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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Clustering of Circular Consensus Sequences: Accurate Error Correction and Assembly of Single Molecule Real-Time Reads from Multiplexed Amplicon Libraries

BACKGROUNDTargeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical challenge for research and applications using HTS data, especially from long-read platforms, is errors arising from technological limits and bioinformatic algorithms.\n\nRESULTSA single molecule real-time (SMRT) sequencing-error correction and assembly pipeline, C3S-LAA, was developed for libraries of pooled amplicons. By uniquely leveraging the structure of SMRT sequence data (comprised of multiple low quality subreads from which higher quality circular consensus sequences are formed) to cluster raw reads, C3S-LAA produced accurate consensus sequences and assemblies of overlapping amplicons from single sample and multiplexed libraries. In contrast, despite read depths in excess of 100X per amplicon, the standard long amplicon analysis module from Pacific Biosciences generated unexpected numbers of amplicon sequences with substantial inaccuracies in the consensus sequences. A bootstrap analysis showed that the C3S-LAA pipeline per se was effective at removing bioinformatic sources of error, but in rare cases a read depth of nearly 400X was not sufficient to overcome minor but systematic errors inherent to amplification or sequencing.\n\nCONCLUSIONSC3S-LAA uses a novel processing algorithm for SMRT amplicon-sequence data that produces accurate consensus sequences and local sequence assemblies. The community standard long amplicon analysis module from Pacific Biosciences is prone to substantial errors that raise concerns about findings based on this pipeline. The method developed here removed this confounding bioinformatics source of error, allowing for the identification of limited instances of errors due to DNA amplification or sequencing.

bioinformatics↗

bioRxiv2017

De novo profile generation based on sequence context specificity with the long short-term memory network

Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles. We employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information. We demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.

bioinformatics↗

bioRxiv2018

PEPOP: new approaches to mimic non-continous epitopes

Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits as replacing a protein difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitope predicted by bioinformatics tools are commonly used and these sequential epitopes are used as such in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools proposed so far to predict peptide sequences: Superficial and PEPOP. PEPOP can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody. We have developed an improved version of PEPOP dedicated to answer to the experimentalists need for a tool able to handle proteins and to turn them into peptides. The PEPOP web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.\n\nWe present the user-friendly, well-structured web-site of PEPOP and its validation through the use of predicted immunogenic or antigenic peptides mimicking discontinuous epitopes in different experimental ways. PEPOP proposes 35 methods of peptide design to guide experimentalists in using peptides potentially capable of replacing the cognate protein in its interaction with an Ab.

bioinformatics↗

bioRxiv2018

IsoProt: A fully reproducible one-stop-shop for the analysis of iTRAQ/TMT data

Reproducibility has become a major concern in biomedical research. In proteomics, bioinformatic workflows can quickly consist of multiple software tools each with its own set of parameters. Their usage involves the definition of often hundreds of parameters as well as data operations to ensure tool interoperability. Hence a manuscripts methods section is often insufficient to completely describe and reproduce a data analysis workflow. Here we present IsoProt: A complete and reproducible bioinformatic workflow deployed on a portable container environment to analyse data from isobarically-labeled, quantitative proteomics experiments. The workflow uses only open source tools and provides a user-friendly and interactive browser interface to configure and execute the different operations. Once the workflow is executed, the results including the R code to perform statistical analyses can be downloaded as an HTML or PDF document providing a complete record of the performed analyses. IsoProt therefore represents a reproducible bioinformatics workflow that will yield identical results on any computer platform.

bioinformatics↗

bioRxiv2018

doepipeline: a systematic approach for optimizing multi-level and multi-step data processing workflows

BackgroundSelecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.\n\nResultsWe present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.\n\nConclusionsOur proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.

bioinformatics↗

bioRxiv2019

Cerebro: Interactive visualization of scRNA-seq data

SummaryDespite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user.\n\nThrough an interactive and intuitive graphical interface, users can i) explore similarities and heterogeneity between samples and cells clusters in 2D or 3D projections such as t-SNE or UMAP, ii) display the expression level of single genes or genes sets of interest, iii) browse tables of most expressed genes and marker genes for each sample and cluster.\n\nWe provide a simple example to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists which facilitates effective interaction to shorten the gap between analysis and interpretation of the data.\n\nAvailabilityCerebro and example data sets are available at https://github.com/romanhaa/Cerebro. Similarly, the R packages cerebroApp and cerebroPrepare R packages are available at https://github.com/romanhaa/cerebroApp and https://github.com/romanhaa/cerebroPrepare, respectively. All components are released under the MIT License.

bioinformatics↗

bioRxiv2019

Thirty two novel nsSNPs May effect on HEXA protein Leading to Tay-Sachs disease (TSD) Using a Computational Approach

BackgroundGenetic polymorphisms in the HEXA gene are associated with a neurodegenerative disorder called Tay-Sachs disease (TSD) (GM2 gangliosidosis type 1). This study aimed to predict the possible pathogenic SNPs of this gene and their impact on the protein using different bioinformatics tools.\n\nMethodsSNPs retrieved from the NCBI database were analyzed using several bioinformatics tools. The different algorithms collectively predicted the effect of single nucleotide substitution on both structure and function of the hexosaminidase A protein.\n\nResultsFifty nine mutations were found to be highly damaging to the structure and function of the HEXA gene protein.\n\nConclusionAccording to this study, thirty two novel nsSNP in HEXA are predicted to have possible role in Tay-Saches Disease using different bioinformatics tools. Our findings could help in genetic study and diagnosis of Tay-Saches Disease.

bioinformatics↗

bioRxiv2019

Tximeta: reference sequence checksums for provenance identification in RNA-seq

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

bioinformatics↗

bioRxiv2019

The Association between SLC25A15 Gene Polymorphisms and Hyperornithinemia-hyperammonemia-homocitrullinuria Syndrome: Using In Silico Analysis

BackgroundHyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome is an autosomal recessive inborn error of the urea cycle. It is caused by mutations in the SLC25A15 gene that codes the mitochondrial ornithine transporter. The aim of this study is to detect and identify the pathogenic SNPs in SLC25A15 gene through a combination set of bioinformatics tools and their effect on the structure and function of the protein.\n\nMethodsThe deleterious SNPs in SLC25A15 are detected by various bioinformatics tools, with addition to identifying their effects on the structure and function of this gene.\n\nResults20 deleterious SNPs out 287of were found to have their own damaging effects on the structure and function of the SLC25A15 gene.\n\nConclusionThis study is the first in silico analysis of SLC25A15 using a selection of bioinformatics tools to detect functional and structural effects of deleterious SNPs. Finding the pathogenic SNPs is a promising start to innovate new, useful SNP diagnostic markers for medical testing and for safer novel therapies specifically targeting mutant SLC25A15.

bioinformatics↗

bioRxiv2026

MOAflow: how re-design a pipeline with Nextflow streamlines data analysis

BackgroundAdvances in high-throughput DNA sequencing technologies have dramatically reduced the time and cost required to generate genomic data. As sequencing is no longer a limiting factor, increasing attention must be paid to optimizing the analyses of the large-scale datasets produced. Efficient processing of such data is essential to reduce computational time and operational costs. In this context, workflow management systems (WMSs) have become key instruments for orchestrating complex bioinformatic pipelines. Among these systems, Nextflow has emerged as one of the most widely adopted solutions in bioinformatics. MethodsTo improve scalability and computational efficiency, we employed Nextflow to re-design an already existing pipeline dedicated to the analysis of MNase-defined cistrome-Occupancy (MOA-seq) data. The re-engineering process focused on modularizing the workflow and integrating containerization technologies to ensure reproducibility and easier deployment across heterogeneous computing environments. ResultsThe resulting workflow, named MOAflow, represents a modernized and fully containerized pipeline for MOA-seq data analysis. With only Docker and Nextflow required, the pipeline guarantees high portability and reproducibility. The data of the original article was used to benchmark the new pipeline. Its outputs closely match those of the original study with minor variations. ConclusionsMOAflow demonstrates how the adoption of robust WMS can substantially enhance the performance and usability of pre-existing bioinformatic pipelines. By leveraging containerization and Nextflow, it ensures consistent results across platforms while minimizing setup complexity. This work highlights the value of modern WMS-driven approaches in meeting the computational demands.

bioinformatics↗

bioRxiv2026

EV-Net: A computational framework to model extracellular vesicles-mediated communication

SummaryExtracellular vesicles (EVs) are bilayer vesicles that carry a diverse cargo of molecules, such as nucleic acids, proteins and metabolites. These EVs can be transported throughout the organism to specific recipient tissues. For this reason, EVs have been recognized as pivotal mediators of cell-to-cell communication (CCC). Importantly, alterations in EV-mediated communication have been linked to pathological processes, further highlighting their biological relevance. However, the in silico exploration of the functional effects of EV cargo in recipient tissues remains limited due to the lack of dedicated tools that can be applied to EV omics datasets. Most current bioinformatics tools for assessing CCC rely on ligand-mediated communication and therefore cannot be used to explore EV-mediated communication. To address this gap, we developed EV-Net, a bioinformatics tool designed to explore the effects of EV cargo on recipient tissues. EV-Net was built by adapting NicheNet, a CCC bioinformatics tool that relies on ligand-receptor mediated communication, for the analysis of EVs proteomics and RNA-seq data. The EV-Net framework enables the identification and prioritization of EV cargo molecules with high regulatory potential in a recipient tissue of interest. This prioritization facilitates the systematic translation of EV cargo profiles into testable biological hypotheses. Availability and documentationThe source code of EV-Net is stored in GitHub https://github.com/torrejoNia/EV-Net alongside instructions on how to install it. Comprehensive tutorials and additional documentation are available at https://torrejonia.github.io/EV-Net/. The datasets used in the use cases were deposited in Zenodo. The corresponding Zenodo links are provided in the tutorials for each use case. This software is distributed under a GLP3 licence.

bioinformatics↗

bioRxiv2026

Skill-Augmented Frontier Agents Nearly Saturate BixBench-Verified-50

Large language model (LLM) agents are increasingly used for biological data analysis, but prior benchmark results have given a mixed picture of whether they are ready for routine bioinformatics work. The original BixBench study reported only [~] 17-21% accuracy for frontier agents on open-answer bioinformatics questions [1]. Subsequent curation of BixBench-Verified-50 removed or revised ambiguous items, revealing much higher performance for modern agents [2]. Here we evaluate three frontier-model configurations on the 50 verified questions using the same local benchmark, prompt structure, answer format, and grading pipeline: GPT-5.4 with Claude Scientific Skills and no web access, Claude Opus 4.7 with Claude Scientific Skills and no web access, and GPT-5.5 with Claude Scientific Skills, bioSkills, and web access. The three configurations achieve 88.0% (44/50), 84.0% (42/50), and 98.0% (49/50) accuracy, respectively. The remaining GPT-5.5 error is not a clear analytical failure: the agent correctly computed Spearman correlations on the distributed CRISPRGeneEffect.csv values and selected CCND1, whereas the reference answer is recovered only after interpreting stronger essentiality as the opposite sign of the raw gene-effect score. Offline errors mainly occurred when agents lacked pathway, organism-annotation, BUSCO, or PhyKIT-related resources. These results show that frontier agents equipped with high-quality scientific skills can nearly saturate a curated bioinformatics benchmark, while also emphasizing that question wording, score sign conventions, and access to current external resources remain decisive for reliable evaluation.

bioinformatics↗

bioRxiv2026

Benchmarking full-length ITS metabarcoding across Illumina 2x500, PacBio, and Oxford Nanopore sequencing using mock and soil communities

Metabarcoding is a powerful tool for biodiversity comparisons, where standard-size DNA barcodes (>500 bases) offer better taxonomic resolution than shorter ones. Still, the choice of sequencing platforms and bioinformatics pipelines may strongly affect inferred diversity due to various technical biases. We assessed the relative performance of Illumina MiSeq i100 (2x500 paired-end), PacBio Revio and Oxford Nanopore MinION sequencing and bioinformatics pipelines, using full-length ITS amplicon sequencing datasets from a 103-species mock community and 45 composite soil samples. Despite numerous low-quality reads, PacBio yielded the lowest overall error rate and highest number of taxa. Illumina revealed the highest proportion of chimeric and index-switched reads, along with a strong bias towards shorter amplicons. MinION data analysed using PRONAME and Minovar - a bioinformatics pipeline presented here - had the largest proportion of low-quality data, and rare taxa were lost during data filtering and read polishing steps. Although Minovar enabled amplicon sequence variant (ASV) level precision for common taxa, we recommend clustering ASVs into OTUs. For PacBio, standard filtering approaches outperformed the ASV approach because they retained rare taxa. For Illumina, a stringent ASV approach or removal of rare OTUs would limit artefacts. Across all platforms, excess PCR cycles promoted chimeric and low-quality reads and lost quantitativity in biodiversity assessments. With moderate differences in effect sizes, all analytical approaches supported the conclusion that sampling design determines how we see soil biodiversity responses to land use. For biodiversity surveys based on the full-length ITS metabarcoding, we recommend using PacBio sequencing with standard, non-ASV pipelines.

bioinformatics↗

bioRxiv2026

Benchmarking sequence performance on the DNBSEQ-T7 using Genome in a Bottle reference genomes

Advances in sequencing technologies have improved the accuracy, throughput, and completeness of human genome characterization, enabling more reliable detection of genetic variation. Well-characterized reference genomes are critical for benchmarking sequencing platforms and bioinformatics analysis pipelines. Here, we present whole genome sequencing datasets generated for the Ashkenazi Jewish trio reference samples from the Genome in a Bottle Consortium. Libraries were prepared using three distinct MGI-based workflows: PCR-free library preparation, FastFS DNA library preparation, and Universal DNA library preparation. Sequencing was performed on the MGI DNBSEQ-T7 platform, generating a minimum of 400 million paired-end reads per sample, corresponding to 30X mean genome coverage. Raw reads were processed using a standardized GATK bioinformatics workflow. Sequencing performance and variant detection accuracy were evaluated using the Genome in a Bottle high-confidence benchmark variant sets. All workflows demonstrated high sequencing quality and concordance with GIAB benchmark truth sets, with PCR-free libraries showing the strongest indel calling performance and lowest Mendelian violation rates across the Ashkenazi trio. This dataset provides a resource for benchmarking DNBSEQ-T7 sequencing and bioinformatics workflows, and for evaluating the impact of library preparation strategies on whole genome variant detection performance.

bioinformatics↗

bioRxiv2026

Taxonomic profilers and their influence on metagenomic diversity analyses

Estimating taxonomic profiles is a central task in microbiome research. Several bioinformatic tools have been developed for this purpose, differing in algorithmic strategy, reference database flexibility, sensitivity parameters, and the type of abundance they estimate. As a result, taxonomic profiles carry an unwanted methodological signal whose driving characteristics remains understudied. While benchmarks have evaluated the performance of some of these tools, they rely on simulated data; little work has been done to compare them using real metagenomes in the presence of noise and uncharacterised diversity. Overall, the impact of taxonomic profiler choice and parameterisation on scientific conclusions remains poorly understood. First, we provide a much-needed characterisation of four taxonomic profilers to help researchers better understand the available bioinformatic tools and inform their methodological choices. Then, we leverage 1,211 shotgun metagenomes from eight datasets to compare these taxonomic profilers across 13 methodological designs. Based on diversity indices, we found substantial variability in estimated taxonomic composition depending on methodological features such as reference database and algorithmic strategy. Alpha diversity analysis was substantially sensitive totool choice (particularly among k-mer-based tools) and reference database. Beta diversity showed sensitivity to both database and parameter choices, yet this variability barely affected statistical inference. Our findings highlight the sensitivity of taxonomic diversity analyses to taxonomic profiling methodology and the importance for researchers to consider assessing the robustness of their results to choice of tool, parameter, and reference database. Crucially, differences in sample diversity across methodologies are symptomatic of differences in estimated taxonomic composition, which can affect any analysis based on taxonomic abundances. Overall, this study underscores the importance of tool selection and parametrisation, and of conducting sensitivity analyses to support robust and reliable scientific conclusions. AUTHOR SUMMARYMicrobiome research relies on bioinformatic tools to determine which microbes are present in a sample and estimate their relative abundances, a process known as taxonomic profiling. Because this task requires comparing tens of millions of DNA sequences against thousands of microbial genomes, a wide range of computational strategies have been developed to make profiling accurate and efficient. As a result, many taxonomic profilers are now available, yet do not provide the same results. Although previous studies have evaluated their precision and sensitivity, less attention has been given to how the choice of profiler may influence the scientific conclusions drawn from microbiome data. Here, we selected four widely used taxonomic profilers to examine whether researchers would reach the same biological conclusions when comparing microbiomes across groups of samples, such as healthy and diseased individuals. We show that different profilers can lead to different biological conclusions and identify key tool characteristics that contribute to these discrepancies. Moreover, we provide a comparative overview of these four profilers, offering practical guidance for researchers seeking to choose the most appropriate tool for their study. These findings highlight the importance of software choice in microbiome research and support more transparent and reproducible data analysis practices.

bioinformatics↗

bioRxiv2026

Accounting for allelic diversity and multicopy gene detection improves the accuracy of antibiotic resistance genotypic determination

BackgroundGenomic prediction of antimicrobial resistance (AMR) relies on the accurate detection of resistance genes or allelic variants of core genes from raw or assembled genomes sequences. For several bacterial species and antibiotics, AMR genotype-phenotype discrepancies are common, indicating that important sources of error remain unresolved. For Enterococcus faecium, we focused on identifying the sources of discrepancies for tetracycline resistance, for which genotypic detection had shown particularly low accuracy. We investigated the effect of structural variation in antibiotic resistance genes (ARGs)--including gene duplications, truncations, interruptions, and mixed configurations of complete and partial gene copies-- as a source of genotype-phenotype discrepancies from short{square}read data. We conduct further extended investigations to other antibiotic families and into another bacterial species: Escherichia coli. MethodsWe analyzed collections of E. faecium and E. coli genomes, integrating high{square}quality complete assemblies, simulated Illumina short reads, and matched AMR phenotypic data. The integrity, copy number, and allelic diversity of ARGs were examined for multiple antibiotic classes, and their impact on ARG detection and accuracy of AMR determination was assessed using several commonly used bioinformatic tools (SRST2, ARIBA and AMRFinderPlus). ResultsFor E. faecium, after ruling out the effect of specific tet allelic variants on tetracycline susceptibility, we found that the integrity and copy number of tet(M) had a major effect on detection accuracy. Duplicated and incomplete ARGs are also common in E. faecium genomes, particularly for macrolides (erm(B)) and aminoglycosides (ant(6)-Ia and aph(3)-IIIa). In E. coli, similar patterns were observed for tet(A), erm(B) and aminoglycoside{square}associated genes (aph(3{square})-IIIa and ant(6)-Ia). Across ARGs in both species, short-read mapping methods wrongly reported interrupted genes as complete in some instances, while assembly{square}based methods often failed to resolve complete copies of duplicated genes. Detection accuracy improved when tools were adapted to account for gene integrity and when extended AMR databases incorporating species{square}specific alleles were included. ConclusionsOur findings reveal that bioinformatic limitations in dealing with ARG copy number and completeness, and in accounting for allelic variation, underly a substantial source of genotype-phenotype errors, highlighting the need for improved AMR databases and bioinformatic tools that consider these factors to achieve reliable genomic prediction of AMR.

bioinformatics↗

bioRxiv2026

Client-server interfaces enable efficient agent-driven variant calling

BackgroundLarge language model (LLM) agents increasingly automate bioinformatics analyses, but most existing bioinformatics tools were built for standalone use by human experts. An agent driving such a tool must reason about its installation, configuration, and execution from documentation for human, spending many turns, tokens, and tool calls per result. How a method is exposed to an agent can therefore matter as much as the method itself. By designing agentic interfaces for these tools, agent can reduce such overhead and improve the reliability of agent-driven analyses. FindingsTo test this design, we re-architected Clair3, a widely used deep-learning-based long-read variant caller, into a client-server system, Clair3-Connect. The client performs all genomics related processing and holds the identifiable data. The server runs only neural-network inference, and the client sends only feature tensors to the server, while sample identifiers and genomic context remain on the client. The client exposes schema-defined agent-facing tools that an agent invokes through single structured calls. On an APOE diplotyping task, all 60 agent runs were correct. The agentic tools used 12K tokens in 3 turns, 6.8 to 14 times fewer tokens than the shell-driven baselines (81K-163K tokens), at about a quarter the wall-clock time and far more stably (4% versus 35% token usage variation). Dropping the pileup and phasing stages to keep the client light left SNP F1 within 0.1-0.3 points of standard Clair3 by 50x coverage, while mutual TLS and AES-256-GCM encryption added 7.2% to end-to-end runtime. ConclusionsRecasting an established algorithm as developer-built, agentic tools behind a secure client-server boundary makes it more efficient, reliable, and easier to deploy for an LLM agent than a third-party wrapper, which cannot recover the defaults and conventions only its developers know. Agentic interfaces should be a first-class deliverable of bioinformatics tool development.

bioinformatics↗

bioRxiv2017

Epigenetic Regulation of Gene Expression in Cancer: Techniques, Resources, and Analysis

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, non-coding RNAs, post-translational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations, once thought to be crucial only for the malignant phenotype maintenance, are now recognized as critical also for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. While associated with cancer, the precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems, and bioinformatics algorithms for their analysis. Advances in bioinformatics data integration techniques that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer, and are therefore also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations to cause tumorigenesis initiation and progression. This deeper understanding is essential to future studies that will precisely infer patients prognosis and select patients who will be responsive to emerging epigenetic therapies.

cancer biology↗