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PubMed2003

Computational strategies for analyzing data in gene expression microarray experiments.

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Algorithms↗
PubMed2026

Characterization of ZIC5 expression in esophageal squamous cell carcinoma and its association with patient survival.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy known for its aggressive nature and poor prognosis. The present study aimed to investigate the expression levels and clinical importance of the Zic family member 5 (ZIC5) gene in ESCC. Gene expression data and survival information obtained from The Cancer Genome Atlas and Gene Expression Omnibus were utilized. In 176 patients with surgically resected ESCC, immunohistochemical analysis was conducted to validate the expression of ZIC5 protein in cancerous and adjacent tissues. The findings of the present study revealed a significant upregulation of ZIC5 in ESCC compared with normal tissues (P<0.05), which was further corroborated by immunohistochemistry exhibiting a notable association between ZIC5 expression and clinical parameters such as tumor size, invasion depth, lymph node metastasis and TNM staging (P<0.05). Survival analysis further indicated that high ZIC5 expression was an independent prognostic factor for poor outcomes in patients with ESCC (hazard ratio=1.519; 95% CI: 1.017-2.269; P<0.05). In addition, bioinformatic analyses predicted that hsa-microRNA-212-5p may regulate ZIC5 mRNA and gene enrichment analysis suggested that ZIC5 may facilitate ESCC progression through involvement in the cell cycle and DNA repair pathways. In conclusion, ZIC5 is highly expressed in ESCC and associated with a poor prognosis, indicating its potential as a therapeutic target and biomarker for ESCC management. Further studies are warranted to elucidate the precise mechanisms underlying the role of ZIC5 in ESCC progression.

ESCC↗
PubMed2006

Bioinformatics and its applications in plant biology.

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.

Computational Biology↗
PubMed2006

A two-entropies analysis to identify functional positions in the transmembrane region of class A G protein-coupled receptors.

Residues in the transmembrane region of G protein-coupled receptors (GPCRs) are important for ligand binding and activation, but the function of individual positions is poorly understood. Using a sequence alignment of class A GPCRs (grouped in subfamilies), we propose a so-called "two-entropies analysis" to determine the potential role of individual positions in the transmembrane region of class A GPCRs. In our approach, such positions appear scattered, while largely clustered according to their biological function. Our method appears superior when compared to other bioinformatics approaches, such as the evolutionary trace method, entropy-variability plot, and correlated mutation analysis, both qualitatively and quantitatively.

Amino Acid Sequence↗
PubMed2005

Integrating protein annotation resources through the Distributed Annotation System.

Using the Distributed Annotation System (DAS) we have created a protein annotation resource available at our web page: http://www.cbs.dtu.dk, as a part of the BioSapiens Network of Excellence EU FP6 project. The DAS protocol allows us to gather layers of annotation data for a given sequence and thereby gain an overview of the sequence's features. A user-friendly graphical client has also been developed (http://www.cbs.dtu.dk/cgi-bin/das), which demonstrates the possibility of integrating DAS annotation data from multiple sources into a simple graphical view. The client displays protein feature annotations from the Center for Biological Sequence Analysis as well as from the BioSapiens reference UniProt server (http://www.ebi.ac.uk/das-srv/uniprot/das) at the European Bioinformatics Institute. Other DAS data sources for protein annotation will be added as they become available.

Computer Graphics↗
PubMed2025

MicroRNA-486: a dual-function biomarker for diagnosis and tumor immune microenvironment characterization in non-small cell lung cancer.

BACKGROUND: This investigation evaluates the clinical significance and molecular mechanisms of microRNA-486 (miR-486) as a potential biomarker in non-small cell lung cancer (NSCLC) through an integrative analytical approach. METHODS: We conducted systematic search and meta-analysis of diagnostic studies from major biomedical databases from inception through April 04, 2025, followed by comprehensive bioinformatics interrogation. Protein-protein interaction (PPI) networks were constructed using STRING to identify key hub genes regulated by miR-486. Validation of hub genes employed TCGA datasets, while immune infiltration analysis utilized TIMER2.0 platform. RESULTS: The meta-analysis indicated that miR-486, both individually and in combination, could be effective biomarkers for NSCLC detection. Afterwards, functional enrichment analyses of miR-486 target genes highlighted significant ontology terms and pathways crucial to the initiation and progression of NSCLC. PPI networks revealed key proteins and modules that participate in multiple essential pathways associated with NSCLC pathogenesis. Furthermore, the identified hub genes were validated for differential expression in cancerous versus normal tissues, suggesting their potential diagnostic utility, while subsequent survival analyses confirmed their prognostic value through significant associations with overall survival. Notably, these hub genes were found to be significantly associated with immune infiltration levels, immune microenvironment scores, and immune-related proteins in NSCLC. CONCLUSIONS: This dual-modality investigation establishes miR-486 as a multi-functional biomarker in NSCLC, demonstrating both diagnostic utility and immunoregulatory potential through tumor microenvironment modulation.

Humans↗
PubMed1995

X-HUSAR, an X-based graphical interface for the analysis of genomic sequences.

Management and analysis of nucleotide and protein sequence and structure data constitute a traditional area of bioinformatics. Since the analytical programs are frequently developed by researchers, rather than software engineers, they tend to suffer from idiosyncratic and non-ergonomic man-machine interfaces. We report on HUSAR, our 140+ collection of third-party, as well as in-house developed or adapted, sequence manipulation and analysis tools, well integrated into the UNIX operating system environment and accessible via consistent menu-aware interface. Most of the HUSAR programs can be completely specified by UNIX command-line options; they can thus be run in batches or combined into pipes. Adding such a program into the HUSAR environment is almost a 'plug-and-play' exercise. HUSAR has been recently complemented with a graphical client interface, X-HUSAR, to support users on UNIX platforms with X11 windowing systems. The whole X-HUSAR interface is based on a single generic program, COMLIGEN, and a number of specific configuration files. COMLIGEN interprets those files and renders appropriate windows, menus, and other interactive elements, which help the end user in selecting application programs and specifying their options. Efforts of extending both HUSAR and X-HUSAR are roughly linear to the size of the collection.

Animals↗
PubMed2001

A combination of chemical derivatisation and improved bioinformatic tools optimises protein identification for proteomics.

The identification of individual protein species within an organism's proteome has been optimised by increasing the information produced from mass spectral analysis through the chemical derivatisation of tryptic peptides and the development of new software tools. Peptide fragments are subjected to two forms of derivatisation. First, lysine residues are converted to homoarginine moieties by guanidination. This procedure has two advantages, first, it usually identifies the C-terminal amino acid of the tryptic peptide and also greatly increases the total information content of the mass spectrum by improving the signal response of C-terminal lysine fragments. Second, an Edman-type phenylthiocarbamoyl (PTC) modification is carried out on the N-terminal amino acid. The renders the first peptide bond highly susceptible to cleavage during mass spectrometry (MS) analysis and consequently allows the ready identification of the N-terminal residue. The utility of the procedure has been demonstrated by developing novel bioinformatic tools to exploit the additional mass spectral data in the identification of proteome proteins from the yeast Saccharomyces cerevisiae. With this combination of novel chemistry and bioinformatics, it should be possible to identify unambiguously any yeast protein spot or band from either two-dimensional or one-dimensional electropheretograms.

Databases, Factual↗
PubMed2005

Molecular cloning and stress-dependent regulation of potassium channel gene in Chinese cabbage (Brassica rapa ssp. Pekinensis).

Potassium channels are important for many physiological functions in plants, one of which is to regulate plant adaption to stress conditions. In this study, KCT2, the gene encoding a membrane-bound protein potassium channel (GenBank accession number: ), was isolated from Chinese cabbage (Brassica rapa ssp. Pekinensis) by RACE-PCR technique. Bioinformatics methods were performed for the gene structure and molecular similarity analysis. The KCT2 expression patterns under various stress conditions were studied by semi-quantitative RT-PCR. DNA gel blot was used to analyze genomic organization. The putative KCT2 was found to contain five membrane-spanning segments, a pore-forming domain (P-domain) between the last two transmembrane spans, a TxxTxGYGD motif in the P-domain and a putative cyclic nucleotide-binding-like domain within a long C-terminal region. KCT2 is closest to KAT2 in Arabidopsis. KCT2 could be a one-copy gene with different isoforms or belong to a small gene family with four or five members. KCT2 was expressed more strongly in leaves than in shoots and roots. KCT2 transcription products were up-regulated by a 4-h-incubation in abscisic acid (ABA) and various stress treatment including cold stress (4 degrees C) for 24 h, drought stress for 1h, and salt stress for 12 h. KCT2 transcription was not affected by anoxia stress for 8h and was down-regulated with cold stress for 48 h. KCT2 was cloned for the first time from the genus Brassica. Expression analysis indicated that in the early stage of plant adaption to stress conditions KCT2 is up-regulated, which results in a stimulation of potassium transport.

Amino Acid Motifs↗
PubMed2001

Database-assisted promoter analysis.

The analysis of regulatory sequences is greatly facilitated by database-assisted bioinformatic approaches. The TRANSFAC database contains information on transcription factors and their origins, functional properties and sequence-specific binding activities. Software tools enable us to screen the database with a given DNA sequence for interacting transcription factors. If a regulatory function is already attributed to this sequence then the database-assisted identification of binding sites for proteins or protein classes and subsequent experimental verification might establish functionally relevant sites within this sequence. The binding transcription factors and interacting factors might already be present in the database.

Binding Sites↗
PubMed2004

Long-range comparison of human and mouse Sprr loci to identify conserved noncoding sequences involved in coordinate regulation.

Mammalian epidermis provides a permeability barrier between an organism and its environment. Under homeostatic conditions, epidermal cells produce structural proteins, which are cross-linked in an orderly fashion to form a cornified envelope (CE). However, under genetic or environmental stress, specific genes are induced to rapidly build a temporary barrier. Small proline-rich (SPRR) proteins are the primary constituents of the CE. Under stress the entire family of 14 Sprr genes is upregulated. The Sprr genes are clustered within the larger epidermal differentiation complex on mouse chromosome 3, human chromosome 1q21. The clustering of the Sprr genes and their upregulation under stress suggest that these genes may be coordinately regulated. To identify enhancer elements that regulate this stress response activation of the Sprr locus, we utilized bioinformatic tools and classical biochemical dissection. Long-range comparative sequence analysis identified conserved noncoding sequences (CNSs). Clusters of epidermal-specific DNaseI-hypersensitive sites (HSs) mapped to specific CNSs. Increased prevalence of these HSs in barrier-deficient epidermis provides in vivo evidence of the regulation of the Sprr locus by these conserved sequences. Individual components of these HSs were cloned, and one was shown to have strong enhancer activity specific to conditions when the Sprr genes are coordinately upregulated.

Animals↗
PubMed2003

Subsystem identification through dimensionality reduction of large-scale gene expression data.

The availability of parallel, high-throughput biological experiments that simultaneously monitor thousands of cellular observables provides an opportunity for investigating cellular behavior in a highly quantitative manner at multiple levels of resolution. One challenge to more fully exploit new experimental advances is the need to develop algorithms to provide an analysis at each of the relevant levels of detail. Here, the data analysis method non-negative matrix factorization (NMF) has been applied to the analysis of gene array experiments. Whereas current algorithms identify relationships on the basis of large-scale similarity between expression patterns, NMF is a recently developed machine learning technique capable of recognizing similarity between subportions of the data corresponding to localized features in expression space. A large data set consisting of 300 genome-wide expression measurements of yeast was used as sample data to illustrate the performance of the new approach. Local features detected are shown to map well to functional cellular subsystems. Functional relationships predicted by the new analysis are compared with those predicted using standard approaches; validation using bioinformatic databases suggests predictions using the new approach may be up to twice as accurate as some conventional approaches.

Algorithms↗
PubMed2003

Disruption of a novel member of a sodium/hydrogen exchanger family and DOCK3 is associated with an attention deficit hyperactivity disorder-like phenotype.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a complex condition with high heritability. However, both biochemical investigations and association and linkage studies have failed to define fully the underlying genetic factors associated with ADHD. We have identified a family co-segregating an early onset behavioural/developmental condition, with features of ADHD and intellectual disability, with a pericentric inversion of chromosome 3, 46N inv(3)(p14:q21). METHODS: We hypothesised that the inversion breakpoints affect a gene or genes that cause the observed phenotype. Large genomic clones (P1 derived/yeast/bacterial artificial chromosomes) were assembled into contigs across the two inversion breakpoints using molecular and bioinformatic technologies. Restriction fragments crossing the junctions were identified by Southern analysis and these fragments were amplified using inverse PCR. RESULTS: The amplification products were subsequently sequenced to reveal that the breakpoints lay within an intron of the dedicator of cytokinesis 3 (DOCK3) gene at the p arm breakpoint, and an intron of a novel member of the solute carrier family 9 (sodium/hydrogen exchanger) isoform 9 (SLC9A9) at the q arm. Both genes are expressed in the brain, but neither of the genes has previously been implicated in developmental or behavioural disorders. CONCLUSION: These two disrupted genes are candidates for involvement in the pathway leading to the neuropsychological condition in this family.

Adult↗
PubMed2002

Functional genomics in neuropsychiatric disorders and in neuropharmacology.

The rapidly accumulating amount of information concerning gene and protein expression patterns produced by functional genomics, proteomics and bioinformatics is presently providing new targets for drug development. Furthermore, the analysis of gene expression in cells and tissues affected by a disease may reveal the underlying metabolic pathways and cellular processes affected. Finally, changes in gene expression may be used in either diagnostics or the monitoring of drug responses. This review focuses on advances in the use of functional genomics in neurological and neuropsychiatric diseases and neuropsychopharmacology. Although the number of published studies in this field is still limited, it already appears that this strategy may become a fruitful means in the analysis of the aetiology of neuropsychiatric disorders and the search for novel neuropharmacological drugs.

Central Nervous System Agents↗
PubMed2004

Differential gene induction in macrophage-like human cells by two types of Porphyromonas gingivalis: a microarray study.

Several studies have provided clinical evidence that FimA clonal variation may contribute to the periodontopathogenicity of Porphyromonas gingivalis (P.g.). We studied the gene expression profiling of the macrophage-like human cell line U937 after infection of two types of P.g. (fimA type I; Pg-I and fimA type II; Pg-II) using microarray. Of 1088 genes examined, 394 genes were detectable. Bioinformatics algorithms were used to analyze the detectable genes. Hierarchical clustering analysis showed that gene expression patterns of Pg-II and the control (no infection) were grouped together. K-means clustering grouped 79 genes into Pg-II dominance and 88 genes into Pg-I dominance. A large number of genes related to cell signaling, extracellular communication proteins, cell receptors (by ligands), protein turnover and cell adhesion receptors/proteins were grouped into clusters of Pg-I dominance. Our results indicate that compared with Pg-I, Pg-II induces a low host response as measured by its weak induction of gene expression.

Bacteroidaceae Infections↗
PubMed2003

Putting data integration into practice: using biomedical terminologies to add structure to existing data sources.

A major purpose of biomedical terminologies is to provide uniform concept representation, allowing for improved methods of analysis of biomedical information. While this goal is being realized in bioinformatics, with the emergence of the Gene Ontology as a standard, there is still no real standard for the representation of clinical concepts. As discoveries in biology and clinical medicine move from parallel to intersecting paths, standardized representation will become more important. A large portion of significant data, however, is mainly represented as free text, upon which conducting computer-based inferencing is nearly impossible. In order to test our hypothesis that existing biomedical terminologies, specifically the UMLS Metathesaurus and SNOMED CT, could be used as templates to implement semantic and logical relationships over free text data that is important both clinically and biologically, we chose to analyze OMIM (Online Mendelian Inheritance in Man). After finding OMIM entries' conceptual equivalents in each respective terminology, we extracted the semantic relationships that were present and evaluated a subset of them for semantic, logical, and biological legitimacy. Our study reveals the possibility of putting the knowledge present in biomedical terminologies to its intended use, with potentially clinically significant consequences.

Databases, Genetic↗