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The globin family of genes and proteins has been a recurrent object of study for many decades. This interest has generated a vast amount of knowledge. However it has also created an inconsistent and confusing nomenclature, due to the lack of a systematic approach to naming genes and failure to reflect the phylogenetic relationships among genes of the gene family. To alleviate the problems with the existing system, here we propose a standardized nomenclature for the alpha and beta globin family of genes, based on a phylogenetic analysis of vertebrate alpha and beta globins, and following the Guidelines for Human Gene Nomenclature.

Although the genotype-phenotype map plays a central role both in Quantitative and Evolutionary Genetics, the formalization of a completely general and satisfactory model of genetic effects, particularly accounting for epistasis, remains a theoretical challenge. Here, we use a two-locus genetic system in simulated populations with epistasis to show the convenience of using a recently developed model, NOIA, to perform estimates of genetic effects and the decomposition of the genetic variance that are orthogonal even under deviations from the Hardy-Weinberg proportions. We develop the theory for how to use this model in interval mapping of quantitative trait loci using Halley-Knott regressions, and we analyze a real data set to illustrate the advantage of using this approach in practice. In this example, we show that departures from the Hardy-Weinberg proportions that are expected by sampling alone substantially alter the orthogonal estimates of genetic effects when other statistical models, like F-2 or G2A, are used instead of NOIA. Finally, for the first time from real data, we provide estimates of functional genetic effects as sets of effects of natural allele substitutions in a particular genotype, which enriches the debate on the interpretation of genetic effects as implemented both in functional and in statistical models. We also discuss further implementations leading to a completely general genotype-phenotype map.

Regulation of transcription is a central mechanism in all living cells that now can be investigated with high-throughput technologies. Data produced from such experiments give new insights to how transcription factors (TFs) coordinate the gene transcription and thereby regulate the amounts of proteins produced. These studies are also important from a medical perspective since TF proteins are often involved in disease. To learn more about transcriptional regulation, we have developed strategies for analysis of data from microarray and massively parallel sequencing (MPS) experiments.

Our computational results consist of methods to handle the steadily increasing amount of data from high-throughput technologies. Microarray data analysis tools have been assembled in the LCB-Data Warehouse (LCB-DWH) (paper I), and other analysis strategies have been developed for MPS data (paper V). We have also developed a de novo motif search algorithm called BCRANK (paper IV).

The analysis has lead to interesting biological findings in human liver cells (papers II-V). The investigated TFs appeared to bind at several thousand sites in the genome, that we have identified at base pair resolution. The investigated histone modifications are mainly found downstream of transcription start sites, and correlated to transcriptional activity. These histone marks are frequently found for pairs of genes in a bidirectional conformation. Our results suggest that a TF can bind in the shared promoter of two genes and regulate both of them.

From a medical perspective, the genes bound by the investigated TFs are candidates to be involved in metabolic disorders. Moreover, we have developed a new strategy to detect single nucleotide polymorphisms (SNPs) that disrupt the binding of a TF (paper IV). We further demonstrated that SNPs can affect transcription in the immediate vicinity. Ultimately, our method may prove helpful to find disease-causing regulatory SNPs.

Disease-associated SNPs detected in large-scale association studies are   frequently located in non-coding genomic regions, suggesting that they may be involved in transcriptional regulation. Here we describe a new strategy for detecting regulatory SNPs (rSNPs), by combining   computational and experimental approaches. Whole genome ChIP-chip data   for USF1 was analyzed using a novel motif finding algorithm called   BCRANK. 1754 binding sites were identified and 140 candidate rSNPs were   found in the predicted sites. For validating their regulatory function,   seven SNPs found to be heterozygous in at least one of four human cell   samples were investigated by ChIP and sequence analysis (haploChIP). In   four of five cases where the SNP was predicted to affect binding, USF1   was preferentially bound to the allele containing the consensus motif.   Allelic differences in binding for other proteins and histone marks   further reinforced the SNPs regulatory potential. Moreover, for one of   these SNPs, H3K36me3 and POLR2A levels at neighboring heterozygous SNPs   indicated effects on transcription. Our strategy, which is entirely   based on in vivo data for both the prediction and validation steps, can   identify individual binding sites at base pair resolution and predict   rSNPs. Overall, this approach can help to pinpoint the causative SNPs   in complex disorders where the associated haplotypes are located in regulatory regions. Availability: BCRANK is available from Bioconductor  (http://www.bioconductor.org).

The Linnaeus Centre for Bioinformatics Data Warehouse (LCB-DWH) is a web-based infrastructure for reliable and secure microarray gene expression data management and analysis that provides an online service for the scientific community. The LCB-DWH is an effort towards a complete system for storage (using the BASE system), analysis and publication of microarray data. Important features of the system include: access to established methods within R/Bioconductor for data analysis, built-in connection to the Gene Ontology database and a scripting facility for automatic recording and re-play of all the steps of the analysis. The service is up and running on a high performance server. At present there are more than 150 registered users.

Molecular investigations of deep-level relationships within and among the animal phyla have been hampered by a lack of slowly evolving genes that are amenable to study by molecular systematists. To provide new data for use in deep-level metazoan phylogenetic studies, primers were developed to amplify a 1.3-kb region of the subunit of the nuclear-encoded sodium–potassium ATPase gene from 31 bilaterians representing several phyla. Maximum parsimony, maximum likelihood, and Bayesian analyses of these sequences (combined with ATPase sequences for 23 taxa downloaded from GenBank) yield congruent trees that corroborate recent findings based on analyses of other data sets (e.g., the 18S ribosomal RNA gene). The ATPase-based trees support monophyly for several clades (including Lophotrochozoa, a form of Ecdysozoa, Vertebrata, Mollusca, Bivalvia, Gastropoda, Arachnida, Hexapoda, Coleoptera, and Diptera) but do not support monophyly for Deuterostomia, Arthropoda, or Nemertea. Parametric bootstrapping tests reject monophyly for Arthropoda and Nemertea but are unable to reject deuterostome monophyly. Overall, the sodium–potassium ATPase -subunit gene appears to be useful for deep-level studies of metazoan phylogeny.

Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.