Jens G. Reich

Alternative splicing and genome complexity

Alternative splicing of mRNA allows many gene products with different functions to be produced from a single coding sequence. It has recently been proposed as a mechanism by which higher-order diversity is generated. Here we show, using large-scale expressed sequence tag (EST) analysis, that among seven different eukaryotes the amount of alternative splicing is comparable, with no large differences between humans and other animals.

EST comparison indicates 38% of human mRNAs contain possible alternative splice forms

Expressed sequence tag (EST) databases represent a large volume of information on expressed genes including tissue type, expression profile and exon structure. In this study we create an extensive data set of human alternative splicing. We report the analysis of 7867 non‐redundant mRNAs, 3011 of which contained alternative splice forms (38% of all mRNAs analysed). From a total of 12 572 ESTs 4560 different possible alternative splice forms were detected. Interestingly, 70% of the alternative splice forms correspond to exon deletion events with only 30% exonic insertions. We experimentally verified 19 different splice forms from 16 genes in a total subset of 20 studied; all of the respective genes are of medical relevance.

A Complete Enumeration and Classification of Two-Locus Disease Models

There are 512 two-locus, two-allele, two-phenotype, fully penetrant disease models. Using the permutation between two alleles, between two loci, and between being affected and unaffected, one model can be considered to be equivalent to another model under the corresponding permutation. These permutations greatly reduce the number of two-locus models in the analysis of complex diseases. This paper determines the number of nonredundant two-locus models (which can be 102, 100, 96, 51, 50, or 58, depending on which permutations are used, and depending on whether zero-locus and single-locus models are excluded). Whenever possible, these nonredundant two-locus models are classified by their property. Besides the familiar features of multiplicative models (logical AND), heterogeneity models (logical OR), and threshold models, new classifications are added or expanded: modifying-effect models, logical XOR models, interference and negative interference models (neither dominant nor recessive), conditionally dominant/recessive models, missing lethal genotype models, and highly symmetric models. The following aspects of two-locus models are studied: the marginal penetrance tables at both loci, the expected joint identity-by-descent (IBD) probabilities, and the correlation between marginal IBD probabilities at the two loci. These studies are useful for linkage analyses using single-locus models while the underlying disease model is two-locus, and for correlation analyses using the linkage signals at different locations obtained by a single-locus model.

ASTD: The Alternative Splicing and Transcript Diversity database

The Alternative Splicing and Transcript Diversity database (ASTD) gives access to a vast collection of alternative transcripts that integrate transcription initiation, polyadenylation and splicing variant data. Alternative transcripts are derived from the mapping of transcribed sequences to the complete human, mouse and rat genomes using an extension of the computational pipeline developed for the ASD (Alternative Splicing Database) and ATD (Alternative Transcript Diversity) databases, which are now superseded by ASTD. For the human genome, ASTD identifies splicing variants, transcription initiation variants and polyadenylation variants in 68%, 68% and 62% of the gene set, respectively, consistent with current estimates for transcription variation. Users can access ASTD through a variety of browsing and query tools, including expression state-based queries for the identification of tissue-specific isoforms. Participating laboratories have experimentally validated a subset of ASTD-predicted alternative splice forms and alternative polyadenylation forms that were not previously reported. The ASTD database can be accessed at http://www.ebi.ac.uk/astd.

Multi-Locus Nonparametric Linkage Analysis of Complex Trait Loci with Neural Networks

Complex traits are generally taken to be under the influence of multiple genes, which may interact with each other to confer susceptibility to disease. Statistical methods in current use for localizing such genes essentially work under single-gene models, either implicitly or explicitly. In genomic screens for complex disease genes, some of the marker loci must be in tight linkage with disease susceptibility genes. We developed a general multi-locus approach to identify sets of such marker loci. Our approach focuses on affected sib pair data and employs a nonparametric pattern recognition technique using artificial neural networks. This technique analyzes all markers simultaneously in order to detect patterns of locus interactions. When applied to previously published sib pair data on type I diabetes, our approach finds the same genes as in the published report in addition to some new loci. For a specific two-locus model of inheritance, the power of our approach is higher than that of the currently used analysis standard.

EASED: Extended Alternatively Spliced EST Database

We established a database of alternative splice forms (ASforms) for nine eukaryotic organisms. ASforms are defined by comparing high-scoring ESTs with mRNA sequences using BLAST, taking known exon-intron information (from the Ensembl database). Filtering programs compare the ends of each aligned sequence pair for deletions or insertions in the EST sequence, which indicate the existence of alternative splice forms with respect to the exon-intron boundaries. Moreover, we defined the alternative splice profile of each human sequence. It indicates the number of alternatively spliced ESTs (NAE), the number of constitutively spliced ESTs (NCE) as well as the number of alternative splice sites (NSS) per mRNA. NAE and NCE correspond to the EST coverage and can be used as a quality indicator for the predicted alternative splice variants. The NSS value specifies the splice propensity of a gene. Additionally, the tissue type information of all ESTs was included. This allows (i) restriction of the search to certain tissues and (ii) calculation of the tissue-NAEs, tissue-NCEs and tissue-NSS. These scores are suitable for the estimation of tissue specificity of certain ASforms. Furthermore, the developmental stage and disease information of the ESTs is available. EASED is accessible at http://eased.bioinf.mdc-berlin.de/.

Prediction of nonsynonymous single nucleotide polymorphisms in human disease-associated genes.

Analysis of human genetic variation can shed light on the problem of the genetic basis of complex disorders. Nonsynonymous single nucleotide polymorphisms (SNPs), which affect the amino acid sequence of proteins, are believed to be the most frequent type of variation associated with the respective disease phenotype. Complete enumeration of nonsynonymous SNPs in the candidate genes will enable further association studies on panels of affected and unaffected individuals. Experimental detection of SNPs requires implementation of expensive technologies and is still far from being routine. Alternatively, SNPs can be identified by computational analysis of a publicly available expressed sequence tag (EST) database following experimental verification. We performed in silico analysis of amino acid variation for 471 of proteins with a documented history of experimental variation studies and with confirmed association with human diseases. This allowed us to evaluate the level of completeness of the current knowledge of nonsynonymous SNPs in well studied, medically relevant genes and to estimate the proportion of new variants which can be added with the help of computer-aided mining in EST databases. Our results suggest that approx. 50% of frequent nonsynonymous variants are already stored in public databases. Computational methods based on the scan of an EST database can add significantly to the current knowledge, but they are greatly limited by the size of EST databases and the nonuniform coverage of genes by ESTs. Nevertheless, a considerable number of new candidate nonsynonymous SNPs in genes of medical interest were found by EST screening procedure.

Kohonen map as a visualization tool for the analysis of protein sequences: multiple alignments, domains and segments of secondary structures

The method of Kohonen maps, a special form of neural networks, was applied as a visualization tool for the analysis of protein sequence similarity. The procedure converts sequence (domains, aligned sequences, segments of secondary structure) into a characteristic signal matrix. This conversion depends on the property or replacement score vector selected by the user. Similar sequences have small distance in the signal space. The trained Kohonen network is functionally equivalent to an unsupervised non-linear cluster analyzer. Protein families, or aligned sequences, or segments of similar secondary structure, aggregate as clusters, and their proximity may be inspected on a color screen or on paper. Pull-down menus permit access to background information in the established text-oriented way.

Parameter estimation and enzyme kinetic models.

Biochemists have always sought to use kinetic and other measurements to derive a description of enzyme catalysis at the molecular level. However accurate the data and sophisticated the experimental techniques, all efforts may be futile unless they are coupled with appropriate modeling and simulation strategies to construct a mathematical model of the object of study. An adequate model might be a simple formula such as the Michaelis equation or alternatively it may be an extremely complex set of equations. This depends entirely on the available data. The survey presented here aims to show that the mathematical techniques which lead to the kinetic model are by no means as trivial as they appear to be from the study of simple textbook cases. It will be seen that in many recent papers the difficulties have been investigated, but so far satisfactory solutions for the more complicated cases have not been described.

A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels

Complex phenotypes such as serum lipid concentrations involve numerous genes and require the analysis of the combined effects of these gene products. We modeled the interactions of six key lipid metabolism genes by means of differential equations. We tested the model by inserting the effects of known mutations in the low-density lipoprotein receptor gene and the lipoprotein lipase gene, as well as the effects of a high-fat diet, and observed that the predictions corresponded very well to published measurements. Models such as the one that we present here will become indispensable for analyzing and understanding the effects of variation in multiple genes.