Lars Juhl Jensen

A new non-linear normalization method for reducing variability in DNA microarray experiments

BackgroundMicroarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current normalization techniques are most often linear and therefore not capable of fully correcting for these effects.ResultsWe present here a simple and robust non-linear method for normalization using array signal distribution analysis and cubic splines. These methods compared favorably to normalization using robust local-linear regression (lowess). The application of these methods to oligonucleotide arrays reduced the relative error between replicates by 5-10% compared with a standard global normalization method. Application to cDNA arrays showed improvements over the standard method and over Cy3-Cy5 normalization based on dye-swap replication. In addition, a set of known differentially regulated genes was ranked higher by the t-test. In either cDNA or Affymetrix technology, signal-dependent bias was more than ten times greater than the observed print-tip or spatial effects.ConclusionsIntensity-dependent normalization is important for both high-density oligonucleotide array and cDNA array data. Both the regression and spline-based methods described here performed better than existing linear methods when assessed on the variability of replicate arrays. Dye-swap normalization was less effective at Cy3-Cy5 normalization than either regression or spline-based methods alone.

Prediction of human protein function from post-translational modifications and localization features

We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects such as the length, isoelectric point and composition of the polypeptide chain.

Prediction of human protein function according to Gene Ontology categories.

MOTIVATIONnThe human genome project has led to the discovery of many human protein coding genes which were previously unknown. As a large fraction of these are functionally uncharacterized, it is of interest to develop methods for predicting their molecular function from sequence.nnnRESULTSnWe have developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors can all be predicted. Although the method relies on protein sequences as the sole input, it does not rely on sequence similarity, but instead on sequence derived protein features such as predicted post translational modifications (PTMs), protein sorting signals and physical/chemical properties calculated from the amino acid composition. This allows for prediction of the function for orphan proteins where no homologs can be found. Using this method we propose two novel receptors in the human genome, and further demonstrate chromosomal clustering of related proteins.

On the total number of genes and their length distribution in complete microbial genomes

In sequenced microbial genomes, some of the annotated genes are actually not protein-coding genes, but rather open reading frames that occur by chance. Therefore, the number of annotated genes is higher than the actual number of genes for most of these microbes. Comparison of the length distribution of the annotated genes with the length distribution of those matching a known protein reveals that too many short genes are annotated in many genomes. Here we estimate the true number of protein-coding genes for sequenced genomes. Although it is often claimed that Escherichia coli has about 4300 genes, we show that it probably has only approximately 3800 genes, and that a similar discrepancy exists for almost all published genomes.

Comparison of computational methods for the identification of cell cycle-regulated genes

MOTIVATIONnDNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes.nnnRESULTSnHere, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.

Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation

MOTIVATIONnThe whole genomes submitted to GenBank contain valuable information about the function of genes as well as the upstream sequences and whole cell expression provides valuable information on gene regulation. To utilize these large amounts of data for a biological understanding of the regulation of gene expression, new automatic methods for pattern finding are needed.nnnRESULTSnTwo word-analysis algorithms for automatic discovery of regulatory sequence elements have been developed. We show that sequence patterns correlated to whole cell expression data can be found using Kolmogorov-Smirnov tests on the raw data, thereby eliminating the need for clustering co-regulated genes. Regulatory elements have also been identified by systematic calculations of the significance of correlations between words found in the functional annotation of genes and DNA words occurring in their promoter regions. Application of these algorithms to the Saccharomyces cerevisiae genome and publicly available DNA array data sets revealed a highly conserved 9-mer occurring in the upstream regions of genes coding for proteasomal subunits. Several other putative and known regulatory elements were also found.nnnAVAILABILITYnUpon request.

Three views of microbial genomes.

We describe here GenomeAtlases as a method for visualising three different aspects of complete microbial chromosomes: repeats, DNA structural characteristics, and base composition. We have applied this method to all publicly available genomes, and find a general strand preference of global repeats. The atlas for the Mycoplasma genitalium genome is presented as an example, and results from all three views are consistent with known characteristics of the genome.

Cyclebase.org—a comprehensive multi-organism online database of cell-cycle experiments

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase.org, for viewing and downloading these data. The user interface facilitates searches for genes of interest as well as downloads of genome-wide results. Individual genes are displayed with graphs of expression profiles throughout the cell cycle from all available experiments. These expression profiles are normalized to a common timescale to enable inspection of the combined experimental evidence. Furthermore, state-of-the-art computational analyses provide key information on both individual experiments and combined datasets such as whether or not a gene is periodically expressed and, if so, the time of peak expression. Cyclebase is available at http://www.cyclebase.org.

Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text

Objective Drugs have tremendous potential to cure and relieve disease, but the risk of unintended effects is always present. Healthcare providers increasingly record data in electronic patient records (EPRs), in which we aim to identify possible adverse events (AEs) and, specifically, possible adverse drug events (ADEs). Materials and methods Based on the undesirable effects section from the summary of product characteristics (SPC) of 7446 drugs, we have built a Danish ADE dictionary. Starting from this dictionary we have developed a pipeline for identifying possible ADEs in unstructured clinical narrative text. We use a named entity recognition (NER) tagger to identify dictionary matches in the text and post-coordination rules to construct ADE compound terms. Finally, we apply post-processing rules and filters to handle, for example, negations and sentences about subjects other than the patient. Moreover, this method allows synonyms to be identified and anatomical location descriptions can be merged to allow appropriate grouping of effects in the same location. Results The method identified 1u2005970u2005731 (35u2005477 unique) possible ADEs in a large corpus of 6011 psychiatric hospital patient records. Validation was performed through manual inspection of possible ADEs, resulting in precision of 89% and recall of 75%. Discussion The presented dictionary-building method could be used to construct other ADE dictionaries. The complication of compound words in Germanic languages was addressed. Additionally, the synonym and anatomical location collapse improve the method. Conclusions The developed dictionary and method can be used to identify possible ADEs in Danish clinical narratives.

Prediction of novel archaeal enzymes from sequence-derived features

The completely sequenced archaeal genomes potentially encode, among their many functionally uncharacterized genes, novel enzymes of biotechnological interest. We have developed a prediction method for detection and classification of enzymes from sequence alone (available at http://www.cbs.dtu.dk/services/ArchaeaFun/). The method does not make use of sequence similarity; rather, it relies on predicted protein features like cotranslational and posttranslational modifications, secondary structure, and simple physical/chemical properties.