PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Bioinformatics”

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 379 records · Page 21Linked to original sources

Putative fasciclin-like arabinogalactan-proteins (FLA) in wheat (Triticum aestivum) and rice (Oryza sativa): identification and bioinformatic analyses.

Putative plant adhesion molecules include arabinogalactan-proteins having fasciclin-like domains. In animal, fasciclin proteins participate in cell adhesion and communication. However, the molecular basis of interactions in plants is still unknown and none of these domains have been characterized in cereals. This work reports the characterization of 34 wheat (Triticum aestivum) and 24 rice (Oryza sativa) Fasciclin-Like Arabinogalactan-proteins (FLAs). Bioinformatics analyses show that cereal FLAs share structural characteristics with known Arabidopsis FLAs including arabinogalactan-protein and fasciclin conserved domains. At least 70% of the wheat and rice FLAs are predicted to be glycosylphosphatidylinositol-anchored to the plasma membranes. Expression analyses determined from the relative abundance of ESTs in the publicly available wheat EST databases and from RNA gel blots indicate that most of these genes are weakly expressed and found mainly in seeds and roots. Furthermore, most wheat genes were down regulated by abiotic stresses except for TaFLA9 and 12 where cold treatment induces their expression in roots. Plant fasciclin-like domains were predicted to have 3-D homology with FAS1 domain of the fasciclin I insect neural cell adhesion molecule with an estimated precision above 70%. The structural analysis shows that negatively charged amino acids are concentrated along the beta1-alpha3-alpha4-beta2 edges, while the positively charged amino acids are concentrated on the back side of the folds. This highly charged surface distribution could provide a way of mediating protein-protein interactions via electrostatic forces similar to many other adhesion molecules. The identification of wheat FLAs will facilitate studying their function in plant growth and development and their role in stress response.

Amino Acid Sequence↗

Identification of 96 single nucleotide polymorphisms in eight genes involved in iron metabolism: efficiency of bioinformatic extraction compared with a systematic sequencing approach.

Single nucleotide polymorphisms (SNPs) can significantly contribute to the characterization of the genes predisposing to iron overloads or deficiencies. We report an SNP survey of coding and non-coding regions of eight genes involved in iron metabolism, by two successive methods. First, we made use of the public domain sequence data, by using assembled expressed sequence tags, non-redundant sequences, and SNP database screening. We extracted 77 potential SNPs of which only 31 could be further validated by sequencing DNA from 44 unrelated multi-ethnic individuals. Our results indicate that a bioinformatic approach may be effective only in those cases where candidate SNPs are extracted from two different data sources or in cases of experimentally confirmed SNPs. Second, additional systematic sequencing of DNA from 24 unrelated Breton subjects increased the number of SNPs over a total length of 86 kb to 96. The average distance between the SNPs and minor allele frequencies were higher than reported by others authors; this discrepancy may reflect the nature of the genes studied and the ethnic homogeneity of our test population.

Cation Transport Proteins↗

Experimental and bioinformatics comparison of gene expression between T cells from TIL of liver cancer and T cells from UniGene.

BACKGROUND: The major difficulty of mapping parallel gene expression obtained from solid tumors is mainly due to contaminating cells. In this study, by applying a strategy of parallel gene expression at a cell-cluster or colony level, we have identified the gene expression pattern of T cells within tumor-infiltrating lymphocytes (TJLs) obtained from two liver cancer patients. METHODS: Here a new method was utilized to analyze the parallel gene expression. By using bioinformatics analysis, the data were also compared with T-cell gene expression present in UniGene. RESULTS: Our results demonstrated that 18 genes in specimen A and 13 genes in specimen B were highly expressed after the removal of a nonspecific TIL cDNA library, by pairing gene hybridization; the genes were expressed in CD3+ cells from peripheral blood mononuclear cells (PBMC). By using BlastN search, 17 of the 18, and 12 of the 13 sequences were exhibited, respectively, in Homo sapiens, with a range of BlastN E values of 0 to 4 x 10(-13). The LocusLink distribution in chromosomes obtained from both specimens was not significantly different; 17 of 19 putative genes (both specimen A and specimen B) were observed in the UniGene cluster in Homo sapiens, except for dihydropyrimidinase-related protein-3 and diacyglycerol kinase alpha. Interestingly, only 4 of 19 (21%) putative genes were displayed in the T-cell UniGene database (i.e., LD-78 in Hs. 73817, IL-8 in Hs. 624, TRAIL in Hs. 83429, and Fas ligand in Hs. 2007). CONCLUSIONS: By comparison with the reported data and UniGene, the parallel gene expression of T cells obtained from TIL can provide essential new insights into T-cell activity, T-cell extravasation into tumor tissues, and T-cell cytotoxicity against tumor cells.

Adult↗

Bioinformatic mining of type I microsatellites from expressed sequence tags of channel catfish (Ictalurus punctatus).

Gene-derived markers are pivotal to the analysis of genome structure, organization, and evolution and necessary for comparative genomics. However, gene-derived markers are relatively difficult to develop. This project utilized the genomic resources of channel catfish expressed sequence tags (ESTs) to identify simple sequence repeats (SSRs), or microsatellites. It took the advantage of ESTs for the establishment of gene identities, and of microsatellites for the acquisition of high polymorphism. When microsatellites are tagged to genes, the microsatellites can then be used as gene markers. A bioinformatic analysis of 43,033 ESTs identified 4855 ESTs containing microsatellites. Cluster analysis indicated that 1312 of these ESTs fell into 569 contigs, and the remaining 3534 ESTs were singletons. A total of 4103 unique microsatellite-containing genes were identified. The dinucleotide CA/TG and GA/TC pairs were the most abundant microsatellites. AT-rich microsatellite types were predominant among trinucleotide and tetranucleotide microsatellites, consistent with our earlier estimation that the catfish genome is highly AT-rich. Our preliminary results indicated that the majority of the identified microsatellites were polymorphic and, therefore, useful for genetic linkage mapping of catfish. Mapping of these gene-derived markers is under way, which will set the foundation for comparative genome analysis in catfish.

Animals↗

Bioinformatics and proteomics approaches for aging research.

Aging is a natural phenomenon that affects the entire physiology of an organism. Elucidating the molecular mechanisms underlying this complex process remains a major challenge today. Humans make poor models for research into aging because of their long life span. Thus, most of the current knowledge is through studies conducted in lower organisms. Large differences in life spans make it difficult to extrapolate the results of experiments carried out in model organisms to humans. Recent advances in genomic and proteomic technologies now permit generation of data pertaining to aging on a large-scale. In addition, several web-based community resources and databases are available that provide easy access to the available data. Use of bioinformatics and systems biology type of approaches provide a framework to start dissecting this complex biological phenomenon. Here, we discuss various genomic, transcriptomic and proteomic approaches that have the potential to provide a comprehensive mechanistic insight into the aging process.

Aging↗

Bioinformatic analysis reveals the potential association of ESRP1 with the splicing of cytoskeleton-associated genes in doxorubicin-resistant MCF7 breast cancer cells.

BACKGROUND: Breast cancer remains one of the most prevalent malignancies among women, with doxorubicin resistance posing a significant challenge that undermines treatment success and survival outcomes. Aberrant alternative splicing (AS), driven by dysregulation or mutations in splicing factors (SFs), is implicated in cancer initiation, progression, and drug resistance. This study aims to investigate the association of the epithelial cell-specific splicing factor ESRP1 with doxorubicin resistance in breast cancer, focusing on how ESRP1 deficiency correlates with AS changes that promote chemoresistance. METHODS: We analyzed RNA-sequencing (RNA-seq) data from doxorubicin-resistant (MCF7-DR) and parental (MCF7) breast cancer cell lines to identify enhanced alternative splicing events (ASEs) and changes in ESRP1 expression; we further leveraged The Cancer Genome Atlas (TCGA)-BRCA cohort to construct an SF-RASE correlation network for screening core SFs (including ESRP1). An integrative analysis combining crosslinking immunoprecipitation (CLIP-seq) data and The Cancer Genome Atlas (TCGA) database was performed to validate ESRP1 binding targets and assess the association between ESRP1-related splicing and cytoskeleton organization. RESULTS: We observed extensive AS changes and significantly downregulated ESRP1 expression in MCF7-DR cells. Integrative analysis identified 61 high-confidence ASEs that correlate with ESRP1 expression. Further bioinformatic integration suggests that ESRP1 expression is associated with the splicing patterns of SPTBN1, MAP2K7, FGFR3, and CYB561A3-four genes involved in cytoskeleton organization-though direct experimental verification to confirm a causal regulatory relationship between ESRP1 and the splicing of these genes is still pending. CONCLUSIONS: Our findings suggest that ESRP1 expression is closely associated with doxorubicin resistance in breast cancer cells, with concomitant alterations in key ASEs linked to cytoskeletal remodeling that correlate with ESRP1. Exploring the ESRP1-related splicing network may offer new strategies to overcome chemoresistance and improve patient outcomes. However, the small cell line sample size (n = 2 per group) constrains the robustness of ASE and SF-ASE correlation findings, and these results should be interpreted with caution and require further validation with larger sample cohorts.

Alternative splicing↗

Bioinformatics in the pharmaceutical industry.

The great advances in human healthcare that are presaged by the Human Genome Project can be realized by the pharmaceutical industry. A prerequisite for this will be the successful integration of bioinformatics into most aspects of drug discovery. Although, from a scientific viewpoint, this is not a difficult problem, there are formidable technological obstacles. Once these are overcome, rapid progress can be expected.

Amino Acid Sequence↗

Bioinformatics and drug discovery.

Bioinformatics involves both the automatic processing of large amounts of existing data and the creation of new types of information resource. Both will be required if the data are to be transformed into information and used to help in the discovery of drugs.

Biotechnology↗

The EF-Handome: combining comparative genomic study using FamDBtool, a new bioinformatics tool, and the network of expertise of the European Calcium Society.

By combining a bioinformatics tool (FamDBTool) and the expertise of a network of calcium binding proteins specialists (European Calcium Society), we aim to accelerate and to rationalize the curation of the public general biological database such as Swissprot and Entrez Gene with respect to specific protein families. In this paper, we show the feasibility of such rationale in order to set and to curate the human, mouse and rat sets of EF-Hand genes, the EF-Handome.

Animals↗

Identification of novel steroid target genes through the combination of bioinformatics and functional analysis of hormone response elements.

Steroid hormone receptors including androgen receptor (AR), glucocorticoid receptor (GR), progesterone receptor (PR), and mineralocorticoid receptor (MR) recognize and bind to identical consensus hormone response elements (HREs), which consist of two hexameric half-sites (5'-AGAACA-3') arranged as inverted repeats with a 3-bp spacer. Although only a few near-consensus HRE sequences have been identified in the transcriptional regulatory regions of known steroid target genes, it has been unclear whether the exact consensus sequences function as bona fide HREs in vivo. A genome-wide in silico screening of palindromic HREs identified 565 exact consensus sequences in human genome (NCBI 35 assembly). In this study, of 565 exact consensus elements, functional in vivo receptor binding was evaluated regarding 26 sequences located within 10 kb upstream to the 5' end of annotated genes through chromatin immunoprecipitation (ChIP) assay using cells endogenously expressing steroid hormone receptors. Hormone responsiveness of proximal gene expression was examined through quantitative RT-PCR. As far as performing ChIP assay for AR, GR, and PR, 14 of 26 elements significantly recruited at least one of the receptors by hormone treatment (>2-fold enrichment versus vehicle). In terms of gene expression in the vicinity of the above 14 functional perfect HREs, four genes were upregulated by >2-fold with hormone treatment. The present data suggest that the combination of bioinformatics analysis and quantitative experimental evaluation is useful to identify novel functional HREs that may contribute to the transcriptional regulation of steroid target genes.

Cell Line, Tumor↗

SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer.

Breast cancer has never had any good serum tumor markers. Therefore, we developed and evaluated a proteomics approach to searching for new biomarkers and building diagnostic models. SELDI-TOF-MS ProteinChip was used to detect the serum protein patterns of 49 breast cancer patients, 51 patients with benign breast diseases, and 33 healthy women. The diagnostic models were developed and validated using bioinformatics tools such as artificial neural networks and discriminant analysis. In total, four models were built and their sensitivities and specificities were satisfactory. The abilities of these models to diagnose stage I breast cancer were not worse than for stages II-IV (P>0.05). Four candidate biomarkers of breast cancer were found. The high sensitivity and specificity achieved by this method show great potential for the early detection of breast cancer and facilitation of discovering new and improved biomarkers.

Adult↗

Bioinformatics analysis of mycoplasma metabolism: important enzymes, metabolic similarities, and redundancy.

In this work we apply a bioinformatics approach to determine the most important enzymes of the metabolic network of mycoplasmas. The genomes of several mycoplasmas shared predicted important enzymes. Our method allows us to determine both enzymes that are isolated from the metabolic network of the organism and those that are redundant. We also compare the similarities of the mycoplasmas metabolic networks with the phylogenetic relationships predicted from their 16s rRNA sequences.

Computational Biology↗

Comprehensive bioinformatics analysis identifies candidate ciliogenesis-related genes preferentially associated with N0-stage lung squamous cell carcinoma.

PURPOSE: There is few research on which genes play an important role in tumors without lymph metastasis. This study aimed to identify candidate molecular alterations preferentially associated with N0-stage LUSC. METHODS: we conducted a comprehensive bioinformatics analysis using publicly available The Cancer Genome Atlas (TCGA) data. Differentially expressed genes (DEGs) were identified separately by comparing N0 tumors and N+ tumors with normal lung tissues. Genes dysregulated in both N0 and N+ tumors were excluded to identify candidate N0-associated genes PPI networks were constructed using STRING and Cytoscape, with module analysis performed via MCODE. Hub genes were identified using multiple Cytohubba algorithms. Functional enrichment analyses were conducted using GO, and KEGG pathways using DAVID. Gene interaction networks were further explored using GeneMANIA. Immune cell infiltration was evaluated with TIMER. Associations with pathological stage and patient survival were assessed using GEPIA and other relevant tools. RESULTS: A total of 1103 candidate N0-associated DEGs were identified, including 748 upregulated and 355 downregulated genes. The PPI network contained five major MCODE clusters. One cluster (MCODE 4) included TTC30A, TTC30B, BBS7, and KIF3B genes implicated in ciliogenesis. TTC30B showed significant differential expression across pathological stages in the overall LUSC cohort. Seven consensus hub genes (ERBB2, CHUK, CASP8, NOTCH1, HNF4A, CREBBP, and IRS1) were identified based on their consistent ranking across multiple CytoHubba algorithms. Upregulated candidate N0-associated genes were primarily enriched in immune-related processes, including B-cell-mediated immunity and humoral responses, whereas downregulated genes were enriched in lysosomal and trans-Golgi network-related pathways. Exploratory immune infiltration analyses identified associations between the four ciliogenesis-related genes and several immune cell populations. CONCLUSIONS: This study identified candidate molecular signatures preferentially associated with N0-stage LUSC, including ciliogenesis-related genes and consensus hub genes. These findings provide hypotheses regarding molecular features of N0-stage LUSC and warrant further validation in independent cohorts and experimental studies.

Humans↗

Bioinformatic and expression analysis of the putative gliotoxin biosynthetic gene cluster of Aspergillus fumigatus.

Gliotoxin is a secondary metabolite produced by several fungi including the opportunistic animal pathogen Aspergillus fumigatus. It is a member of the epipolythiodioxopiperazine (ETP) class of toxins characterised by a disulphide bridged cyclic dipeptide. A putative cluster of 12 genes involved in gliotoxin biosynthesis has been identified in A. fumigatus by a comparative genomics approach based on homology to genes from the sirodesmin (another ETP) biosynthetic gene cluster of Leptosphaeria maculans. The physical limits of the cluster in A. fumigatus have been defined by bioinformatics and by identifying the genes that are co-regulated and whose timing of expression correlates with the production of gliotoxin in culture.

Aspergillus fumigatus↗

Molecular and bioinformatic analysis of the FB-NOF transposable element.

The Drosophila melanogaster transposable element FB-NOF is known to play a role in genome plasticity through the generation of all sort of genomic rearrangements. Moreover, several insertional mutants due to FB mobilizations have been reported. Its structure and sequence, however, have been poorly studied mainly as a consequence of the long, complex and repetitive sequence of FB inverted repeats. This repetitive region is composed of several 154 bp blocks, each with five almost identical repeats. In this paper, we report the sequencing process of 2 kb long FB inverted repeats of a complete FB-NOF element, with high precision and reliability. This achievement has been possible using a new map of the FB repetitive region, which identifies unambiguously each repeat with new features that can be used as landmarks. With this new vision of the element, a list of FB-NOF in the D. melanogaster genomic clones has been done, improving previous works that used only bioinformatic algorithms. The availability of many FB and FB-NOF sequences allowed an analysis of the FB insertion sequences that showed no sequence specificity, but a preference for A/T rich sequences. The position of NOF into FB is also studied, revealing that it is always located after a second repeat in a random block. With the results of this analysis, we propose a model of transposition in which NOF jumps from FB to FB, using an unidentified transposase enzyme that should specifically recognize the second repeat end of the FB blocks.

AT Rich Sequence↗

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↗

A bioinformatics framework for genotype-phenotype correlation in humans with Marfan syndrome caused by FBN1 gene mutations.

Mutations in the human FBN1 gene are known to be associated with the Marfan syndrome, an autosomal dominant inherited multi-systemic connective tissue disorder. However, in the absence of solid genotype-phenotype correlations, the identification of an FBN1 mutation has only little prognostic value. We propose a bioinformatics framework for the mutated FBN1 gene which comprises the collection, management, and analysis of mutation data identified by molecular genetic analysis (DHPLC) and data of the clinical phenotype. To query our database at different levels of information, a relational data model, describing mutational events at the cDNA and protein levels, and the disease's phenotypic expression from two alternative views, was implemented. For database similarity requests, a query model which uses a distance measure based on log-likelihood weights for each clinical manifestation, was introduced. A data mining strategy for discovering diagnostic markers, classification and clustering of phenotypic expressions was provided which enabled us to confirm some known and to identify some new genotype-phenotype correlations.

Computational Biology↗

An interdepartmental Ph.D. program in computational biology and bioinformatics: the Yale perspective.

Computational biology and bioinformatics (CBB), the terms often used interchangeably, represent a rapidly evolving biological discipline. With the clear potential for discovery and innovation, and the need to deal with the deluge of biological data, many academic institutions are committing significant resources to develop CBB research and training programs. Yale formally established an interdepartmental Ph.D. program in CBB in May 2003. This paper describes Yale's program, discussing the scope of the field, the program's goals and curriculum, as well as a number of issues that arose in implementing the program. (Further updated information is available from the program's website, www.cbb.yale.edu.)

Computational Biology↗