Jens Hanke

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.

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.

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.

EST analysis online: WWW tools for detection of SNPs and alternative splice forms.

One of the first priorities on completion of the human genome is to extract the coded genes from the raw genomic sequence. Primarily, this information will be provided by random ESTs and cDNA production. The percentage of AS found within this EST data will have a direct effect on the final number of human genes identified in the genome. In recent studies carried out by our group and others, 35% of genes examined by EST matching contained at least one alternative splice form6xAlternative splicing of human genes: more the rule than the exception. Hanke, J. et al. Trends Genet. 1999; 15: 383–427Abstract | Full Text | Full Text PDF | PubMed | Scopus (112)See all References, 7xFrequent alternative splicing of human genes. Mironov, A. et al. Genome Res. 1999; 9: 1288–1293Crossref | PubMed | Scopus (403)See all References. Indeed, this is probably an underestimate as EST coverage is estimated to only 30% of all exons4xPrediction of nonsynonymous single nucleotide polymorphisms in human disease-associated genes. Sunyaev, S. et al. J. Mol. Med. 1999; 77: 754–760Crossref | PubMedSee all References4. In addition, exons contained completely within published introns are not matched by mRNAs.The tools described here should allow an easy exploitation of up-to-date EST information to reveal SNPs and AS in human genes. The accuracy is sufficient to study the candidates experimentally for various applications including SNP haplotype prediction and novel AS forms implicated in disease progression. The tools have simple cut-and-paste interfaces, direct access to public SNP and AS databases, plus helpful pointers to explain and evaluate the results.

Associative database of protein sequences.

MOTIVATION We present a new concept that combines data storage and data analysis in genome research, based on an associative network memory. As an illustration, 115 000 conserved regions from over 73 000 published sequences (i.e. from the entire annotated part of the SWISSPROT sequence database) were identified and clustered by a self-organizing network. Similarity and kinship, as well as degree of distance between the conserved protein segments, are visualized as neighborhood relationship on a two-dimensional topographical map. RESULTS Such a display overcomes the restrictions of linear list processing and allows local and global sequence relationships to be studied visually. Families are memorized as prototype vectors of conserved regions. On a massive parallel machine, clustering and updating of the database take only a few seconds; a rapid analysis of incoming data such as protein sequences or ESTs is carried out on present-day workstations. AVAILABILITY Access to the database is available at http://www.bioinf.mdc-berlin.de/unter2.html++ + CONTACT (hanke,lehmann,reich)@mdc-berlin.de; bork@embl-heidelberg.de