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At least 451 records · Page 25Linked to original sources
PubMed2002

Computational discovery of sense-antisense transcription in the human and mouse genomes.

BACKGROUND: Overlapping but oppositely oriented transcripts have the potential to form sense-antisense perfect double-stranded (ds) RNA duplexes. Over recent years, the number and variety of examples of mammalian gene-regulatory phenomena in which endogenous dsRNA duplexes have been proposed or demonstrated to participate has greatly increased. These include genomic imprinting, RNA interference, translational regulation, alternative splicing, X-inactivation and RNA editing. We computationally mined public mouse and human expressed sequence tag (EST) databases to search for additional examples of bidirectionally transcribed genomic regions. RESULTS: Our bioinformatics approach identified over 217 candidate overlapping transcriptional units, almost all of which are novel. From experimental validation of a subset of our predictions by orientation-specific RT-PCR, we estimate that our methodology has a specificity of 84% or greater. In many cases, regions of sense-antisense overlap within the 5'- or 3'-untranslated regions of a given transcript correlate with genomic patterns of mouse-human conservation. CONCLUSIONS: Our results, in conjunction with the literature, bring the total number of predicted and validated examples of overlapping but oppositely oriented transcripts to over 300. Several of these cases support the hypothesis that a subset of the instances of substantial mouse-human conservation in the 5' and 3' UTRs of transcripts might be explained in part by functionality of an overlapping transcriptional unit.

Alternative Splicing↗
PubMed2000

From microbial genome sequence to applications.

Whole genome sequences of microbial pathogens present new opportunities for clinical applications. Chief among these are development of antimicrobials, diagnostics, and vaccines. While antimicrobial development is a more difficult, long-term prospect, new diagnostics and vaccines are likely to be the first products of microbial genomics. To take advantage of whole genome sequences, methods for production of gene products in surrogate hosts (heterologous expression) are required that will work for large-scale, high-throughput gene expression. This will allow genomic information from even the most experimentally difficult pathogens to be mined for applications. In addition, screening methods to test gene products for their potential as vaccine candidates are needed for large-scale screening. These areas for technological development should be stimulated by the potential for converting genomic sequence information into applications.

Animals↗
PubMed2006

CryptoDB: a Cryptosporidium bioinformatics resource update.

The database, CryptoDB (http://CryptoDB.org), is a community bioinformatics resource for the AIDS-related apicomplexan-parasite, Cryptosporidium. CryptoDB integrates whole genome sequence and annotation with expressed sequence tag and genome survey sequence data and provides supplemental bioinformatics analyses and data-mining tools. A simple, yet comprehensive web interface is available for mining and visualizing the data. CryptoDB is allied with the databases PlasmoDB and ToxoDB via ApiDB, an NIH/NIAID-fundedBioinformatics Resource Center. Recent updates to CryptoDB include the deposition of annotated genome sequences for Cryptosporidium parvum and Cryptosporidium hominis, migration to a relational database (GUS), a new query and visualization interface and the introduction of Web services.

Animals↗
PubMed2004

GOLD.db: genomics of lipid-associated disorders database.

BACKGROUND: The GOLD.db (Genomics of Lipid-Associated Disorders Database) was developed to address the need for integrating disparate information on the function and properties of genes and their products that are particularly relevant to the biology, diagnosis management, treatment, and prevention of lipid-associated disorders. DESCRIPTION: The GOLD.db http://gold.tugraz.at provides a reference for pathways and information about the relevant genes and proteins in an efficiently organized way. The main focus was to provide biological pathways with image maps and visual pathway information for lipid metabolism and obesity-related research. This database provides also the possibility to map gene expression data individually to each pathway. Gene expression at different experimental conditions can be viewed sequentially in context of the pathway. Related large scale gene expression data sets were provided and can be searched for specific genes to integrate information regarding their expression levels in different studies and conditions. Analytic and data mining tools, reagents, protocols, references, and links to relevant genomic resources were included in the database. Finally, the usability of the database was demonstrated using an example about the regulation of Pten mRNA during adipocyte differentiation in the context of relevant pathways. CONCLUSIONS: The GOLD.db will be a valuable tool that allow researchers to efficiently analyze patterns of gene expression and to display them in a variety of useful and informative ways, allowing outside researchers to perform queries pertaining to gene expression results in the context of biological processes and pathways.

Adipocytes↗
PubMed2004

In silico gene function prediction using ontology-based pattern identification.

MOTIVATION: With the emergence of genome-wide expression profiling data sets, the guilt by association (GBA) principle has been a cornerstone for deriving gene functional interpretations in silico. Given the limited success of traditional methods for producing clusters of genes with great amounts of functional similarity, new data-mining algorithms are required to fully exploit the potential of high-throughput genomic approaches. RESULTS: Ontology-based pattern identification (OPI) is a novel data-mining algorithm that systematically identifies expression patterns that best represent existing knowledge of gene function. Instead of relying on a universal threshold of expression similarity to define functionally related groups of genes, OPI finds the optimal analysis settings that yield gene expression patterns and gene lists that best predict gene function using the principle of GBA. We applied OPI to a publicly available gene expression data set on the life cycle of the malarial parasite Plasmodium falciparum and systematically annotated genes for 320 functional categories based on current Gene Ontology annotations. An ontology-based hierarchical tree of the 320 categories provided a systems-wide biological view of this important malarial parasite.

Algorithms↗
PubMed2006

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.

Algorithms↗
PubMed2002

The repetitive sequence database and mining putative regulatory elements in gene promoter regions.

At least 43% of the human genome is occupied by repetitive elements. Moreover, around 51% of the rice genome is occupied by repetitive elements. The analysis of repetitive elements reveals that repetitive elements in our genome may have been very important in the evolutionary genomics. The first part of this study is to describe a database of repetitive elements - RSDB. The RSDB database contains repetitive elements, which are classified into the following categories: exact, tandem, and similar. The interfaces needed to query and show the results and statistical data, such as the relationship between repetitive elements and genes, cross-references of repetitive elements among different organisms, and so on, are provided. The second part of this study then attempts to mine the putative binding site for information on how combinations of the known regulatory sites and overrepresented repetitive elements in RSDB are distributed in the promoter regions of groups of functionally related genes. The overrepresented repetitive elements appearing in the associations are possible transcription factor binding sites. Our proposed approach is applied to Saccharomyces cerevisiae and the promoter regions of Yeast ORFs. The complete contents of RSDB and partial putative binding sites are available to the public at www.rsdb.csie.ncu.edu.tw. The readers may download partial query results.

Animals↗
PubMed2005

Finding genomic ontology terms in text using evidence content.

BACKGROUND: The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biological entities and properties in the text, and then decide which pairs represent valid annotations. METHODS: This document introduces a novel unsupervised method for recognizing biological properties in unstructured text, involving the evidence content of their names. RESULTS: This document shows the results obtained by the application of our method to BioCreative tasks 2.1 and 2.2, where it identified Gene Ontology annotations and their evidence in a set of articles. CONCLUSION: From the performance obtained in BioCreative, we concluded that an automatic annotation system can effectively use our method to identify biological properties in unstructured text.

Computational Biology↗
PubMed2006

[High throughput screening and analysis of prostate cancer-related genes through mining databases].

BACKGROUND & OBJECTIVE: Investigation of differentially expressed genes in prostate cancer tissues may help to understand the molecular mechanism of prostate cancer and provide diagnostic markers or new targets for therapy. The Cancer Genome Anatomy Project (CGAP) public database provides an unprecedented opportunity for cancer researchers to mine genes differentially expressed in cancer tissues by bioinformatic methods. This study was to explore the feasibility of incorporating the Internet-available Serial Analysis of Gene Expression (SAGE) and cDNA databases to find human prostate cancer-related genes. METHODS: SAGE digital gene expression displayer (DGED) and cDNA DGED were used to analyze differentially expressed (>5 folds) genes in malignant prostate tissues compared with normal prostate tissues. The SAGE tags were filtered by their confidence and our 3 criteria. To test the confidence of tags of virtual digital analysis for gene identification, the modified GLGI (generation of longer cDNA fragments from SAGE tags for gene identification) was used to get the cDNA 3' end downstream of 20 tags. Main functions of all candidate genes and their relations to prostate cancer were annotated. RESULTS: Fifty-three differentially expressed genes were screened out by SAGE DGED, 26 of them were up-regulated and 27 were down-regulated in prostate cancer; 28 differentially expressed genes were got by cDNA DGED, 15 of them were up-regulated and 13 were down-regulated in prostate cancer. CONCLUSION: Reasonable use of public databases by the Internet-available tools is a simple, effective approach to get cancer-related genes, and might provide useful clues for further investigation although the results require experimental validation.

DNA, Complementary↗
PubMed2005

GeneTide--Terra Incognita Discovery Endeavor: a new transcriptome focused member of the GeneCards/GeneNote suite of databases.

GeneCards is an automatically mined database of human genes that strives to create, along with its auxiliary databases--GeneLoc, GeneNote and GeneAnnot--the most inclusive resource of gene-centered information of the human genome. GeneTide, the Gene Terra Incognita Discovery Endeavor (http://genecards.weizmann.ac.il/genetide/), the newest addition to this family, is a transcriptome-focused database which aims to enhance GeneCards with additional expressed sequence tag (EST)-based genes. This is achieved by comprehensively mapping >85% of the approximately 5.6 million human ESTs currently available at dbEST to known genes by means of data mining and integration of genomic resources including UniGene, DoTS, AceView and in-house resources. GeneTide thus creates comprehensive links between ESTs and GeneCards genes. Furthermore, groups of unassociated transcripts serve as a basis for defining novel EST-based GeneCards Candidates (EGCs). These EGCs, nearly 25,000 of which were defined in version 0.3 of GeneTide, are further annotated with various parameters, including splicing evidence and expression data extracted from the GeneNote database, to determine their validity as possible de novo genes.

Databases, Genetic↗
PubMed2002

Use of a common promoter by two juxtaposed and intronless mouse early embryonic genes, Rnf33 and Rnf35: implications in zygotic gene expression.

Rnf33 and Rnf35 are mouse RING finger protein genes that are transcribed temporally in the preimplantation mouse embryo, predominantly at the two-cell embryonic stage. The genes are juxtaposed in a 20-kb genomic region and are both intronless except for a single intron in the 5' untranslated region (5'-UTR). Based on analysis of the Rnf33/35 genomic sequence and cDNA sequences derived by in silico mining, we found that the Rnf33 and Rnf35 mRNAs are apparently transcribed from the same putative promoter and may be products of alternative splicing of the same pre-mRNA generated through differential 3' cleavage and polyadenylation. We also detected a second variant of Rnf35 in two-cell embryo generated through a second splicing event using an unconventional 5' splice junction. Our observations on the mode of transcription of Rnf33 and Rnf35 are consistent with the hypothesis that transcription of zygotic genes is promiscuous, and that the solo 5'-UTR intron may serve to facilitate efficient translation.

Animals↗
PubMed2025

MED12-STAT1-TAP2 axis regulates CD8 + T cell cytotoxicity and mediates immunotherapy outcome in non-small cell lung cancer.

Although immunotherapy for late-stage non-small cell lung carcinoma (NSCLC) has been clinically utilized, its prognosis remains highly heterogeneous, prompting us to investigate novel predictive immunotherapy biomarkers for NSCLC. We analyzed the correlations between MED12 nonsynonymous mutations and survival, clinical, genomic, transcriptomic information, and immune infiltration information through data mining across multiple datasets. We also investigated the mechanism of MED12 using luciferase assay, Western blot, ChIP-PCR, and siRNA. MED12 is significantly associated with survival in completely independent immunotherapy datasets, including MSKCC (N = 350), Naiyer2015 (N = 34), our own (N = 295) and the pan-cancer dataset, but not in the TCGA dataset, where patients received non-immunotherapy regimens. Mutations in MED12 showed no significant correlation with known metrics (TMB, IPS/CTLA4/PD1 status, PD-1/PD-L1 expression, and TCR/BCR status) or DNA Damage Repair (DDR) pathway mutations, yet they carried independent prognostic information according to the Cox multivariate regression. On the other hand, MED12 mutation is significantly associated with multiple immune-related pathways and immune infiltration of CD8 + T cells and activated NK cells. Lactate dehydrogenase assay revealed that knockdown of TAP2 restored the upregulation of CD8 + T cell cytotoxicity triggered by MED12 knockdown. ChIP-PCR, luciferase assay and siRNA knock down assay indicate that MED12 binds to the promoter region of STAT1 to suppress its transcription, while the transcription factor STAT1 promotes the transcription of TAP2, thus inhibiting the antigen processing and presentation. Collectively, MED12 mutation is an independent and valuable biomarker for predicting the response to immune checkpoint inhibitor (ICI)therapy in NSCLC by modulating CD8 + T cell cytotoxicity via the STAT1/TAP2 axis.

Humans↗
PubMed2025

Target and biomarker exploration portal for drug discovery.

MOTIVATION: The discovery of novel drug targets and precision biomarkers remains a major challenge in drug development, with traditional differential expression analysis often overlooking key regulatory proteins. Here, we present a novel, web-based bioinformatics tool, the Target and Biomarker Exploration Portal (TBEP), designed to accelerate the drug discovery process by integrating large-scale biomedical data with network analysis techniques. RESULTS: TBEP harnesses machine-learning approaches to mine and combine multimodal datasets, including human genetics, functional genomics, and protein-protein interaction networks, to decode causal disease mechanisms and uncover novel therapeutic targets and precision biomarkers for specific phenotypes. A unique feature of the tool is its ability to process large-scale data in real-time, facilitated by an efficient cloud-based architecture. Additionally, the tool incorporates an integrated large language model (LLM), which assists researchers in exploring and interpreting complex biological relationships within the generated networks and multi-omics data using natural language (English). By offering an intuitive, interactive interface, the LLM enhances the exploration of biological insights, making it easier for scientists to derive actionable conclusions. This powerful integration of network analysis, multi-omics data, and LLM provides a robust framework for accelerating the identification of novel drug targets. AVAILABILITY AND IMPLEMENTATION: The tool is publicly available at https://tbep.missouri.edu. The source code, documentation and installation instructions are available at GitHub repository: https://github.com/mizzoudbl/tbep.

Drug Discovery↗
PubMed2003

Fungal biology and agriculture: revisiting the field.

Plant pathology has made significant progress over the years, a process that involved overcoming a variety of conceptual and technological hurdles. Descriptive mycology and the advent of chemical plant-disease management have been followed by biochemical and physiological studies of fungi and their hosts. The later establishment of biochemical genetics along with the introduction of DNA-mediated transformation have set the stage for dissection of gene function and advances in our understanding of fungal cell biology and plant-fungus interactions. Currently, with the advent of high-throughput technologies, we have the capacity to acquire vast data sets that have direct relevance to the numerous subdisciplines within fungal biology and pathology. These data provide unique opportunities for basic research and for engineering solutions to important agricultural problems. However, we also are faced with the challenge of data organization and mining to analyze the relationships between fungal and plant genomes and to elucidate the physiological function of pertinent DNA sequences. We present our perspective of fungal biology and agriculture, including administrative and political challenges to plant protection research.

Agriculture↗
PubMed2006

Survey and analysis of microsatellites from transcript sequences in Phytophthora species: frequency, distribution, and potential as markers for the genus.

BACKGROUND: Members of the genus Phytophthora are notorious pathogens with world-wide distribution. The most devastating species include P. infestans, P. ramorum and P. sojae. In order to develop molecular methods for routinely characterizing their populations and to gain a better insight into the organization and evolution of their genomes, we used an in silico approach to survey and compare simple sequence repeats (SSRs) in transcript sequences from these three species. We compared the occurrence, relative abundance, relative density and cross-species transferability of the SSRs in these oomycetes. RESULTS: The number of SSRs in oomycetes transcribed sequences is low and long SSRs are rare. The in silico transferability of SSRs among the Phytophthora species was analyzed for all sets generated, and primers were selected on the basis of similarity as possible candidates for transferability to other Phytophthora species. Sequences encoding putative pathogenicity factors from all three Phytophthora species were also surveyed for presence of SSRs. However, no correlation between gene function and SSR abundance was observed. The SSR survey results, and the primer pairs designed for all SSRs from the three species, were deposited in a public database. CONCLUSION: In all cases the most common SSRs were trinucleotide repeat units with low repeat numbers. A proportion (7.5%) of primers could be transferred with 90% similarity between at least two species of Phytophthora. This information represents a valuable source of molecular markers for use in population genetics, genetic mapping and strain fingerprinting studies of oomycetes, and illustrates how genomic databases can be exploited to generate data-mining filters for SSRs before experimental validation.

Codon↗
PubMed2002

[Construction and application of an in silico elongation system of nucleic acid sequence].

Normally it is difficult to obtain full-length cDNA sequence of novel genes. More and more expressed sequence tags(ESTs) have been obtained since the start-up of human genome project. Powerful system is badly needed for data mining on these EST sequences. Based on a personal computer coupled with Linux operating system and EST database, the Blast software and Phrap software were used to construct a platform for in silico elongation of ESTs in our lab. The performance was tested using 11386 EST sequences and 511 partial-length cDNA sequences. Results demonstrated that 8373 EST and 389 cDNA sequence were elongated using this system. Thus the platform seems to be a fast way for full-length cDNA sequence cloning of new genes.

English Abstract↗
PubMed1999

Antisense oligonucleotides for target validation and gene function determination.

Antisense technology is attracting attention from the biotechnology and pharmaceutical industries because it provides a high-throughput and systematic approach to drug target validation and gene function discovery. Antisense represents a logical approach to gene function analysis and discovery as it is specific, broadly applicable, and can be designed with minimal information (ie, expressed sequence tags). This technology in combination with other emerging technologies (eg, microarray technology), will enable efficient 'mining' of the sequence data generated by the human genome project. This review addresses recent advances in the antisense field and discusses the potential use of antisense technology for functional genomics approaches.

Journal Article↗
PubMed2025

EucaMOD: a comprehensive multi-omics database for functional genomics research and molecular breeding of fast-growing eucalyptus trees.

Eucalyptus, one of the most widely planted plantation tree species globally, is primarily found in tropical and subtropical regions and contributes significantly to economic and social benefits. With advances in sequencing technologies, there is an increasing demand for the systematic analysis of multi-omics data among Eucalyptus species to enhance genetic breeding efforts. Although several early genomic databases have been established for eucalyptus, they have not been updated in a timely manner and lack recent multi-omics data, rendering them insufficient for current research needs. To address this gap, we developed the eucalyptus multi-omics database (EucaMOD, http://eucalyptusggd.net/eucamod), a comprehensive resource for cross-omics studies. In this study, we functionally annotated 45 eucalyptus genomes and structurally annotated 15, conducting comparative genomics and pan-proteomics analyses across all genomes. Additionally, we analyzed eucalyptus transcriptome, epigenome, and variome data through standardized workflows, enabling the in-depth mining and reanalysis of multi-omics datasets. EucaMOD is the most comprehensive multi-omics database for eucalyptus to date and includes data from 45 genomes (39 species), 870 mRNA-seq samples, 17 miRNA-seq samples, 52 epigenomic datasets (histone modifications and transcription factor binding), and genetic variation data from 1219 samples. To support functional genomics and molecular breeding research, the database is organized into the following 11 modules: Home, Species, Genomics, Comparative genomics, Pan-proteomics, Transcriptomics, Epigenetics, Variomics, Tools, Download, and Help. EucaMOD also offers online analysis tools for data mining, providing free public services to aid eucalyptus gene function and genetic engineering studies.

Eucalyptus↗