Rainer Fuchs

Motivation: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein–protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied. Results: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.

Rainer Fuchs; From Sequence to Biology: The Impact on Bioinformatics , Bioinformatics, Volume 18, Issue 4, 1 April 2002, Pages 505–506, https://doi.org/10.1093

A general and detailed noise model for the DNA microarray measurement of gene expression is presented and used to derive a Bayesian estimation scheme for expression ratios, implemented in a program called PFOLD, which provides not only an estimate of the fold-change in gene expression, but also confidence limits for the change and a P-value quantifying the significance of the change. Although the focus is on oligonucleotide microarray technologies, the scheme can also be applied to cDNA based technologies if parameters for the noise model are provided. The model unifies estimation for all signals in that it provides a seamless transition from very low to very high signal-to-noise ratios, an essential feature for current microarray technologies for which the median signal-to-noise ratios are always moderate. The dual use, as decision statistics in a two-dimensional space, of the P-value and the fold-change is shown to be effective in the ubiquitous problem of detecting changing genes against a background of unchanging genes, leading to markedly higher sensitivities, at equal selectivity, than detection and selection based on the fold-change alone, a current practice until now.

Motivation: Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. Results: We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies. Availability: The source code of the program implementing the algorithm is available upon request from the authors. Contact: alex_lukashin@biogen.com

Rainer Fuchs; Analyse This…Or: Intelligent Help for The Rest of Us , Bioinformatics, Volume 16, Issue 6, 1 June 2000, Pages 491–493, https://doi.org/10.1093/

In this study we describe a new information resource that provides integrated access to information on IMAGE (integrated molecular analysis of genomes and their expression) cDNA library clones and derived expressed sequence tags (ESTs). We have developed an automated procedure that collates data from various public sources into a single ACeDB database. This database is a valuable tool for electronic cloning experiments and gene expression studies. It allows researchers to find information about cDNA libraries, plate addresses, insert sizes, and sequence data for IMAGE clones, the assignment of ESTs to UniGene clusters, and the chromosomal location of those genes in an efficient, graphically oriented manner.

A data base is described in which over 2,500 mutations in the p53 gene of human tumors and tumor cell lines are compiled from a systematic search of reports published before 1 January 1994. Data from 1994 are being added intermittently, with a systematic search and update scheduled for December, 1994. The compilation has been deposited with the EMBL Data Library and is available in electronic form free of charge. This report contains a rationale for the compilation, a brief summary of the major findings and a description of the data base.

A tool is presented that helps to find biological functions for new protein sequences. Running on any Macintosh computer system, MacPattern provides a unique combination of different algorithms, speed and user-friendliness. It supports searches for protein patterns using the PROSITE database, protein block searches with the BLOCKS database, and the identification of statistically significant protein segments. MacPattern allows batch processing of sequences and automatic translations of nucleotide sequence data. It is particularly suited for genome analysis or cDNA sequencing projects.

Profile searches using aligned short protein blocks are an effective method for identifying putative protein functions. An algorithm is presented that accelerates block searches by a factor 2–5 with only limited lack of sensitivity: this algorithm is particularly suited for application in large-scale genome research.