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Bioinformatics

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bioRxiv2024

Identification of key genes and signaling pathway in the pathogenesis of Huntington's disease via bioinformatics and next generation sequencing data analysis

Huntingtons disease (HD) is the primary cause of progressive motor deficits, psychiatric symptoms, and cognitive impairment. The exact molecular mechanisms of HD pathogenesis are largely unknown. This investigation aims to identify the hub genes, miRNA and TFs in HD and explore their potential molecular regulatory network. Next generation sequencing (NGS) dataset (GSE105041) was extracted from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), Gene ontology and REACTOME pathway enrichment analysis, protein-protein interaction (PPI) network and module analysis, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network analysis, and receiver operating characteristic curve (ROC) analysis were applied to identify hub genes, miRNA and TFs, and key drivers of HD pathogenesis. We identified 958 DEGs in the discovery phase, consisting of 479 up regulated genes and 479 down regulated genes. GO and REACTOME enrichment analyses of the 479 up regulated genes and 479 down regulated genes showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and MHC class II antigen presentation. Further analysis of the PPI network using Cytoscape and PEWCC plugins identified 10 hub genes, including LRRK2, MTUS2, HOXA1, IL7R, ERBB3, EGFR, TEX101, WDR76, NEDD4L and COMT. Possible target miRNAs and TFs, including hsa-mir-1292-5p, hsa-mir-4521, ESRRB and SREBF1, were predicted by constructing a miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network. This investigation used bioinformatics methods to explore the molecular pathogenesis of HD, and identified potential molecular markers. These molecular markers might provide novel ideas and methods for the early diagnosis, treatment, and monitoring of HD.

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bioRxiv2024

Identification of Hub Genes and CDK1-Targeting Therapeutics in Hepatocellular Carcinoma: Bioinformatics and Simulation Study

BackgroundHepatocellular carcinoma (HCC) remains a significant global health challenge, often associated with chronic hepatitis B (HBV) and hepatitis C (HCV) infections. Novel therapeutic approaches are urgently needed to improve patient outcomes. ObjectiveTo identify and validate potential therapeutic targets and novel drug candidates for HBV, HCV, and associated HCC using integrative bioinformatics and molecular dynamics simulations. MethodsGene expression datasets from HCC studies were analyzed to identify dysregulated genes. Protein-protein interaction networks were constructed to determine hub genes. Virtual screening of kinase compound libraries against CDK1 was performed, followed by molecular docking and dynamics simulations. Drug-likeness and metabolic properties of hit compounds were evaluated. ResultsSeven hub genes were identified, including five upregulated (CDK1, AURKB, CCNA2, BUB1B, RRM2) and two downregulated (AGXT2, ESR1) genes. Virtual screening yielded three promising hit compounds (G213-0272, E130-0174, Z666960592) targeting CDK1. E130-0174 demonstrated the most favorable profile, with stable CDK1 binding and no inhibition of major cytochrome P450 enzymes. Two repurposed drugs, alvocidib and riviciclib, also showed potential as CDK1 inhibitors. ConclusionsThis study identified novel CDK1 inhibitors with promising profiles for HCC treatment. The integrative approach combining bioinformatics and molecular dynamics simulations provides a robust framework for identifying potential therapeutic targets and drug candidates in HCC and related liver diseases. Further experimental validation is warranted to confirm these findings and advance the development of new HCC treatments.

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bioRxiv2025

g.nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis by Non-Computational Biologists

Reproducibility and accessibility are cardinal principles in the rapidly evolving field of bioinformatics. As the collection of biological data grows, proper use of pipelines to analyze datasets can become a bottleneck restricting efficient analysis. Biologists who collect data and test hypotheses may not have strong computational backgrounds and may not be able to fully understand the underlying strengths and weaknesses of computational approaches or fully exploit their data. Some data may be misunderstood and, perhaps more importantly, critical findings may remain unobserved. High throughput RNA sequencing (RNAseq) has advanced our understanding of transcriptomics across diverse applications. Here we introduce g.nome, a bioinformatics platform that integrates contemporary tools necessary for independent analysis. A user-friendly graphical interface simplifies running jobs and allows simplified analysis of different datasets by non-bioinformaticians. g.nome was used to analyze the consequences of localizing the critical RNAi factor argonaute (AGO) to nuclei of colorectal cancer cell line HCT116. Analysis using the pipeline facilitated the straightforward identification of splicing changes and the prioritization of these splicing changes for validation and further experimental analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/652286v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f0fa7org.highwire.dtl.DTLVardef@ccac5borg.highwire.dtl.DTLVardef@147c331org.highwire.dtl.DTLVardef@5fe66c_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

ECCFP: a consecutive full pass based bioinformatic analysis for eccDNA identification using Nanopore sequencing data

It is commonly known that extrachromosomal circular DNA (eccDNA) has the potential as a molecular marker because of its close relationship with cancer progress and its prevalent existence in eukaryotic organisms. The mainstream technique of eccDNA detection is using high-throughput sequencing supported by bioinformatics analysis. Although these have various analysis pipelines for sequencing data, they are restricted by sequencing platforms or have shortcomings in accuracy and efficiency. To address these limitations, we design ECCFP, a bioinformatic analysis pipeline that detects eccDNAs amplified by rolling circle amplification (RCA) from long-read sequencing data and outputs eccDNA genomic coordinates and consensus sequences. This pipeline proposes a rigorous algorithm to retain all consecutive full passes derived from individual reads to obtain candidate eccDNAs, followed by systematic consolidation of candidate eccDNAs to detect unique eccDNAs. Using simulated datasets and experimental eccDNA sequencing datasets, we estimated ECCFP in several aspects and compared it with other existing pipelines. It exhibits a marked reduction in false positive rates compared with eccDNA_RCA_nanopore and superior sensitivity relative to CReSIL and FLED. Besides, inverse PCR and Sanger sequencing further validated the existence and accuracy of the position of detected eccDNAs by ECCFP. Collectively, ECCFP provides a more efficient choice for eccDNA detection from long-read sequencing data.

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bioRxiv2025

Unveiling the evolutionary code of NOTCH3: mammalian bioinformatics sheds light on human pathogenicity.

NOTCH3 is a highly conserved transmembrane receptor implicated in CADASIL, a hereditary small vessel disease driven by mutations in its extracellular EGF-like repeats. The mechanism by which these mutations cause pathology remains unclear. We present the first large-scale comparative bioinformatic analysis of NOTCH3 across 113 mammalian species, uncovering three novel insights: i) a remarkable evolutionary conservation of all 204 cysteines, with the only exception being eight naturally occurring cysteine mutations in jaguar (EGFr13-15); ii) a unique deletion in Brandts bat regulatory region, which may expose it to proteases, potentially altering signaling; iii) a rare human NOTCH3-X1 isoform, absent in most mammals but shared with select primates, a bat, and elephants, involving a cysteine-depleting deletion spanning EGFr20-22. These features provide novel evolutionary insights into human pathogenicity and suggest testable targets for in vivo experiments. Our study highlights the potential of comparative bioinformatics to identify previously hidden functional elements in disease-associated mammalian proteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/678708v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@5b54cforg.highwire.dtl.DTLVardef@1d0725org.highwire.dtl.DTLVardef@1c0417corg.highwire.dtl.DTLVardef@10b4b5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

Bioinformatic inference of the exercise-responsive control of p70 S6 kinase through RPS6KB1 expression

Previous research suggests that the absolute levels of p70 S6 kinase (p70S6K) are a key determinant of the rate of skeletal muscle protein synthesis (MPS). p70S6K levels are in part determined by the transcriptional control of the gene encoding p70S6K, RPS6KB1, but the molecular mechanisms governing its expression are poorly understood. The purpose of this study was to infer the molecular regulatory network governing RPS6KB1 expression. We applied a novel bioinformatic network inference algorithm called CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) to infer the signaling network downstream of canonical exercise sensors controlling RPS6KB1-specific transcription factors (TFs) after acute aerobic (AE) or resistance exercise (RE). CARNIVAL integrates a prior knowledge network, TF and signaling pathway activities inferred from transcriptomic data, and perturbation targets to predict the network that best explains the data. The networks revealed intracellular sensors and hormone receptors controlling RPS6KB1-specific TFs. Both exercise types resulted in AMPK-mediated SNAI1 regulation, but HIF1A was distinctly controlled (AE: PHD1-3, FIH; RE: AMPK). AE controlled FOXA1 via insulin, TGF-{beta}, and myostatin signalling, while RE controlled CEBPA via MAP3Ks. Our study is the first to apply a comprehensive bioinformatic network inference algorithm to infer causal exercise-responsive signaling networks. The results of our analysis motivate experimentally testable hypotheses pertaining to the molecular control of RPS6KB1 transcription in human skeletal muscle in response to aerobic and resistance exercise.

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bioRxiv2017

Analytic combinatorics for bioinformatics I: seeding methods

Seeding heuristics are the most widely used strategies to speed up sequence alignment in bioinformatics. Such strategies are most successful if they are calibrated, so that the speed-versus-accuracy trade-off can be properly tuned. In the widely used case of read mapping, it has been so far impossible to predict the success rate of competing seeding strategies for lack of a theoretical framework. Here I present an approach to estimate such quantities based on the theory of analytic combinatorics. In a nutshell, the strategy is to specify a combinatorial construction of reads where the seeding heuristic fails, translate this specification into a generating function using formal rules, and finally extract the probabilities of interest from the singularities of the generating function. I use this approach to construct simple estimators of the success rate of the seeding heuristic under different types of sequencing errors. I also show how the analytic combinatorics strategy can be used to compute the associated type I and type II error rates (mapping the read to the wrong location, or being unable to map the read). Finally, I show how analytic combinatorics can be used to estimate average quantities such as the expected number of errors in reads where the seeding heuristic fails. Overall, this work introduces a theoretical and practical framework to find the success rate of seeding heuristics and related problems in bioinformatics.

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bioRxiv2018

WAVES: a Web Application for Versatile Enhanced bioinformatic Services

SummaryWAVES is a web application dedicated to bioinformatic tool integration. It provides an efficient way to implement a service for any bioinformatic software. Such services are automatically made available in three ways: web pages, web forms to include in remote websites, and a RESTful web services API to access remotely from applications. In order to fulfill the services computational needs, WAVES can perform computation on various resources and environments, such as Galaxy instances.\n\nAvailability and implementationWAVES was developed with Django, a Python-based web framework. It was designed as a reusable web application. It is fully portable, as only a Python installation is required to run Django. It is licensed under GNU General Public License. Source code and documentation are available from https://github.com/lirmm/waves-core.\n\nContactlefort@lirmm.fr

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bioRxiv2018

SATurn: A modular bioinformatics framework for the design of robust maintainable web-based and standalone applications

SummarySATurn is a modular, open-source, bioinformatics platform designed to specifically address the problems of maintenance and longevity commonly associated with the development of simple tools funded by academic research grants. Applications developed in SATurn can be deployed as web-based tools, standalone applications, or hybrid tools which have the benefits of both. Within the Structural Genomics Consortium (SGC) we have utilized SATurn to create a bioinformatics portal which routinely supports a diverse group of scientists including those interested in structural biology, cloning, glycobiology & chemicalbiology.\n\nSupplementary information:

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bioRxiv2018

Next-generation Sequence-analysis Toolkit (NeST): A standardized bioinformatics framework for analyzing Single Nucleotide Polymorphisms in next-generation sequencing data

Rapid advancements in next-generation sequencing (NGS) technologies have led to the development of numerous bioinformatics tools and pipelines. As these tools vary in their output function and complexity and some are not well-standardized, it is harder to choose a suitable pipeline to identify variants in NGS data. Here, we present NeST (NGS-analysis Toolkit), a modular consensus-based variant calling framework. NeST uses a combination of variant callers to overcome potential biases of an individual method used alone. NeST consists of four modules, that integrate open-source bioinformatics tools, a custom Variant Calling Format (VCF) parser and a summarization utility, that generate high-quality consensus variant calls. NeST was validated using targeted-amplicon deep sequencing data from 245 Plasmodium falciparum isolates to identify single-nucleotide polymorphisms conferring drug resistance. The results were verified using Sanger sequencing data for the same dataset in a supporting publication [28]. NeST offers a user-friendly pipeline for variant calling with standardized outputs and minimal computational demands for easy deployment for use with various organisms and applications.

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bioRxiv2018

Novel deleterious nsSNPs within MEFV gene that could be used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using bioinformatics analysis

BackgroundFamilial Mediterranean Fever (FMF) is the most common auto inflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin, we aimed to identify the pathogenic SNPs in MEFV by computational analysis software.\n\nMethodsWe carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function.\n\nResult23 novel mutations out of 857 nsSNPs that are found to be deleterious effect on the MEFV structure and function.\n\nConclusionThis is the first in silico analysis in MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.

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bioRxiv2019

Bioshake: a Haskell EDSL for bioinformatics pipelines

BackgroundTypical bioinformatics analysis comprise long running computational pipelines. An important part of producing reproducible research is the management and execution of these computational pipelines to allow robust execution and to minimise errors. Bioshake is an embedded domain specific language embedded in Haskell for specifying and executing computational pipelines in bioinformatics that significantly reduces the possibility of errors occurring. ResultsUnlike other pipeline frameworks, Bioshake raises many properties to the type level to allow the correctness of a pipeline to be statically checked during compilation, catching errors before any lengthy execution process. Bioshake builds on the Shake build tool to provide robust dependency tracking, parallel execution, reporting, and resumption capabilities. Finally, Bioshake abstracts execution so that jobs can either be executed directly or submitted to a cluster. ConclusionsBioshake is available at http://github.com/papenfusslab/bioshake.

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bioRxiv2019

Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction

AI recently shows great promise in the field of bioinformatics, such as protein structure prediction. The Critical Assessment of protein Structure Prediction (CASP) is a nationwide experiment that takes place biannually, which centered around analyzing the best current systems for predicting protein tertiary structures. In this paper, we research on available AI methods and features, and then explore novel methods based on reinforcement learning. Such method will have profound implications for R&D in bioinformatics and add an additional platform to the management of innovation in biotechnology.

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bioRxiv2019

Comprehensive bioinformatics analysis of L1CAM gene revealed Novel Pathological mutations associated with L1 syndrome

BackgroundMutations in the human L1CAM gene cause a group of neurodevelopmental disorders known as L1 syndrome (CRASH syndrome). The L1CAM gene provides instructions for producing the L1 protein, which is found all over the nervous system on the surface of neurons. L1 syndrome involves a variety of characteristics but the most common characteristic is muscle stiffness. Patients with L1 syndrome can also suffer from difficulty speaking, seizures, and underdeveloped or absent tissue connecting the left and right halves of the brain. MethodThe human L1CAM gene was studied from dbSNP/NCBI, 1499 SNPs were Homo sapiens; of which 450 were missense mutations. This selected for Comprehensive bioinformatics analysis by several in silico tools to investigate the effect of SNPs on L1CAM proteins structure and function. Results34 missense mutations (26 novel mutations) out of 450 nsSNPs that are found to be the most deleterious that effect on the L1CAM structural and functional level. ConclusionBetter understanding of L1 syndrome caused by mutations in L1CAM gene was achieved using Comprehensive bioinformatics analysis. These findings describe 35 novel L1 mutations which improve our understanding on genotype-phenotype correlation. And can be used as diagnostic markers for L1 syndrome and besides in cancer diagnosis specifically in breast cancer.

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bioRxiv2019

nf-core: Community curated bioinformatics pipelines

The standardization, portability, and reproducibility of analysis pipelines is a renowned problem within the bioinformatics community. Most pipelines are designed for execution on-premise, and the associated software dependencies are tightly coupled with the local compute environment. This leads to poor pipeline portability and reproducibility of the ensuing results - both of which are fundamental requirements for the validation of scientific findings. Here, we introduce nf-core: a framework that provides a community-driven, peer-reviewed platform for the development of best practice analysis pipelines written in Nextflow. Key obstacles in pipeline development such as portability, reproducibility, scalability and unified parallelism are inherently addressed by all nf-core pipelines. We are also continually developing a suite of tools that assist in the creation and development of both new and existing pipelines. Our primary goal is to provide a platform for high-quality, reproducible bioinformatics pipelines that can be utilized across various institutions and research facilities.

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bioRxiv2019

Radiator: a cloud-based framework for deploying reusable bioinformatics tools

SummaryAccessibility and usability of compute-intensive bioinformatics tools can be increased with simplified web-based graphic user interfaces. However, deploying such tools as web applications presents additional barriers, including the complexity of developing a usable interface, network latency in transferring large datasets, and cost, which we encountered in developing a web-based version of our command-line tool CellNet. Learning and generalizing from this experience, we have devised a lightweight framework, Radiator, to facilitate deploying bioinformatics tools as web applications. To achieve reproducibility, usability, consistent accessibility, throughput, and cost-efficiency, Radiator is designed to be deployed on the cloud. Here, we describe the internals of Radiator and how to use it.\n\nAvailability and ImplementationCode for Radiator and the CellNet Web Application are freely available at https://github.com/pcahan1 under the MIT license. The CellNet WebApp, Radiator, and Radiator-derived applications can be launched through public Amazon Machine Images from the cloud provider Amazon Web Services (AWS) (https://aws.amazon.com/).

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bioRxiv2019

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

IntroductionTumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment.\n\nObjectiveHere we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase 3 clinical trial.\n\nMethodsIn the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne(R) CDx assay.\n\nResultsTMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearmans r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearmans r = 0.90).\n\nConclusionsParameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.\n\nKey PointsO_LITumor mutational burden (TMB) is a clinically relevant biomarker for efficacy of immunotherapy in patients with cancer\nC_LIO_LIVariations in TMB assessment parameters can shift the final TMB value. Harmonization and standardization are important to the successful clinical implementation of TMB testing\nC_LIO_LITMB values assessed by different methods are highly correlated. Harmonization of TMB testing in patients with cancer is therefore achievable\nC_LI

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bioRxiv2019

An Accurate Bioinformatics Tool For Anti-Cancer Peptide Generation Through Deep Learning Omics

The Anti-cancer targets play crucial role in signalling processes of cells. We have developed an Anti-Cancer Scanner (ACS) tool for identification of Anti-cancer targets in form of peptides. ACS tool also allows fast fingerprinting of the Anti-cancer targets of significance in the current bioinformatics research. There are tools currently available which predicts the above-mentioned features in single platform. In the present work, we have compared the features predicted by ACS with other on-line available methods and evaluated the performance of the ACS tool. ACS scanned the Anti-cancer target protein sequences provided by the user against the Anti-cancer target data-sets. It has been developed in PERL language and it is scalable having an extensible application in bioinformatics with robust coding architecture. It achieves a prediction accuracy of 95%, which is much higher than the existing tools.

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