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bioRxiv2023

Cont-ID:Detection of samples cross-contamination in viral metagenomic data

BackgroundHigh Throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be influenced by cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies. ResultsWe present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one external alien control. Using 273 real datasets, including 68 virus species from different hosts (fruit tree, plant, human) and several library preparation protocols (Ribodepleted total RNA, small RNA and double stranded RNA), we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed. ConclusionsCross-contamination between samples when detecting viruses using HTS can be monitored and highlighted by Cont-ID (provided an alien control is present). Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.

bioinformatics↗

bioRxiv2023

aaHash: recursive amino acid sequence hashing

MotivationK-mer hashing is a common operation in many foundational bioinformatics problems. However, generic string hashing algorithms are not optimized for this application. Strings in bioinformatics use specific alphabets, a trait leveraged for nucleic acid sequences in earlier work. We note that amino acid sequences, with complexities and context that cannot be captured by generic hashing algorithms, can also benefit from a domain-specific hashing algorithm. Such a hashing algorithm can accelerate and improve the sensitivity of bioinformatics applications developed for protein sequences. ResultsHere, we present aaHash, a recursive hashing algorithm tailored for amino acid sequences. This algorithm utilizes multiple hash levels to represent biochemical similarities between amino acids. aaHash performs [~]10X faster than generic string hashing algorithms in hashing adjacent k-mers. Availability and implementationaaHash is available online at https://github.com/bcgsc/btllib and is free for academic use.

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bioRxiv2023

Benchmarking AI-based plasmid annotation tools for antibiotic resistance genes mining from metagenome of the Virilla River, Costa Rica

Bioinformatics and Artificial Intelligence (AI) stand as rapidly evolving tools that have facilitated the annotation of mobile genetic elements (MGEs), enabling the prediction of health risk factors in polluted environments, such as antibiotic resistance genes (ARGs). This study aims to assess the performance of four AI-based plasmid annotation tools (Plasflow, Platon, RFPlasmid, and PlasForest) by employing defined performance parameters for the identification of ARGs in the metagenome of one sediment sample obtained from the Virilla River, Costa Rica. We extracted and sequenced complete DNA from the sample, assembled the metagenome, and then performed the plasmid prediction with each bioinformatic tool, and the ARGs annotation using the Resistance Gene Identifier web portal. Sensitivity, specificity, precision, negative predictive value, accuracy, and F1score were calculated for each ARGs prediction result of the evaluated plasmidomes. Notably, Platon emerged as the highest performer among the assessed tools, exhibiting exceptional scores. Conversely, Plasflow seems to face difficulties distinguishing between chromosomal and plasmid sequences, while PlasForest has encountered limitations when handling small contigs. RFPlasmid displayed diminished specificity and was outperformed by its taxon-dependent workflow. We recommend the adoption of Platon as the preferred bioinformatic tool for resistome investigations in the taxon-independent environmental metagenomic domain. Meanwhile, RFPlasmid presents a compelling choice for taxon-dependent prediction due to its exclusive incorporation of this approach. We expect that the results of this study serve as a guiding resource in selecting AI-based tools for accurately predicting the plasmidome and its associated genes.

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bioRxiv2023

CATHI: An interactive platform for comparative genomics and homolog identification

AO_SCPLOWBSTRACTC_SCPLOWBioinformatics has established itself as a central pillar of modern biology. Specifically, comparative genomics enables scientists to study a vast number of genomes efficiently. These comparative analyses shed light on the evolution and potential function of genomes and genes, but are also increasingly used as a key tool for metabolic engineering and synthetic biology by identifying appropriate targets for modification. While numerous sophisticated tools for comparative genomics and homolog identification exist, those tools predominantly target highly skilled bioinformatics users. Consequently, many biologists either defer such analyses to their more versed bioinformatic collaborators or resort to suboptimal tools. Here, we present an intuitive solution available on all major operating systems, easily accessed through common web browsers. CATHI - Comparative Analysis Tool for Homolog Identification - integrates a suite of best-practice bioinformatic tools, encompassing BLAST for homology searches, MAFFT for multiple sequence alignment, FastTree2 for phylogeny reconstruction, and clinker for synteny analysis. Specifically tailored to biologists, CATHI orchestrates predefined settings and automated pipelines, obviating the need for programming expertise. This platform empowers researchers to confidently engage in detailed comparative genomics studies by streamlining the analytical process. The interactive framework provides users with a plethora of options. This includes real-time execution and progress monitoring, facilitates dynamic result tracking, and a set of search functions across NCBI databases like CDD or ProtFam. Users can interactively engage in data exploration, filtering, and visualization through CATHIs intuitive interface. Furthermore, the seamless export of project data in standard formats (FASTA, Newick, CSV, and HTML) facilitates the integration with further third-party tools such as TreeViewer and Jalview. To benchmark CATHI, we revisited the comparative analysis of cyanobacterial circadian clock proteins conducted by Schmelling et al. in 2017, revealing consistent global patterns among identified homologs, while also highlighting individual variations attributed to the expansion of available databases.

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bioRxiv2023

INAEME: Integral Neoantigen Analysis with Entirety of Mutational Events

Neoantigens are peptides presented on the surface of cancer cells that can be recognized by the immune system. Multiple novel therapeutic approaches involve the administration of neoantigens to trigger immunity-induced tumor regression. Identifying neoantigens includes a personalized approach consisting of detailed analyses of the sequenced tumor tissue and its comparison with wild type to identify somatic mutations. Altered peptides are translated from nucleotides around somatic mutations, and their binding affinity and immunogenicity need further evaluation. Still, the entire bioinformatics analysis is very complex, and accurate prediction of the neoantigen candidates represents a true challenge. Here, we present the novel, integral bioinformatic analysis workflow for neoantigen discovery, denoted INAEME (Integral Neoantigen Analysis with Entirety of Mutational Events). The workflow performs integral processing of an individuals DNA tumor-normal and RNA tumor raw reads to output prioritized neoantigen candidates. Through conducted analysis and benchmarks, our main goal was to demonstrate the necessity of taking into account a wide scope of mutational events so far not considered in the existing solutions, including phasing of variants, influence of both somatic and germline variants, positions of all transcripts, neighboring variants, and frameshifts. The influence of each mutational event on the accuracy of predicted neoepitope candidates is tested across 300 TCGA samples from multiple cancer types, including melanoma, hepatocellular carcinoma, and lung squamous cancer. The observed loss of neoantigen nests, going from 8.45% up to 23.65%, underscores the importance of accounting for the entirety of mutational events to accurately identify robust neoantigen candidates for cancer immunotherapy and vaccine development. The adaptation of the described methods in the bioinformatics analysis minimizes the existence of false positives, which are only later discovered in a laboratory environment using expensive methods such as mass spectrometry or microscopy.

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bioRxiv2023

mtx-COBRA: Subcellular localization prediction for bacterial proteins

BackgroundBacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets. MethodWe present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Metas protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset. ResultsUsing benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb. Conclusionsmtx-COBRA provides an accessible pipeline with greater efficiency to classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

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bioRxiv2024

RUDEUS, a machine learning classification system to study DNA-Binding proteins

DNA-binding proteins are essential in different biological processes, including DNA replication, transcription, packaging, and chromatin remodelling. Exploring their characteristics and functions has become relevant in diverse scientific domains. Computational biology and bioinformatics have assisted in studying DNA-binding proteins, complementing traditional molecular biology methods. While recent advances in machine learning have enabled the integration of predictive systems with bioinformatic approaches, there still needs to be generalizable pipelines for identifying unknown proteins as DNA-binding and assessing the specific type of DNA strand they recognize. In this work, we introduce RUDEUS, a Python library featuring hierarchical classification models designed to identify DNA-binding proteins and assess the specific interaction type, whether single-stranded or double-stranded. RUDEUS has a versatile pipeline capable of training predictive models, synergizing protein language models with supervised learning algorithms, and integrating Bayesian optimization strategies. The trained models have high performance, achieving a precision rate of 95% for DNA-binding identification and 89% for discerning between single-stranded and doublestranded interactions. RUDEUS includes an exploration tool for evaluating unknown protein sequences, annotating them as DNA-binding, and determining the type of DNA strand they recognize. Moreover, a structural bioinformatic pipeline has been integrated into RUDEUS for validating the identified DNA strand through DNA-protein molecular docking. These comprehensive strategies and straightforward implementation demonstrate comparable performance to high-end models and enhance usability for integration into protein engineering pipelines.

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bioRxiv2024

Rare Copy Number Variant analysis in case-control studies using SNP Array Data: a scalable and automated data analysis pipeline

BackgroundRare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data. ResultsThe pipeline performs two major tasks: (1) CNV detection and quality control, and (2) rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility. ConclusionsOur pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

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bioRxiv2024

Harnessing Data-Intelligence-Intensive Multi-Agent System for Life Science Research

Advancements in high-throughput sequencing technologies and artificial intelligence offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of artificial intelligence technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human expertise to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human experts. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves the overall state-of-the-art performance across all tasks, while showcases exceptional task completeness. Furthermore, in the application of constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a hierarchical annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories in life sciences.

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bioRxiv2024

Unbiased and comprehensive identification of viral encoded circular RNAs in a large range of viral species and families

Non-coding RNAs play a significant role in viral infection cycles, with recent attention focused on circular RNAs (circRNAs) originating from various viral families. Notably, these circRNAs have been associated with oncogenesis and alterations in viral fitness. However, identifying their expression has proven more challenging than initially anticipated due to unique viral characteristics. This challenge has the potential to impede progress in our understanding of viral circRNAs. Key hurdles in working with viral genomes include: (1) the presence of repetitive regions that can lead to misalignment of sequencing reads, and (2) unconventional splicing mechanisms that deviate from conserved eukaryotic patterns. To address these challenges, we developed vCircTrappist, a bioinformatic pipeline tailored to identify backsplicing events and pinpoint loci expressing circRNAs in RNA sequencing data. Applying this pipeline, we obtained novel insights from both new and existing datasets encompassing a range of animal and human pathogens belonging to Herpesviridae, Retroviridae, Adenoviridae and Orthomyxoviridae families. Subsequent RT-PCR and Sanger sequencings validated the accuracy of the developed bioinformatic tool for a selection of new candidate viral encoded circRNAs. These findings demonstrate that vCircTrappist is an open and unbiased approach for comprehensive identification of virus-derived circRNAs. Significance StatementCircular RNAs (circRNAs) were revealed to have prominent roles in cellular life in the past decade. They were more recently shown to be expressed by viruses, influencing their infectious cycles and host-pathogen relationship. In this context, viruses that were not previously associated with cellular splicing processes are shown to express circRNAs through unknown mechanisms. These non-canonical circRNAs were already shown to be important in the viral cycle and pathogenesis of the viruses they are encoded from. Here, we propose a bioinformatics pipeline that bypasses the limitations of the existing tools in the identification of viral circRNA. Using this pipeline, we discovered numerous candidates and invite the reader to start its own exploration in the realm of viral encoded circRNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/600382v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1003330org.highwire.dtl.DTLVardef@20d366org.highwire.dtl.DTLVardef@116f38forg.highwire.dtl.DTLVardef@1d04a76_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

Evaluation of a Real-time Plasmid Transmission Detection Pipeline

The spread of antimicrobial resistance among bacteria by horizontal plasmid transmissions poses a major challenge for clinical microbiology. Here, we evaluate a new real-time plasmid transmission detection pipeline implemented in the SeqSphere+ (Ridom GmbH, Munster, Germany) software. Within the pipeline, a local Mash plasmid database is created and Mash searches with a distance threshold of 0.001 are used to trigger plasmid transmission early warning alerts (EWA). Clonal transmissions are detected using cgMLST allelic differences. The integrated tools MOB-suite, NCBI AMRFinderPlus, CGE MobileElementFinder, pyGenomeViz, and MUMmer are used to characterize plasmids and for visual pairwise plasmid comparisons, respectively. We evaluated the pipeline using published hybrid assemblies (Oxford Nanopore Technology/Illumina) of a surveillance and outbreak dataset with plasmid transmissions. To emulate prospective usage, samples were imported in chronological order of sampling date. Different combinations of the user-adjustable parameters sketch size (1,000 vs 10,000) and plasmid size correction were tested and discrepancies between resulting clusters were analyzed with Quast. When using a sketch size of 1,000 with size correction turned on, the SeqSphere+ pipeline agreed with the published data and produced the same clonal and carbapenemase-carrying plasmid clusters. EWAs were in the correct chronological order. In summary, the developed pipeline presented here is suitable for integration into clinical microbiology settings with limited bioinformatics knowledge due to its automated analyses and alert system, which are combined with the GUI-based SeqSphere+ platform. Thus, with its integrated sample database, (near) real-time plasmid transmission detection is within reach in bacterial routine-diagnostic settings when long-read sequencing is employed. ImportancePlasmid-mediated spread of antimicrobial resistance (AMR) is a major challenge for clinical microbiology and monitoring of potential plasmid transmissions is essential to combat further dissemination. Whole-genome sequencing (WGS) is often used to surveil nosocomial transmissions but usually limited to the detection of clonal transmissions (based on chromosomal markers). Recent advances in long-read sequencing technologies enable full reconstruction of plasmids and the detection of very similar plasmids but so far easy-to-use bioinformatic tools for this purpose were missing. Here we present an evaluation of an innovative real-time plasmid transmission detection pipeline. It is integrated into the GUI-based SeqSphere+ software, which already offers cgMLST based pathogen outbreak detection. It requires very limited bioinformatics knowledge, and its database, automated analyses, and alert system make it well suited for prospective clinical application.

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bioRxiv2024

Pymportx: Facilitating Next-Generation Transcriptomics Analysis in Python

The efficient importation of quantified gene expression data is pivotal in transcriptomics. Historically, the R package Tximport addressed this need by enabling seamless data integration from various quantification tools. However, the Python community lacked a corresponding tool, restricting cross-platform bioinformatics interoperability. We introduce Pymportx, a Python adaptation of Tximport, which replicates and extends the original packages functionalities. Pymportx maintains the integrity and accuracy of gene expression data while improving processing speed and integration within the Python ecosystem. It supports new data formats and includes tools for enhanced data exploration and analysis. Available under the MIT license, Pymportx integrates smoothly with Pythons bioinformatics tools, facilitating a unified and efficient workflow across the R and Python ecosystems. This advancement not only broadens access to Pythons extensive toolset but also fosters interdisciplinary collaboration and the development of cutting-edge bioinformatics analyses. Availability and ImplementationPymportx is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/victorsanchezarevalo/Pymportx.

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bioRxiv2024

APHIX: Analysis Pipeline for HIV-1 Isoform eXploration Using Long-read RNA Sequencing Data

HIV-1 uses 4 major splice donors and 8 major splice acceptors as well as dozens of minor, cryptic, and uncharacterized splice sites to produce over one hundred distinct transcript isoforms from a single 9.2 kb genome. As a result, existing bioinformatic pipelines struggle to accurately analyze spliced HIV sequences due to the complex nature of HIV alternative splicing compared to human mRNA splicing. Previous approaches to identify HIV isoforms from long-read sequencing data used pipelines that are not publicly available, are convoluted to operate, or are locked into a specific HIV strain, which limits their wide adoption to other experimental designs or systems. To address this gap, we have developed a bioinformatic pipeline called APHIX that fully automates spliced isoform assignment, splice site usage quantification, and non-coding exon detection. APHIX takes a FASTQ/A of long-read transcripts and a HIV genome reference sequence and fully automates HIV isoform analysis. APHIX calculates splice site usage counts and percentages for each donor and acceptor site and their pairwise combinations, accurately assigns isoforms, and automatically identifies transcripts containing non-coding exons. APHIX is compatible with long-reads sequences generated from multiple platforms and library preps, including direct DNA and RNA sequencing. APHIX can also be adapted to multiple HIV-1 clades and strains by providing the appropriate reference sequence during bioinformatic processing. Overall, APHIX enables comprehensive processing of spliced sequences with reproducible results in a manner that is faster and easier to run compared to other methods.

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bioRxiv2025

An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study

The computational complexity of many key bioinformatics problems has resulted in numerous alternative heuristic solutions, where no single approach consistently outperforms all others. This creates difficulties for users trying to identify the most suitable tool for their dataset and for developers managing and evaluating alternative methods. As data volumes grow, deploying these methods becomes increasingly difficult, highlighting the need for standardized frameworks for seamless tool deployment and comparison in HPC environments. Multiple sequence aligners (MSAs) rank among the most commonly employed modeling techniques in bioinformatics, playing a crucial role in applications such as protein structure prediction, phylogenetic reconstruction, and variant effect prediction. The NP-hardness of MSAs makes them a major example of problems where heuristics stand central, as no optimal solution can be currently obtained, within the limits of operational computational requirements. Here, we present a pilot design of an nf-core framework for streamlined tool deployment and rigorous performance evaluation focusing on the MSAs software ecosystem. By integrating the most popular MSA tools and focusing on a modular, and extensible architecture, we aspire to provide a key platform supporting MSA deployment, evaluation, and algorithmics development to the MSA community, and a proof-of-principle to the wider bioinformatics community.

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bioRxiv2025

In silico discovery of pathogenic PD-L1 nsSNVs with altered glycosylation and immunotherapy binding

Immune checkpoint inhibitors (ICIs), particularly anti-PD-L1 monoclonal antibodies, block extracellular interactions between programmed death ligand-1 (PD-L1) and programmed cell death protein-1 (PD-1) to enhance antitumour immunity. Here, we present a streamlined workflow integrating public databases, bioinformatics tools, and in silico molecular dynamics (MD) simulations and in vitro experimental assays to identify consequential pathogenic nonsynonymous PD-L1 variants (PD-L1SNVs). Four variants predicted as pathogenic across six bioinformatic tools (SIFT, PolyPhen-2, PANTHER, SNP-PhD, SNP&GO, and Pmut) destabilized the anti-PD-L1 ICI atezolizumab-drug binding epitope in MD studies, correlating with reduced drug affinity. Live-cell assays directly linked PD-L1SNVs to intracellular trafficking defects and aberrant glycosylation, even distal from N-glycosylation sites. Allostery modeling (AlloSigMA) linked these disruptions to long-range structural perturbations. Our work establishes bioinformatics-driven predictions of functionally impactful pathogenic PD-L1SNVs and highlight how glycosylation/trafficking defects may serve as key mechanisms compromising therapeutic efficacy. These insights in predicting and validating pathogenic PD-L1SNVs has significant implications for guiding the personalized selection of effective PD-L1-targeted therapies in clinical settings. One Sentence Summary: Uncovering the implication of non-synonymous single nucleotide variants (nsSNVs) on the structure and function of Programmed Death Ligand-1.

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bioRxiv2025

Comparative Analysis of De Novo Assemblers and Quantification Software for RNA-sequencing Data in Non-Model Arthropods

BackgroundRNA-sequencing has greatly improved our understanding of the transcriptomic regulation of fundamental biological processes. Although the method has matured significantly within the last decade, bioinformatic processing of the resulting high-dimensional data sets is still challenging and the performance of algorithms can vary between data sets. As a consequence, for most non-model organisms, in particular arthropods, there is no or limited literature evidence which software is best suited to handle taxon-specific data characteristics. Therefore, we evaluated the performance of different de nonvo transcriptome assembler (Trinity, rnaSPAdes, IDBA-tran) and transcript quantification software (RSEM, Salmon) on transcriptomic data of a non-model insect and freshwater crustacean species, as well as the impact of different quality trimming strategies on the downstream bioinformatic processing results. ResultsWhile the trimming strategy had no considerable effect on the quality of transcriptome assemblies, the choice of the assembler had a substantial impact. IDBA-tran was less sensitive than the two other assemblers and produced the most fragmented transcriptome assemblies. The low remapping rates of reads against IDBA-tran assemblies further suggest that the input read data was not effectively leveraged by this algorithm. In contrast, Trinity and rnaSPAdes both generated comprehensive and contiguous de novo transcriptome assemblies, although Trinity appeared to be slightly more sensitive. This increased sensitivity, however, was associated with a higher redundancy in Trinity-generated assemblies compared to assemblies produced with rnaSPAdes. When the quality of the transcriptome assembly was high, RSEM and Salmon were able to identify the origin of at least 90% of the read data in the reference. Despite their different underlying quantification approaches, the estimated transcript counts of both tools were highly correlated and their expression signal was consistent. Notably, the alignment-free quantification algorithm Salmon was substantially faster than the alignment-based approach of RSEM. Furthermore, it was also slightly more sensitive, increasing the average re-mapping rate to [~]98%. ConclusionSince the performance of bioinformatic algorithms, especially of de novo assemblers, varies for different RNA-sequencing data sets, establishing an appropriate analysis workflow remains an important task. Our results show that the better performing combinations of algorithms produce congruent count data sets with consistent expression signal, highlighting the robustness of RNA-sequencing data analysis software.

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bioRxiv2025

ViralQuest: A user-friendly interactive pipeline for viral-sequences analysis and curation

BackgroundHigh-throughput sequencing (HTS) has become an essential, unbiased tool in virology for identifying known and novel viruses. However, analyzing the large and complex datasets generated by HTS presents significant bioinformatics challenges. The process of accurately identifying and characterizing viral sequences from assembled contigs remains a bottleneck, often requiring specialized expertise and involving non-standardized parameters. There is a pressing need for robust, user-friendly, and reproducible pipelines to streamline this post-assembly analysis. ResultsTo address these challenges, we developed ViralQuest, a bioinformatics tool that automates the in-depth characterization of viral sequences from pre-assembled contigs. The pipeline integrates multiple lines of evidence for robust identification, using Diamond BLASTx against the Viral RefSeq database and pyHMMER searches against the RVDB, Vfam, and eggNOG profile HMM databases. For detailed characterization, ViralQuest performs taxonomic classification based on the ICTV nomenclature and functional annotation via Pfam domain analysis. Novel features of ViralQuest include an AI-powered summarization module that uses a Large Language Model (LLM) to generate contextual narratives for key viral findings and a comprehensive confidence score to rank putative viral contigs. All results are consolidated into a single, interactive HTML report that includes dynamic visualizations of contigs, ORFs, and protein domains, alongside detailed data tables that are exportable in TSV and SVG formats. ConclusionViralQuest provides an accessible and comprehensive solution for the post-assembly analysis of viral metagenomic data. By combining rigorous bioinformatics methods with novel AI-driven features and an intuitive reporting interface, it streamlines the complex process of viral identification and characterization. The tool enhances the interpretability and reliability of results, making in-depth virome analysis more accessible to the broader research community. ViralQuest is available on GitHub at https://github.com/gabrielvpina/viralquest/.

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bioRxiv2025

Powerful read processing with matchbox

The wide variety of protocols and applications for DNA and RNA sequencing makes flexible tools for read processing an important step in sequence analysis. Beyond trimming and demultiplexing, custom read-level processing is commonly required for data exploration, QC and analysis. Existing tools are often task-specific and dont generalise to new bioinformatic problems. Thus, there is a need for a tool flexible enough to handle the full variety of read processing tasks, and fast and scalable enough to retain high performance on growing sequencing datasets. We introduce matchbox, a read processor that enables fluent manipulation and analysis of FASTA/FASTQ/SAM/BAM files. With a lightweight scripting language designed around error-tolerant pattern-matching, users can write their own matchbox scripts to tackle a wide variety of bioinformatic problems, and incorporate them into existing pipelines and work-flows. We demonstrate matchboxs versatility in a number of contexts: demultiplexing long-read scRNA-seq data with 10X or SPLiT-seq barcodes; restranding RNA-seq reads; assessing CRISPR editing efficiency; and haplotyping macrosatel-lite repeat regions. matchbox achieves a computational performance comparable to existing tools, while addressing a broader range of bioinformatic needs, representing a new state-of-the-art in sequence processing. matchbox is implemented in Rust and available open-source at https://github.com/jakob-schuster/matchbox.

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