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bioRxiv2021

Design considerations for workflow management systems use in production genomics research and the clinic

BackgroundThe changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. ResultsThis work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, "which WfMS should be chosen for a given bioinformatics application regardless of analysis type?". ConclusionsThe choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.

bioinformatics↗

bioRxiv2022

Reference genome choice and filtering thresholds jointly influence phylogenomic analyses

AO_SCPLOWBSTRACTC_SCPLOWMolecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant-calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate to what extent the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find greatest topological accuracy when filtering sites for minor allele count >3-4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with minor allele count >1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short read genomic data for phylogenetic inference.

bioinformatics↗

bioRxiv2023

Bayesian phylogenetics on globally emerging SARS-CoV-2 variant BA.2.86 suggest global distribution and rapid evolution

Using bioinformatic pipelines and Bayseian phylogenetic analyses, we characterized a SARS-CoV-2 variant designated by the World Health Organization as a variant under monitoring in August 2023. Here we analyze the genomes of this SARS-CoV-2 variant, BA.2.86, deposited into GISAID within the two weeks of its emergence (2023-08-14 first submission to 2023-08-31), including the first BA.2.86 genome reported from a traveler originating from Japan. We present bioinformatics methods using publicly available tools to help analysts identify the lineage-defining 12 nucleotide insertion (S:Ins16MPLF), which is often masked by most bioinformatics pipelines. We also applied maximum-likelihood and Bayesian phylogenetics to demonstrate the high mutational rate of the tree branch leading to the emergence of BA.2.86, hinting at possible origins, and predict that BA.2.86 emerged around May 2023 and spread globally rapidly. Taken together, these results provide a framework for more rigorous bioinformatics approaches for teams performing genomic surveillance on viral respiratory pathogens.

bioinformatics↗

bioRxiv2024

Comparison of the performance of multiple whole-genome sequence-based tools for the identification of Bacillus cereus sensu stricto biovar Thuringiensis

The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins also known as Bt toxins or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for production of crystal proteins. A total of 58 diverse B. cereus s.l. strains isolated from clinical, food, environmental, and commercial biopesticide products were underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS, and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatic tools performed well overall, with IDOPS achieving both high sensitivity and specificity (>0.90). IMPORTANCEBacillus cereus s.s. biovar Thuringiensis (Bt) is used as an organic biopesticide. It is differentiated from the foodborne pathogen Bacillus cereus s.s. by the production of insecticidal crystal proteins. Thus, reliable genomic identification of biovar Thuringiensis is necessary to ensure food safety and facilitate risk assessment. This study assessed the accuracy of WGS-based identification of Bt compared to phenotypic microscopy-based screening for crystal protein production. Multiple bioinformatics tools were compared to assess their performance in predicting crystal protein production. Among them, IDOPS performed best overall at WGS- based Bt identification.

bioinformatics↗

bioRxiv2024

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying reference databases. Each of these tools generates a report of detected AMR genes or variants in a distinct, non-standard, format. This presents a huge barrier to the comparison of results and to the modularity of tools for AMR gene prediction within bioinformatic workflows. In collaboration with 17 public health laboratories across 10 countries, the Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) data structures working group has developed and piloted a standardized output specification for the bioinformatic detection of AMR from microbial genomes. In this report, we discuss hAMRonization, a python package and command-line utility, which implements PHA4GEs AMR specification to combine the outputs of disparate antimicrobial resistance gene detection tools into a single unified format. hAMRonization can be easily extended and currently supports 18 different tools (both species-agnostic and species-specific) for the detection of genes and/or variants conferring AMR. The harmonized reports are available in tabular form, JSON format or through an interactive HTML file (e.g., https://maguire-lab.github.io/assets/interactive_report_demo.html) that can be opened within the browser for navigable data exploration. As of 2024-03-07 hAMRonization has been downloaded [~]12,500 times, incorporated into >9 public bioinformatic tools and workflows, and been internally adopted by several national and international public health groups. The hAMRonization tool and underlying specification are open-source and freely available through PyPI, conda and GitHub (https://github.com/pha4ge/hAMRonization).

bioinformatics↗

bioRxiv2025

SoEM-Web: a user-friendly platform for the analysis and visualization of small-organelle-enriched metagenomics data

BackgroundMarine microeukaryotes play a crucial role in global biogeochemical cycles and ecosystem functioning. However, their diversity and distribution patterns remain poorly understood. To improve the biodiversity assessment of environmental samples, we recently developed a PCR-free, small-organelle-enriched metagenomics (SoEM) method, accompanied by a bioinformatics workflow for analyzing SoEM metagenomic data. However, the complexity of these analyses typically requires extensive bioinformatics expertise, potentially limiting their broader adoption. MethodsTo address this challenge, we present SoEM-Web, a user-friendly web application for the interactive analysis, classification, and visualization of SoEM metagenomic data. SoEM-Web integrates all necessary procedures, including raw sequencing data preprocessing, paired-end read merging, automated taxonomic identification, and diversity visualization, into a streamlined workflow accessible through an intuitive interface. This application is designed to facilitate SoEM data analysis for researchers who may not have advanced bioinformatics skills. ResultsWe demonstrated the capabilities of SoEM-Web by reanalyzing previously published datasets. This reanalysis not only confirmed the utility of the platform but also revealed additional taxa, emphasizing the value of updated reference datasets. The application successfully simplifies complex bioinformatics processes, enhancing accessibility and reproducibility in marine microeukaryote diversity research. SoEM-Web aims to enhance accessibility and reproducibility in marine microeukaryote diversity research. The web server is freely available at http://compsysbio.re.kr/soem-web/.

bioinformatics↗

bioRxiv2025

Eduomics: a Nextflow pipeline to simulate -omics data for education

Moving past learning just algorithms and code is a key challenge of bioinformatics education: the ideal goal is for students to acquire higher-order knowledge such as the ability to solve biological problems with the appropriate tools, and more importantly learn to interpret the results in the broader context where bioinformatics is needed. To design such a teaching and learning experience, data simulations play a key role: however, there is a massive barrier to adoption. Different data types are produced by different tools, requiring educators to learn each of them and adapt their workflow to the necessary dependencies, requirements and input files. Additionally, most existing data simulation solutions are meant for benchmarking and methods development rather than education and cannot provide the context needed to teach students the critical interpretation skills they need to move beyond problem-based learning to what we call storyline-based learning. A significant effort must be placed also when many datasets with the same characteristics are needed, such as in tutoring or assessment in higher education. Here, we present eduomics: a Nextflow pipeline meant to automate the simulation workflow for both genomic and transcriptomic next-generation sequencing data, and to produce realistic clinical scenarios to provide students with clues and a biomedical story necessary for the interpretation of their results. Eduomics removes barriers to adoption, by requiring the user to just decide which chromosome datasets should be simulated on, and which type of data they would like to simulate. There is no need to learn specific tools and resolve their dependencies. The use of Gemini API provides an innovative approach to generate plausible clinical scenarios, consistent with the genes where either a pathological mutation or differential expression has been simulated. With eduomics, we offer an accessible and scalable solution to design comprehensive learning experiences and innovate bioinformatics education. Author SummaryModern bioinformatics education faces a dual challenge: students must learn how to analyse data and interpret results within the biological problems they aim to solve, while instructors who wish to design such a comprehensive learning experience should introduce appropriately simulated data in their teaching. However, the complexity of existing simulation tools is an often daunting barrier to overcome: current solutions require different software for different data types and provide no support for teaching interpretation skills. Most available simulators were designed for benchmarking and method development, not for education: therefore they lack the biological and clinical context needed to move beyond the traditional problem-based learning. To address these challenges, we developed eduomics, a Nextflow-based end-to-end pipeline that automates the simulation of genomic and transcriptomic data in a fully automated manner for educational use. Educators only need to choose the type of data to simulate, while the pipeline handles all underlying tools and dependencies. As a groundbreaking element, we integrated the Google Gemini API to generate patient-inspired clinical scenarios that gives students realistic clues and a biomedical narrative in which to interpret the results. This approach complements the traditional problem-based learning by offering a more complete, storyline-based learning experience. By combining automated data simulation and clinical storytelling, eduomics provides an accessible and scalable solution that support an engaging and immersive learning experience for both educators and students.

bioinformatics↗

bioRxiv2019

BioSAILs: versatile workflow management for high-throughput data analysis

MotivationHigh-throughput analysis in the current era of systems biology encompasses a range of analysis workflows for different applications, such as transcriptomics, epigenetics, variant discovery, de novo genome assembly, etc. Many research institutes house a genomics core facility that is responsible for generating high-throughput sequencing data and often includes a core bioinformatics team that carries out data analysis. Core teams must both keep track of data and maintain numerous software packages and databases - which must be scalable according to ever-changing research needs - and they must allocate computational resources to them. Typical data analysis pipelines involve multiple software packages and require the ability to install, execute, and maintain software stacks on a variety of hardware/operating system configurations, including high-performance computing (HPC) facilities, stand-alone servers, and Cloud services. At the same time, individual researchers need to analyze their data and share their analysis steps with collaborators. Having to rely on ad hoc scripts and rigid internally developed pipelines is inefficient, difficult to track and maintain, and ultimately, limits the ability to adapt and evolve as research methods progress.\n\nResultsHere, we present BioSAILs (Bioinformatics Standardized Analysis Information Layers), a scientific workflow management system (WMS) developed by the Core Bioinformatics team at NYU Abu Dhabi. BioSAILs comprises two central components, BioX command and HPCRunner command, supported by BioStacks software stacks. BioX structures executable workflows that may be either run directly on a desktop or lab server, or submitted by HPCRunner to HPC infrastructure or the Cloud. BioSAILs is supported by a range of pre-configured, customizable BioStacks software stacks for various applications (e.g. RNA-seq, de novo genome/transcriptome assembly, variant discovery, etc.) Software stacks are built using Conda and BioConda (6), have been pre-packaged into modules using EasyBuild, and can be deployed with or without BioSAILs. Within the BioSAILs WMS, users can also invoke commands that take advantage of containerized software in Docker (10) and Singularity (8). Finally, the BioSAILs web resource provides documentation, blog posts related to various BioSAILs analysis tasks, forums, knowledge base & FAQs, as well as an interactive web-based workflow editor/creator. BioSAILs is production-level software that has been in use as the main WMS at NYU Abu Dhabi for the past 2 years and is continuously developed and maintained by the core bioinformatics team.\n\nAvailabilityBioSAILs is open source software, available under the GNU license. Online documentation, support and guides can be found at biosails.abudhabi.nyu.edu. The public Github project is available at Github - BioSAILs. HPC-Runner and BioX-Workflow can be installed through Conda using the BioConda channel.

bioinformatics↗

bioRxiv2026

Using user-centered design to better understand challengesfaced during genetic analyses by novice genomicresearchers

The lack of user-centered design principles in the current landscape of commonly-used bioinformatics software tools poses challenges for novice genomics researchers (NGRs) entering the genomics ecosystem. Comparing the usability of one analysis software to that of another is a non-trivial task and requires evaluation criteria that incorporates perspectives from both existing literature and a diverse, underrepresented user base of NGRs. To better characterize these barriers, we utilized a two-pronged approach consisting of a literature review of existing bioinformatics tools and semi-structured interviews of the needs of NGRs. From both knowledge sources, the key attributes that resulted in poor adoption and sustained use of most bioinformatics tools included poor documentation, lack of readily-accessible informational content, challenges with installation and dependency coordination, and inconsistent error messages/progress indicators. Combining the findings from the literature review and the insights gained by interviewing the NGRs, an evaluation rubric was created that can be utilized to grade existing and future bioinformatics tools. This rubric acts as a summary of key components needed for software tools to cater to the diverse needs of both NGRs and experienced users. Due to the rapidly evolving nature of genomics research, it becomes increasingly important to critically evaluate existing tools and develop new ones that will help build a strong foundation for future exploration.

bioinformatics↗

bioRxiv2020

A new method to accurately identify single nucleotide variants using small FFPE breast samples.

Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however, it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic algorithm to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing algorithms. We found that the SNV-identification accuracy declined when there was less than 40ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ (DCIS) of the breast to their adjacent invasive ductal carcinoma (IDC) samples. We observed an increased number of mutations (paired-samples sign test, p<0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, p<0.01). Our algorithm provides a significant improvement in detecting SNVs in FFPE samples over previous approaches. Key PointsO_LIThe sequencing of reduced quantities of DNA extracted from FFPE samples leads to substantial sequencing errors that require correction in order to obtain accurate detection of somatic mutations. C_LIO_LIWe developed and validated a new bioinformatic algorithm to robustly identify somatic single nucleotide variants using small amounts of DNA extracted from archival FFPE samples of breast cancers. C_LIO_LIVariant calling software packages need to be optimized to reduce the impact of sequencing errors. Our bioinformatics pipeline represents a significant methodological advance compared to the currently available bioinformatic tools used for the analysis of small FFPE samples. C_LI

genomics↗

bioRxiv2020

Towards end-to-end disease prediction from raw metagenomic data

Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and are stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

genomics↗

bioRxiv2022

Evaluation of metagenomic, 16S rRNA gene and ultra-plexed PCR-based sequencing approaches for profiling antimicrobial resistance gene and bacterial taxonomic composition of polymicrobial samples

BackgroundShotgun metagenomic sequencing is increasingly popular in taxonomic and resistome-profiling of polymicrobial samples due to its agnostic nature and data versatility. However, caveats include high- cost, sequencing depth/sensitivity trade-offs, and challenging bioinformatic deconvolution. Targeted PCR-based profiling optimises sensitivity and cost-effectiveness, but can only identify prespecified targets and may introduce amplification biases. Ultra-high multiplexed PCR is a potential compromise. As no comprehensive comparative evaluation exists, we evaluated performance of each method in taxonomic/resistome-profiling of a well-defined DNA mock sample and seven "real- world" wastewater samples. ResultsWe tested three sequencing approaches (short-read shotgun metagenomics, Illumina Ampliseq ultra-plexed AMR Research Panel, 16S rRNA gene sequencing) with seven bioinformatic pipelines (ResPipe, Illumina DNA Amplicon App, One Codex Metagenomic-/Targeted Loci classification and Ampliseq Report, DADA2, and an in-house pipeline for AmpliSeq data [AmpliSeek]). Metagenomics outperformed 16S rRNA gene sequencing in accurately reconstituting a mock taxonomic profile and optimising the identification of diverse wastewater taxa, while 16S rRNA gene sequencing produced more even taxonomic outputs which may be quantitatively inaccurate but enhance detection of low abundance taxa. Shotgun metagenomic and AmpliSeq sequencing performed equally well in profiling AMR genes present in a mock sample, but AmpliSeq identified more genes in more complex, "real-world" samples, likely related to sensitivity of detection at the metagenomic sequencing depth used. ConclusionsA complementary sequencing approach employing 16S rRNA gene or shallow-metagenomic sequencing for taxonomic profiling, and the AmpliSeq AMR panel for high-resolution resistome profiling represents a potential lower-cost alternative to deep shotgun sequencing and may also be more sensitive for the detection of low-prevalence AMR genes. However, our evaluation highlights that both the sequencing and bioinformatics approach used can significantly influence results; for AmpliSeq AMR gene profiling, we developed AmpliSeek which outperformed the other pipelines tested and is open source. Sequencing approach and bioinformatic pipeline should be considered in the context of study goals and sample type, with study-specific validation when feasible.

genomics↗

bioRxiv2022

The history and organization of the Workshop on Population and Speciation Genomics

With the advent of high-throughput genome sequencing, bioinformatics training has become essential for research in evolutionary biology and related fields. However, individual research groups are often not in the position to teach students about the most up-to-date methodology in the field. To fill this gap, extended bioinformatics courses have been developed by various institutions and provide intense training over the course of two or more weeks. Here, we describe our experience with the organization of a course in one of the longest-running extended bioinformatics series of work-shops, the Evomics Workshop on Population and Speciation Genomics that takes place biennially in the UNESCO world heritage town of [C]esky Krumlov, Czech Republic. We list the key ingredients that make this workshop successful in our view, and describe the routine for workshop organization that we have optimized over the years. We report the results of a survey conducted among past workshop participants that quantifies measures of effective teaching and provide examples of how the workshop setting has led to the cross-fertilisation of ideas and ultimately scientific progress. We expect that our account may be useful for other groups aiming to set up their own extended bioinformatics courses.

scientific communication and education↗

bioRxiv2023

Performance of amplicon and capture based next-generation sequencing approaches for the epidemiological surveillance of Omicron SARS-CoV-2 and other variants of concern.

To control the SARS-CoV-2 pandemic, healthcare systems have focused on ramping up their capacity for epidemiological surveillance through viral whole genome sequencing. In this paper, we tested the performance of two protocols of SARS-CoV-2 nucleic acid enrichment, an amplicon enrichment using different versions of the ARTIC primer panel and a hybrid-capture method using KAPA RNA Hypercap. We focused on the challenge of the Omicron variant sequencing, the advantages of automated library preparation and the influence of the bioinformatic analysis in the final consensus sequence. All 94 samples were sequenced using Illumina iSeq 100 and analysed with two bioinformatic pipelines: a custom-made pipeline and an Illumina-owned pipeline. We were unsuccessful in sequencing six samples using the capture enrichment due to low reads. On the other hand, amplicon dropout and mispriming caused the loss of mutation G21987A and the erroneous addition of mutation T15521A respectively using amplicon enrichment. Overall, we found high sequence agreement regardless of method of enrichment, bioinformatic pipeline or the use of automation for library preparation in eight different SARS-CoV-2 variants. Automation and the use of a simple app for bioinformatic analysis can simplify the genotyping process, making it available for more diagnostic facilities and increasing global vigilance.

microbiology↗

bioRxiv2025

Synthetic community Hi-C benchmarking provides a baseline for virus-host inferences

Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least ten studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z [≥] 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone - i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.

microbiology↗

bioRxiv2025

Integrated single-cell and spatial analysis identifies context-dependent myeloid-T cell interactions in head and neck cancer immune checkpoint blockade response

BackgroundApproximately 15-20% of head and neck cancer squamous cell carcinoma (HNSCC) patients respond favorably to immune checkpoint blockade (ICB). Previous single-cell RNA-Seq (scRNA-Seq) studies identified immune features, including macrophage subset ratios and T-cell subtypes, in HNSCC ICB response. However, the spatial features of HNSCC-infiltrated immune cells in response to ICB treatment need to be better characterized. MethodsHere, we perform a systematic evaluation of cell interactions between immune cell types within the tumor microenvironment using spatial omics data using complementary techniques from both 10X Visium spot-based spatial transcriptomics and Nanostring CosMx single-cell spatial omics with RNA gene panel including 435 ligands and receptors. In this study, we used integrated bioinformatics analyses to identify cellular neighborhoods of co-localizing cell types in single-cell spatial transcriptomics and proteomics data. In addition, we used both publicly available scRNA-Seq and in-house spatial RNA-Seq data to identify spatially constrained Ligand-Receptor interactions in Responder patients. ResultsWith 522,399 single cells profiled with both RNA and protein from 26 patients, in addition to spot-resolved spatial RNA-Seq from 8 patients treated with ICB together with bioinformatics analysis of publicly available single-cell and bulk RNA-Seq, we have identified a spatial and cell-type specific context-dependency of myeloid and T cell interaction difference between Responders and Non-Responders. We defined further cellular neighborhood and the sources of chemokine CXCL9/10-CXCR3 interactions in Responders, emerging targets in ICB, as well as CXCL16-CXCR6, CCL4/5-CCR5, and other underappreciated and potential markers and targets for ICB response in HNSCC. In addition, we have contributed a rich data resource of cell-cell Ligand Receptor interactions for the immunotherapy and HNSCC research community. DiscussionOur work provides a comprehensive single-cell and spatial atlas of immune cell interactions that correlate with response to ICB in HNSCC. We showcase how integrating multiple technologies and bioinformatics approaches can provide new insights into potential immune-based biomarkers of ICB response. Our results suggested refining future studies using preclinical animal models in a more context-specific manner to elucidate potential underlying mechanisms that lead to improved ICB responses. What is already known on this topicMost cancer patients still do not experience clinical benefits from immune checkpoint blockade (ICB), necessitating the development of response biomarkers and new immunotherapeutic targets. What this study addsHere, we use integrated high-dimensional omics and bioinformatics approaches to identify immune cell-cell interaction markers associated with ICB response in patients with Head and neck squamous cell carcinoma. How this study might affect research, practice or policyWe identified spatial and cell-type specificity of Ligand-Receptor interactions between myeloid and T cells in ICB Responder patients that may help inform further mechanistic studies and biomarker development.

cancer biology↗

bioRxiv2025

BioFrame: Enhancing Reproducibility and Accessibility in Genomics through Web-Based Workflow Design

Since, Bioinformatic research is getting attraction that can be seen with sudden increase in development of tools as well as publications. However, there are challenges in bioinformatics which are making the results difficult to obtain while facing reproducibility, scalability, and accessibility of computational workflows. Though command line tools are powerful but majority of the bioinformatic researchers have little or no expertise in the extensive computational skills. To address these issues, we present here the easy to use and getting results with ease through our tool, BioFrame. BioFrame employs a containerized microservices architecture orchestrated via Docker Compose, separating concerns across presentation, orchestration, execution, and storage layers. The bioinformatic tools are encapsulated here as versioned container images with declarative metadata schemas, enabling dynamic tool discovery and parameter validation. Therefore, through user designed workflows one can easily get directed acyclic graphs while using the web interface.

genomics↗

bioRxiv2025

Unraveling the Transcriptional Landscape within a Minimized Bacterium via Comparative Analysis

Stochastic nature of gene expression leads to the complex formation of the bacterial transcriptome and proteome. In contrast to typical transcriptome studies, we employ a near wild-type, Syn1.0, of the naturally genome-reduced Mycoplasmas, and the dramatically further genome-reduced JCVI-syn3A thus avoiding additional contributions from many non-essential cellular functions. To aid in profiling the transcriptional landscape within these bacteria, we present a bioinformatic analysis of the genetic sequence motifs implicated in modulating the stochastic gene expression events, coupled with genome-wide short-read (Illumina) and long-read (Oxford Nanopore Technologies and Pacfic Biosciences) RNA sequencing. The bioinformatic analysis coupled with information from structural studies assigns strengths of the Shine-Dalgarno signatures and identifies both transcription initiation and termination sites, leading to predictions of RNA isoforms in Syn1.0 (and related organisms). The long-read and short-read RNA sequencing characterized the predicted transcriptional activity, and the long-read methods provide direct insight into the RNA isoform complexity within Syn1.0. Comparison of the RNA sequencing results with that of the bioinformatic analysis highlights the inability of bioinformatics alone to capture the results of bacterial transcription without including effects of RNA degradation. This study emphasizes the need for comparative analysis and potential dangers of genome reduction, exemplified through the discovery of altered gene expression patterns of JCVI-syn1.0 and JCVI-syn3A, achieved via the union of our transcriptome study with their proteomics data. Analysis of the transcriptomics data sets through a Jupyter notebook allows any genomic region to be easily examined. Table of Content Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/681674v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@78a9e0org.highwire.dtl.DTLVardef@1d8c9fcorg.highwire.dtl.DTLVardef@1b4e129org.highwire.dtl.DTLVardef@2a8c3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗