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bioRxiv2024

Maloja: simple and scalable Snakemake workflow orchestration in the cloud

As sequencing technologies have matured, bioinformatics tasks have become more complex, computationally demanding, and data intensive. Workflow management software has been developed to aid in simplifying the replicable chaining of complex bioinformatics jobs, and cloud computing has emerged as a potential solution to the computational demands of this work. However, the capacity to effectively deploy these resources is limited by the expertise required to implement these solutions. Here, we develop Maloja, an easily deployed cloud workflow orchestrator. This tool interprets existing scientific workflows written in Snakemake and deploys them in appropriately scaled AWS cloud resources. We test the utility of this new toolset using previously published and custom built Snakemake workflows for ecological genomics tasks, revealing how this tool can facilitate the use of cloud resources without prior cloud architecture expertise.

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bioRxiv2024

The discovery of phages in the Substantia Nigra and its implication for Parkinson's Disease

BackgroundA century ago, a mystery between virus and Parkinsons disease (PD) was described. Owing to the limitation of human brain biopsy and the challenge of electron microscopy in observing virions in human brain tissue, it has been difficult to study the viral etiology of PD. Recent discovery of virobiota reveals that viruses coexist with humans as symbionts. Newly-developed transcriptomic sequencing and novel bioinformatic approaches for mining the encrypted virome in human transcriptome make it possible to study the relationship between symbiotic viruses and PD. Nevertheless, whether viruses exist in the human substantial nigra (SN), and whether symbiotic viruses underlie PD pathogenesis remain unknown. MethodsWe collected current worldwide human SN transcriptomic datasets from the United States, the United Kingdom, the Netherlands and Switzerland. We used bioinformatic approaches including viruSITE and the Virus-Track to identify the existence of viruses in the SN of patients. The comprehensive RNA sequencing-based virome analysis pipeline was used to characterize the virobiota in the SN. The Pearsons correlation analysis was used to examine the association between the viral RNA fragment counts (VRFC) and PD-related human gene sequencing reads in the SN. The differentially expressed genes (DEGs) in the SN between PD patients and non-PD individuals were used to examine the molecular signatures of PD and also evaluate the impact of symbiotic viruses on the SN. FindingsWe observed the existence of viruses in the human SN. A dysbiosis of virobiota was found in the SN of PD patients. A significant correlation between VRFC and PD-related human gene expression was detected in the SN of PD patients. These PD-related human genes correlated to VRFC were named as the virus-correlated PD-related genes (VPGs). We identified three bacteriophages (phages), including the Proteus phage VB_PmiS-Isfahan, the Escherichia phage phiX174 and the Lactobacillus phage Sha1, that might impair the gene expression of neural cells in the SN of PD patients. The Proteus phage VB_PmiS-Isfahan was a common virus in the SN of patients from the UK, the Netherlands, and Switzerland. VPGs and DEGs together highlighted that the phages might dampen dopamine biosynthesis and weaken cGAS-STING function. InterpretationThis is the first study to discover the involvement of phages in PD pathogenesis. A life-long low symbiotic viral load in the SN may be a contributor to PD pathogenesis. Our findings unlocked the black box between brain virobiota and PD, providing a novel insight into PD etiology from the perspective of phages-human symbiosis.

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bioRxiv2024

VEHoP: A Versatile, Easy-to-use, and Homology-based Phylogenomic pipeline accommodating diverse sequences

Phylogenomics has emerged as a transformative approach in systematics, conservation biology, and biomedicine, enabling the inference of evolutionary relationships by leveraging hundreds to thousands of genes from genomic or transcriptomic data. However, acquiring high-quality genomes and transcriptomes necessitates samples with intact DNA and RNA, substantial sequencing investments, and extensive bioinformatic processing, such as genome/transcriptome assembly and annotation. This challenge is particularly pronounced for rare or difficult-to-collect species, such as those inhabiting the deep sea, where only fragmented DNA reads are often available due to environmental degradation or suboptimal preservation conditions. To address these limitations, we introduce VEHoP (Versatile, Easy-to-use Homology-based Phylogenomic pipeline), a tool designed to infer protein-coding regions from diverse inputs, including raw reads (short and long), draft genomes, transcriptomes, and annotated genomes. VEHoP automates the generation of orthologous sequence alignments, concatenated matrices, and phylogenetic trees, streamlining phylogenomic analyses for researchers across disciplines. The tool aims to (1) expand taxonomic sampling by accommodating a wide range of input data types and (2) simplify phylogenomic workflows, making them accessible to researchers with varying levels of bioinformatic expertise. We evaluated VEHoPs performance using datasets from oysters, catfish, and insects, demonstrating its ability to produce robust phylogenetic trees with strong bootstrap support, outperforming assembly-free methods. Additionally, we applied VEHoP to reconstruct the phylogeny of the enigmatic deep-sea gastropod order Neomphalida, successfully resolving a well-supported phylogenetic backbone for this poorly understood group. VEHoP is freely available on GitHub (https://github.com/ylify/VEHoP), with dependencies easily installable via Bioconda.

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bioRxiv2024

A novel method for integrating genomic and Tn-Seq data to identify common in vivo fitness mechanisms across multiple bacterial species

Sepsis is life-threatening organ dysfunction due to an unregulated immune response to infection. Bacteremia is a leading cause of sepsis, and members of the Enterobacterales cause nearly half of bacteremia cases annually. While previous Tn-Seq studies to identify novel bacteremia-fitness genes have provided valuable insight into virulence mechanisms, evidence for common pathways across species is lacking. To identify common fitness pathways in five bacteremia- caused Enterobacterales species, we utilized the JCVI pan-genome pipeline to integrate Tn-Seq fitness data with multiple available functional data types. Core genes from species pan-genomes were used to construct a multi-species core pan-genome, producing 2,850 core gene clusters found in four out of the five species. Integration of Tn-Seq fitness data enabled identification of 373 protein clusters that were conserved in all five species. A scoring rubric and filter was applied to these clusters, which incorporated Tn-Seq fitness defects, operon localization, and antibiotic susceptibility data, which reduced the number of bacteremia-fitness genes and identified seven common fitness mechanisms. Independent mutational validation of one prioritized fitness gene, tatC, showed reduced fitness in vivo and increased susceptibility to beta- lactams that were restored following tatC complementation in trans. By integrating known operon structures and antibiotic susceptibility with Tn-Seq fitness data, common genes within the core pan-genome emerged and revealed mechanisms that are essential for colonization of, or survival in, the mammalian bloodstream. Our prediction and validation of tatC as a common bacteremia fitness factor and contributor of antibiotic resistance supports the utility of this bioinformatic approach. This study represents a major step forward to prioritize potentially novel targets for therapy against these deadly widespread sepsis infections. Author SummaryBacteremia is a leading cause of sepsis, a life-threatening condition where an unregulated immune response to infection causes systemic organ failure. Nearly half of bacteremia cases are caused by members of the Gram-negative bacterial taxonomic order Enterobacterales. Given the public health impact of bacteremia and the reduction of existing antibiotic treatment options, novel strategies are needed to combat these infections. Pan-genome software was used to predict seven shared fitness pathways in these bacteria that may serve as novel targets for treatment of bacteremia. Briefly, a scoring rubric was applied to shared pan-genome clusters, which incorporated multiple data types, including Tn-Seq fitness defects, operon localization, and antibiotic susceptibility data to rank and prioritize fitness genes. To validate one of our predictions, mutations were constructed in tatC, which showed both reduced fitness in mice and increased susceptibility to beta-lactam antibiotics; complementation restored fitness and antibiotic susceptibility to wild type levels. This study takes a novel bioinformatics approach to build a core pan-genome across multiple distantly related bacteria to integrate computational and experimental data to predict important shared fitness genes and represents a major step forward toward identifying novel targets of therapy against these deadly widespread life-threatening infections.

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bioRxiv2024

A survey of ADP-ribosyltransferase families in the pathogenic Legionella

BackgroundADP-ribosyltransferases (ARTs) are a superfamily of enzymes implicated in various cellular processes, including pathogenic mechanisms. The Legionella genus, known for causing Legionnaires disease, possesses diverse ART-like effectors. This study explores the proteomes of 41 Legionella species to bioinformatically identify and characterise novel ART-like families, providing insights into their potential roles in pathogenesis and host interactions. MethodsWe conducted a comprehensive bioinformatic survey of 41 Legionella species to identify proteins with significant sequence or structural similarity to known ARTs. Sensitive sequence searches were performed to detect candidate ART-like families. Subsequent validation, including structure prediction of such families, was achieved using artificial intelligence-driven tools, such as AlphaFold. Comparative analyses were performed to assess sequence and structural similarities between the novel ART-like families and known ARTs. ResultsOur analysis identified 63 proteins with convincing similarity to ARTs, organised into 39 ART-like families, including 26 novel families. Key findings include: O_LIDUF2971 family: exhibits sequence similarity to DarT toxins and other DNA-acting ARTs. C_LIO_LIDUF4291 family: the largest newly identified family shows structural and sequence similarity to the diphtheria toxin, suggesting the ability to modify proteins. C_LI Most members of the novel ART families are predicted effectors. Although experimental validation of the predicted ART effector functions is necessary, the novel ART-like families identified present promising targets for understanding Legionella pathogenicity and developing therapeutic strategies. We publish a complete catalogue of our results in the astARTe database: http://bioinfo.sggw.edu.pl/astarte/.

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bioRxiv2024

IDENTIFICATION OF IMMUNE RESPONSE AND RNA NETWORK OF RHEUMATOID ARTHRITIS AND MOLECULAR DOCKING OF CELASTRUS PANICULATUS AS POTENTIAL THERAPEUTIC AGENT

BackgroundRheumatoid Arthritis (RA) is an acute autoimmune disease leading to critical joint damage and bone destruction, weakening extra-articular organs over time. The pathogenesis of RA is complex and still undiscovered. This study aims to identify immune response, microRNA-hub genes network (miRNA), and drug candidates against RA via bioinformatics analysis. MethodologyThree Gene Expression Omnibus (GEO) datasets were obtained from the NCBI database and classified into upregulated and downregulated differentially expressed genes (DEGs) using GEO2R tool. Gene enrichment analysis, protein-protein interaction network analysis, top 10 hub genes identification, miRNA-hub genes network analysis, and immune response identification were performed using various bioinformatic tools. Moreover, Celastrus paniculatus phytochemical compounds were retrieved and subjected to autodocking with upregulated and downregulated hub genes that are closely associated with RA. The drug-likeness and PreADMET analysis were performed. ResultsGSE30662, GSE766, GSE72100 datasets revealed 243 upregulated DEGs and 285 downregulated DEGs which exhibited RPS27A, UBB, UBC, UBA52, PSMD4, PSMD1, PSMD7, PSMB7, PSMD8, PSMA7 as top 10 upregulated hub genes and ACTB, TP53, AKT1, GAPDH, CTNNB1, EGFR, TNF, IL6, MYC, ANXA5 as top 10 downregulated hub genes. The miRNA network disclosed hsa-mir-23b-3p, as highly associated with upregulated hub genes whereas hsa-mir-34a-5p and hsa-mir-155-5p with downregulated hub genes. Additionally, immune responses of specific hub genes of RA were revealed while the docking analysis showed oleic acid and zeylasterone as novel drug candidates against RA. ConclusionThus, hsa-mir-23b-3p, hsa-mir-34a-5p, and hsa-mir-155-5p can serve as therapeutic targets of RA while oleic acid and zeylasterone become potential drug candidates against RA.

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bioRxiv2024

GENOMICON-Seq: A comprehensive tool for the simulation of mutations in amplicon and whole exome sequencing

GENOMICON-Seq is a comprehensive genomic sequencing simulation tool that enables the assessment of laboratory and bioinformatics parameters influencing the detection of mutations. The tool generates genomes with mutations, mimicking processes such as low-frequency mutations, APOBEC3 activity in viruses, somatic mutations and single base substitution (SBS) mutational signals. GENOMICON-Seq adds amplicon and whole exome sequencing biases and errors. It outputs sequencing reads compatible with mutation detection tools and a report on mutation origin (generated mutations and PCR errors), nucleotide context, and position. GENOMICON-Seq aids in the evaluation of bioinformatics tools and experimental designs, reducing the need for costly real-world sequencing experiments.

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bioRxiv2024

Classifying epithelial-mesenchymal transition states in single cell cancer data using large language models

Single-cell foundation models (scFMs) have been widely adopted for cell type annotation, yet their suitability for modelling cellular plasticity, where cells transition along continuous, context-dependent state trajectories, remains unclear. Here, we systematically benchmark state-of-the-art scFMs against conventional machine learning and bioinformatics approaches on epithelial-mesenchymal plasticity (EMP), a prototypical plastic cellular process. We show that naive fine-tuning of scFMs often fails to resolve intermediate states and is strongly influenced by tissue- and stimulus-specific signals. We propose a parameter-efficient dual-task adaptation strategy for EMP foundation models (EMP-FM) that combines discrete classification with pseudotime-guided regression, which improves cell state resolution in controlled settings (up to 85% AUROC), but remains sensitive to domain shifts. Across diverse in vitro and in vivo datasets, scFMs do not consistently outperform conventional methods, which often achieve comparable performance with lower complexity. Together, our results delineate both the potential and current limitations of scFMs for modelling cellular plasticity and support their complementary use alongside established bioinformatics approaches.

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bioRxiv2024

Bolero: a dedicated workflow to decipher Hepatitis B virus transcriptome from long-reads sequencing method coupled to 5RACE amplification of transcripts

Hepatitis B virus (HBV) represents a major health burden, as it affects close to 290 million people worldwide. Although prophylactic vaccines are available, current therapeutic compounds do not usually achieve HBV eradication due to the persistence of the covalently closed circular (ccc)DNA that serves as viral reservoir. Thus, novel biomarkers that reliably reflect intrahepatic cccDNA transcriptional activity would be highly relevant for the monitoring of infected individuals, as well as the evaluation of new treatments targeting HBV. In this context, the development of 5 rapid amplification of complementary DNA ends (5RACE) as a strategy to capture and amplify full-length HBV RNAs, coupled with long-read and full-length sequencing approaches (e.g., Oxford Nanopore Technology), has recently enabled the detailed characterization of these molecules. The analysis of such data requires a dedicated bioinformatics pipeline due to the highly condensed nature of the HBV genome, which is characterized by the production of multiple transcripts and spliced variants that overlap each other. Here, we present Bolero, a computational method and built-in workflow designed to handle HBV sequencing data and evaluate the relative expression of viral RNAs and their spliced variants. The analysis of HBV-infected cell lines demonstrates that our bioinformatics pipeline is efficient for the identification and quantification of individual HBV mRNAs. Thus, Bolero represents a useful tool to study cccDNA transcriptional activity and the heterogeneity of HBV RNA spliced variants. Author summaryTranscriptomic analyses have brought comprehensive insights in the mechanisms controlling gene expression. Moreover, with the recent advances in sequencing technologies and computational methods, researchers can nowadays not only quantify gene expression, but also study alternative splicing, polyadenylation, transcription initiation, and even rare phenomena such as distant gene fusions. However, conventional analysis tools still rely heavily on the assumption of linear genomes with minimal overlap between open reading frames, rendering them insufficient for studying complex viruses such as hepatitis B virus (HBV). Unlike typical linear genomes, HBV genome consists in a circular DNA molecule, which results in an extensive sequence overlap between its transcripts. To tackle these challenges, we developed an innovative approach coupling 5 rapid amplification of complementary DNA ends (5RACE) and long-read sequencing to comprehensively explore the HBV transcriptome. Furthermore, we developed Bolero, a computational method designed to handle the peculiarities of HBV sequencing data, which allows a detailed characterization of the HBV transcriptome.

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bioRxiv2024

Particular sequence characteristics induce bias in the detection of polymorphic transposable element insertions

Transposable elements (TEs) have an important role in genome evolution but are challenging for bioinformatics detection due to their repetitive nature and ability to move and replicate within genomes. New sequencing technologies now enable the characterization of nucleotide and structural variations within species. Among them, TE polymorphism is critical to identify as it may influence species adaptation or trigger diseases. Despite the development of numerous bioinformatic programs, identifying the most effective tool is challenging due to non-overlapping results and varying efficiency across studies. Benchmarking efforts have highlighted some of the limitations of these tools, often evaluated on either real or simulated data. However, real data may be incomplete or contain unannotated TEs, while simulated data may not accurately reflect real genomes. This study introduces a simulation method generating data based on real genomes to control all genomic parameters. Evaluating several TE polymorphic detection tools using data from Drosophila melanogaster and Arabidopsis thaliana, our study investigates factors like copy size, sequence divergence, and GC content that influence detection efficiency. Our results indicate that only a few programs perform satisfactorily and that all are sensitive to TE and genomic characteristics that may differ according to the species considered. Using Bos taurus population data as a case study to identify polymorphic LTR-retrotransposon insertions, we found low-frequency insertions particularly challenging to detect due to a high number of false positives. Increased sequencing coverage improved sensitivity but reduced precision. Our work underscores the importance of selecting appropriate tools and thresholds according to the specific research questions.

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bioRxiv2024

AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis

The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using of a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with to the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRRs set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.

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bioRxiv2024

scGNN+: Adapting ChatGPT for Seamless Tutorial and Code Optimization

Foundation models have transformed AI by leveraging large-scale data to efficiently perform diverse tasks, and their applications in bioinformatics are primarily focused on data-centric tasks like cell type annotation and gene expression analysis. However, their potential extends beyond data analysis, offering significant opportunities in software development and optimization, such as code refinement, tutorial generation, and advanced visualization. For example, models like OpenAI Codex can suggest optimized code snippets and generate well-documented, reproducible workflows, enhancing accessibility and reducing computational complexity. Despite these advantages, the use of foundation models for improving computational tool engineering in single-cell research remains underutilized. To address this gap, we developed scGNN+, a web-based platform that combines the power of graph neural networks with the capabilities of ChatGPT to enhance reproducibility, code optimization, and visualization. scGNN+ further simplifies the process for users by generating standardized, well-annotated code, making complex procedures more accessible to non-programmers. Additionally, ChatGPT integration allows users to create high-quality, customizable visualizations through natural language prompts, improving data interpretation and presentation. Ultimately, scGNN+ offers a user-friendly, reproducible, and optimized solution for single-cell research, leveraging the full potential of foundation models in bioinformatics. scGNN+ is publicly available at https://bmblx.bmi.osumc.edu/scgnn+.

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bioRxiv2024

Enhancing the annotation of small ORF-altering variants using MORFEE: introducing MORFEEdb, a comprehensive catalog of SNVs affecting upstream ORFs in human 5'UTRs

Non-canonical small Open Reading Frames (sORFs) are among main regulators of gene expression. The most studied ones are upstream ORFs (upORFs) located in the 5UTR of coding genes. Internal ORFs (intORFs) in the coding sequence and downstream ORFs (dORFs) in the 3UTR have received less attention. Different bioinformatics tools permit to predict single nucleotide variants (SNVs) altering upORFs, mainly those creating AUGs or deleting stop codons, but no tool predict variants altering non-canonical translation initiation sites and those altering intORFs or dORFs. We propose an upgrade of our MORFEE bioinformatics tool to identify SNVs that may alter all types of sORFs in coding transcripts from a VCF file. Moreover, we generate an exhaustive catalog, named MORFEEdb, reporting all possible SNVs altering existing upORFs or creating new ones in human transcripts and provide an R script for visualizing the results. MORFEEdb has been implemented in the public platform Mobidetails. Finally, the annotation of ClinVar variants with MORFEE reveals that more than 45% of UTR-SNVs can alter upORFs or dORFs. In conclusion, MORFEE and MORFEEdb have the potential to improve the molecular diagnosis of rare human diseases and to facilitate the identification of functional variants from genome-wide association studies of complex traits.

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bioRxiv2024

The open-closed mod-minimizer algorithm

Sampling algorithms that deterministically select a subset of k-mers are an important building block in bioinformatics applications. For example, they are used to index large textual collections, like DNA, and to compare sequences quickly. In such applications, a sampling algorithm is required to select one k-mer out of every window of w consecutive k-mers. The folklore and most used scheme is the random minimizer that selects the smallest k-mer in the window according to some random order. This scheme is remarkably simple and versatile, and has a density (expected fraction of selected k-mers) of 2/(w + 1). In practice, lower density leads to faster methods and smaller indexes, and it turns out that the random minimizer is not the best one can do. Indeed, some schemes are known to approach optimal density 1/w when k [->] {infty}, like the recently introduced mod-minimizer (Groot Koerkamp and Pibiri, WABI 2024). In this work, we study methods that achieve low density when k [≤] w. In this small-k regime, a practical method with provably better density than the random minimizer is the miniception (Zheng et al., Bioinformatics 2021). This method can be elegantly described as sampling the smallest closed sycnmer (Edgar, PeerJ 2021) in the window according to some random order. We show that extending the miniception to prefer sampling open syncmers yields much better density. This new method - the open-closed minimizer - offers improved density for small k [≤] w while being as fast to compute as the random minimizer. Compared to methods based on decycling sets, that achieve very low density in the small-k regime, our method has comparable density while being computationally simpler and intuitive. Furthermore, we extend the mod-minimizer to improve density of any scheme that works well for small k to also work well when k > w is large. We hence obtain the open-closed mod-minimizer, a practical method that improves over the mod-minimizer for all k.

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bioRxiv2024

Identifying Family Structures from Obituaries and Matching them to Patients in an Electronic Heath Record.

MotivationFamily data is a valuable data source in bioinformatic research. This is because family members often share common genetic and environmental exposures. Collecting this family data is traditionally very labor intensive but advances in electronic health record (EHR) data mining has proven useful when identifying pedigrees linked to longitudinal health histories. These are called e-pedigrees. Unfortunately, e-pedigrees tend to miss the oldest generations who inherently have the longest and richest health histories. A good source of family data from older generations includes obituaries, as they have a formulaic nature making them a good candidate for natural language processing that can extract relationships to the decedent. While there have been several studies on obtaining such data from obituaries, we demonstrate for the first-time approaches that tie that information to an EHR. ResultsNLP extraction resulted in 8,166,534 family members being abstracted from 567,279 obituaries published in the state of Wisconsin. After matching decedent and family members to patients in the EHR, we identified 109,365 unique patients that were put in 34,158 pedigrees. The largest pedigree consisted of 21 individuals. Heritability of adult height was quantified (H2= 0.51 +- .04, P=< 1.00e-07) demonstrating this datas use in genetic research. The heritability data, coupled with overlapping data in a biobank, suggested 80% - 90% of familial relationships were accurately defined. The totality of these findings demonstrate obituaries with the oldest generations can be highly informative for bioinformatic research. Availability and ImplementationCode is available on GitHub at https://github.com/jgmayer672/ObituaryNLP.

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bioRxiv2024

Integrated population clustering and genomic epidemiology with PopPIPE

Genetic distances between bacterial DNA sequences can be used to cluster populations into closely related subpopulations, and as an additional source of information when detecting possible transmission events. Due to their variable gene content and order, reference-free methods offer more sensitive detection of genetic differences, especially among closely related samples found in outbreaks. However, across longer genetic distances, frequent recombination can make calculation and interpretation of these differences more challenging, requiring significant bioinformatic expertise and manual intervention during the analysis process. Here we present a Population analysis PIPEline (PopPIPE) which combines rapid reference-free genome analysis methods to analyse bacterial genomes across these two scales, splitting whole populations into subclusters and detecting plausible transmission events within closely related clusters. We use k-mer sketching to split populations into strains, followed by split k-mer analysis and recombination removal to create alignments and subclusters within these strains. We first show that this approach creates high quality subclusters on a population-wide dataset of Streptococcus pneumoniae. When applied to nosocomial vancomycin resistant Enterococcus faecium samples, PopPIPE finds transmission clusters which are more epidemiologically plausible than core genome or MLST-based approaches. Our pipeline is rapid and reproducible, creates interactive visualisations, and can easily be reconfigured and re-run on new datasets. Therefore PopPIPE provides a user-friendly pipeline for analyses spanning species-wide clustering to outbreak investigations. Impact statementAs time passes, bacterial genomes accumulate small changes in their sequence due to mutations, or larger changes in their content due to horizontal gene transfer. Using their genome sequences, it is possible to use phylogenetics to work out the most likely order in which these changes happened, and how long they took to happen. Then, one can estimate the time that separates any two bacterial samples - if it is short then they may have been directly transmitted or acquired from the same source; but if it is long they must have been acquired separately. This information can be used to determine transmission chains, in conjunction with dates and locations of infections. Understanding transmission chains enables targeted infection control measures. However, correctly calculating the genetic evidence for transmission is made difficult by correctly distinguishing different types of sequence changes, dealing with large amounts of genome data, and the need to use multiple complex bioinformatic tools. We addressed this gap by creating a computational workflow, PopPIPE, which automates the process of detecting possible transmissions using genome sequences. PopPIPE applies state-of-the-art tools and is fast and easy to run - making this technology will be available to a wider audience of researchers. Data summaryThe code for this pipeline is available at https://github.com/bacpop/PopPIPE and as a docker image https://hub.docker.com/r/poppunk/poppipe. Raw sequencing reads for Enterococcus faecium isolates have been deposited at the NCBI under BioProject accession number PRJNA997588.

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bioRxiv2024

PathoFact 2.0: An Integrative Pipeline for Predicting Antimicrobial Resistance Genes, Virulence Factors, Toxins and Biosynthetic Gene Clusters in Metagenomes

SummaryAntimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases. Availability and ImplementationPathoFact 2.0 is available for download at https://gitlab.lcsb.uni.lu/ESB/PathoFact2/.

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bioRxiv2024

AmpliPiper: A versatile amplicon-seq analysis tool for multilocus DNA barcoding

The advent of third generation sequencing technology has revolutionized parallelized sequencing of DNA fragments of varying lengths, such as PCR amplicons, which provides unprecedented new opportunities for large-scale and diverse DNA barcoding projects that, for example, aim to quantify the accelerating biodiversity crisis. However, the broad-scale application of these new technologies for biodiversity research is often hindered by the demand for advanced bioinformatics skills to carry out quantitative analyses. To facilitate the application of multilocus amplicon sequencing (amplicon-seq) data for biodiversity and integrative taxonomic research questions, we present AmpliPiper, an automated and user-friendly software pipeline which carries out bioinformatics analyses of multilocus amplicon-seq data generated with Oxford Nanopore (ONT) sequencing. AmpliPiper combines analysis methods for DNA barcoding data that include demultiplexing of pooled amplicon-seq data, haplotype-specific consensus sequence reconstruction, species identification based on comparison to the BOLD and GenBank databases, phylogenetic analyses and species delimitation. We demonstrate the applicability and workflow of our approach based on a newly generated dataset of 14 hoverfly (Syrphidae) samples that were amplified and sequenced at four marker genes. We further benchmark our approach with Sanger sequencing and simulated amplicon-seq data which show that DNA barcoding with ONT is both accurate and sensitive to detect even subtle genetic variation.

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