Martin Vingron

Processing and quality control of DNA array hybridization data

MOTIVATION The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY http://www.dkfz.de/tbi CONTACT M.Vingron@dkfz-heidelberg.de

Transcriptional profiling on all open reading frames of Saccharomyces cerevisiae

Open reading frames (6116) of the budding yeast Saccharomyces cerevisiae were PCR‐amplified from genomic DNA using 12,232 primers specific to the ends of the coding sequences; the success rate of amplification was 97%. PCR‐products were made accessible to hybridization by being arrayed at very high density on solid support media using various robotic devices. Probes made from total RNA preparations were hybridized for the analysis of the transcriptional activity of yeast under various growth conditions and of different strains. Experimental factors that proved critical to the performance, such as different RNA isolation procedures and the assessment of hybridization results, for example, were investigated in detail. Various software tools were developed that permit convenient handling and sound analysis of the large data quantities obtained from transcriptional profiling studies. Comprehensive arrays are being distributed within the European Yeast Functional Analysis Network (EUROFAN) and beyond.

TIF-IA, the factor mediating growth-dependent control of ribosomal RNA synthesis, is the mammalian homolog of yeast Rrn3p

Cells carefully modulate the rate of rRNA transcription in order to prevent an overinvestment in ribosome synthesis under less favorable nutritional conditions. In mammals, growth‐dependent regulation of RNA polymerase I (Pol I) transcription is mediated by TIF‐IA, an essential initiation factor that is active in extracts from growing but not starved or cycloheximide‐treated mammalian cells. Here we report the molecular cloning and functional characterization of recombinant TIF‐IA, which turns out to be the mammalian homolog of the yeast factor Rrn3p. We demonstrate that TIF‐IA interacts with Pol I in the absence of template DNA, augments Pol I transcription in vivo and rescues transcription in extracts from growth‐arrested cells in vitro.

Computational aspects of expression data

Abstract Several experimental techniques are available nowadays to study the spectrum of genes expressed in a cell at a specific moment. Typically, such methods generate large amounts of expression data that may be hard to interpret. Here we review computational questions and approaches resulting from the various experimental techniques.

Transcriptional profiling: is it worth the money?

Transcriptional profiling on DNA arrays has become a synonym for the type of analyses that aim to understand cellular functioning in a comprehensive manner. In this review, the status of the technology is briefly discussed, with emphasis on some inherent weaknesses and problems.

In silico analysis of gene expression patterns during early development of Xenopus laevis.

The information as to where and when a mRNA is present in a given cell is essential to bridge the gap between the DNA sequence of a gene and its physiological function. Therefore, a major component of functional genomics is to characterize the levels and the spatio-temporal domains of gene expression. Currently, there is just a few specialised public databases available storing the data on gene expression while they are needed as a resource for the field. Moreover, there is a need to develop and assess computational tools to compare and analyse expression profiles in a suitable way for biological interpretation. Here we describe our recent work on developing a database on gene expression for the frog Xenopus laevis, and on setting up and using new tools for the analysis and comparison of gene expression patterns. We used histogram clustering to compare expression profiles at both gene and tissue levels using a set of data coming from the characterization of the expression of genes during early development of Xenopus. This enabled us to draw a tree of tissue relatedness and to identify coexpressed genes by in silico analysis.

Contig selection in physical mapping

In physical mapping one orders a set of genetic landmarks or a library of cloned fragments of DNA according to their position in the genome. This is a preparatory step for efficient sequencing. Our approach to physical mapping divides the problem into smaller and easier subproblems by partitioning the probe set into independent parts (contigs). The focus is on the selection of probe sets which can be grouped together into contigs. We introduce a new distance function between probes, the averaged rank distance (ARD). The ARD measures the reliability of certain probe configurations in physical maps which are generated by bootstrap resampling of the raw data. This mimics an independent experiment repetition in silico. The ARD measures the distances of probes within a contig and smoothes the distances of probes in different contigs. It shows distinct jumps at contig borders. This makes it appropriate for contig selection by clustering. We designed a physical mapping algorithm that makes use of these observations and seems to be particularly well suited to the delineation of reliable contigs. We evaluated our method on data sets from two physical mapping projects. In comparison to a physical map of Pasteurella haemolytica that was computed using simulated annealing, the newly computed map is considerably cleaner. On data from Xylella fastidiosa the contigs produced by the new method could be compared to a map produced by a group of experts and the two maps largely agree in the definition of the contigs. The results of our method have already proven helpful for the design of experiments aiming at further improving the quality of a map.

Tree fitting: an algebraic approach using profile distances

Distance methods play a central role in the field of phylogeny reconstruction, providing fast, efficient algorithms which yield reliable trees. The taxonomy problem is; given a set of DNA or amino acid sequences from several species, accurately reconstruct a phylogenetic tree representing their evolutionary history. Distance methods approach this problem by inferring a distance matrix of species-to-species evolutionary distances, and finding a tree which approximates the distance matrix. Our results consider the approach of using profile distances instead of leaf-to-leaf distances. We consider the vector space of tree metrics with regard to a basis generated by profile distances Given a fixed tree topology, we show how to project edge weights onto a topology based upon its set of profile distances. The projected edge weights provide topological insight, as negative edge weights will point to false edges in the topology. Although the presence of such negative edge weights is not guaranteed, we show that if the test tree is sufficiently close to the target tree in topology, negative edge weights will highlight the false edges. An algorithm is presented which uses this information to accurately reconstruct tree metrics.