial fibroblast dysfunction. Elucidating the molecular mechanisms underlying synovial tissue impairment in OA is crucial for developing targeted therapeutic interventions. Huoxuezhitongsan (HXZTS), a traditional Chinese medicine formula, has shown promising therapeutic effects in treating OA, but its mechanisms of action on synovial tissue remain unclear. MethodThis study integrated network pharmacology, bioinformatics analysis, molecular docking, and experimental validation to elucidate the potential targets, pathways, and mechanisms by which HXZTS exert therapeutic effects on osteoarthritic synovial tissue. ResultsNetwork analysis identified 21 active ingredients and 111 common targets of HXZTS for OA, with key compounds like quercetin, {beta}-sitosterol, and stigmasterol. Enrichment analysis revealed HXZTS modulates inflammation through TNF, IL-17, and MAPK signaling pathways, oxidative stress, apoptosis, angiogenesis, and metabolic regulation. Molecular docking demonstrated strong binding affinities of HXZTS compounds with targets like AKT1, TNF, TP53, IL6, and VEGFA. RT-qPCR validation confirmed the downregulation of inflammatory mediators like IL6 and PTGS2, alongside the upregulation of genes associated with potential cartilage repair effects, such as MMP1 and MMP3, upon HXZTS treatment. ConclusionsHXZTS exerts therapeutic effects on osteoarthritic synovial tissue through a multifaceted mechanism involving modulation of inflammation, oxidative stress, apoptosis, angiogenesis, and metabolic regulation. These findings provide insights into the potential application of HXZTS as a complementary or alternative therapeutic approach for managing OA and a foundation for developing novel targeted therapies.

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bioRxiv2025

Bioinformatic analysis of differentially expressed long non-coding RNAs in skeletal muscle following aerobic and resistance exercise

Emerging evidence suggests that long non-coding RNA (lncRNA) molecules influence the adaptive response to exercise, but how lncRNA responses differ between endurance and resistance exercise modalities is poorly understood. The purpose of this study was to bioinformatically infer the expression of lncRNA in skeletal muscle following acute aerobic exercise (AE) and resistance exercise (RE). We downloaded publicly available RNA-seq data, performed a differential expression analysis, and compared lncRNA expression profiles between different exercise types (AE vs. RE) at three time points: baseline, 1 hour post-exercise, and 4 hours post-exercise. We observed distinct lncRNA profiles between acute AE and RE at different time points, suggesting that lncRNA perform different roles in controlling the response to different exercise modalities in skeletal muscle. Future studies should investigate the specific roles of these lncRNAs in the response to acute exercise in skeletal muscle.

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bioRxiv2025

Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts

Health and life scientists frequently rely on quantitative experts to perform complex data analyses, yet interpretation of these results often becomes a bottleneck due to communication barriers across disciplines. Large Language Models (LLMs) have the potential to act as intermediaries that translate analytical outputs into accessible scientific narratives, but their ability to reliably interpret outputs from real-world biomedical data analyses across disciplines remains unclear. In this study, we investigate how state-of-the-art LLMs behave when integrated into real bioinformatics analysis workflows as report-generation assistants for interdisciplinary teams. We evaluated LLM-generated summaries using both automated and human evaluation frameworks to ensure holistic evaluation. Automated assessment employed multiple choice questions designed using Bloom's taxonomy to assess multiple levels of understanding, while human evaluation tasked scientists to score summaries for factual consistency, lack of harmfulness, comprehensiveness, and coherence. All generally produced readable and largely safe summaries, confirming their value for first-pass translation of technical analyses, however frequently misinterpreted visualisations, produced verbose summaries and rarely offered novel insights beyond what was already contained in the analytics. Our findings suggest that LLMs are best suited for easing interdisciplinary communication rather than replacing domain expertise and human oversight remains essential to guarantee accuracy, interpretative depth, and the generation of genuinely novel scientific insights.

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bioRxiv2017

Improving bioinformatics prediction of microRNA targets by ranks aggregation

microRNAs are non-coding RNAs which down-regulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available softwares still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of three important prediction algorithms (miRanda, PITA and SVmicrO) showing additional characteristics in order to rerank them into a single list. This database is freely available through a webtool called miRabel (http://bioinfo.univ-rouen.fr/mirabel/) which can take either a list of miRNAs, genes or signaling pathways as search inputs. Receiver Operating Characteristic curves and Precision-Recall curves analysis carried out using experimentally validated data and very large datasets show that miRabel significantly improves the prediction of miRNA targets compared to the three algorithms used separatly. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR and miRWalk. Interestingly, a F-score analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to accurately identify miRNA targets and integrate them into a biological context.

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bioRxiv2018

TETyper: a bioinformatic pipeline for classifying variation and genetic contexts of transposable elements from short-read whole-genome sequencing data

Much of the worldwide dissemination of antibiotic resistance has been driven by resistance gene associations with mobile genetic elements (MGEs), such as plasmids and transposons. Although increasing, our understanding of resistance spread remains relatively limited, as methods for tracking mobile resistance genes through multiple species, strains and plasmids are lacking. We have developed a bioinformatic pipeline for tracking variation within, and mobility of, specific transposable elements (TEs), such as transposons carrying antibiotic resistance genes. TETyper takes short-read whole-genome sequencing data as input and identifies single-nucleotide mutations and deletions within the TE of interest, to enable tracking of specific sequence variants, as well as the surrounding genetic context(s), to enable identification of transposition events. To investigate global dissemination of Klebsiella pneumoniae carbapenemase (KPC) and its associated transposon Tn4401, we applied TETyper to a collection of >3000 publicly available Illumina datasets containing blaKPC. This revealed surprising diversity, with >200 distinct flanking genetic contexts for Tn4401, indicating high levels of transposition. Integration of sample metadata revealed insights into associations between geographic locations, host species, Tn4401 sequence variants and flanking genetic contexts. To demonstrate the ability of TETyper to cope with high copy number TEs and to track specific short-term evolutionary changes, we also applied it to the insertion sequence IS26 within a defined K. pneumoniae outbreak. TETyper is implemented in python and is freely available at https://github.com/aesheppard/TETyper.

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bioRxiv2018

Container-based bioinformatics with Pachyderm

MotivationComputational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages, and (iii) a data management layer that tracks data as it moves through the processing pipeline.\n\nResultsPachyderm is an open-source workflow system and data management framework that fulfills these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud-agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability.\n\nAvailabilityPachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out-of-the-box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow-plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC-MS-Pachyderm).\n\nContactjon.novella@farmbio.uu.se

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bioRxiv2018

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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bioRxiv2018

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

The systematic identification of regulatory elements that control gene expression remains a challenge. Genetic screens that use untargeted mutagenesis have the potential to identify protein-coding genes, non-coding RNAs and regulatory elements, but their analysis has mainly focused on identifying the former two. To identify regulatory elements, we conducted a new bioinformatics analysis of insertional mutagenesis screens interrogating WNT signaling in haploid human cells. We searched for specific patterns of retroviral gene trap integrations (used as mutagens in haploid screens) in short genomic intervals overlapping with introns and regions upstream of genes. We uncovered atypical patterns of gene trap insertions that were not predicted to disrupt coding sequences, but caused changes in the expression of two key regulators of WNT signaling, suggesting the presence of cis-regulatory elements. Our methodology extends the scope of haploid genetic screens by enabling the identification of regulatory elements that control gene expression.

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bioRxiv2018

The application of Hadoop in Structural Bioinformatics

The paper reviews the use of the Hadoop platform in Structural Bioinformatics applications. Specifically, we review a number of implementations using Hadoop of high-throughput analyses, e.g. ligand-protein docking and structural alignment, and their scalability in comparison with other batch schedulers and MPI. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. We do note there is some evidence that MPI implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop e.g. Spark improve, usage of cloud platforms (e.g. Azure and AWS) increases and approaches such as the Workflow Definition Language are taken up.

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bioRxiv2018

SciPipe - A workflow library for agile development of complex and dynamic bioinformatics pipelines

BackgroundThe complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation and aid reproducibility of analyses. Many contemporary workflow tools are specialized and not designed for highly complex workflows, such as with nested loops, dynamic scheduling and parametrization, which is common in e.g. machine learning.\n\nFindingsSciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on Flow-based programming principles to support agile development of workflows based on a library of self-contained, re-usable components. It supports running subsets of workflows for improved iterative development, and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX and PDF on-demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline.\n\nConclusionsSciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine leaning, through a flexible programming API suitable for scientists used to programming or scripting.

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bioRxiv2018

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

Bioinformatics workflows for analyzing genomic data obtained from xenografted tumor (e.g., human tumors engrafted in a mouse host) must address several challenges, including separating mouse and human sequence reads and accurate identification of somatic mutations and copy number aberrations when paired normal DNA from the patient is not available. We report here data analysis workflows that address these challenges and result in reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from patient derived xenograft models. We validated our analytical approaches using simulated data and by assessing concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). The commands and parameters for the workflows are available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.

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bioRxiv2018

Neutools: A collection of bioinformatics web tools for neurogenomic analysis

With a large and growing number of neurogenomic data, from epigenetic, transcriptomic to proteomic data deposited to the public domains, visualization and mining of these data alongside one own data have become extremely useful for identifying potential genetic targets and/or biological pathways to further validate and characterize in model systems. Here, a series of easy-to-use web tools (Neutools) were developed using Shiny/R codes for neuroscientists to perform basic bioinformatics data analysis and visualization. Specifically, NeuVenn calculates and plots overlap statistics for multiple input gene sets; NeuGene and NeuChIP visualize gene expression data and histone ChIP-Seq data generated from brain related tissue and cell culture, respectively. NeuVar annotates human brain GWAS variants and epigenetic features based on user-specified genes and regions. Neutools are freely available resources for all academic users to use.

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bioRxiv2018

Aminoacyl tRNA Synthetases as Malarial Drug Targets: A Comparative Bioinformatics Study

Treatment of parasitic diseases has been challenging due to the development of drug resistance by parasites, and thus there is need to identify new class of drugs and drug targets. Protein translation is important for survival of plasmodium and the pathway is present in all the life cycle stages of the plasmodium parasite. Aminoacyl tRNA synthetases are primary enzymes in protein translation as they catalyse the first reaction where an amino acid is added to the cognate tRNA. Currently, there is limited research on comparative studies of aminoacyl tRNA synthetases as potential drug targets. The aim of this study is to understand differences between plasmodium and human aminoacyl tRNA synthetases through bioinformatics analysis. Plasmodium falciparum, P. fragile, P. vivax, P. ovale, P. knowlesi, P. bergei, P. malariae and human aminoacyl tRNA synthetase sequences were retrieved from UniProt database and grouped into 20 families based on amino acid specificity. Despite functional and structural conservation, multiple sequence analysis, motif discovery, pairwise sequence identity calculations and molecular phylogenetic analysis showed striking differences between parasite and human proteins. Prediction of alternate binding sites revealed potential druggable sites in PfArgRS, PfMetRS and PfProRS at regions that were weakly conserved when compared to the human homologues. These differences provide a basis for further exploration of plasmodium aminoacyl tRNA synthetases as potential drug targets.

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bioRxiv2018

NGOMICS-WF, a Bioinformatic Workflow Tool for Batch Omics Data Analysis

In recent years, high throughput next generation omics data have been broadly used in many research fields. However, analyzing omics data is still very challenging, especially for researchers with less experience in bioinformatics, software, and programming. Omics data analysis workflows usually are composed of many processes involving different software tools that demand different compute resources. So, it is often difficult to perform a full workflow in an automated way, despite that many computational tools and resources are available. Here I present NGOMICS-WF, a lightweight workflow tool for batch omics data analysis. This workflow tool has been used in several projects. It can assist researchers to configure and run workflows for various types of omics data on Linux computers or Linux computer clusters. The software package is available from https://github.com/weizhongli/ngomicswf. In addition to the workflow tool, several pre-configured workflows that were tested and published are also available in the package. These pre-configured workflows can be directly used or be used as templates to configure new workflows to analyze metagenomic, metatranscriptomic, RNA-seq and 16S data.

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bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

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bioRxiv2018

Different molecular signatures in lung cancer types from integrative bioinformatic analyses of RNASeq data

BackgroundGenomic initiatives such as The Cancer Genome Atlas (TCGA) project contain data on profiling of thousands of tumors with different-omics approaches, providing a valuable source of information which may be used to decipher cancer signaling and related alterations. Managing and analyzing data from large-scale projects such as TCGA is a demanding task. Indeed, it is difficult to dissect the high complexity hidden in genomic data and to adequately account for tumor heterogeneity.\n\nResultsIn this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patient samples from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC). In particular, we used RNA-Seq gene expression data to identify both co-expression modules and differentially expressed genes to accurately discriminate between LUAD and LUSC. Moreover, we identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, as well as two dual role genes. Our results have also been cross-validated against other transcriptomics data of lung cancer patients.\n\nConclusionsOur integrative approach allowed to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC is linked to the activation of three specific oncogenic pathways which promote the evasion of antitumor immune response, providing new future directions for the design of target therapies.

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bioRxiv2019

Identification of key genes and pathways associated with Crohn's disease by bioinformatics analysis

Crohns disease is a type of inflammatory bowel disease posing a significant threat to human health all over the world. Genome-wide gene expression profiles of mucosal colonic biopsies have provided some insight into the molecular mechanisms of Crohns disease. However, the exact pathogenesis is unclear. This study aimed to identify key genes and significant signaling pathways associated with Crohns disease by bioinformatics analysis. To identify key genes, an integrated analysis of gene expression signature was conducted using a robust rank aggregation approach. A total of 179 Crohns disease patients and 94 healthy controls from nine public microarray datasets were included. MMP1 and CLDN8 were two key genes screened from the differentially expressed genes. Connectivity Map predicted several small molecules as possible adjuvant drugs to treat CD. Besides, we used weighted gene co-expression network analysis to explore the co-expression modules associated with Crohns disease pathogenesis. Seven main functional modules were identified, of which black module showed the highest correlation with Crohns disease. The genes in black module mainly enriched in Interferon Signaling and defense response to virus. Blue module was another important module and enriched in several signaling pathways, including extracellular matrix organization, inflammatory response and blood vessel development. There were also several other meaningful functional modules which enriched in many biological processes. The present study identified a number of key genes and pathways correlated with Crohns disease and potential drugs to combat it, which might offer insights into Crohns disease pathogenesis and provide a clue to potential treatments.

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bioRxiv2019

Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

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