SEARCH · PubMed Health

Results for “Bioinformatics analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,369 records · Page 76Linked to original sources

PubMed2026

Comparative essentialome analysis of six Pectobacteriaceae strains using the TNSEEK pipeline identifies conserved and strain-specific fitness determinants.

Transposon sequencing (Tn-seq) is a powerful technique for defining the essential genes required for bacterial survival. However, gene essentiality can vary significantly across taxonomic levels, and comparing large Tn-seq datasets from multiple strains presents considerable analytical challenges. To address this, we developed TNSEEK, a fully automated bioinformatics pipeline for the systematic and comparative analysis of Tn-seq experiments. We applied TNSEEK to analyse newly generated data for six soft rot Pectobacteriaceae strains, encompassing species from the Dickeya and Pectobacterium genera, grown in a rich medium. This approach identified a core essentialome of 225 genes, primarily involved in fundamental cellular maintenance, conserved across all 6 strains, a set comparable in size to that of the neighbouring Enterobacteriaceae family. Only a few genus-specific essential genes were found, highlighting interesting distinct metabolic capabilities between Dickeya and Pectobacterium genera. In striking contrast, we discovered a large variable essentialome comprising 181 strain-specific genes, many of which are of unknown function. A portion of these strain-specific essential genes are components of defence systems and prophage genomic regions. The unexpected essentiality of selected components of these modules is consistent with cellular dependency on cognate toxic, restriction or immunity functions encoded by defence-associated loci under the tested growth condition. Furthermore, a comparison with the Escherichia coli essentialome demonstrates that discrepancies in gene essentiality can often be attributed to differences in growth conditions, particularly temperature, as well as variations in genetic redundancy. In conclusion, the TNSEEK pipeline provides a reproducible framework for comparative analysis of mariner/Himar1 Tn-seq datasets across multiple strains.

Pectobacterium↗

PubMed2026

Integrative multi-omics quantitative trait loci prioritize CASP7 as a candidate protective gene for cataract.

Cataracts are the leading cause of vision loss worldwide. Despite surgery being the only effective treatment, its economic burden highlights the necessity of exploring the pathogenesis of cataracts. In this study, we analyzed 4 large-scale GWAS (genome-wide association study) datasets for cataracts and performed SMR analysis along with heterogeneity in dependent instruments (HEIDI) testing to explore the effects of methylation, expression, and protein QTLs on cataracts. We further validated shared genetic variants through COLOC analysis. Additionally, we searched datasets related to cataracts from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. By integrating summary-based Mendelian randomization (SMR) results with bioinformatics findings, CASP7 showed a consistent protective-direction association with cataract risk (mQTL: OR [95% CI] = 0.959 [0.941-0.977], FDR-adjusted P = .039; eQTL: OR [95% CI] = 0.897 [0.860-0.937], FDR-adjusted P = .0046; pQTL: OR [95% CI] = 0.597 [0.483-0.738], FDR-adjusted P = .00083). GEO-based analyses provided transcriptomic support for CASP7 involvement in cataract-related lens biology. These findings prioritize CASP7 as a genetically supported candidate protective gene associated with cataract risk. Because this study is based on public summary-level and transcriptomic datasets, the results should be interpreted cautiously and require functional validation in human lens-relevant systems.

Quantitative Trait Loci↗

PubMed2001

The BioTools Suite. A comprehensive suite of platform-independent bioinformatics tools.

The BioTools Suite is a set of three comprehensive, platform-independent software packages (PepTool, GeneTool, and ChromaTool) developed for sequence assembly and analysis. In addition to supporting a large number of standard bioinformatics functions, these programs also incorporate a number of useful innovations including uniform graphical-user interface (GUI) design, direct internet connectivity, a novel approach to feature annotation, and a variety of enhanced algorithms for large scale proteome and genome analysis. This article describes the key features, recent changes, and general operation of all three programs.

Computational Biology↗

PubMed2006

SHARP2: protein-protein interaction predictions using patch analysis.

UNLABELLED: SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.

Algorithms↗

PubMed2005

A comprehensive software suite for the analysis of cDNAs.

We have developed a comprehensive software suite for bioinformatics research of cDNAs; it is aimed at rapid characterization of the features of genes and the proteins they code. Methods implemented include the detection of translation initiation and termination signals, statistical analysis of codon usage, comparative study of amino acid composition, comparative modeling of the structures of product proteins, prediction of alternative splice forms, and metabolic pathway reconstruction.

Alternative Splicing↗

PubMed2005

cDNA microarray in isolation of novel differentially expressed genes related to human glioma and clone of a novel full-length gene.

BACKGROUND: This investigation was undertaken to obtain differentially expressed genes related to human glioma using cDNA microarray and the characterization of one novel full-length gene. METHODS: Total RNA was extracted from human glioma tissues and normal brain tissues, and mRNA was used to make probes. After hybridization and washing, the results were scanned using a computer system. The gene named 681F05 clone was an expressed gene to human glioma through four-time hybridization and scanning. Subsequently northern blot analysis was performed by northern blot, 5'RACE and bioinformatics. RESULTS: Fifteen differentially expressed genes to human glioma were obtained through four-time hybridization and scanning. Northern blot analysis confirmed that 681F05 clone was low-expressed in human brain tissues and over-expressed in human glioma tissues. The analysis of BLASTn and BLASTx showed that 681F05 clone is two cDNA clones encoding two novel proteins that are highly identified to the cyclophilin isoform 10 of C. Elgans, respectively. Sequence analysis revealed the two cDNA clones are two different splicing variants of a novel cycophilin-like gene (PPIL3a and PPIL3b). CONCLUSIONS: cDNA microarray technology can be successfully used to identify differentially expressed genes. The novel full-length gene of human PPIL3 may be correlated with the formation of human glioma.

Amino Acid Sequence↗

PubMed2005

Computationally analyzing the possible biological function of YJL103C--an ORF potentially involved in the regulation of energy process in yeast.

Although the complete genomes of a number of organisms have been sequenced, the biological functions of many genes are still not known. Because experimentally studying the functions of those genes one by one requires tremendous time, it is vital to use published resources like microarray gene expression data for computational analysis of gene functions. One example is YJL103C, a yeast gene of unknown function in the Saccharomyces Genome Database (SGD). It is possible to quickly infer its biological function by computational analysis. In this study, we present an efficient model to explore the biological function of a novel gene using microarray data. We showed that the expression pattern of YJL103C is most similar to the genes in the energy group and respiratory chain subgroup. We further found that YJL103C contains a HAP2,3,4 box in its promoter region and a cytochrome C heme-binding signature in its protein sequence. Our findings define a potential role for YJL103C in the regulation of energy metabolism, specifically in the process of oxidative phosphorylation. Similar bioinformatics methods can be applied to infer the biological functions of other novel genes in organisms for which microarray data are available. In this work, we selected a single gene of unknown function as a case study. By focusing on the power of computer analysis and bioinformatics on the available microarray data, we have determined the likely biological function of YJL103C. Our study provides a method by which to explore the potential function of other genes currently annotated as having an unknown function in any organism for which global gene expression data are available.

Binding Sites↗

PubMed2002

Finding and decrypting of promoters contributes to the elucidation of gene function.

The combination of full-scale genomic sequencing with high throughput expression analysis provides a new and largely unexploited basis for in silico functional genomics. Recent break through developments in locating and analyzing promoters now allow extending functional genomics in silico far beyond identification of protein sequences into the complex regulatory structures and mechanisms of the genome. However, only first examples of this new type of approach are emerging at present and intensive further developments of bioinformatics tools will be required before such analysis can become large-scale routine in genomic sequence analysis. Nevertheless, the door to a new dimension of functional analysis of the genomic sequence is open. Finally, only the tight integration of the enormous amount of knowledge gained from proteins sequence analysis with the complementary information about gene regulation will afford us with a more complete picture of the networks than constitute life.

Computational Biology↗

PubMedDate not supplied

[G-protein coupled peptide receptors and their ligands in human genome].

The G-protein coupled peptide receptors as well as their ligands, endogenous peptides, are involved in regulation of many important physiological processes in the organism and therefore represent attractive targets for pharmaceutical investigation and drug design. With the completion of the human draft genome sequencing, it has become possible to take a comprehensive picture of all genes encoding both peptide receptors and peptides themselves. In the present study a first attempt has been made to carry out a comprehensive analysis of G-protein coupled peptide receptors and their respective endogenous peptide ligands in the human genome. We searched the genome sequence by means of sequential application of standard bioinformatical methods (such as homology search, hierarchical cluster analysis, building of hmm-profiles etc.) with the goal of identifying all the components of peptide ligand/receptor system in the human genome. As a result of this search it was concluded that the probable number of functional peptide receptors in the human genome is 218, and the probable peptide precursors' number is 126 amino acid sequences. These two groups include, respectively, 12 novel G-protein coupled peptide receptors and 10 novel peptide precursors, discovered in the present study. The probable biological functions of newly discovered candidates were determined based on the sequence similarity to the earlier known proteins. Classification of all peptide GPCRs and their ligands based on the ligand specificity was performed for all probable G-protein coupled peptide receptors. The issue of ligand-receptor specificity in the human genome is also discussed.

Amino Acid Sequence↗

PubMed2005

Pokemon expression in malignant glioma: an application of bioinformatics methods.

OBJECT: In this report the authors review the role of bioinformatics in the design of a research project in which the molecular genetics of malignant glioma were studied. A project to characterize Pokemon expression in malignant glioma was developed, refined, and implemented using bioinformatics methods. METHODS: Using the resources available from the National Center for Biotechnology Information, the messenger RNA (mRNA) sequence for Pokemon was determined. With this information and online primer design tools, novel primers were designed that would specifically amplify Pokemon mRNA by using reverse transcription-polymerase chain reaction assays. CONCLUSIONS: The promise of bioinformatics is in the rapid and widespread dissemination and analysis of genomic information. This information is then used in research investigating the genetic basis of disease. In this paper the authors review the bioinformatics methods used in their study of Pokemon expression in malignant glioma.

Base Sequence↗

PubMed2005

A novel approach for clustering proteomics data using Bayesian fast Fourier transform.

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Algorithms↗

PubMed2026

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

MOTIVATION: DNA methylation (DNAme) is the best studied epigenetic mechanism that plays pivotal role in tissue differentiation and epigenetic disruption has been correlated to diverse disease types (e.g. cancer, metabolic disorders). While various DNAme array platforms have been discovered, data analysis remains a challenging task which often requires in-depth bioinformatic expertise. Here, we developed a user-friendly web-based application for data analysis and visualization that accommodates users ranging from early-career basic/translational researchers to experienced bioinformaticians. RESULTS: CpGene is a web application for analyzing DNA methylation array data. It supports Illumina 450K, EPIC, and EPICv2 methylation array platforms and processes .idat files with integrated preprocessing, normalization, and quality control. Biomarker discovery is available through either classic differential methylation point analysis or machine learning-based feature selection as well as gene enrichment analysis. Results are summarized with clear visualizations, to aid interpretation. By combining these functions in a unified interface, CpGene streamlines methylation analysis and helps identify CpG sites and genes with biological and clinical relevance. AVAILABILITY AND IMPLEMENTATION: CpGene is openly accessible as a web service through http://cpgene.duckdns.org:8001/ and it's source code is available on https://github.com/kostaslazaros/cpgenene.

DNA Methylation↗

PubMed2000

Genomic organization of the human kallikrein gene family on chromosome 19q13.3-q13.4.

Kallikreins are a subgroup of serine proteases with diverse physiological functions. Recently, growing evidence indicates that many kallikrein genes are involved in malignancy. In rodents, kallikreins are encoded by a large multigene family, but in humans only three kallikreins were thought to exist. Based on the homology between the human and rodent kallikrein loci, we studied a 300 kb region of genomic sequences around the putative KLK1 gene locus on chromosome 19q13.3-q13.4. By using linear sequence information, restriction analysis, end sequencing, PCR and blotting techniques, as well as bioinformatic approaches, we were able to construct the first detailed map of the human kallikrein gene family. Comparative analysis of genes located in this area, provides strong evidence that the human kallikrein gene family locus on chromosome 19 is considerably larger than previously thought, containing at least fifteen genes. We have established, for the first time, the common structural features that apply to all members of the expanded kallikrein multigene family. Our map specifies the distance between genes to one base pair accuracy, the relative location, and the direction of transcription of all 15 genes. Determination of the true size of the kallikrein family in humans is important for our understanding of the contribution of the kallikreins to human biology and pathophysiology.

Chromosome Mapping↗

PubMed1999

Analysis of the proteome of Mycobacterium tuberculosis in silico.

Novel bioinformatics routines have been used to provide a more detailed definition of the proteome of Mycobacterium tuberculosis H37Rv. Over half of the current proteins result from gene duplication or domain shuffling events while one-sixth show no similarity to polypeptides described in other organisms. Prominent among the genes that appear to have been duplicated on numerous occasions are those involved in fatty acid metabolism, regulation of gene expression, and the unusually glycine-rich PE and PPE proteins. Protein similarity analysis, coupled with inspection of the genetic neighbourhood, was used to explore possible functional relatedness. This uncovered four large mce operons whose proteins may mediate initial interactions between the tubercle bacillus and host cells, together with a cluster of genes that might encode components of a structure required for secretion of ESAT-6 like proteins. Close linkage of the mmpL genes, encoding large membrane proteins, with those required for fatty acid metabolism suggests involvement in lipid transport. Compared to free-living bacteria, M. tuberculosis has a significantly smaller transport protein repertoire and this may reflect its intracellular lifestyle.

Amino Acid Motifs↗

PubMed2025

AmpSeqR: an R package for amplicon deep sequencing data analysis.

Amplicon sequencing (AmpSeq) is a methodology that targets specific genomic regions of interest for polymerase chain reaction (PCR) amplification so that they can be sequenced to a high depth of coverage. Amplicons are typically chosen to be highly polymorphic, usually with several highly informative, high frequency single nucleotide polymorphisms (SNPs) segregating in an amplicon of 100-200 base pair (bp). This allows high sensitivity detection and quantification of the frequency of each sequence within each sample making it suitable for applications such as low frequency somatic mosaicism detection or minor clone detection in mixed samples. AmpSeq is being increasingly applied to both biological and medical studies, in applications such as cancer, infectious diseases and brain mosaicism studies. Current bioinformatics pipelines for AmpSeq data processing lack downstream analysis, have difficulty distinguishing between true sequences and PCR sequencing errors and artifacts, and often require bioinformatic expertise. We present a new R package: AmpSeqR, designed for the processing of deep short-read amplicon sequencing data, with a focus on infectious diseases. The pipeline integrates several existing R packages combining them with newly developed functions to perform optimal filtering of reads to remove noise and improve the accuracy of the detected sequences data, permitting detection of very low frequency clones in mixed samples. The package provides useful functions including data pre-processing, amplicon sequence variants (ASVs) estimation, data post-processing, data visualization, and automatically generates a comprehensive Rmarkdown report that contains all essential results facilitating easy inclusion into reports and publications. AmpSeqR is publicly available at https://github.com/bahlolab/AmpSeqR.

High-Throughput Nucleotide Sequencing↗

PubMed2005

Molecular characterization of suicide by microarray analysis.

Several lines of evidence support the idea that individuals who commit suicide have a certain biological predisposition, part of which is given by genes. Studies investigating genetic factors increasing suicide predisposition have been limited by current knowledge of the suicide neurobiology and have typically investigated one or a few genes at a time, whereas it is anticipated that several genes account for the total genetic variance mediating suicide. This review focuses on the advantages and the interest of using the microarray technology to investigate the neurobiology of suicide and discusses, by means of a data analysis example, the possible methodological problems and bioinformatic strategies that should be employed in order to separate the signal from the large amount of background noise, which is usually generated in such studies. Microarray expression studies and related platforms are promising tools to gain better insight into the neurobiology of suicide.

Brain↗

PubMed2003

Analysis of singleton ORFans in fully sequenced microbial genomes.

Singleton sequence ORFans are orphan ORFs (open reading frames) that have no detectable sequence similarity to any other sequence in the databases. ORFans are of particular interest not only as evolutionary puzzles but also because we can learn little about them using bioinformatics tools. Here, we present a first systematic analysis of singleton ORFans in the first 60 fully sequenced microbial genomes. We show that although ORFans have been underemphasized, the number of ORFans is steadily growing, currently accounting for 23,634 sequences. At the same time, the percentage of ORFans as a fraction of all sequences is slowly diminishing, and is currently about 14%. Short ORFans comprise about 61% of all ORFans. The abundance of short ORFans may be due to a yet unexplained artifact. The data also suggest that the number of longer ORFans may soon diminish as more genomes of closely related organisms become available. To better address the questions about the functions and origins of ORFans, we propose to focus further studies on the longer ORFans, with emphasis on three new types of ORFans: ORFan modules, paralogous ORFans, and orthologous ORFans. We conclude that the large number of ORFans reflects an intrinsic property of the genetic material not yet fully understood. Further computational and experimental studies aimed at understanding Nature's protein diversity should also include ORFans.

Genome↗

PubMed2026

LCR-modules: a collection of workflows for cancer genome analysis.

MOTIVATION: The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10 800 samples. AVAILABILITY: No new data were generated in support of this research. The source code for the LCR-modules is openly available at https://github.com/LCR-BCCRC/lcr-modules.

Software↗