Tailored gene array databases: applications in mechanistic toxicology

MOTIVATION: The development of an annotated global database suitable for a wide range of investigations is a challenging and labor-intensive task. Thus, the development of databases tailored for specific applications remains necessary. For example, in the field of toxicology, no annotated gene array databases are now available that may assist in the correlation of changes in gene activity to cellular functions and processes associated with the toxic response. RESULTS: As an example of a tailored annotated database, an attempt was made to systematize available biological information on genes present on the Affymetrix Rat Toxicology U34 GeneChip, with a focus on how the gene products relate to liver cells and their response to chemical toxins. The information collected was imbedded in a local relational database to analyze data obtained in toxicological gene array experiments with hydrazine-exposed hepatocytes. The advantages and benefits of the tailored database in the biological interpretation of the results are demonstrated.     

GEDA: new knowledge base of gene expression in drug addiction

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and nonredundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.     

euGenes: a eukaryote genome information system

euGenes is a genome information system and database that provides a common summary of eukaryote genes and genomes, at http://iubio.bio.indiana.edu/eugenes/. Seven popular genomes are included: human, mouse, fruitfly, Caenorhabditis elegans worm, Saccharomyces yeast, Arabidopsis mustard weed and zebrafish, with more planned. This information, automatically extracted and updated from several source databases, offers features not readily available through other genome databases to bioscientists looking for gene relationships across organisms. The database describes 150 000 known, predicted and orphan genes, using consistent gene names along with their homologies and associations with a standard vocabulary of molecular functions, cell locations and biological processes. Usable whole-genome maps including features, chromosome locations and molecular data integration are available, as are options to retrieve sequences from these genomes. Search and retrieval methods for these data are easy to use and efficient, allowing one to ask combined questions of sequence features, protein functions and other gene attributes, and fetch results in reports, computable tabular outputs or bulk database forms. These summarized data are useful for integration in other projects, such as gene expression databases. euGenes provides an extensible, flexible genome information system for many organisms.     

Genomic Portraits of the Nervous System in Health and Disease

As the human genome project moves toward its goal of sequencing the entire human genome, gene expression profiling by DNA microarray technology is being employed to rapidly screen genes for biological information. In this review, we will introduce DNA microarray technology, outline the basic experimental paradigms and data analysis methods, and then show with some examples how gene expression profiling can be applied to the study of the central nervous system in health and disease.     

CODON—Software to manual curation of prokaryotic genomes

Genome annotation conceptually consists of inferring and assigning biological information to gene products. Over the years, numerous pipelines and computational tools have been developed aiming to automate this task and assist researchers in gaining knowledge about target genes of study. However, even with these technological advances, manual annotation or manual curation is necessary, where the information attributed to the gene products is verified and enriched. Despite being called the gold standard process for depositing data in a biological database, the task of manual curation requires significant time and effort from researchers who sometimes have to parse through numerous products in various public databases. To assist with this problem, we present CODON, a tool for manual curation of genomic data, capable of performing the prediction and annotation process. This software makes use of a finite state machine in the prediction process and automatically annotates products based on information obtained from the Uniprot database. CODON is equipped with a simple and intuitive graphic interface that assists on manual curation, enabling the user to decide about the analysis based on information as to identity, length of the alignment, and name of the organism in which the product obtained a match. Further, visual analysis of all matches found in the database is possible, impacting significantly in the curation task considering that the user has at his disposal all the information available for a given product. An analysis performed on eleven organisms was used to test the efficiency of this tool by comparing the results of prediction and annotation through CODON to ones from the NCBI and RAST platforms.     

GeneInfoViz: constructing and visualizing gene relation networks

Large amounts of knowledge about genes have been stored in public databases. One of the most challenging problems in Bioinformatics is, given all the information about the genes in the databases, determining the relationships between the genes. For example, how can we determine if genes are related and how closely they are related based on existing knowledge about their biological roles. We developed GeneInfoViz, a web tool for batch retrieval of gene information and construction and visualization of gene relation networks. We created a database containing compiled Gene Ontology information for the genes of several model organisms. Users can batch search for a group of genes and get the Gene Ontology terms that are associated with the genes. Directed acyclic graphs are generated to show the hierarchical structure of the Gene Ontology tree. GeneInfoViz calculates an adjacency matrix to determine whether the genes are related and, if so, how closely they are related based on biological processes, molecular functions, or cellular components they are associated with and then displays a dynamic graph layout of the network among the selected genes.     

Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments

One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.     

Protist.guru: A Comparative Transcriptomics Database for Protists

SummaryDuring the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still largely neglected. To address this gap, we used gene expression data from 17 protist species to create protist.guru: an online database equipped with tools for identifying co-expressed genes, gene families, and co-expression clusters enriched for specific biological functions. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes.AvailabilityThe database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.     

eXPatGen: generating dynamic expression patterns for the systematic evaluation of analytical methods

MOTIVATION: Experimental gene expression data sets, such as those generated by microarray or gene chip experiments, typically have significant noise and complicated interconnectivities that make understanding even simple regulatory patterns difficult. Given these complications, characterizing the effectiveness of different analysis techniques to uncover network groups and structures remains a challenge. Generating simulated expression patterns with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of different analysis methods. A simulation-based approach can systematically evaluate different gene expression analysis techniques and provide a basis for improved methods in dynamic metabolic network reconstruction. RESULTS: We have developed an on-line simulator, called eXPatGen, to generate dynamic gene expression patterns typical of microarray experiments. eXPatGen provides a quantitative network structure to represent key biological features, including the induction, repression, and cascade regulation of messenger RNA (mRNA). The simulation is modular such that the expression model can be replaced with other representations, depending on the level of biological detail required by the user. Two example gene networks, of 25 and 100 genes respectively, were simulated. Two standard analysis techniques, clustering and PCA analysis, were performed on the resulting expression patterns in order to demonstrate how the simulator might be used to evaluate different analysis methods and provide experimental guidance for biological studies of gene expression. AVAILABILITY: http://www.che.udel.edu/eXPatGen/     

Studying Gene Expression System Regulation at the Program Level

Understanding how gene expression systems influence biological outcomes is an important goal for diverse areas of research. Gene expression profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to use this information to increase biological understanding. Yet, the best way to relate this immense amount of information to biological outcomes is far from clear. Here, a novel approach to gene expression systems research is presented that focuses on understanding gene expression systems at the level of gene expression program regulation. It is suggested that such an approach has important advantages over current techniques and may provide novel insights into how gene expression systems are regulated to shape biological outcomes such as the development of disease or response to treatment.     

Collective Instance-Level Gene Normalization on the IGN Corpus

A high proportion of life science researches are gene-oriented, in which scientists aim to investigate the roles that genes play in biological processes, and their involvement in biological mechanisms. As a result, gene names and their related information turn out to be one of the main objects of interest in biomedical literatures. While the capability of recognizing gene mentions has made significant progress, the results of recognition are still insufficient for direct use due to the ambiguity of gene names. Gene normalization (GN) goes beyond the recognition task by linking a gene mention to a database ID. Unlike most previous works, we approach GN on the instance-level and evaluate its overall performance on the recognition and normalization steps in abstracts and full texts. We release the first instance-level gene normalization (IGN) corpus in the BioC format, which includes annotations for the boundaries of all gene mentions and the corresponding IDs for human gene mentions. Species information, along with existing co-reference chains and full name/abbreviation pairs are also provided for each gene mention. Using the released corpus, we have designed a collective instance-level GN approach using not only the contextual information of each individual instance, but also the relations among instances and the inherent characteristics of full-text sections. Our experimental results show that our collective approach can achieve an F-score of 0.743. The proposed approach that exploits section characteristics in full-text articles can improve the F-scores of information lacking sections by up to 1.8%. In addition, using the proposed refinement process improved the F-score of gene mention recognition by 0.125 and that of GN by 0.03. Whereas current experimental results are limited to the human species, we seek to continue updating the annotations of the IGN corpus and observe how the proposed approach can be extended to other species.     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...