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PubMed2007

SYSTOMONAS--an integrated database for systems biology analysis of Pseudomonas.

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at http://www.systomonas.de.

Bacterial Proteins↗

PubMed2001

HUNT: launch of a full-length cDNA database from the Helix Research Institute.

The Helix Research Institute (HRI) in Japan is releasing 4356 HUman Novel Transcripts and related information in the newly established HUNT database. The institute is a joint research project principally funded by the Japanese Ministry of International Trade and Industry, and the clones were sequenced in the governmental New Energy and Industrial Technology Development Organization (NEDO) Human cDNA Sequencing Project. The HUNT database contains an extensive amount of annotation from advanced analysis and represents an essential bioinformatics contribution towards understanding of the gene function. The HRI human cDNA clones were obtained from full-length enriched cDNA libraries constructed with the oligo-capping method and have resulted in novel full-length cDNA sequences. A large fraction has little similarity to any proteins of known function and to obtain clues about possible function we have developed original analysis procedures. Any putative function deduced here can be validated or refuted by complementary analysis results. The user can also extract information from specific categories like PROSITE patterns, PFAM domains, PSORT localization, transmembrane helices and clones with GENIUS structure assignments. The HUNT database can be accessed at http://www.hri.co.jp/HUNT.

Computational Biology↗

PubMed2004

Cytomics--new technologies: towards a human cytome project.

BACKGROUND: Molecular cell systems research (cytomics) aims at the understanding of the molecular architecture and functionality of cell systems (cytomes) by single-cell analysis in combination with exhaustive bioinformatic knowledge extraction. In this way, loss of information as a consequence of molecular averaging by cell or tissue homogenisation is avoided. PROGRESS: The cytomics concept has been significantly advanced by a multitude of current developments. Amongst them are confocal and laser scanning microscopy, multiphoton fluorescence excitation, spectral imaging, fluorescence resonance energy transfer (FRET), fast imaging in flow, optical stretching in flow, and miniaturised flow and image cytometry within laboratories on a chip or laser microdissection, as well as the use of bead arrays. In addition, biomolecular analysis techniques like tyramide signal amplification, single-cell polymerase chain reaction (PCR), and the labelling of biomolecules by quantum dots, magnetic nanobeads, or aptamers open new horizons of sensitivity and molecular specificity at the single-cell level. Data sieving or data mining of the vast amounts of collected multiparameter data for exhaustive multilevel bioinformatic knowledge extraction avoids the inadvertent loss of information from unknown molecular relations being inaccessible to an a priori hypothesis. CHALLENGE: It seems important to address the challenge of a human cytome project using hypothesis-driven molecular information collection from disease associated cell systems, supplemented by systematic and exhaustive knowledge extraction. This will allow the description of the molecular setup of normal and abnormal cell systems within a relational knowledge system, permitting the standardised discrimination of abnormal cell states in disease. As one of the consequences, individualised predictions of further disease course in patients (predictive medicine by cytomics) by characteristic discriminatory data patterns will permit individualised therapies, identification of new pharmaceutical targets, and establishment of a standardised framework of relevant molecular alterations in disease. This special issue of Cytometry, on new technologies in cytomics, focuses on prominent examples of this presently fast-moving scientific field, and represents one of the preconditions for the formulation of a human cytome project.

Cell Biology↗

PubMed2000

Development of a prostate cDNA microarray and statistical gene expression analysis package.

A cDNA microarray comprising 5184 different cDNAs spotted onto nylon membrane filters was developed for prostate gene expression studies. The clones used for arraying were identified by cluster analysis of > 35 000 prostate cDNA library-derived expressed sequence tags (ESTs) present in the dbEST database maintained by the National Center for Biotechnology Information. Total RNA from two cell lines, prostate line 8.4 and melanoma line UACC903, was used to make radiolabeled probe for filter hybridizations. The absolute intensity of each individual cDNA spot was determined by phosphorimager scanning and evaluated by a bioinformatics package developed specifically for analysis of cDNA microarray experimentation. Results indicated 89% of the genes showed intensity levels above background in prostate cells compared with only 28% in melanoma cells. Replicate probe preparations yielded results with correlation values ranging from r = 0.90 to 0.93 and coefficient of variation ranging from 16 to 28%. Findings indicate that among others, the keratin 5 and vimentin genes were differentially expressed between these two divergent cell lines. Follow-up northern blot analysis verified these two expression changes, thereby demonstrating the reliability of this system. We report the development of a cDNA microarray system that is sensitive and reliable, demonstrates a low degree of variability, and is capable of determining verifiable gene expression differences between two distinct human cell lines. This system will prove useful for differential gene expression analysis in prostate-derived cells and tissue.

DNA, Complementary↗

PubMed2003

New trends in bioinformatics: from genome sequence to personalized medicine.

Molecular medicine requires the integration and analysis of genomic, molecular, cellular, as well as clinical data and it thus offers a remarkable set of challenges to bioinformatics. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic, and proteomic data generated by high-throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine.Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and-most importantly-well trained and educated personnel to design, maintain and use these environments. This review outlines the most promising trends in bioinformatics, which may play a major role in the pursuit of future biological discoveries and medical applications.

Computational Biology↗

PubMed2006

The Fnr regulon of Bacillus subtilis.

The Bacillus subtilis transcriptional regulator Fnr is an integral part of the regulatory cascade required for the adaptation of the bacterium to low oxygen tension. The B. subtilis Fnr regulon was defined via transcriptomic analysis in combination with bioinformatic-based binding site prediction. Four distinct groups of Fnr-dependent genes were observed. Group 1 genes (narKfnr, narGHJI, and arfM) are generally induced by Fnr under anaerobic conditions. All corresponding promoters contain an essential Fnr-binding site centered -41.5/-40.5 bp upstream of the transcriptional start point, suggesting their induction by direct Fnr interaction. Group 2 genes (alsSD, ldh lctP, ywcJ, and cydABCD) are characterized by anaerobic repression in the presence of nitrate. Mutational analysis of the Fnr-binding sites found in three of the corresponding promoters excluded their function in Fnr-mediated repression. Genetic evidence showing that group 2 genes are anaerobically repressed by nitrate reductase formation was accumulated. A possible role of the redox regulator YdiH in the regulation of group 2 genes was initially investigated. Group 3 genes are characterized by their Fnr-dependent activation in the presence of nitrate and the lack of an Fnr-binding site in their promoters. The analysis of Group 3 gene transcription (ykuNOP and ydbN) indicated that Fnr induces nitrate reductase production, which leads to the formation of the regulatory compound nitrite from nitrate. Finally, the group 4 operon acoABCL, lacking an Fnr-binding site, requires Fnr-dependent nitrate reductase formation for its general anaerobic induction. A regulatory model for the observed complex Fnr-mediated gene expression was deduced.

Bacillus subtilis↗

PubMed2025

Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. METHODS: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. RESULTS: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR. CONCLUSION: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Humans↗

PubMed2000

Gene expression data analysis.

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices--tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.

Animals↗

PubMed2001

Gene expression data analysis.

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions.

Animals↗

PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗

PubMed2006

In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets.

Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory.

Burkholderia pseudomallei↗

PubMed2025

Integrated multi-omics analysis reveals TMEM147 as an immunosuppressive prognostic biomarker in LUAD.

TMEM147, an endoplasmic reticulum (ER) membrane protein, is implicated in lung adenocarcinoma (LUAD) progression, although its precise role remains unclear. To elucidate its function, this study integrated bioinformatics analyses with experimental validation. First, TMEM147 expression was assessed using TCGA and GEO datasets, with validation performed in LUAD cell lines. Survival analysis evaluated its prognostic significance. Subsequently, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and single-sample gene set enrichment analysis (ssGSEA) were employed to identify associated functional pathways and interactions within the tumor immune microenvironment. Transcription factor binding predictions and in vitro functional assays (migration, invasion, proliferation) further characterized TMEM147's role. Results indicated that TMEM147 was significantly upregulated in LUAD and correlated with poor outcomes in patients. FLI1 was predicted as a key transcriptional regulator of TMEM147. Furthermore, TMEM147 expression influenced immune cell infiltration profiles and was associated with pathways involved in ribonucleoprotein biogenesis and oxidative phosphorylation (OXPHOS). Importantly, silencing TMEM147 significantly reduced cancer cell migration, invasion, and proliferation. These findings collectively suggest that TMEM147 promotes LUAD progression and holds potential as both a prognostic biomarker and a therapeutic target.

Bioinformatics analysis↗

PubMed2003

Bioinformatics: how it is being used to identify bacterial vaccine candidates.

Genomic sequencing has provided a tremendous amount of information that can be useful in vaccine target identification. The sheer volume of information available necessitates the use of new research disciplines and techniques. Using bioinformatics, researchers sift through available data to identify appropriate candidates for biological analysis. This review provides an overview of available bioinformatic techniques for vaccine candidate identification and a few examples of how these techniques are being applied to specific bacterial pathogens.

Antigens, Bacterial↗

PubMed2003

Protein structure prediction and analysis as a tool for functional genomics.

Bioinformatic analyses of whole genome sequences highlight the problem of identifying the biochemical and cellular functions of the many gene products that are at present uncharacterised. Determination of their three-dimensional structures, either experimentally or by prediction, provides a powerful tool to address function, since it is at this level that biological activity is expressed. Here, we discuss the current approaches to protein structure prediction from sequence data, including the ab initio prediction of new folds, methods of fold recognition and comparative modelling based on homology. The value and limitations of such models are also explored. A major factor for the future will be the growth of the database of experimentally determined protein structures, through structural genomics projects. The prospects for this approach are also discussed, together with our experience in a pilot structural genomics project focused on proteins from Mycobacterium tuberculosis, the cause of tuberculosis (TB).

Computer Simulation↗

PubMed2003

Bioinformatics in neurosurgery.

WITH THE COMPLETION of the Human Genome Project, the amount of molecular biological sequence data available in public databases has reached staggering proportions. Data continue to accumulate at an exponential rate in the postgenomic era. Compilation, storage, searching, sharing, studying, and transmitting of all these data present formidable challenges. To keep pace with this extant database, the science of bioinformatics (sometimes called computational biology) has evolved. Bioinformatics is the combination of biology and computers and usually involves the storage or analysis of molecular biological sequence data at either the deoxyribonucleic acid, ribonucleic acid, or protein (amino acid) level. Most bioinformatics tools are freely available on the Internet for use by investigators around the globe. The collective wisdom from bioinformatics databases worldwide will continue to spawn advances in the neurological sciences for generations to come. Neurosurgeons must be aware of the power and potential applications of bioinformatics for the analysis of neurosurgical diseases.

Animals↗

PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗

PubMed2004

Whole-genome analysis of Drosophila gastrulation.

Microarray assays and bioinformatics methods have identified many of the genes and associated regulatory DNAs that control the early phases of gastrulation in Drosophila. The localized activities of these genes are coordinated by a nuclear gradient of the maternal regulatory factor, Dorsal, that is established shortly after fertilization. At least half of the Dorsal target genes encode transcription factors or signaling components that lead to the restricted activation of FGF, EGF, and TGF-beta signaling pathways in the mesoderm, neurogenic ectoderm and dorsal ectoderm, respectively. Recent work has yielded insights into how these signaling pathways control gastrulation, particularly in the context of the Dorsal-mediated gene regulation network

Animals↗

PubMed2003

RNA-related tools on the Bielefeld Bioinformatics Server.

We present four tools for the analysis of RNA secondary structure. They provide animated visualization of multiple structures, prediction of potential conformational switching, structure comparison (including local structure alignment) and prediction of structures potentially containing a certain kind of pseudoknots. All are available via the Bielefeld University Bioinformatics Server (http://bibiserv.techfak.uni-bielefeld.de).

Base Sequence↗