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Bioinformatics

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bioRxiv2024

Bioinformatics Copilot 1.0: A Large Language Model-powered Software for the Analysis of Transcriptomic Data

The field of single-cell transcriptomics has been producing extensive datasets, advancing our understanding of cellular functions in various tissues, and empowering diagnosis, prognosis, and drug development. However, parsing through this data has been a monumental task, often stretching weeks to months. This bottleneck arises due to the sheer volume of data generated--ranging from hundreds of gigabytes to tens of terabytes--that demands extensive time for analysis. Moreover, the data analysis involves an intricate series of steps utilizing various software packages, creating a steep learning curve for biologists. Additionally, the iterative nature of data analysis in this domain necessitates a deep biological insight to formulate relevant questions, conduct analysis, interpret results, and refine hypotheses. This iterative loop has required close collaboration between biologists and bioinformaticians, which is hampered by protracted communication cycles. To address these challenges, we present a large language model-powered software, Bioinformatics Copilot 1.0. It allows users to analyze data through an intuitive natural language interface, without requiring proficiency in programming languages such as Python or R. It is engineered for cross-platform functionality, with support for Mac, Windows, and Linux. Importantly, it facilitates local data analysis, ensuring adherence to stringent data management regulations that govern the use of patient samples in medical and research institutions. We anticipate that this tool will expedite the data analysis workflow in numerous research endeavors, thereby accelerating advancements in the biomedical sciences.

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bioRxiv2024

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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bioRxiv2024

Empowering bioinformatics communities with Nextflow and nf-core

Standardised analysis pipelines are an important part of FAIR bioinformatics research. Over the last decade, there has been a notable shift from point-and-click pipeline solutions such as Galaxy towards command-line solutions such as Nextflow and Snakemake. We report on recent developments in the nf-core and Nextflow frameworks that have led to widespread adoption across many scientific communities. We describe how adopting nf-core standards enables faster development, improved interoperability, and collaboration with the >8,000 members of the nf-core community. The recent development of Nextflow Domain-Specific Language 2 (DSL2) allows pipeline components to be shared and combined across projects. The nf-core community has harnessed this with a library of modules and subworkflows that can be integrated into any Nextflow pipeline, enabling research communities to progressively transition to nf-core best practices. We present a case study of nf-core adoption by six European research consortia, grouped under the EuroFAANG umbrella and dedicated to farmed animal genomics. We believe that the process outlined in this report can inspire many large consortia to seek harmonisation of their data analysis procedures.

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bioRxiv2024

sangerFlow, a Sanger sequencing-based bioinformatics pipeline for pests and pathogens identification

Sequencing of a Polymerase Chain Reaction product (amplicon) is called amplicon sequencing. Amplicon sequencing allows for reliable identification of an organism by amplifying, sequencing, and analysing a single conserved marker gene or DNA barcode. As this approach generally involves a single gene, it is a light-weight protocol compared to multi-locus or whole genome sequencing for diagnostic purposes; yet considerably reliable. Therefore, Sanger-based high-quality amplicon sequencing is widely deployed for species identification and high-throughput biosecurity surveillance. However, keeping up with the data analysis in a large-scale surveillance or diagnostic settings could be a limiting factor because it involves manual quality control of the raw sequencing data, alignment of the forward and reverse reads, and finally web-based Blastn search of all the amplicons. Here, we present a bioinformatics pipeline that automates the entire analysis. As a result, the pipeline is scalable with high-volume of samples and reproducible. Furthermore, the pipeline leverages the modern open-source Nextflow and Singularity concept, thus it does not require software installation except Nextflow and Singularity, software subscription, or programming expertise from the end users making it widely adaptable. Availability and implementationsangerFlow source code and documentation are freely available for download at GitHub, implemented in Nextflow and Singularity.

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bioRxiv2024

Identifying Representative Cell Line Models for TNBC Chemotherapy Drug Resistance via Systems Biology and Bioinformatics

Cancer cell lines, derived from tumors, have become essential tools in life science research and are commonly employed as experimental model systems in cancer research. However, researchers often overlook the similarities between clinical samples when selecting cell line models, potentially impacting the validity and applicability of their findings. In the context of triple-negative breast cancer (TNBC) chemotherapy drug resistance, our study aims to provide guidance for selecting appropriate cell line models by employing a combination of systems biology and bioinformatic approaches. These approaches, including hierarchical clustering analysis, Spearmans rank correlation, and single sample gene set enrichment analysis (ssGSEA), allowed us to identify the most representative cell models that correspond to poor chemotherapy responders among TNBC patients.

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bioRxiv2024

Playbook Workflow Builder: Interactive Construction of Bioinformatics Workflows from a Network of Microservices

Many biomedical research projects produce large-scale datasets that may serve as resources for the research community for hypothesis generation, facilitating diverse use cases. Towards the goal of developing infrastructure to support the findability, accessibility, interoperability, and reusability (FAIR) of biomedical digital objects and maximally extracting knowledge from data, complex queries that span across data and tools from multiple resources are currently not easily possible. By utilizing existing FAIR application programming interfaces (APIs) that serve knowledge from many repositories and bioinformatics tools, different types of complex queries and workflows can be created by using these APIs together. The Playbook Workflow Builder (PWB) is a web-based platform that facilitates interactive construction of workflows by enabling users to utilize an ever-growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem. Via a user-friendly web-based user interface (UI), workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflows are provided in reports containing textual descriptions, as well as interactive and downloadable figures and tables. To demonstrate the ability of the PWB to generate meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases sieves novel targets for individual cancer patients using data from the GTEx, LINCS, Metabolomics, GlyGen, and the ExRNA Communication Consortium (ERCC) Common Fund (CF) Data Coordination Centers (DCCs). The workflows created with the PWB can be published and repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

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bioRxiv2024

BiomiX, a User-Friendly Bioinformatic Tool for Automatized Multiomics Data Analysis and Integration

BiomiX addresses the data analysis bottleneck in high-throughput omics technologies, enabling the efficient, integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data. DESeq2/Limma packages analyze transcriptomics data, while statistical tests determine metabolomics peaks. The metabolomics annotation uses the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package while Methylomics analysis is performed using the ChAMP R package. Multiomics Factor Analysis (MOFA) integration and interpretation identifies common sources of variations among omics. BiomiX provides comprehensive outputs, including statistics and report figures, also integrating EnrichR and GSEA for biological process exploration. Subgroup analysis based on user gene panels enhances comparisons. BiomiX implements MOFA automatically, selecting the optimal MOFA model to discriminate the two cohorts being compared while providing interpretation tools for the discriminant MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. The interpretation is also supported by clinical data correlation with the discriminant MOFA factors and pathways analyses of the top factor contributors. The integration of single and multi-omics analysis in a standalone tool, together with the implementation of MOFA and its interpretability by literature, constitute a step forward in the multi-omics landscape in line with the FAIR data principles. The wide parameter choice grants a personalized analysis at each level based on the user requirements. BiomiX is a user-friendly R-based tool compatible with various operating systems that aims to democratize multiomics analysis for bioinformatics non-experts. Key pointsO_LIBiomiX is the first user-friendly multiomics tool to perform single omics analysis for transcriptomics, metabolomics and methylomics and their data integration by MOFA in the same platform. C_LIO_LIMOFA algorithm was made accessible to non-bioinformaticians and improved to select the best model automatically, testing the MOFA factors performance in groups separation. C_LIO_LILarge improvement of MOFA factors interpretability by correlation, pathways analysis and innovative bibliography research. C_LIO_LIBiomiX is embedded in a network of other online tools as GSEA, metaboanalyst EnrichR etc, to provide a format compatible with further analyses in these tools. C_LIO_LIInterface and usage are intuitive and compatible with all the main operating systems, and rich parameters are set to grant personalized analysis based on the users needs. C_LI

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bioRxiv2024

Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud.

We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based deployment demonstrates several key advantages over alternative strategies: it enables optimal resource provision to any workflow that comprise of multiple applications with diverse computing needs; it provides protection against application-agnostic out-of-memory (OOM) errors or spot instance interruptions; and it reduces efforts required for workflow development, optimization, and management because it runs workflows with minimal or no code modifications. Proof-of-concept experiments show that ACS avoided both under- and over-provisioning in monolithic single-container deployment. Despite being deployed as a single container, it achieved comparable resource utilization efficiency as optimized Nextflow-managed, multi-modular workflows. Analysis of over 18,000 workflow runs demonstrated that ACS can effectively reduce workflow failures by two-thirds. These findings suggest that ACS frees developers from navigating the complexity of deploying robust workflows and rightsizing compute resources in the cloud, leading to significant reduction in workflow development time and savings in cloud computing costs.

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bioRxiv2024

The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

Cardiac arrest (CA) is a common cause of death world wide. The disease has lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of CA, but the molecular mechanisms remain elusive. To identify key genes and pathways in CA, the next generation sequencing (NGS) GSE200117 dataset was downloaded from the Gene Expression Omnibus (GEO) database. DESeq2 tool was used to recognize differentially expressed genes (DEGs). Gene ontology (GO) and REACTOME pathway enrichment analyses were performed to analyze the DEGs and associated signal pathways in the g:Profiler database. The IID database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were then constructed to screen miRNAs, TFs and hub genes by miRNet and NetworkAnalyst database and Cityscape software. Receiver operating characteristic curve (ROC) analysis used to verified the hub genes. In total, 844 DEGs were identified, comprising 414 up regulated genes and 430 down regulated genes. GO and REACTOME pathway enrichment analyses indicated that the DEGs for CA were mainly enriched in organonitrogen compound metabolic process, response to stimulus, translation and immune system. Ten hub genes (up-regulated: HSPA8, HOXA1, INCA1 and TP53; down-regulated: HSPB1, LMNA, SNCA, ADAMTSL4 and PDLIM7) were screened. We also predicted miRNAs (hsa-mir-1914-5p and hsa-mir-598-3p) and TFs (JUN and PRRX2) targeting hub genes. This study uses a series of bioinformatics technologies to obtain hug genes, miRNAs and TFs, and key pathways related to CA. These analysis results provide us with new ideas for finding biomarkers and treatment methods for CA.

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bioRxiv2024

Utilizing Protein Bioinformatics to Delve Deeper Into Immunopeptidomic Datasets

Immunopeptidomics is a growing subfield of proteomics that has the potential to shed new light on a long-neglected aspect of adaptive immunology: a comprehensive understanding of the peptides presented by major histocompatibility complexes (MHC) to T cells. As the field of immunopeptidomics continues to grow and mature, a parallel expansion in the methods for extracting quantitative features of these peptides is necessary. Currently, massive experimental efforts to isolate a given immunopeptidome are summarized in tables and pie charts, or worse, entirely thrown out in favor of singular peptides of interest. Ideally, an unbiased approach would dive deeper into these large proteomic datasets, identifying sequence-level biochemical signatures inherent to each individual dataset and the given immunological niche. This chapter will outline the steps for a powerful approach to such analysis, utilizing the Automated Immune Molecule Separator (AIMS) software for the characterization of immunopeptidomic datasets. AIMS is a flexible tool for the identification of biophysical signatures in peptidomic datasets, the elucidation of nuanced differences in repertoires collected across tissues or experimental conditions, and the generation of machine learning models for future applications to classification problems. In learning to use AIMS, readers of this chapter will receive a broad introduction to the field of protein bioinformatics and its utility in the analysis of immunopeptidomic datasets and other large-scale immune repertoire datasets.

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bioRxiv2024

dScaff - an automatic bioinformatics framework for scaffolding draft de novo assemblies based on reference genome data

Rapid evolution of long-read sequencing technologies requires accurate, fast and user-friendly genome assembly and scaffolding tools. In this article we present Digital Scaffolding (dScaff), a bioinformatics tool that performs scaffolding of de novo genome assemblies based on reference scaffolds or chromosomes. dScaff makes use of a series of bash and R scripts in order to create a minimal complete scaffold from a genome assembly, with future features to be implemented. Herein, we demonstrate the functionality of dScaff on a novel genome assembly pertaining to a recently sequenced local strain of Drosophila suzukii.

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bioRxiv2024

Identification and Diagnostic Potential of Pyroptosis-Related Genes in Endometriosis: A Novel Bioinformatics Analysis

ObjectiveThis study aimed to identify and analyze potential signatures of pyroptosis-related genes in EMs. MethodsTranscriptomic datasets related to endometriosis were retrieved from the GEO databases (GSE7305, GSE7307, and GSE11691). Differential gene expression analysis was performed to identify pyroptosis-related differentially expressed genes (PRDEGs) by intersecting DEGs with a curated list of PRGs. Various bioinformatics tools were employed to explore the biological functions and pathways associated with PRDEGs. ResultsWe identified 26 PRDEGs from combined datasets and constructed an EMs diagnostic model using LASSO regression based on pyroptosis scores. The model included 5 DEGs: KIF13B, BAG6, MYO5A, HEATR, and AK055981. Additionally, 21 Key Module Genes (KMGs) were identified, leading to the classification of 3 distinct EMs subtypes. These subtypes were analyzed for immune cell infiltration, revealing a complex immune landscape in EMs. ConclusionsThis study reveals pyroptosis crucial role in EMs and offers a novel diagnostic model based on pyroptosis-related genes. Modulating pyroptosis may provide a new therapeutic approach for managing EMs.

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bioRxiv2024

SAMURAI: Shallow Analysis of Copy nuMber alterations Using a Reproducible And Integrated bioinformatics pipeline

Shallow whole-genome sequencing (sWGS) offers a cost-effective approach to detect copy number alterations (CNAs). However, there remains a gap for a standardized workflow specifically designed for sWGS analysis. To address this need, in this work we present SAMURAI a bioinformatics pipeline specifically designed for analyzing CNAs from sWGS data in a standardized and reproducible manner. SAMURAI is built using established community standards, ensuring portability, scalability, and reproducibility. The pipeline features a modular design with independent blocks for data pre-processing, copy number analysis, and customized reporting. Users can select workflows tailored for either solid or liquid biopsy analysis (e.g., circulating tumor DNA), with specific tools integrated for each sample type. The final report generated by SAMURAI provides detailed results to facilitate data interpretation and potential downstream analyses.To demonstrate its robustness, SAMURAI was validated using simulated and real-world data sets. The pipeline achieved high concordance with ground truth data and maintained consistent performance across various scenarios. By promoting standardization and offering a versatile workflow, SAMURAI empowers researchers in diverse environments to reliably analyze CNAs from sWGS data. This, in turn, holds promise for advancements in precision medicine.

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bioRxiv2024

Integrating Bioinformatics and Artificial Intelligence Methods to identify disruptive STAT1 variants impacting Protein Stability and Function

The Signal Transducer and Activator of Transcription 1 (STAT1) gene is an essential component of the JAK-STAT signalling pathway. This pathway has a pivotal role in regulating different cellular processes, including immune responses, cell growth, and apoptosis. Mutations in the STAT1 gene contribute to various body pathologies [OMIM #600555], including immune system dysfunction. In the current study, we used eighteen online computational approaches. Six pathogenic variants (R602W, I648T, V642D, L600P, I578N, and W504C) were predicted to significantly disrupt protein stability and function. These findings highlight the potential of approaches to pinpoint pathogenic single nucleotide polymorphisms, providing a time and cost effective alternative to experimental approaches. Up to the best of our knowledge, this is the original inclusive study, where we analyze STAT1 gene variants using both bioinformatics and artificial intelligence based model tools.

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bioRxiv2024

Bioinformatics Analysis Suggests That SE_1780 protein from Staphylococcus epidermidis may be a member of the Fph Family of Lipases

The Protein Databank entry for protein SE_1780 from Staphylococcus epidermidis lists the function as unknown. We leveraged the framework outlined in the Biochemistry Authentic Scientific Inquiry Laboratory and used bioinformatics tools to ascertain the function of the protein. Based on our analysis we posit that SE_1780 is a lipase of the /{beta} hydrolase family with a proposed active site catalytic triad composed of Ser144, Asp235, and His269. Further we identified the lipase FphD as having significant sequence identity to protein SE 1780, and suggest that the protein is a member of the Fph family of lipases from S. epidermidis.

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bioRxiv2024

Combined Linkage Disequilibrium and Linkage Analysis (cLDLA): implementation of a powerful approach to identify the genetic basis of complex traits in a bioinformatics workflow.

Identifying the relationship between the polymorphism segregating in a population and phenotypic differences of a trait observed between the individuals of a population is of major biological interest and represents the basis of forward genetics. Much of the traits of interest are influenced by several polymorphic genes and environmental conditions. Often the loci associated with such measurable traits are referred to as Quantitative trait loci. These loci are identified using several statistical approaches. One of them is combined linkage disequilibrium and linkage analysis (cLDLA). This approach, first proposed by Meuwissen and colleagues in 2002, is shown to be robust against population stratification/family structure and requires a relatively lower sample size compared to a genome-wide association study design. Previously, we have successfully used this approach in mapping several important traits in livestock such as identifying the genetic basis of polled condition in cattle and tail length in sheep. A cLDLA requires several complex computation processing and intermediary file conversion steps; for some of these steps no open-source tools are available. Therefore, running this analysis, manually, can prove challenging, tedious, or error-prone. We present, cldla, a bioinformatics workflow implemented in nextflow which takes the vcf file and phenotype file as inputs and implements all the downstream processing required for cLDLA. Additionally, it also has a separate workflow to estimate SNP-based heritability and features for interactive visualization of the results. The workflow is freely available at: https://github.com/Popgen48/cldla.

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bioRxiv2024

Squeakuences: a portable tool for formatting squeaky-clean sequences to eliminate bioinformatic software incompatibilities

Computational analysis of biological sequences is the cornerstone of modern bioinformatics research. Complex processing and interpretation of data often entails multi-step workflows. The specific requirements and limitations of individual applications can require laborious reformatting and piecemeal data-wrangling to produce a satisfactory input for each step in a pipeline. We present Squeakuences, a command line tool developed to simplify and automate FASTA file preparation for applications such as phylogenetics, gene annotation, and genome analysis. Implemented in a lightweight Python script, Squeakuences identifies and removes potentially problematic elements in sequence identifiers, such as non-alphanumeric characters, white space, and excessive character count. Squeakuences outputs a new clean version of the sequence file for analysis alongside metadata files to track changes. The user can customize Squeakuences behavior using optional arguments to meet individual processing and formatting requirements. We tested the performance of Squeakuences on molecular data from the human reference genome and found that runtime correlates with the number of sequences processed but not with file size. We expect Squeakuences to save time and manual effort when analyzing sequence data. Squeakuences code is freely available at https://github.com/EvanForsythe/Squeakuences.

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bioRxiv2024

Identification of Potential Hub Genes and Therapeutic Targets in Colorectal Cancer Using Integrated Bioinformatics Approaches

In this study, we took a comprehensive approach using bioinformatics to uncover potential therapeutic targets for colorectal cancer (CRC). We started by analyzing gene expression data from GEO2R to identify genes that were differentially expressed in CRC. Then, using FunRich software, we created Venn diagrams to visualize these genes. From the 191 upregulated genes we found, we focused on potential "hub genes" by looking at their network connections and strength, using the STRING database.To understand the roles of these hub genes, we performed functional analyses like Gene Ontology (GO) and pathway enrichment through the DAVID platform. This helped us pinpoint key biological processes and pathways linked to the genes we identified. We also looked at patient survival data from GEPIA, along with information on gene expression related to disease stages and metastatic progression. This helped us identify which hub genes were most relevant for CRC.In addition, we examined genetic changes and gene expression patterns in CRC patients through databases like cBioPortal and the Human Protein Atlas. This gave us more evidence supporting the involvement of these genes in the disease. Ultimately, our analysis highlighted CXCL8, FOXC1, ICOS, and MCF2 as potential hub genes with important roles in CRC. These genes may serve as useful biomarkers for both diagnosing CRC and predicting patient outcomes, and they could also help guide the development of targeted treatments to improve survival rates.

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