Epidemiology. Influenza escapes immunity along neutral networks.
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Antigenic Variation↗
Biomedical subjects
Erik van Nimwegen
Publications and source records attributed to Erik van Nimwegen.
SwissRegulon: a database of genome-wide annotations of regulatory sites.
SwissRegulon (http://www.swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory sites in the intergenic regions of genomes. The regulatory site annotations are produced using a number of recently developed algorithms that operate on multiple alignments of orthologous intergenic regions from related genomes in combination with, whenever available, known sites from the literature, and ChIP-on-chip binding data. Currently SwissRegulon contains annotations for yeast and 17 prokaryotic genomes. The database provides information about the sequence, location, orientation, posterior probability and, whenever available, binding factor of each annotated site. To enable easy viewing of the regulatory site annotations in the context of other features annotated on the genomes, the sites are displayed using the GBrowse genome browser interface and can be queried based on any annotated genomic feature. The database can also be queried for regulons, i.e. sites bound by a common factor.
Algorithms↗
The types and prevalence of alternative splice forms.
The finding that eukaryotic gene structures are extremely complex prompted the development of new experimental techniques for the accurate measurement of transcription start site usage and of the expression of alternative splice forms. On the computational side, analyses of large databases of splice variants revealed differences in the length, motif composition and selection pressure between constitutive and alternatively spliced exons. Such features are being incorporated into novel computational tools for gene structure prediction. The result of these investigations is a continuously improving catalogue of alternative splice forms. How the expression of these alternative splice forms is regulated remains one of the major open questions.
Alternative Splicing↗
SPA: a probabilistic algorithm for spliced alignment.
Recent large-scale cDNA sequencing efforts show that elaborate patterns of splice variation are responsible for much of the proteome diversity in higher eukaryotes. To obtain an accurate account of the repertoire of splice variants, and to gain insight into the mechanisms of alternative splicing, it is essential that cDNAs are very accurately mapped to their respective genomes. Currently available algorithms for cDNA-to-genome alignment do not reach the necessary level of accuracy because they use ad hoc scoring models that cannot correctly trade off the likelihoods of various sequencing errors against the probabilities of different gene structures. Here we develop a Bayesian probabilistic approach to cDNA-to-genome alignment. Gene structures are assigned prior probabilities based on the lengths of their introns and exons, and based on the sequences at their splice boundaries. A likelihood model for sequencing errors takes into account the rates at which misincorporation, as well as insertions and deletions of different lengths, occurs during sequencing. The parameters of both the prior and likelihood model can be automatically estimated from a set of cDNAs, thus enabling our method to adapt itself to different organisms and experimental procedures. We implemented our method in a fast cDNA-to-genome alignment program, SPA, and applied it to the FANTOM3 dataset of over 100,000 full-length mouse cDNAs and a dataset of over 20,000 full-length human cDNAs. Comparison with the results of four other mapping programs shows that SPA produces alignments of significantly higher quality. In particular, the quality of the SPA alignments near splice boundaries and SPA's mapping of the 5' and 3' ends of the cDNAs are highly improved, allowing for more accurate identification of transcript starts and ends, and accurate identification of subtle splice variations. Finally, our splice boundary analysis on the human dataset suggests the existence of a novel non-canonical splice site that we also find in the mouse dataset. The SPA software package is available at http://www.biozentrum.unibas.ch/personal/nimwegen/cgi-bin/spa.cgi.
Algorithms↗
A simple physical model predicts small exon length variations.
One of the most common splice variations are small exon length variations caused by the use of alternative donor or acceptor splice sites that are in very close proximity on the pre-mRNA. Among these, three-nucleotide variations at so-called NAGNAG tandem acceptor sites have recently attracted considerable attention, and it has been suggested that these variations are regulated and serve to fine-tune protein forms by the addition or removal of a single amino acid. In this paper we first show that in-frame exon length variations are generally overrepresented and that this overrepresentation can be quantitatively explained by the effect of nonsense-mediated decay. Our analysis allows us to estimate that about 50% of frame-shifted coding transcripts are targeted by nonsense-mediated decay. Second, we show that a simple physical model that assumes that the splicing machinery stochastically binds to nearby splice sites in proportion to the affinities of the sites correctly predicts the relative abundances of different small length variations at both boundaries. Finally, using the same simple physical model, we show that for NAGNAG sites, the difference in affinities of the neighboring sites for the splicing machinery accurately predicts whether splicing will occur only at the first site, splicing will occur only at the second site, or three-nucleotide splice variants are likely to occur. Our analysis thus suggests that small exon length variations are the result of stochastic binding of the spliceosome at neighboring splice sites. Small exon length variations occur when there are nearby alternative splice sites that have similar affinity for the splicing machinery.
Animals↗
PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.
A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single gene's upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbs's results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.
Algorithms↗
Identification of clustered microRNAs using an ab initio prediction method.
BACKGROUND: MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations. RESULTS: In this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our ab initio method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web. CONCLUSION: Our results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution.
Algorithms↗
Analysis of human immunodeficiency virus cytopathicity by using a new method for quantitating viral dynamics in cell culture.
Human immunodeficiency virus (HIV) causes complex metabolic changes in infected CD4(+) T cells that lead to cell cycle arrest and cell death by necrosis. To study the viral functions responsible for deleterious effects on the host cell, we quantitated the course of HIV type 1 infection in tissue cultures by using flow cytometry for a virally encoded marker protein, heat-stable antigen (HSA). We found that HSA appeared on the surface of the target cells in two phases: passive acquisition due to association and fusion of virions with target cells, followed by active protein expression from transcription of the integrated provirus. The latter event was necessary for decreased target cell viability. We developed a general mathematical model of viral dynamics in vitro in terms of three effective time-dependent rates: those of cell proliferation, infection, and death. Using this model we show that the predominant contribution to the depletion of viable target cells results from direct cell death rather than cell cycle blockade. This allows us to derive accurate bounds on the time-dependent death rates of infected cells. We infer that the death rate of HIV-infected cells is 80 times greater than that of uninfected cells and that the elimination of the vpr protein reduces the death rate by half. Our approach provides a general method for estimating time-dependent death rates that can be applied to study the dynamics of other viruses.
Antigens, CD↗
Transformation fingerprint: induced STAT3-C, v-Src and Ha-Ras cause small initial changes but similar established profiles in mRNA.
Induced transformation of mouse fibroblasts was carried out by releasing tetracycline-repressed expression of an oncogenic mutant of STAT3, STAT3-C, or of v-Src or Ha-Ras. At 15 days after derepression of each oncogene, DNA microarrays showed elevation (>3-fold) of a similar group of approximately 25 mRNAs compared to untransformed cells. RT-PCR confirmed a number of these mRNA elevations. RNA samples were then analysed at intervals during the first 24 h after doxycycline removal to determine the time of early changes. Extensive changes were not observed by array analysis, except in v-Src-expressing cells where about 10 mRNAs were elevated threefold or more. However, RT-PCR did uncover changes in each derepressed cell type that included some of the changes observed after the 15-day transformation period. In addition, STAT3-C target genes such as BclXI and cyclin D1, which were not observed on array analysis, were elevated by RT-PCR analysis. We conclude, therefore, that early after oncogene induction, transcriptional changes, including those initiated by STAT3-C, may occur only in scarce mRNA and/or to a limited extent. However, with additional time and probably additional cell division, a new epigenetic state is established that is mirrored by a changed transcriptional profile emblematic of transformation by each of three oncogenes.
Animals↗
Scaling laws in the functional content of genomes.
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Evolution, Molecular↗
A probabilistic method to detect regulatory modules.
MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.
Algorithms↗
Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes.
Although mRNA decay rates are a key determinant of the steady-state concentration for any given mRNA species, relatively little is known, on a population level, about what factors influence turnover rates and how these rates are integrated into cellular decisions. We decided to measure mRNA decay rates in two human cell lines with high-density oligonucleotide arrays that enable the measurement of decay rates simultaneously for thousands of mRNA species. Using existing annotation and the Gene Ontology hierarchy of biological processes, we assign mRNAs to functional classes at various levels of resolution and compare the decay rate statistics between these classes. The results show statistically significant organizational principles in the variation of decay rates among functional classes. In particular, transcription factor mRNAs have increased average decay rates compared with other transcripts and are enriched in "fast-decaying" mRNAs with half-lives <2 h. In contrast, we find that mRNAs for biosynthetic proteins have decreased average decay rates and are deficient in fast-decaying mRNAs. Our analysis of data from a previously published study of Saccharomyces cerevisiae mRNA decay shows the same functional organization of decay rates, implying that it is a general organizational scheme for eukaryotes. Additionally, we investigated the dependence of decay rates on sequence composition, that is, the presence or absence of short mRNA motifs in various regions of the mRNA transcript. Our analysis recovers the positive correlation of mRNA decay with known AU-rich mRNA motifs, but we also uncover further short mRNA motifs that show statistically significant correlation with decay. However, we also note that none of these motifs are strong predictors of mRNA decay rate, indicating that the regulation of mRNA decay is more complex and may involve the cooperative binding of several RNA-binding proteins at different sites.
Base Composition↗
Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.
Genome-wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene-by-gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (probabilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from WMs when these WMs are unknown. Our analysis suggests that the set of all putative sites for a single genome (e.g., Escherichia coli) is largely inadequate for clustering. When sites from different genomes are combined and all the homologous sites from the various species are used as a block, clustering becomes feasible. We predict 50-100 new regulons as well as many new members of existing regulons, potentially doubling the number of known regulatory sites in E. coli.
Bacteria↗
Splice variation in mouse full-length cDNAs identified by mapping to the mouse genome.
We mapped the collection of The Institute of Physical and Chemical Research (Japan) (RIKEN) 21,076 full-length mouse cDNA clone sequences and the mouse RefSeq sequences to the recently completed draft of the mouse genome. Using this mapping, we identified 3674 mouse genes with multiple transcripts, of which 1098 have splice variants. All but 532 of 21,076 clones (97.5%) mapped to the genome assembly. Alignments of cDNA clone sequences with proteins show that much of the detected splice variation alters coding regions and affects the translated protein. We developed novel analytical techniques to classify observed splice variation and to assess the relation between splice variation and alternative transcription. This analysis indicates that an alternative choice of transcription start or polyadenylation signal frequently induces splice variation.
Alternative Splicing↗