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At least 955 records · Page 53Linked to original sources
PubMed2025

Identifying potential novel biomarkers for varicocele: A bioinformatics approach to genomics analysis.

Introduction: Varicocele, characterized by the enlargement of scrotal veins, is a common contributor to male infertility, but its genetic underpinnings remain largely unknown. Aim: The goal of this study is to identify potential biomarkers associated with varicocele in order to better understand its molecular mechanisms. Materials and methods: Using the three primary databases, NCBI, DisGeNET, and OpenTarget, we analyzed gene variants and found 79 pertinent genes associated with varicocele. Protein-protein interaction analysis was performed using STRING and visualized with Cytoscape. Molecular Complex Detection (MCODE) and CytoHubba tools helped identify significant protein clusters. Results: The gene ontology analysis shows that there are 79 proteins involved in the inflammatory process, the regulation of gene expression, and cellular components that play a role in oxidative stress and angiogenesis. Our results revealed three key biomarkers: Interleukin-1 beta (IL1B), B-cell lymphoma 2 (BCL2), and matrix metalloproteinase-9 (MMP-9). These proteins are involved in critical processes, such as inflammation, oxidative stress, angiogenesis, and vascular damage, that are central to the pathophysiology of varicocele. Conclusion: The identification of IL1B, BCL2, and MMP-9 offers new insights into varicocele’s molecular mechanisms and suggests potential targets for diagnostic and therapeutic strategies, advancing personalized treatment approaches for fertility restoration.

Humans↗
PubMed2000

The untranslated regions of eukaryotic mRNAs: structure, function, evolution and bioinformatic tools for their analysis.

The crucial role of the non-coding portion of genomes is now widely acknowledged. In particular, mRNA untranslated regions are involved in many post-transcriptional regulatory pathways that control mRNA localisation, stability and translation efficiency. A review is given of the most recent research works on the functional characterisation of eukaryotic mRNA untranslated regions. In order to make possible a systematic and detailed sequence analysis of mRNA untranslated regions (UTRs), a non-redundant database of metazoan mRNA untranslated sequences annotated for the occurrence of specific functional elements, UTRdb, was devised. These elements, whose consensus structure has been devised on the basis of experimental assays and of comparative analyses, have been collected in the UTRsite database. A suitable pattern-matching software has been devised to search UTRsite patterns in user-submitted sequences, also assessing their statistical significance. Structural, compositional and evolutionary features of untranslated sequences of metazoan mRNAs have been investigated showing peculiar intra- and interspecific patterns.

Animals↗
PubMed2002

Bioinformatics classification and functional analysis of PhoH homologs.

PhoH protein is a putative ATPase belonging to the phosphate regulon in Escherichia coli. EC-PhoH homologs are present in different organisms, but it is not clear if they are functionally related, besides nothing is known about their regulation. To distinguish true functional orthologs of EC-PhoH in different classes of bacteria and to identify their functional role in bacterial metabolic network we performed phylogenetic analysis of these proteins and comparative study of position and regulation of the related genes. Three groups of proteins were identified. Proteins of the first group (BS-PhoH orthologs) are present in most of bacteria and are proposed to be functionally linked to phospholipid metabolism and RNA modification. Proteins of the second group (BS-YlaK orthologs) are present in most of aerobes and Actinobacterial YlaK orthologs are shown to be members of a fatty acid beta-oxidation regulons. EC-PhoH orthologs are classified in a third group, specific for Enterobacteria. Functional role of PhoH homologs in the lipid and RNA metabolism and proposed interrelation of PhoH paralogs in one organism are discussed.

Bacteria↗
PubMed2006

Bioinformatics and microbial biodiversity: analysis of vibrios by the GenEnv system.

Sequence-based approaches to prokaryotic systematics and typing represent a modern and promising strategy in epidemiology and environmental microbiology. GenEnv, a database-driven system for bacterial typing, was developed in order to provide user friendly tools for supporting biomolecular analysis of bacteria. The family Vibrionaceae represents a heterogeneous taxon of aquatic microrganisms, harbouring a plethora of genomes currently analyzed by different molecular techniques. Under the query "Vibrio", GenEnv retrieved 256 organisms, included in a total number of 19 families. Overall, 548 sequences, comprising 16S rRNA (n=402), rpoB (n=1), gyrB (n=145) were available. In addition, GenEnv system allowed primer design, homology analysis and restriction maps, for immediate applications to the study of Vibrionaceae.

Bacterial Typing Techniques↗
PubMed2006

The MIGenAS integrated bioinformatics toolkit for web-based sequence analysis.

We describe a versatile and extensible integrated bioinformatics toolkit for the analysis of biological sequences over the Internet. The web portal offers convenient interactive access to a growing pool of chainable bioinformatics software tools and databases that are centrally installed and maintained by the RZG. Currently, supported tasks comprise sequence similarity searches in public or user-supplied databases, computation and validation of multiple sequence alignments, phylogenetic analysis and protein-structure prediction. Individual tools can be seamlessly chained into pipelines allowing the user to conveniently process complex workflows without the necessity to take care of any format conversions or tedious parsing of intermediate results. The toolkit is part of the Max-Planck Integrated Gene Analysis System (MIGenAS) of the Max Planck Society available at www.migenas.org (click 'Start Toolkit').

Animals↗
PubMed2004

Current applications of microarrays in head and neck cancer research.

OBJECTIVES/HYPOTHESIS: The objective was to introduce microarray technology and its applications in cancer research to the head and neck clinician. STUDY DESIGN: Literature review combined with methodology and examples from the authors' experiences with microarray analysis of tumors of the head and neck. METHODS: Search of literature and the authors' experience was made for technical details, alternative methods of data analysis, available bioinformatics tools, and applications of microarrays in cancer research. RESULTS: Microarrays allow the simultaneous analysis of the expression of thousands of genes. The use of a well-developed microarray study design leads to informative results. There are various bioinformatics resources widely available to aid in the analysis of microarray data. However, there is not yet a gold standard for analysis because this methodology is still evolving. CONCLUSION: Microarray studies may allow researchers to identify genetic changes relevant to diagnosis and prognosis in patients with head and neck cancer. Although still relatively new, this powerful methodology has immense potential to aid in understanding of the genetic changes that are important in head and neck cancer.

DNA Mutational Analysis↗
PubMed2006

Expression profiling of drug response--from genes to pathways.

Understanding individual response to a drug-what determines its efficacy and tolerability-is the major bottleneck in current drug development and clinical trials. Intracellular response and metabolism, for example through cytochrome P-450 enzymes, may either enhance or decrease the effect of different drugs, dependent on the genetic variant. Microarrays offer the potential to screen the genetic composition of the individual patient. However, experiments are "noisy" and must be accompanied by solid and robust data analysis. Furthermore, recent research aims at the combination of high-throughput data with methods of mathematical modeling, enabling problem-oriented assistance in the drug discovery process. This article will discuss state-of-the-art DNA array technology platforms and the basic elements of data analysis and bioinformatics research in drug discovery. Enhancing single-gene analysis, we will present a new method for interpreting gene expression changes in the context of entire pathways. Furthermore, we will introduce the concept of systems biology as a new paradigm for drug development and highlight our recent research-the development of a modeling and simulation platform for biomedical applications. We discuss the potentials of systems biology for modeling the drug response of the individual patient.

Animals↗
PubMed2004

Bioinformatics in mass spectrometry data analysis for proteomics studies.

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.

Computational Biology↗
PubMed2005

A medical bioinformatics approach for metabolic disorders: biomedical data prediction, modeling, and systematic analysis.

UNLABELLED: During the past century, studies of metabolic disorders have focused research efforts to improve clinical diagnosis and management, to illuminate metabolic mechanisms, and to find effective treatments. The availability of human genome sequences and transcriptomic, proteomic, and metabolomic data provides us with a challenging opportunity to develop computational approaches for systematic analysis of metabolic disorders. In this paper, we present a strategy of bioinformatics analysis to exploit the current data available both on genomic and metabolic levels and integrate these at novel levels of understanding of metabolic disorders. PathAligner is applied to predict biomedical data based on a given disorder. A case study on urea cycle disorders is demonstrated. A Petri net model is constructed to estimate the regulation both on genomic and metabolic levels. We also analyze the transcription factors, signaling pathways and associated disorders to interpret the occurrence and regulation of the urea cycle. AVAILABILITY: PathAligner's metabolic disorder analyzer is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner/pathaligner_MDA.html. Supplementary materials are available at http://www.techfak.uni-bielefeld.de/~mchen/metabolic_disorders.

Animals↗
PubMed2002

Molecular determinants of human uveal melanoma invasion and metastasis.

The molecular analysis of cancer has benefited tremendously from the sequencing of the human genome integrated with the science of bioinformatics. Microarray analysis technology has the potential to classify tumors based on the differential expression of genes. In the current study, a collaborative, multidisciplinary approach was utilized to study the molecular determinants of human uveal melanoma invasion and metastasis. Uveal melanoma is considered the most common primary intraocular cancer in adults, resulting in the death of approximately 50% of patients affected. Unfortunately, at the time of diagnosis, many patients already harbor microscopic metastases, thus underscoring a critical need to identify prognostic markers indicative of metastatic potential. The investigative strategy consisted of isolating highly invasive vs. poorly invasive uveal melanoma cells from a heterogeneous tumor derived from cells that had metastasized from the eye to the liver. The heterogeneous tissue explant MUM-2 led to the derivation of two clonal cell lines: MUM-2B and MUM-2C. Further morphological and functional analyses revealed that the MUM-2B cells were epithelioid, interconverted (expressing mesenchymal and epithelial phenotypes) highly invasive, and demonstrated vasculogenic mimicry. The MUM-2C cells were spindle-like, expressed only a vimentin mesenchymal phenotype, poorly invasive, and were incapable of vasculogenic mimicry. The molecular analysis of the MUM-2B vs. the MUM-2C clones resulted in the differential expression of 210 known genes. Overall, the molecular signature of the MUM-2B cells resembled that of multiple phenotypes--similar to a pluripotent, embryonic-like genotype. Validation of select genes that were upregulated and down-regulated was conducted by semiquantitative RT-PCR measurement. This study provides a molecular profile that will hopefully lead to the development of new molecular targets for therapeutic intervention and possible diagnostic markers to predict the clinical outcome of patients with uveal melanoma.

Adult↗
PubMed2026

Bioinformatics and Quantitative Real-Time Polymerase Chain Reaction Analysis of SUCNR1 and GPR37L1 in Schizophrenia.

Schizophrenia is a severe, complex, and multifactorial mental disorder involving numerous genetic susceptibility elements, leading to substantial disability, morbidity, and mortality. Despite significant progress in understanding its pathophysiology and etiology, specific diagnostic biomarkers for schizophrenia remain elusive. This study aimed to identify candidate molecular markers associated with schizophrenia. An integrated bioinformatics analysis was performed on the public microarray dataset GSE54913. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the most significantly enriched GO terms were related to channel activity, including passive transmembrane transporter activity, ion channel activity, gated channel activity, and substrate-specific channel activity. The top five enriched KEGG pathways were insulin secretion, cAMP signaling pathway, nucleotide excision repair, TNF signaling pathway, and glutathione metabolism. Validation was conducted using quantitative real-time polymerase chain reaction (qRT-PCR) on an independent sample set from Wuhan Rongjun Youfu Hospital. The qRT-PCR results were largely consistent with the microarray analysis (Pearson r = 0.89, 95% CI: 0.66-0.97). Protein-protein interaction (PPI) network analysis identified two hub genes, SUCNR1 and GPR37L1, which were significantly associated with the GO term 'ion channel activity' and enriched in the KEGG pathway 'insulin secretion'. Furthermore, SUCNR1 expression showed a negative correlation with verbal memory scores (r = -0.54, P = 0.015), whereas GPR37L1 expression showed a positive correlation (r = 0.59, P = 0.0034). These findings suggest that altered SUCNR1 and GPR37L1 expression may be associated with schizophrenia and may represent candidate molecular markers for further investigation.

Humans↗
PubMed2026

T-rex: standardized analysis of germline variants in whole-exome sequencing trios.

Whole-exome sequencing (WES) enables the identification of rare germline variants contributing to pediatric diseases. Trio-based sequencing, comparing affected children with their parents, is particularly effective for rare disease genetics. However, WES data analysis requires bioinformatics expertise, varies across institutions, and is often incompatible with clinical workflows. We developed T-Rex (Trio Rare variant analysis of EXomes), a cross-platform desktop application that enables the standardized and local analysis of WES germline Trio data without the need for programming knowledge. T-Rex integrates state-of-the-art tools for alignment, dual-variant calling (GATK HaplotypeCaller + VarScan2), annotation (SNPEff/SNPSift), rare-variant filtering based on population frequencies (gnomAD), and family-based statistical testing, including the Transmission Disequilibrium Test with multiple-testing correction. Benchmarking of the dual-caller strategy on the Genome in a Bottle Ashkenazim Trio demonstrates high precision (99.2%) while maintaining robust sensitivity (91.1%). User testing (n = 13) confirmed quick learning across clinicians and researchers. Application to a cohort of n = 121 pediatric cancer Trio datasets, filtering for rare protein-coding variants (MAF ≤ 0.1% in gnomAD v4.1), validated all assessable previously reported pathogenic variants. Overall, T-Rex enables clinicians to robustly analyze WES Trio data in compliance with data protection regulations without requiring additional software licenses. As one of the first platforms for comprehensive WES Trio analysis that requires no programming expertise while providing reproducible, end-to-end workflows for clinical genomics, T-Rex facilitates collaborative research between clinics and reduces reliance on external providers.

Humans↗
PubMed2001

Electron cryomicroscopy and bioinformatics suggest protein fold models for rice dwarf virus.

The three-dimensional structure of rice dwarf virus was determined to 6.8 A resolution by single particle electron cryomicroscopy. By integrating the structural analysis with bioinformatics, the folds of the proteins in the double-shelled capsid were derived. In the outer shell protein, the uniquely orientated upper and lower domains are composed of similar secondary structure elements but have different relative orientations from that of bluetongue virus in the same Reoviridae family. Differences in both sequence and structure between these proteins may be important in defining virus-host interactions. The inner shell protein adopts a conformation similar to other members of Reoviridae, suggesting a common ancestor that has evolved to infect hosts ranging from plants to animals. Symmetry mismatch between the two shells results in nonequivalent, yet specific, interactions that contribute to the stability of this large macromolecular machine.

Amino Acid Sequence↗
PubMed2025

Out-of-the-box bioinformatics capabilities of large language models (LLMs).

Large Language Models (LLMs), AI agents and co-scientists promise to accelerate scientific discovery across fields ranging from chemistry to biology. Bioinformatics- the analysis of DNA, RNA and protein sequences plays a crucial role in biological research and is especially amenable to AI-driven automation given its computational nature. Here, we assess the bioinformatics capabilities of three popular general-purpose LLMs on a set of tasks covering basic analytical questions that include code writing and multi-step reasoning in the domain. Utilizing questions from Rosalind, a bioinformatics educational platform, we compare the performance of the LLMs vs. humans on 104 questions undertaken by 110 to 68,760 individuals globally. GPT-3.5 provided correct answers for 59/104 (58%) questions, while Llama-3-70B and GPT-4o answered 49/104 (47%) correctly. GPT-3.5 was the best performing in most categories, followed by Llama-3-70B and then GPT-4o. 71% of the questions were correctly answered by at least one LLM. The best performing categories included DNA analysis, while the worst performing were sequence alignment/comparative genomics and genome assembly. Overall, LLMs performance mirrored that of humans with lower performance in tasks in which humans had low performance and vice versa. However, LLMs also failed in some instances where most humans were correct and, in a few cases, LLMs excelled where most humans failed. To the best of our knowledge, this presents the first assessment of general purpose LLMs on basic bioinformatics tasks in distinct areas relative to the performance of hundreds to thousands of humans. LLMs provide correct answers to several questions that require use of biological knowledge, reasoning, statistical analysis and computer code.

Journal Article↗
PubMed2006

Lysosomal, cytoskeletal, and metabolic alterations in cardiomyopathy of cathepsin L knockout mice.

Although lysosomal proteases are expressed in the heart at considerable levels, their specific functions in this organ remain elusive. Mice deficient for the lysosomal cysteine protease cathepsin L (CTSL) develop a late onset dilated cardiomyopathy (DCM) that is characterized by cardiac chamber dilation, fibrosis, and impaired cardiac contraction at 12 month of age. Investigation of the pathogenic sequence of DCM in ctsl-/- mice revealed numerous dysmorphic lysosome-like structures in heart muscle as early as 3 days after birth, whereas skeletal muscle was not affected. Labeling of the acidic cell compartment of neonatal cardiomyocytes and detection of lysosomal markers after subcellular fractionation confirmed increased lysosome content in CTSL deficient myocardium; however, specific storage materials were not detected. The myocardium of ctsl+/+ and ctsl-/- mice revealed no differences in incidence of cell death, proliferation, and capillary density during DCM progression. However, an observed increase in mRNA expression of natriuretic peptides in young adult mice indicates the activation of the adaptive "fetal" gene program, while proteome analysis revealed decreased levels of the sarcomere-associated proteins alpha-tropomyosin, desmin, and calsarcin 1, as well as considerable changes of metabolic enzymes. Bioinformatic pathway analysis suggested a switch to anaerobic catabolism and impairment of mitochondrial respiration. This interpretation was supported by a 50% reduction in resting state oxygen consumption and impaired respiration capacity in ctsl-/- myocardial homogenates. In summary, the data indicate an essential role of CTSL in maintaining the structure of the endosomal/lysosomal compartment in cardiomyocytes. Lysosomal impairment in ctsl-/- hearts results in metabolic and sarcomeric alterations that promote DCM development.

Animals↗
PubMed2005

Combined proteomics and pathways analysis of collecting duct reveals a protein regulatory network activated in vasopressin escape.

Low sensitivity is characteristic of many proteomics methods. Presented here is an approach that combines proteomics based on difference gel electrophoresis (DIGE) with bioinformatic pathways analysis to identify both abundant and relatively nonabundant proteins in inner medullary collecting duct (IMCD) altered in abundance during escape from vasopressin-induced antidiuresis. Rats received the vasopressin analog dDAVP by osmotic minipump plus either a daily water load (vasopressin escape) or only enough water to replace losses (control). Immunoblotting confirmed the hallmark of vasopressin escape, a decrease in aquaporin-2, and demonstrated a decrease in the abundance of the urea transporter UT-A3. DIGE identified 22 mostly high-abundance proteins regulated during vasopressin escape. These proteins were analyzed using pathways analysis software to reveal protein clusters incorporating the proteins identified by DIGE. A single dominant cluster emerged that included many relatively low-abundance proteins (abundances too low for DIGE identification), including several transcription factors. Immunoblotting confirmed a decrease in total and phosphorylated c-myc, a decrease in c-fos, and increases in c-jun and p53. Furthermore, immunoblotting confirmed hypothesized changes in other proteins in the proposed network: Increases in c-src, receptor for activated C kinase 1, calreticulin, and caspase 3 and decreases in steroid receptor co-activator 1, Grp78/BiP, and annexin A4. This combined approach proved capable of uncovering regulatory proteins that are altered in response to a specific physiologic perturbation without being detected directly by DIGE. The results demonstrate a dominant protein regulatory network in IMCD cells that is altered in association with vasopressin escape, providing a new framework for further studies of signaling in IMCD.

Animals↗
PubMed2004

The power of an integrated informatic and molecular approach to type 1 diabetes research.

Recent years have witnessed an explosive growth in available biological data. This includes a tremendous quantity of sequence data (e.g., biological structures, genetic and physical maps, pathways) generated by genome and transcriptome projects focused on humans, mice, and a multitude of other species. Diabetes research stands to greatly benefit from this data, which is distributed across public and private databases and the scientific literature. The increasing quantity and complexity of this biological data necessitates use of novel bioinformatics strategies for its efficient retrieval, analysis, and interpretation. Bioinformatic capability is becoming increasingly indispensable for fast and comprehensive analysis of biological data by diabetes researchers. There is great potential for diabetes scientists and clinicians to take advantage of recent bioinformatics and knowledge discovery developments to radically transform and advance this field of research. This paper will review advances in the field of bioinformatics relevant to diabetes research and preview a new specialty diabetes database, Diabetaeta, that we are creating to serve as a central bioinformatic portal for type 1 diabetes research, as well as serving as a public repository for beta cell gene and protein expression data.

Animals↗