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PubMed2025

Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study.

BACKGROUND: Reliance on complex, custom-built bioinformatics pipelines is a barrier to the implementation of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in high-burden settings in some low-income and middle-income countries (LMICs). Automated analysis pipelines could address this inequity in access to WGS-based diagnostics and surveillance. This study aimed to systematically evaluate the performance and usability of publicly available WGS pipelines for M tuberculosis. METHODS: We identified automated M tuberculosis WGS analysis pipelines through searches of PubMed and GitHub from database inception up to Aug 31, 2024. Accuracy, cost, accessibility, and scalability were assessed for each pipeline. We evaluated the accuracy of genotypic drug susceptibility testing (gDST) using publicly available sequences with phenotypic susceptibility data for 12 antituberculosis drugs. We estimated pooled sensitivity and specificity for each pipeline, across all drugs, by conducting a bivariate meta-analysis, with random effects representing between-drug variability. Lineage classifications were compared, and a previously epidemiologically well-characterised dataset was used to compare measures of genomic relatedness. FINDINGS: Among 28 candidate pipelines, 16 were excluded as they were unmaintained and inexecutable. 12 pipelines (11 compatible with Illumina and four compatible with Nanopore), all free to use, were included for evaluation. Six pipelines processed and stored data remotely, but for five of these six, scalability was limited by the need to upload sequences through web portals. For local processing pipelines, scalability was dependent on substantial local computational resources, data storage capacity, and command-line interfaces that limited user-friendliness. Only one of six remote-processing pipelines removed human DNA sequences before server upload. gDST was similarly accurate across ten of 11 Illumina-compatible pipelines and three of four Nanopore-compatible pipelines. All pipelines classified the main lineages consistently, although there were differences at sublineage resolution. Outputs from three of four pipelines reporting genomic relatedness were compatible with commonly cited single nucleotide polymorphism difference thresholds. INTERPRETATION: Numerous automated analysis pipelines capable of enhancing equity in M tuberculosis WGS are available. Given the overall similarities between the pipelines evaluated in this study in terms of gDST performance, lineage classification, and genomic relatedness inference, non-functional attributes such as availability, accessibility, scalability, and privacy could represent the point of difference for prospective users in LMICs with a high burden of tuberculosis. FUNDING: The Rhodes Trust, Wellcome, Ellison Institute of Technology, and the UK National Institute for Health and Care Research Oxford Biomedical Research Centre.

Mycobacterium tuberculosis↗
PubMed2025

A genome-wide approach for the discovery of novel repeat expansion disorders in the Undiagnosed Diseases Network cohort.

PURPOSE: The Undiagnosed Diseases Network is a National Institutes of Health funded research study that aims to solve a broad clinical spectrum of challenging rare disease cases. Participants receive care from multiple clinical specialists, who collaborate to perform deep phenotyping and state-of-the-art multiomics analyses. As bioinformatics of short-read sequencing has matured, the discovery of repeat expansion disorders (REDs) is accelerating. REDs comprise approximately 60 characterized disorders, which exhibit a broad spectrum of phenotypes. Thus, a largely unbiased genome-wide approach in a phenotypically diverse sample will add to the diagnostic depth, explore the limits of short-read genome analysis, and establish novel candidate RED loci. METHODS: Here, we present a genome-wide analysis of repeat expansions conducted on 1018 genomes from the Undiagnosed Diseases Network. By leveraging 2 distinct bioinformatics tools, ExpansionHunter Denovo and STRling, we showed that repeat expansions can be accurately detected in short-read genomes. RESULTS: We demonstrated that a genotype-first approach can diagnose atypical cases of known REDs and provide valuable clinical insights. We present clinical details on participants with expansions in ATXN7, DMPK, FMR1, GLS, HTT, RFC1, AFF3, and MARCH6. Importantly, we highlight 2 cases of juvenile Huntington disease that were discovered through our analysis. Finally, we present a list of novel candidate short tandem repeats (TR) that could potentially be pathogenic if expanded. CONCLUSION: Importantly, our approach showcases the bioinformatic advancements in genome analysis for RED detection and highlights its practical applications.

Humans↗
PubMed2026

Identification of circulating parasite and host biomarkers in the serum proteome of Trypanosoma vivax-infected sheep under immunosuppression.

Bovine trypanosomiasis, caused by Trypanosoma vivax, presents a major threat to livestock health, primarily due to the lack of efficient field diagnostic tools. This study aimed to identify potential parasite and host-derived biomarkers through a longitudinal proteomic analysis of serum from sheep experimentally infected with a T. vivax isolate. Utilizing LC-MS/MS and bioinformatics, 154 proteins were identified, comprising 150 host (Ovis aries) and four pathogen proteins. Principal Component Analysis (PCA) demonstrated a clear separation of samples according to infection stages: Control, Infection (15 parasites/field), and Peak Infection (30-60 parasites/field) Among the parasite proteins, TvY486_0014340, TvY486_0040500, and TvY486_0042480 were identified as candidate antigens for future evaluation. In silico analysis revealed these proteins contain multiple B-cell epitopes with no cross-reactivity to related Trypanosoma species, supporting their potential for immunodiagnostic development. Additionally, three host proteins-folate receptor 3 (FOLR3) and complement components C1QA and C1QC-were significantly modulated across all infection phases. The upregulation of FOLR3 likely reflects a compensatory response to parasite-induced anemia, while the downregulation of C1Q components suggests immune evasion strategies. These findings highlight specific parasite antigens and host regulatory patterns that may serve as candidate biomarkers for the diagnosis and monitoring of T. vivax infection, facilitating the development of improved point-of-care assays.

Animals↗
PubMed2005

In silico identification of new secretory peptide genes in Drosophila melanogaster.

Bioactive peptides play critical roles in regulating most biological processes in animals. The elucidation of the amino acid sequence of these regulatory peptides is crucial for our understanding of animal physiology. Most of the (neuro)peptides currently known were identified by purification and subsequent amino acid sequencing. With the entire genome sequence of some animals now available, it has become possible to predict novel putative peptides. In this way, BLAST (Basic Local Alignment Searching Tool) analysis of the Drosophila melanogaster genome has allowed annotation of 36 secretory peptide genes so far. Peptide precursor genes are, however, poorly predicted by this algorithm, thus prompting an alternative approach described here. With the described searching program we scanned the Drosophila genome for predicted proteins with the structural hallmarks of neuropeptide precursors. As a result, 76 additional putative secretory peptide genes were predicted in addition to the 43 annotated ones. These putative (neuro)peptide genes contain conserved motifs reminiscent of known neuropeptides from other animal species. Peptides that display sequence similarities to the mammalian vasopressin, atrial natriuretic peptide, and prolactin precursors and the invertebrate peptides orcokinin, prothoracicotropic hormones, trypsin modulating oostatic factor, and Drosophila immune induced peptides (DIMs) among others were discovered. Our data hence provide further evidence that many neuropeptide genes were already present in the ancestor of Protostomia and Deuterostomia prior to their divergence. This bioinformatic study opens perspectives for the genome-wide analysis of peptide genes in other eukaryotic model organisms.

Algorithms↗
PubMed2005

Comparative analysis of the kinomes of three pathogenic trypanosomatids: Leishmania major, Trypanosoma brucei and Trypanosoma cruzi.

BACKGROUND: The trypanosomatids Leishmania major, Trypanosoma brucei and Trypanosoma cruzi cause some of the most debilitating diseases of humankind: cutaneous leishmaniasis, African sleeping sickness, and Chagas disease. These protozoa possess complex life cycles that involve development in mammalian and insect hosts, and a tightly coordinated cell cycle ensures propagation of the highly polarized cells. However, the ways in which the parasites respond to their environment and coordinate intracellular processes are poorly understood. As a part of an effort to understand parasite signaling functions, we report the results of a genome-wide analysis of protein kinases (PKs) of these three trypanosomatids. RESULTS: Bioinformatic searches of the trypanosomatid genomes for eukaryotic PKs (ePKs) and atypical PKs (aPKs) revealed a total of 176 PKs in T. brucei, 190 in T. cruzi and 199 in L. major, most of which are orthologous across the three species. This is approximately 30% of the number in the human host and double that of the malaria parasite, Plasmodium falciparum. The representation of various groups of ePKs differs significantly as compared to humans: trypanosomatids lack receptor-linked tyrosine and tyrosine kinase-like kinases, although they do possess dual-specificity kinases. A relative expansion of the CMGC, STE and NEK groups has occurred. A large number of unique ePKs show no strong affinity to any known group. The trypanosomatids possess few ePKs with predicted transmembrane domains, suggesting that receptor ePKs are rare. Accessory Pfam domains, which are frequently present in human ePKs, are uncommon in trypanosomatid ePKs. CONCLUSION: Trypanosomatids possess a large set of PKs, comprising approximately 2% of each genome, suggesting a key role for phosphorylation in parasite biology. Whilst it was possible to place most of the trypanosomatid ePKs into the seven established groups using bioinformatic analyses, it has not been possible to ascribe function based solely on sequence similarity. Hence the connection of stimuli to protein phosphorylation networks remains enigmatic. The presence of numerous PKs with significant sequence similarity to known drug targets, as well as a large number of unusual kinases that might represent novel targets, strongly argue for functional analysis of these molecules.

Animals↗
PubMed2002

Building an asynchronous web-based tool for machine learning classification.

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not yet been used for this purpose, hence proper comparisons between classification methods have not been conducted. We developed free software that implements logistic regression with stepwise variable selection as a quick and simple method for initial exploration of important genetic markers in disease classification. To implement the algorithm and allow our collaborators in remote locations to evaluate and compare its results against those of other methods, we developed a user-friendly asynchronous web-based application with a minimal amount of programming using free, downloadable software tools. With this program, we show that classification using logistic regression can perform as well as other more sophisticated algorithms, and it has the advantages of being easy to interpret and reproduce. By making the tool freely and easily available, we hope to promote the comparison of classification methods. In addition, we believe our web application can be used as a model for other bioinformatics laboratories that need to develop web-based analysis tools in a short amount of time and on a limited budget.

Algorithms↗
PubMed1998

Analysis of the genome of Mycobacterium tuberculosis H37Rv.

The powerful combination of genomics and bioinformatics is providing a wealth of information about Mycobacterium tuberculosis, the aetiological agent of human tuberculosis, that will facilitate the conception and development of new therapies. The starting point for genome sequencing was the integrated map of the 4.4 Mb circular chromosome of the widely used, virulent reference strain, M. tuberculosis H37Rv. Cosmids and bacterial artificial chromosomes were selected from ordered libraries and subjected to systematic shotgun sequence analysis. This approach simplified sequence assembly as the genome is rich in repetitive DNA. In common with most bacteria, > 90% of the potential coding capacity is used, and probable or tentative functions could be attributed to > 70% of the genes. The potential biological roles of two of the principal driving forces in genome dynamics, insertion sequence elements and polymorphic multigene families are discussed.

DNA Transposable Elements↗
PubMed2025

nf-core/pacvar: a pipeline for analyzing long-read PacBio whole genome and repeat expansion sequencing data.

MOTIVATION: Pacific Biosciences (PacBio) single-molecule, long-read sequencing enables whole genome annotation and the characterization of 20 complex repetitive repeat regions, especially relevant to neurodegenerative diseases, through their PureTarget panel. Long-read whole-genome sequencing (WGS) also allows for the detection of structural variants that would be difficult to detect with traditional short-read sequencing. However, the raw unaligned Binary Alignment Map data need to be processed before analysis. There is a need for an intuitive comprehensive bioinformatic pipeline that can analyze these data. RESULTS: We present nf-core/pacvar, a comprehensive pipeline for analyzing both PacBio single-molecule PureTarget and WGS data that demultiplexes and parallelizes pre-processing, variant calling and repeat characterization. nf-core/pacvar is compatible with little configuration and has few dependencies. This pipeline enables rapid end-to-end, parallel processing of PacBio single-molecule whole genome and targeted repeat expansion sequencing. AVAILABILITY AND IMPLEMENTATION: nf-core/pacvar is available on nf-core website (https://nf-co.re/pacvar/) and on github (https://github.com/nf-core/pacvar) under MIT License (DOI: 10.5281/zenodo.14813048).

Software↗
PubMed2005

Diagnostic protein discovery using liquid chromatography/mass spectrometry for proteolytic peptide targeting.

A peptide targeting method has been developed for diagnostic protein discovery, which combines proteolytic digestion of fractionated plasma proteins and liquid chromatography coupled to electrospray time-of-flight mass spectrometry (LC/ESI-TOFMS) profiling. Proteolysis prior to profiling overcomes molecular weight limitations and compensates for the poor sensitivity of matrix-assisted laser desorption/ionization (MALDI) protein profiling. LC/MS increases the peak capacity compared to crude fractionation techniques or single sample MALDI analysis. Differentially expressed peptides are targeted in the mass chromatograms using bioinformatic techniques and subsequently sequenced with MALDI tandem MS. In a model study comparing pancreatic cancer patients to controls, 74% of the peptide targets were successfully sequenced. This profiling method was superior to previous experiments using single sample MALDI analysis for protein profiling or proteolytic peptide profiling, because more potential protein markers were identified.

Adult↗
PubMed1999

An ontology for bioinformatics applications.

MOTIVATION: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

Animals↗
PubMed2002

BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data.

The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the management and analysis of all areas of microarray experimentation. All software necessary to run a local server is freely available.

Database Management Systems↗
PubMed2005

Bioinformatics in microbial biotechnology--a mini review.

The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics--sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics--identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.

Journal Article↗
PubMed2026

Screening and identification of key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods.

OBJECTIVE: Radiotherapy (RT) plays a crucial role in the comprehensive treatment of rectal cancer. However, the impact of radiotherapy on the tumor microenvironment (TME), especially its effect on immune cell infiltration and immune-related gene expression, has not been fully studied. This study aims to screen and analyze key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods for the purpose of identifying potential biomarkers and providing new insights for the personalized therapy of rectal cancer. METHODS: Using data from the Public Gene Expression Database (GEO) and the Cancer Genomics Database (TCGA), the impact of radiotherapy on the immune microenvironment of rectal cancer was explored using bioinformatics tools. Through screening differentially expressed genes (DEGs), correlation analysis, TIMER database analysis, immune infiltration score, and correlation analysis between key genes and prognosis, the effects of radiotherapy on the immune microenvironment of rectal cancer were investigated. RESULTS: Totally 7 upregulated and 4 downregulated differentially expressed genes were identified, among which MASP1, LTK, SLC9A3R2 were negatively correlated with myeloid suppressor cell infiltration (MDSCs), while ZP2 was positively correlated. The expression of MASP1 and SLC9A3R2 was closely related to the level of immune cell infiltration and played significant roles in the immune microenvironment. High expression of MASP1 was significantly correlated with survival benefits from immune checkpoint inhibitor therapy, while SLC9A3R2 was closely related to the efficacy of PD-L1 inhibitors and CTLA4 inhibitors. CONCLUSIONS: MASP1 and SLC9A3R2, as two key genes that may be related to the immune microenvironment of rectal cancer radiotherapy, deserve further exploration of their roles in the mechanism. The combination of radiotherapy and immunotherapy holds promising prospects in the treatment of rectal cancer, and exploration of related mechanisms will provide new strategies and targets for the treatment of various tumors and rectal cancer.

Bioinformatics↗
PubMed1998

Informatics for mouse genetics and genome mapping.

Bioinformatics has become an essential part of biological research. The rapid pace of technology development and the ability to carry out biological experimentation in large scale require computerized systems for data management, analysis, and display. Experimentation with the mouse, a major model organism of the Human Genome Initiative, has intensified the need for bioinformatics tools for mouse mapping and genome analysis. This article describes the Mouse Genome Database in the United States, a primary resource for mouse genomic data, as well as resources at the Mammalian Genetics Unit in the United Kingdom and the Animal Genome Database of Japan. Internet addresses are provided for major genetic and physical mapping resources, major genome data sites, and resources of molecular information.

Animals↗
PubMed2000

Bioinformatics. A user's perspective.

This review provides an overview of bioinformatics from the user's point of view. Bioinformatics, defined as the application of computers, databases, and computational methods to the management of biologic information, is essential for almost every aspect of data management in modern biology. The rapid accumulation of genomic sequence information together with the wide availability of new technologies that analyze global gene expression patterns have created an information overload. Molecular biology labs are increasingly dependent on computers, large-capacity databases, search and analysis tools, and high-quality Internet connections. Currently available bioinformatics tools are discussed and a general approach is outlined. Using the resources and approaches in this review, readers should be able to form their own view of bioinformatics and tailor the solutions to the information overload according to their needs.

Computational Biology↗
PubMed2004

Cytochrome C oxidase III interacts with hepatitis B virus X protein in vivo by yeast two-hybrid system.

AIM: To screen and identify the proteins which interact with hepatitis B virus (HBV) X protein in hepatocytes by yeast two-hybrid system and to explore the effects of X protein in the development of hepatocellular carcinoma (HCC). METHODS: With HBV X gene amplified by polymerase chain reaction (PCR), HBV X bait plasmid, named pAS2-1-X, was constructed by yeast-two hybridization system3 and verified by auto-sequencing assay. pAS2-1-X was transformed into the yeast AH109, and X-BD fusion protein expressed in the yeast cells was detected by Western blotting. The yeast cells cotransformed with pAS2-1-X and normal human liver cDNA library were grown in selective SC/-trp-leu-his-ade medium. The second screen was performed with beta-gal activity detection, and false positive clones were eliminated by segregation analysis, true positive clones were amplified, sequenced and analyzed with bioinformatics. Mating experiment was performed to confirm the binding of putative proteins to X protein in the yeast cells. RESULTS: Bait plasmid pAS2-1-X was successfully constructed and pAS2-1-X correctly expressed BD-X fusion protein in yeast AH109. One hundred and three clones grew in the selective SC/-trp-leu-his-ade medium, and only one clone passed through beta-gal activity detection and segregation analysis. The inserted cDNA fragment showed high homology with Homo sapiens cytochrome C oxidase III (COXIII). Furthermore, mating experiment identified that the binding of COXIII to X protein was specific. CONCLUSION: COXIII protein is a novel protein that can interact with X protein in vivo by yeast two-hybrid system, and may contribute to the development of HCC through the interaction with X protein.

Carcinoma, Hepatocellular↗
PubMed2002

Cluster analysis of an extensive human breast cancer cell line protein expression map database.

In the current study, the protein expression maps (PEMs) of 26 breast cancer cell lines and three cell lines derived from normal breast or benign disease tissue were visualised by high resolution two-dimensional gel electrophoresis. Analysis of this data was performed with ChiClust and ChiMap, two analytical bioinformatics tools that are described here. These tools are designed to facilitate recognition of specific patterns shared by two or more (a series) PEMs. Both tools use PEMs that were matched by an image analysis program and locally written programs to create a match table that is saved in an object relational database. The ChiClust tool uses clustering and subclustering methods to extract statistically significant protein expression patterns from a large series of PEMs. The ChiMap tool calculates a differential value (either as percentage change or a fold change) and represents these graphically. All such differentials or just those identified using ChiClust can be submitted to ChiMap. These methods are not dependent on any particular commercial image analysis program, and the whole software package gives an integrated procedure for the comparison and analysis of a series of PEMs. The ChiClust tool was used here to order the breast cell lines into groups according to biological characteristics including morphology in vitro and tumour forming ability in vivo. ChiMap was then used to highlight eight major protein feature-changes detected between breast cancer cell lines that either do or do not proliferate in nude mice. Mass spectrometry was used to identify the proteins. The possible role of these proteins in cancer is discussed.

Algorithms↗