Dataset Information

Determination and validation of principal gene products.

ABSTRACT: MOTIVATION:Alternative splicing has the potential to generate a wide range of protein isoforms. For many computational applications and for experimental research, it is important to be able to concentrate on the isoform that retains the core biological function. For many genes this is far from clear. RESULTS:We have combined five methods into a pipeline that allows us to detect the principal variant for a gene. Most of the methods were based on conservation between species, at the level of both gene and protein. The five methods used were the conservation of exonic structure, the detection of non-neutral evolution, the conservation of functional residues, the existence of a known protein structure and the abundance of vertebrate orthologues. The pipeline was able to determine a principal isoform for 83% of a set of well-annotated genes with multiple variants.

SUBMITTER: Tress ML

PROVIDER: S-EPMC2734078 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determination and validation of principal gene products.

Tress Michael L ML Wesselink Jan-Jaap JJ Frankish Adam A López Gonzalo G Goldman Nick N Löytynoja Ari A Massingham Tim T Pardi Fabio F Whelan Simon S Harrow Jennifer J Valencia Alfonso A

Bioinformatics (Oxford, England) 20071115 1

<h4>Motivation</h4>Alternative splicing has the potential to generate a wide range of protein isoforms. For many computational applications and for experimental research, it is important to be able to concentrate on the isoform that retains the core biological function. For many genes this is far from clear.<h4>Results</h4>We have combined five methods into a pipeline that allows us to detect the principal variant for a gene. Most of the methods were based on conservation between species, at the ...[more]

PMID: 18006548

Similar Datasets

Project description:Echinochrome A (Ech A, 1) is one of the main pigments of several sea urchin species and is registered in the Russian pharmacopeia as an active drug substance (Histochrome®), used in the fields of cardiology and ophthalmology. In this study, Ech A degradation products formed during oxidation by O2 in air-equilibrated aqueous solutions were identified, isolated, and structurally characterized. An HPLC method coupled with diode-array detection (DAD) and mass spectrometry (MS) was developed and validated to monitor the Ech A degradation process and identify the appearing compounds. Five primary oxidation products were detected and their structures were proposed on the basis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) as 7-ethyl-2,2,3,3,5,7,8-heptahydroxy-2,3-dihydro-1,4-naphthoquinone (2), 6-ethyl-5,7,8-trihydroxy-1,2,3,4-tetrahydronaphthalene-1,2,3,4-tetraone (3), 2,3-epoxy-7-ethyl-2,3-dihydro-2,3,5,6,8-pentahydroxy-1,4-naphthoquinone (4), 2,3,4,5,7-pentahydroxy-6-ethylinden-1-one (5), and 2,2,4,5,7-pentahydroxy-6-ethylindane-1,3-dione (6). Three novel oxidation products were isolated, and NMR and HR-ESI-MS methods were used to establish their structures as 4-ethyl-3,5,6-trihydroxy-2-oxalobenzoic acid (7), 4-ethyl-2-formyl-3,5,6-trihydroxybenzoic acid (8), and 4-ethyl-2,3,5-trihydroxybenzoic acid (9). The known compound 3-ethyl-2,5-dihydroxy-1,4-benzoquinone (10) was isolated along with products 7-9. Compound 7 turned out to be unstable; its anhydro derivative 11 was obtained in two crystal forms, the structure of which was elucidated using X-ray crystallography as 7-ethyl-5,6-dihydroxy-2,3-dioxo-2,3-dihydrobenzofuran-4-carboxylic acid and named echinolactone. The chemical mechanism of Ech A oxidative degradation is proposed. The in silico toxicity of Ech A and its degradation products 2 and 7-10 were predicted using the ProTox-II webserver. The predicted median lethal dose (LD50) value for product 2 was 221 mg/kg, and, for products 7-10, it appeared to be much lower (?2000 mg/kg). For Ech A, the predicted toxicity and mutagenicity differed from our experimental data.

Project description:BackgroundThere are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.ResultsWe present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation.ConclusionWe provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

Dataset Information

Determination and validation of principal gene products.

Publications

Determination and validation of principal gene products.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets