Biology subjects

Bioinformatics

Explore bioRxiv bioinformatics preprints. Search computational biology methods, sequence analysis and single-cell analysis; follow source links for manuscript details.

Follow this topic with RSS

At least 55 records · Page 3Linked to original sources

bioRxiv2022

A cost-free CURE: Using bioinformatics to identify DNA-binding factors at a specific genomic locus

Research experiences provide diverse benefits for undergraduates. Many academic institutions have adopted course-based undergraduate research experiences (CUREs) to improve student access to research opportunities. However, potential instructors of a CURE might still face financial or practical hurdles that prevent implementation. Bioinformatics research offers an alternative that is free, safe, compatible with remote learning, and may be more accessible for students with disabilities. Here, we describe a bioinformatics CURE that leverages publicly available datasets to discover novel proteins that target an instructor-determined genomic locus of interest. We use the free, user-friendly bioinformatics platform Galaxy to map ChIP-seq datasets to a genome, which removes the computing burden from students. Both faculty and students directly benefit from this CURE, as faculty can perform candidate screens and publish CURE results. Students gain not only basic bioinformatics knowledge, but also transferable skills, including scientific communication, database navigation, and primary literature experience. The CURE is flexible and can be expanded to analyze different types of high-throughput data or to investigate different genomic loci in any species.

scientific communication and education↗

bioRxiv2016

Towards an ontology-based recommender system for relevant bioinformatics workflows

BackgroundWith the large and diverse type of biological data, bioinformatic solutions are being more complex and computationally intensive. New specialized data skills need to be acquired by researchers in order to follow this development. Workflow Management Systems rise as an efficient way to automate tasks through abstract models in order to assist users during their problem solving tasks. However, current solutions could have several problems in reusing the developed models for given tasks. The large amount of heterogenous data and the lack of knowledge in using bioinformatics tools could mislead the users during their analyses. To tackle this issue, we propose an ontology-based workflow-mining framework generating semantic models of bioinformatic best practices in order to assist scientists. To this end, concrete workflows are extracted from scientific articles and then mined using a rich domain ontology.\n\nResultsIn this study, we explore the specific topics of phylogenetic analyses. We annotated more than 300 recent articles using different ontological concepts and relations. Relative supports (frequencies) of discovered workflow components in texts show interesting results of relevant resources currently used in the different phylogenetic analysis steps. Mining concrete workflows from texts lead us to discover abstract but relevant patterns of the best combinations of tools, parameters and input data for specific phylogenetic problems.\n\nConclusionsExtracted patterns would make workflows more intuitive and easy to be reused in similar situations. This could provide a stepping-stone into the identification of best practices and pave the road to a recommender system.

bioinformatics↗

bioRxiv2020

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

BackgroundShortening the time-to-result for pathogen detection and identification and antibiotic susceptibility testing for patients with Hospital-Acquired and Ventilator-Associated pneumonia (HAP-VAP) is of great interest. For this purpose, clinical metagenomics is a promising non-hypothesis driven alternative to traditional culture-based solutions: when mature, it would allow direct sequencing all microbial genomes present in a BronchoAlveolar Lavage (BAL) sample with the purpose of simultaneously identifying pathogens and Antibiotic Resistance Genes (ARG). In this study, we describe a new bioinformatics method to detect pathogens and their ARG with good accuracy, both in mono- and polymicrobial samples. MethodsThe standard approach (hereafter called TBo), that consists in taxonomic binning of metagenomic reads followed by an assembly step, suffers from lack of sensitivity for ARG detection. Thus, we propose a new bioinformatics approach (called TBwDM) with both models and databases optimized for HAP-VAP, that performs reads mapping against ARG reference database in parallel to taxonomic binning, and joint reads assembly. ResultsIn in-silico simulated monomicrobial samples, the recall for ARG detection increased from 51% with TBo to 97.3% with TBwDM; in simulated polymicrobial infections, it increased from 41.8% to 82%. In real sequenced BAL samples (mono and polymicrobial), detected pathogens were also confirmed by traditional culture approaches. Moreover, both recall and precision for ARG detection were higher with TBwDM than with TBo (35 points difference for recall, and 7 points difference for precision). ConclusionsWe present a new bioinformatics pipeline to identify pathogens and ARG in BAL samples from patients with HAP-VAP, with higher sensitivity for ARG recovery than standard approaches and the ability to link ARG to their host pathogens.

bioinformatics↗

bioRxiv2020

Bio-Node - Bioinformatics in the Cloud

MotivationThe applicability and reproducibility of bioinformatics methods and results often depend on the structure and software architecture of their development. Exponentially growing data sets require ever more optimization and performance with conventional computing capacities lacking this process. This creates a large overhead for software development in a research area which is primarily interested in solving complex biological problems rather than developing new, performant software solutions. In pure computer science, new structures in the field of web development have produced more efficient processes for container-based software solutions. The advantages of these structures have rarely been explored in a broader scientific scale. This is also the case with the trend of migrating computations from on premise resources to the cloud. ResultsWe created Bio-Node, a new platform for large scale bio data analysis utilizing cloud compute resources (publicly available at https://bio-node.de). Bio-Node enables building complex workflows using a sophisticated web interface. We applied Bio-Node to implement bioinformatic workflows for rapid metagenome function annotation. We further developed "Auto-Clustering", a workflow that automatically extracts the most suited clustering parameters for specific data types and subsequently enables to optimally segregate unknown samples of the same type. Compared to existing methods and approaches Bio-Node improves performance and costs of bioinformatics data analyses while providing an easier and faster development process with focus on reproducibility and reusability. Supplementary information is available at https://doi.org/10.5281/zenodo.3753146

bioinformatics↗

bioRxiv2021

SARS-CoV-2 RECoVERY: a multi-platform open-source bioinformatic pipeline for the automatic construction and analysis of SARS-CoV-2 genomes from NGS sequencing data

BackgroundSince its first appearance in December 2019, the novel Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2), spread worldwide causing an increasing number of cases and deaths (35,537,491 and 1,042,798, respectively at the time of writing, https://covid19.who.int). Similarly, the number of complete viral genome sequences produced by Next Generation Sequencing (NGS), increased exponentially. NGS enables a rapid accumulation of a large number of sequences. However, bioinformatics analyses are critical and require combined approaches for data analysis, which can be challenging for non-bioinformaticians. ResultsA user-friendly and sequencing platform-independent bioinformatics pipeline, named SARS-CoV-2 RECoVERY (REconstruction of CoronaVirus gEnomes & Rapid analYsis) has been developed to build SARS-CoV-2 complete genomes from raw sequencing reads and to investigate variants. The genomes built by SARS-CoV-2 RECoVERY were compared with those obtained using other software available and revealed comparable or better performances of SARS-CoV2 RECoVERY. Depending on the number of reads, the complete genome reconstruction and variants analysis can be achieved in less than one hour. The pipeline was implemented in the multi-usage open-source Galaxy platform allowing an easy access to the software and providing computational and storage resources to the community. ConclusionsSARS-CoV-2 RECoVERY is a piece of software destined to the scientific community working on SARS-CoV-2 phylogeny and molecular characterisation, providing a performant tool for the complete reconstruction and variants analysis of the viral genome. Additionally, the simple software interface and the ability to use it through a Galaxy instance without the need to implement computing and storage infrastructures, make SARS-CoV-2 RECoVERY a resource also for virologists with little or no bioinformatics skills. Availability and implementationThe pipeline SARS-CoV-2 RECoVERY (REconstruction of COronaVirus gEnomes & Rapid analYsis) is implemented in the Galaxy instance ARIES (https://aries.iss.it).

bioinformatics↗

bioRxiv2022

RLSuite: An integrative R-loop bioinformatics framework

R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids. While R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community.

bioinformatics↗

bioRxiv2023

IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods

Integration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying integration sites (IS) from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline. Here we present IS-seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems. We validated the performance of IS-Seq testing it against the most widely used analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.

bioinformatics↗

bioRxiv2023

BTR: A Bioinformatics Tool Recommendation System

MotivationThe rapid expansion of Bioinformatics research has resulted in a vast array of computational tools utilized in the development of scientific analysis pipelines. However, constructing these pipelines is a laborious and intricate task, one which demands extensive domain knowledge and careful consideration at all stages. As the Bioinformatics landscape continues to evolve, researchers, both novice and expert, may find themselves overwhelmed when working in unfamiliar fields. Consequently, this may result in the selection of unsuitable or suboptimal tools during workflow development. ResultsIn this paper, we propose the Bioinformatics Tool Recommendation system (BTR), an innovative deep learning model designed to recommend the most suitable tools for a given workflow-in-progress. BTR utilizes recent advances in graph neural network technology and introduces a novel approach, representing the entire workflow as a graph to capture essential context and structural information. Additionally, natural language processing techniques are integrated to enhance the quality of tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, highlighting its potential to greatly facilitate scientific workflow construction. Availability and implementationThe Python source code is available at https://github.com/ryangreenj/bioinformatics_tool_recommendation

bioinformatics↗

bioRxiv2023

Haxe as a Swiss knife for bioinformatic applications: the SeqPHASE case story

1Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript and Python. Although Haxe is becoming more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxes source code conversion functionality when it comes to implementing bioinformatic software.

bioinformatics↗

bioRxiv2023

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

bioinformatics↗

bioRxiv2023

BioLLMBench: A Comprehensive Benchmarking of Large Language Models in Bioinformatics

The rapid advancements in artificial intelligence, particularly in Large Language Models (LLMs) such as GPT-4, Gemini, and LLaMA, have opened new avenues for computational biology and bioinformatics. We report the development of BioLLMBench, a novel framework designed to evaluate LLMs in bioinformatics tasks. This study assessed GPT-4, Gemini, and LLaMA through 2,160 experimental runs, focusing on 24 distinct tasks across six key areas: domain expertise, mathematical problem-solving, coding proficiency, data visualization, research paper summarization, and machine learning model development. Tasks ranged from fundamental to expert-level challenges, and each area was evaluated using seven specific metrics. A Contextual Response Variability Analysis was implemented to understand how model responses varied under different conditions. Results showed diverse performance: GPT-4 led in most tasks, achieving a 91.3% proficiency in domain knowledge, while Gemini excelled in mathematical problem-solving with a 97.5% proficiency score. GPT-4 also outperformed in machine learning model development, though Gemini and LLaMA struggled to generate executable code. All models faced challenges in research paper summarization, scoring below 40% using the ROUGE metric. Model performance variance increased when using a new chat window, though average scores remained similar. The study also discusses the limitations and potential misuse risks of these models in bioinformatics.

bioinformatics↗

bioRxiv2024

Exploring the impacts of human breast milk functional lipidome on infant health and growth outcomes in early life using lipid bioinformatics

Human breast milk lipidome is complex, and how changes in the functional lipid metabolism converge systematically to alter infants health outcomes is poorly understood. We used human breast milk and infant-mother dyads as a test system to demonstrate how the application of improved lipid bioinformatics can be effective in discerning systematic changes in functional lipid metabolism providing novel discoveries of how lactational programming in early life can influence infant health and growth outcomes. The study consisted of 40 mother-infant dyads where breast milk, maternal diet, infant anthropometrics [fat mass index (FMI), length z score, BMI z score, fat-free mass index (FFMI)], and infant atopic disease outcome (ear infection, cold, wheezing, diarrhea, and eczema) were collected at one and four months postpartum. Integrated Lipid Bioinformatics analyses were conducted using XLSTAT, Metaboanalyst 5.0. R software, Lipid Search, Xcalibur, and Cytoscape software. The results showed breast milk lipidome ordinated into distinct clusters based on maternal BMI status, and differences in developmental and atopic disease outcomes following redundancy analysis. Specifically, lipids from obese mothers clustered with FMI and eczema, while lipids from non-obese mothers clustered with FFM and wheezing. Receiver operating analysis was effective in identifying potential lipid biomarkers that were significantly associated with infant FMI, FFMI, and eczema during early life. Sphingolipid and glycerophospholipid pathways were significantly associated with the altered breast milk lipidome impacting infant development and atopic disease outcome during the first year of life. The findings following the advanced lipid bioinformatics suggest that the breastmilk functional lipid metabolism appears to play a key role in lipid-mediated lactational programming influencing development and atopic disease outcome, and present opportunities for potential dietary intervention in early life.

bioinformatics↗

bioRxiv2024

Bioinformatics Copilot 2.0 for Transcriptomic Data Analysis

Large language models, when integrated into bioinformatics tools, offer valuable support for biologists in the analysis of single-cell transcriptomic data. Here, we introduce Bioinformatics Copilot 2.0, an advanced tool that builds upon its predecessor with four significant enhancements: 1) Internal server processing: users can now process large datasets on an internal server, thereby ensuring data privacy. 2) User-controlled analysis: users can take control of their data analysis when tasks exceed the copilots capabilities.3) Real-time information access: users can access up-to-date information from online resources, such as gene function queries and recent publications, thus enhancing data interpretation. 4) Data analysis and result documentation: the copilot now supports the generation of PDFs that include images and detailed analysis. These advancements represent a step towards building a bioinformatics autopilot. Demonstration videos showcasing these features are available at www.biochemml.com/tools.

bioinformatics↗

bioRxiv2025

Defective But Promising: Evaluating Bioinformatic Pipelines for Utility of Defective Interfering RNA Discovery in Plant Viral Infections

We explored the utility of the currently available bioinformatics programs ViReMa, DI-tector, DVGfinder, DG-Seq, and VODKA2 for identifying junction points in plant virus high-throughput sequencing (HTS) data that could be tested downstream for antiviral capacity. Specifically, we looked at whether the outputs from these bioinformatic tools generally agree and whether the most frequently identified "defective viral genomes" (DVGs) from these programs are promising defective interfering RNA (DI) candidates for downstream validation. We also explored the possibility of these tools helping us address a larger research question of whether DI RNA are consistently generated and maintained in a specific virus-host combination when conditions are permissive for their replication and accumulation, our "DI prevalence" hypothesis. This was conducted by running eight previously published RNAseq datasets through all five programs and comparing degree of output overlap, most common junction point identified, and whether previously published DI junction points were found. Our results demonstrate a low degree of agreement regarding identified junction points between programs, promise regarding looking at the most commonly occurring junction for DI candidates, and support for our DI prevalence hypothesis. We conclude that bioinformatics workflows have a place in the toolbox of DI and DVG research, but they should not be used alone. We suggest the use of multiple programs on a dataset to better inform decisions regarding deletions to re-create and screen downstream and reiterate the importance of other avenues of evidence in DVG/DI characterization.

bioinformatics↗

bioRxiv2025

PromptBio: A Multi-Agent AI Platform for Bioinformatics Data Analysis

PromptBio is a modular AI platform for scalable, reproducible, and user-adaptable bioinformatics analysis, powered by generative AI and natural language interaction. It supports three complementary modes of analysis designed to meet diverse research needs. PromptGenie is a multi-agent system that enables stepwise, human-in-the-loop workflows using prevalidated domain-standard tools. Within PromptGenie, specialized agents--including DataAgent, OmicsAgent, AnalysisAgent, and QAgent--collaborate to manage tasks such as data ingestion, pipeline execution, statistical analysis, and interactive summarization. DiscoverFlow provides integrated, automated workflows for large-scale multi-omics analysis, offering end-to-end execution and streamlined orchestration. ToolsGenie complements these modes by dynamically generating executable bioinformatics code for custom, user-defined analyses, enabling flexibility beyond standardized workflows. PromptGenie and DiscoverFlow leverage a suite of domain-specific tools, including Omics Tools for standardized omics pipelines, Analysis Tools for downstream statistical interpretation, and MLGenie for machine learning and multi-omics modeling. We present the design, capabilities, and validation of these components, highlight their integration into automated and customizable workflows, and discuss extensibility, monitoring, and compliance. PromptBio aims to democratize high-throughput bioinformatics through a large language model-powered, natural language understanding, workflow generation and agent orchestration.

bioinformatics↗

bioRxiv2025

plasmoRUtils: A one-stop R Package for Plasmodium and other Apicomplexan parasite-related Bioinformatics analysis

Bioinformatics analysis of non-model organisms remains challenging due to the limited availability of specialized tools, as most R packages are optimized for well-annotated model species. This problem is exacerbated by genomic and proteomic data being scattered across multiple databases, each employing different identifiers based on varying reference annotations. Comprehensive databases have been developed to disseminate knowledge related to Apicomplexan genomics and proteomics, such as VEupathDB. Several specialised databases, particularly for the malaria parasite Plasmodium, have been developed such as ApicoTFDB, malaria.tools, MPMP, MIIP, Phenoplasm, PlasmoBase, alongside broader resources like HitPredict and TED. However, these platforms often suffer from manual query interfaces, outdated identifiers, and inefficient data retrieval methods, complicating their use for large-scale bioinformatic analyses. To address these limitations, we present plasmoRUtils, an R package designed to streamline database access and data harmonization for Apicomplexan research. plasmoRUtils enables the retrieval of data tables using single-line R functions and standardized Ensembl gene IDs as input. Thanks to the APIs available for some databases such as VEuPathDB, the package also provides functions to build CLI-based queries for VEuPathDBs component databases. Additional support includes performing overrepresentation analyses and estimating parasite transcriptomic age/stage using single-cell or bulk RNA-Seq references. By automating data retrieval and transformation within RStudio, plasmoRUtils eliminates the need for manual database queries, facilitating end-to-end workflow development without leaving the R environment. Available on GitHub https://github.com/Rohit-Satyam/plasmoRUtils, with comprehensive documentation, plasmoRUtils represents a critical step toward efficient and reproducible bioinformatics for Apicomplexan research.

bioinformatics↗

bioRxiv2017

miCloud: a plug and play, on-premises bioinformatics cloud, providing seamless integration with Illumina genome sequencers

Benchtop genome sequencers such as the Illumina MiSeq or MiniSeq [1], [2] are revolutionizing genomics research for smaller, independent laboratories, by enabling access to low-cost Next Generation Sequencing (NGS) technology in-house. These benchtop genome sequencing instruments require only standard laboratory equipment, in addition to minimal time for sample preparation. However, post-sequencing bioinformatics data analysis still presents a significant bottleneck, for research laboratories lacking specialized software and technical data analysis skills on their teams. While bioinformatics computes clouds providing solutions following a Software as a Service (SaaS) are available ([3]-[6], review in [7]), currently, there are only a few options which are user-friendly for non-experts while at the same time are also low-cost or free. One primary example is Illumina BaseSpace [8] that is very easy to access by non-experts, and also offers an integrated solution where data are streamed directly from the MiSeq sequencing instrument to the cloud. Once the data is on the BaseSpace cloud, users can access a range of bioinformatics applications with pre-installed algorithms through an intuitive web interface. Nonetheless, BaseSpace can be a costly solution as a yearly subscription depending on whether the user is associated with an academic or private institution, ranges in price from $999 - $4,999. Additional \"iCredits\" [9] might need to be purchased for frequent users that exhaust the base credit allowance as part of the subscription. Considering the reduction of computer hardware cost in recent years, a multi-core Intel Xeon server with 64 GigaByte (GB) of memory and multiple TeraByte (TB) of storage is priced less than the yearly subscription to Basespace [10], and similarly when compared to renting compute cycles from providers such as Amazon Web Services (AWS) [11]. Furthermore, the current generation of laptops usually come with 6-10 GigaBytes (GB) of memory and 1 TeraByte (TB) of storage, providing enough computational capacity to analyze data from small NGS experiments [12] that include only a few samples.

bioinformatics↗

bioRxiv2018

INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

A new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data. Here, we launch INSaFLU (\"INSide the FLU\"), which, to the best of our knowledge, is the first influenza-specific bioinformatics free web-based suite that deals with primary data (reads) towards the automatic generation of the output data that are actually the core first-line \"genetic requests\" for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform advanced, multi-step software intensive analyses in a user-friendly manner without previous training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and highly flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus completely cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tune data analysis. This platform additionally flags samples as \"putative mixed infections\" if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional \"consensus-based\" influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants, but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission. In summary, INSaFLU supplies public health laboratories and influenza researchers with an open \"one size fits all\" framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus.\n\nINSaFLU can be accessed through https://insaflu.insa.pt (see homepage view in Figure 1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC=\"FIGDIR/small/253161_fig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@1111419org.highwire.dtl.DTLVardef@3e99c2org.highwire.dtl.DTLVardef@1e0a953org.highwire.dtl.DTLVardef@8c9ea6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO INSaFLU homepage\n\nC_FIG

bioinformatics↗