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PubMed2006

Visual analysis of statistical results from microarray studies of human breast cancer.

Computational and statistical analysis of microarray data is a daunting challenge. Perhaps even more daunting is the biological interpretation of microarray data analysis results. We have previously developed the exploratory visual analysis (EVA) software and database for exploring data analysis results in the context of biological information on each gene available in public databases such as Entrez Gene. EVA brings a flexible combination of statistics and biological annotation to the user's desktop in a straightforward visual interface. Using a publicly available microarray dataset of gene expression response to chemotherapeutic agents in human breast cancer cell lines, we demonstrate the usefulness of the EVA system for interpreting statistical results. EVA can extend previous analyses as well as aid in making novel discoveries. Thus, we anticipate EVA will prove a useful addition to the repertoire of computational methods for microarray data analysis. The EVA software is freely available to academic users.

Antineoplastic Agents↗
PubMed2003

High-throughput functional affinity purification of mannose binding proteins from Oryza sativa.

We have used affinity chromatography in combination with mass spectrometry to isolate, identify, and assign a preliminary functional annotation to a large number of both known and novel proteins from rice. Rice (Oryza sativa) leaf, root, and seed tissue extracts were fractionated by column affinity chromatography using alpha-D-mannose as the ligand. Bound fractions were eluted and subjected to one-dimensional electrophoresis, followed by high-performance liquid chromatography-tandem mass spectrometric analysis of separated proteins. This multiplexed technology resulted in the isolation and identification of 136 distinct mannose binding proteins from rice. A comparative analysis demonstrates very little overlap of identified proteins between the respective tissues, and confirms the correctly compartmentalized presence of a significant number of proteins from largely tissue-specific biochemical pathways. Over 30% of the identified proteins with a previously annotated function are directly involved in sugar metabolism, including several highly expressed known rice lectins. Direct comparison of the peptide sequences identified in this study to those peptides identified in the most comprehensive survey of the rice proteome to date indicates that our current data represents a significant enrichment of proteins unique to this dataset. Nearly 15% of the identified proteins, identified on the basis of exact peptide matching to sequences in the rice genomic database, represent proteins without a previously known functional annotation, indicating the potential of this combined chromatographic approach to assign a preliminary function to novel proteins in a high-throughput fashion.

Binding, Competitive↗
PubMed2005

Serendipitous discovery of Wolbachia genomes in multiple Drosophila species.

BACKGROUND: The Trace Archive is a repository for the raw, unanalyzed data generated by large-scale genome sequencing projects. The existence of this data offers scientists the possibility of discovering additional genomic sequences beyond those originally sequenced. In particular, if the source DNA for a sequencing project came from a species that was colonized by another organism, then the project may yield substantial amounts of genomic DNA, including near-complete genomes, from the symbiotic or parasitic organism. RESULTS: By searching the publicly available repository of DNA sequencing trace data, we discovered three new species of the bacterial endosymbiont Wolbachia pipientis in three different species of fruit fly: Drosophila ananassae, D. simulans, and D. mojavensis. We extracted all sequences with partial matches to a previously sequenced Wolbachia strain and assembled those sequences using customized software. For one of the three new species, the data recovered were sufficient to produce an assembly that covers more than 95% of the genome; for a second species the data produce the equivalent of a 'light shotgun' sampling of the genome, covering an estimated 75-80% of the genome; and for the third species the data cover approximately 6-7% of the genome. CONCLUSIONS: The results of this study reveal an unexpected benefit of depositing raw data in a central genome sequence repository: new species can be discovered within this data. The differences between these three new Wolbachia genomes and the previously sequenced strain revealed numerous rearrangements and insertions within each lineage and hundreds of novel genes. The three new genomes, with annotation, have been deposited in GenBank.

Animals↗
PubMed2006

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.

Animals↗
PubMed2026

T-rex: standardized analysis of germline variants in whole-exome sequencing trios.

Whole-exome sequencing (WES) enables the identification of rare germline variants contributing to pediatric diseases. Trio-based sequencing, comparing affected children with their parents, is particularly effective for rare disease genetics. However, WES data analysis requires bioinformatics expertise, varies across institutions, and is often incompatible with clinical workflows. We developed T-Rex (Trio Rare variant analysis of EXomes), a cross-platform desktop application that enables the standardized and local analysis of WES germline Trio data without the need for programming knowledge. T-Rex integrates state-of-the-art tools for alignment, dual-variant calling (GATK HaplotypeCaller + VarScan2), annotation (SNPEff/SNPSift), rare-variant filtering based on population frequencies (gnomAD), and family-based statistical testing, including the Transmission Disequilibrium Test with multiple-testing correction. Benchmarking of the dual-caller strategy on the Genome in a Bottle Ashkenazim Trio demonstrates high precision (99.2%) while maintaining robust sensitivity (91.1%). User testing (n = 13) confirmed quick learning across clinicians and researchers. Application to a cohort of n = 121 pediatric cancer Trio datasets, filtering for rare protein-coding variants (MAF ≤ 0.1% in gnomAD v4.1), validated all assessable previously reported pathogenic variants. Overall, T-Rex enables clinicians to robustly analyze WES Trio data in compliance with data protection regulations without requiring additional software licenses. As one of the first platforms for comprehensive WES Trio analysis that requires no programming expertise while providing reproducible, end-to-end workflows for clinical genomics, T-Rex facilitates collaborative research between clinics and reduces reliance on external providers.

Humans↗
PubMed2005

[Porphyria and radiotherapy: yet a constellation of risk?].

BACKGROUND: Little is known concerning the relation of porphyrias to radiation treatment for cancer. The recent literature does not report negative outcomes at least in single cases of breast cancer, bladder cancer or in a patient with lymphoma. Theoretically, there is a risk for radiation treatment in cases of porphyrias. CASE REPORT: Two patients with porphyria are described who underwent radiotherapy for glioblastoma multiforme. Case histories, radiation treatment data and the proof of porphyria are given in detail. The patients received a concomitant radiochemotherapy with infusions of ACNU [1-(4-amino-2-methyl-5-pyrimidinyl)-methyl-(2-chloroethyl)-3-nitrosourea] every 6 weeks. The total radiation doses at the ICRU point were 60 and 38 Gy, respectively, given in single daily fractions of 2 Gy. RESULTS: Both patients responded well as far as objective tumor regression is concerned but died early after 7 and 1.5 months following diagnosis. The first patient experienced a large brain necrosis and deterioration of her porphyria. The second patient died of cardiopulmonary insufficiency still during radiotherapy with persistent or even increased signs of porphyria. The tumor showed extensive necrosis already after 38 Gy. CONCLUSION: In view of annotations in the literature and this case report, caution is advised as for irradiation of brain or nervous tissues in case of porphyrias. Molecular biology data seems to support this warning to some extent.

Aged↗
PubMed2004

High-throughput protein analysis integrating bioinformatics and experimental assays.

The wealth of transcript information that has been made publicly available in recent years requires the development of high-throughput functional genomics and proteomics approaches for its analysis. Such approaches need suitable data integration procedures and a high level of automation in order to gain maximum benefit from the results generated. We have designed an automatic pipeline to analyse annotated open reading frames (ORFs) stemming from full-length cDNAs produced mainly by the German cDNA Consortium. The ORFs are cloned into expression vectors for use in large-scale assays such as the determination of subcellular protein localization or kinase reaction specificity. Additionally, all identified ORFs undergo exhaustive bioinformatic analysis such as similarity searches, protein domain architecture determination and prediction of physicochemical characteristics and secondary structure, using a wide variety of bioinformatic methods in combination with the most up-to-date public databases (e.g. PRINTS, BLOCKS, INTERPRO, PROSITE SWISSPROT). Data from experimental results and from the bioinformatic analysis are integrated and stored in a relational database (MS SQL-Server), which makes it possible for researchers to find answers to biological questions easily, thereby speeding up the selection of targets for further analysis. The designed pipeline constitutes a new automatic approach to obtaining and administrating relevant biological data from high-throughput investigations of cDNAs in order to systematically identify and characterize novel genes, as well as to comprehensively describe the function of the encoded proteins.

Automation↗
PubMed2006

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.

Algorithms↗
PubMed2005

An analysis of extensible modelling for functional genomics data.

BACKGROUND: Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time. RESULTS: In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure. CONCLUSION: From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.

Chemistry Techniques, Analytical↗
PubMedDate not supplied

Automatic annotation of BIND molecular interactions from three-dimensional structures.

Software to automate the process of extracting molecular interactions from three-dimensional (3D) structures has been developed that records these as Biomolecular Interaction Network Database (BIND) pairwise interaction records. Full annotation of BIND records is provided through a database processing tool called MMDBind, including detailed atom-atom and residue-residue level interaction information. BIND three-dimensional interaction annotation is synthesized by combining information from the Molecular Modeling Database (MMDB), and the HET (heterogen) group dictionary of small molecules in the macromolecular Crystallographic Information Format (mmCIF). Interactions are validated using the Protein Quaternary Structure (PQS) system. A total of 18,166 interactions were removed as being redundant or biologically irrelevant after PQS validation. This first pass MMDBind annotation creates two new divisions of BIND, 3D Biopolymers (BIND-3DBP) comprising 16,737 initial interaction records, and 3D Small Molecules (BIND-3DSM) comprising 48,219 records. Visualization of interacting residues and nucleotides within a macromolecular structure is possible directly from the BIND database owing to added 3D feature annotation within the BIND records that can be conveniently seen using Cn3D ("see-in-3D") after query from the BIND Data Manager. These interaction records provide a further demonstration of the completeness of the BIND data specification and its capabilities as storage and exchange format for all kinds of molecular interactions, including RNA, DNA, protein, and small molecules. Data from the 3DBP and 3DSM sets are available for downloading in Abstract Syntax Notation.1 (ASN.1) or Extensible Markup Language (XML) formats at ftp://ftp.bind.ca/DB/MMDBBind. Data from the 3DBP set is available for interactive query from the BIND Data Manager at www.bind.ca.

Crystallography, X-Ray↗
PubMed2005

Entamoeba histolytica: construction and applications of subgenomic databases.

Knowledge about the influence of environmental stress such as the action of chemotherapeutic agents on gene expression in Entamoeba histolytica is limited. We plan to use oligonucleotide microarray hybridization to approach these questions. As the basis for our array, sequence data from the genome project carried out by the Institute for Genomic Research (TIGR) and the Sanger Institute were used to annotate parts of the parasite genome. Three subgenomic databases containing enzymes, cytoskeleton genes, and stress genes were compiled with the help of the ExPASy proteomics website and the BLAST servers at the two genome project sites. The known sequences from reference species, mostly human and Escherichia coli, were searched against TIGR and Sanger E. histolytica sequence contigs and the homologs were copied into a Microsoft Access database. In a similar way, two additional databases of cytoskeletal genes and stress genes were generated. Metabolic pathways could be assembled from our enzyme database, but sometimes they were incomplete as is the case for the sterol biosynthesis pathway. The raw databases contained a significant number of duplicate entries which were merged to obtain curated non-redundant databases. This procedure revealed that some E. histolytica genes may have several putative functions. Representative examples such as the case of the delta-aminolevulinate synthase/serine palmitoyltransferase are discussed.

5-Aminolevulinate Synthetase↗
PubMed2005

A web tool for comparative genomics: G-compass.

In order to assist the progression of comparative genomics, we have developed a new web-based tool, named G-compass, for browsing and analysis of genome alignments. G-compass utilizes 829,311 pieces of genome alignments between human and mouse that were originally produced for this tool. The quality of the genome alignment set was evaluated by using several statistics. As a result, the alignment set is found to cover approximately 17% of the human genome and 82% of the annotated exons. The averages of nucleotide sequence identity and sequence length are 71.2% and 673.6 bp, respectively. In comparison with public data, it appeared that our data is more expansive and possesses greater genome coverage. G-compass incorporates unique functions such as window analysis of individual alignments. Furthermore, with G-compass and the joint help of H-InvDB, we were able to find highly conserved genomic segments and a human specific antisense transcript candidate, demonstrating that G-compass is useful for facilitating biological discoveries. G-compass is publicly accessible on the WWW at http://www.jbirc.aist.go.jp/g-compass/.

Animals↗
PubMedDate not supplied

Classical detection theory and the cryo-EM particle selection problem.

Particle selection is an essential but tedious step in the determination of macromolecular structures by single particle reconstruction. This paper presents an automatic, multi-reference particle detection scheme that is based on the classical matched filter principle. It makes use of a pre-whitening filter to standardize the noise, a reduced representation of the references by means of principal component analysis, and a statistic for distinguishing particles from image artifacts. Standardizing the noise allows the noise-induced false-positive frequency to be estimated, and also allows the distribution of the discrimination statistic to be calculated a priori. The method is demonstrated with an annotated dataset of cryo-EM images.

Algorithms↗
PubMed2006

Comparison of aerobic and photosynthetic Rhodobacter sphaeroides 2.4.1 proteomes.

The analysis of proteomes from aerobic and photosynthetic Rhodobacter sphaeroides 2.4.1 cell cultures by liquid chromatography-mass spectrometry yielded approximately 6,500 high confidence peptides representing 1,675 gene products (39% of the predicted proteins). The identified proteins corresponded primarily to open reading frames (ORFs) contained within the two chromosomal elements of this bacterium, but a significant number were also observed from ORFs associated with 5 naturally occurring plasmids. Using the accurate mass and time (AMT) tag approach, comparative studies showed that a number of proteins were uniquely detected within the photosynthetic cell culture. The estimated abundances of proteins observed in both aerobic respiratory and photosynthetic grown cultures were compared to provide insights into bioenergetic models for both modes of growth. Additional emphasis was placed on gene products annotated as hypothetical to gain information as to their potential roles within these two growth conditions. Where possible, transcriptome and proteome data for R. sphaeroides obtained under the same culture conditions were also compared.

Aerobiosis↗
PubMed2005

Molecular analyses of disease pathogenesis: application of bovine microarrays.

The molecular analysis of disease pathogenesis in cattle has been limited by the lack of availability of tools to analyze both host and pathogen responses. These limitations are disappearing with the advent of methodologies such as microarrays that facilitate rapid characterization of global gene expression at the level of individual cells and tissues. The present review focuses on the use of microarray technologies to investigate the functional pathogenomics of infectious disease in cattle. We discuss a number of unique issues that must be addressed when designing both in vitro and in vivo model systems to analyze host responses to a specific pathogen. Furthermore, comparative functional genomic strategies are discussed that can be used to address questions regarding host responses that are either common to a variety of pathogens or unique to individual pathogens. These strategies can also be applied to investigations of cell signaling pathways and the analyses of innate immune responses. Microarray analyses of both host and pathogen responses hold substantial promise for the generation of databases that can be used in the future to address a wide variety of questions. A critical component limiting these comparative analyses will be the quality of the databases and the complete functional annotation of the bovine genome. These limitations are discussed with an indication of future developments that will accelerate the validation of data generated when completing a molecular characterization of disease pathogenesis in cattle.

Animals↗
PubMed2003

Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria.

Mitochondria are tailored to meet the metabolic and signaling needs of each cell. To explore its molecular composition, we performed a proteomic survey of mitochondria from mouse brain, heart, kidney, and liver and combined the results with existing gene annotations to produce a list of 591 mitochondrial proteins, including 163 proteins not previously associated with this organelle. The protein expression data were largely concordant with large-scale surveys of RNA abundance and both measures indicate tissue-specific differences in organelle composition. RNA expression profiles across tissues revealed networks of mitochondrial genes that share functional and regulatory mechanisms. We also determined a larger "neighborhood" of genes whose expression is closely correlated to the mitochondrial genes. The combined analysis identifies specific genes of biological interest, such as candidates for mtDNA repair enzymes, offers new insights into the biogenesis and ancestry of mammalian mitochondria, and provides a framework for understanding the organelle's contribution to human disease.

Animals↗
PubMed2002

Proteomic map and database of lymphoblastoid proteins.

Advances in genomics have led to the accumulation of an unprecedented amount of data, giving rise to a new field in biochemistry, proteomics. We used a combination of two dimensional gel electrophoresis, analysis and annotation using third-generation software, and mass spectrometry to establish the proteome maps of lymphoblastoid B-cells, a prerequisite for analysis of drug effects and lymphocyte cell diseases. About 1200 protein spots were detected and characterised in terms of their isoelectric point, molecular mass and expression. The present status of proteomic technologies, as well as a description of the usefulness of human hematopoietic cells proteomic database are discussed.

Databases, Protein↗
PubMed1999

A molecular scanner to automate proteomic research and to display proteome images.

Identification and characterization of all proteins expressed by a genome in biological samples represent major challenges in proteomics. Today's commonly used high-throughput approaches combine two-dimensional electrophoresis (2-DE) with peptide mass fingerprinting (PMF) analysis. Although automation is often possible, a number of limitations still adversely affect the rate of protein identification and annotation in 2-DE databases: the sequential excision process of pieces of gel containing protein; the enzymatic digestion step; the interpretation of mass spectra (reliability of identifications); and the manual updating of 2-DE databases. We present a highly automated method that generates a fully annoated 2-DE map. Using a parallel process, all proteins of a 2-DE are first simultaneously digested proteolytically and electro-transferred onto a poly(vinylidene difluoride) membrane. The membrane is then directly scanned by MALDI-TOF MS. After automated protein identification from the obtained peptide mass fingerprints using PeptIdent software (http://www.expasy.ch/tools/peptident.html + ++), a fully annotated 2-D map is created on-line. It is a multidimensional representation of a proteome that contains interpreted PMF data in addition to protein identification results. This "MS-imaging" method represents a major step toward the development of a clinical molecular scanner.

Automation↗