17,642 research outputs found
A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs
BACKGROUND: In recent years, intensive computational efforts have been directed towards the discovery of promoter motifs that correlate with mRNA expression profiles. Nevertheless, it is still not always possible to predict steady-state mRNA expression levels based on promoter signals alone, suggesting that other factors may be involved. Other genic regions, in particular 3' UTRs, which are known to exert regulatory effects especially through controlling RNA stability and localization, were less comprehensively investigated, and deciphering regulatory motifs within them is thus crucial. RESULTS: By analyzing 3' UTR sequences and mRNA decay profiles of Saccharomyces cerevisiae genes, we derived a catalog of 53 sequence motifs that may be implicated in stabilization or destabilization of mRNAs. Some of the motifs correspond to known RNA-binding protein sites, and one of them may act in destabilization of ribosome biogenesis genes during stress response. In addition, we present for the first time a catalog of 23 motifs associated with subcellular localization. A significant proportion of the 3' UTR motifs is highly conserved in orthologous yeast genes, and some of the motifs are strikingly similar to recently published mammalian 3' UTR motifs. We classified all genes into those regulated only at transcription initiation level, only at degradation level, and those regulated by a combination of both. Interestingly, different biological functionalities and expression patterns correspond to such classification. CONCLUSION: The present motif catalogs are a first step towards the understanding of the regulation of mRNA degradation and subcellular localization, two important processes which - together with transcription regulation - determine the cell transcriptome
Metabolic and Chaperone Gene Loss Marks the Origin of Animals: Evidence for Hsp104 and Hsp78 Sharing Mitochondrial Clients
The evolution of animals involved acquisition of an emergent gene repertoire
for gastrulation. Whether loss of genes also co-evolved with this developmental
reprogramming has not yet been addressed. Here, we identify twenty-four genetic
functions that are retained in fungi and choanoflagellates but undetectable in
animals. These lost genes encode: (i) sixteen distinct biosynthetic functions;
(ii) the two ancestral eukaryotic ClpB disaggregases, Hsp78 and Hsp104, which
function in the mitochondria and cytosol, respectively; and (iii) six other
assorted functions. We present computational and experimental data that are
consistent with a joint function for the differentially localized ClpB
disaggregases, and with the possibility of a shared client/chaperone
relationship between the mitochondrial Fe/S homoaconitase encoded by the lost
LYS4 gene and the two ClpBs. Our analyses lead to the hypothesis that the
evolution of gastrulation-based multicellularity in animals led to efficient
extraction of nutrients from dietary sources, loss of natural selection for
maintenance of energetically expensive biosynthetic pathways, and subsequent
loss of their attendant ClpB chaperones.Comment: This is a reformatted version from the recent official publication in
PLoS ONE (2015). This version differs substantially from first three arXiV
versions. This version uses a fixed-width font for DNA sequences as was done
in the earlier arXiv versions but which is missing in the official PLoS ONE
publication. The title has also been shortened slightly from the official
publicatio
Motif Discovery through Predictive Modeling of Gene Regulation
We present MEDUSA, an integrative method for learning motif models of
transcription factor binding sites by incorporating promoter sequence and gene
expression data. We use a modern large-margin machine learning approach, based
on boosting, to enable feature selection from the high-dimensional search space
of candidate binding sequences while avoiding overfitting. At each iteration of
the algorithm, MEDUSA builds a motif model whose presence in the promoter
region of a gene, coupled with activity of a regulator in an experiment, is
predictive of differential expression. In this way, we learn motifs that are
functional and predictive of regulatory response rather than motifs that are
simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model
of the transcriptional control logic that can predict the expression of any
gene in the organism, given the sequence of the promoter region of the target
gene and the expression state of a set of known or putative transcription
factors and signaling molecules. Each motif model is either a -length
sequence, a dimer, or a PSSM that is built by agglomerative probabilistic
clustering of sequences with similar boosting loss. By applying MEDUSA to a set
of environmental stress response expression data in yeast, we learn motifs
whose ability to predict differential expression of target genes outperforms
motifs from the TRANSFAC dataset and from a previously published candidate set
of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed
binding sites associated with environmental stress response from the
literature.Comment: RECOMB 200
Application of regulatory sequence analysis and metabolic network analysis to the interpretation of gene expression data
We present two complementary approaches for the interpretation of clusters of
co-regulated genes, such as those obtained from DNA chips and related methods.
Starting from a cluster of genes with similar expression profiles, two basic
questions can be asked:
1. Which mechanism is responsible for the coordinated transcriptional response
of the genes? This question is approached by extracting motifs that are shared
between the upstream sequences of these genes. The motifs extracted are putative
cis-acting regulatory elements.
2. What is the physiological meaning for the cell to express together these
genes? One way to answer the question is to search for potential metabolic
pathways that could be catalyzed by the products of the genes. This can be
done by selecting the genes from the cluster that code for enzymes, and trying
to assemble the catalyzed reactions to form metabolic pathways.
We present tools to answer these two questions, and we illustrate their use with
selected examples in the yeast Saccharomyces cerevisiae. The tools are available
on the web (http://ucmb.ulb.ac.be/bioinformatics/rsa-tools/;
http://www.ebi.ac.uk/research/pfbp/; http://www.soi.city.ac.uk/~msch/)
Predicting Genetic Regulatory Response Using Classification
We present a novel classification-based method for learning to predict gene
regulatory response. Our approach is motivated by the hypothesis that in simple
organisms such as Saccharomyces cerevisiae, we can learn a decision rule for
predicting whether a gene is up- or down-regulated in a particular experiment
based on (1) the presence of binding site subsequences (``motifs'') in the
gene's regulatory region and (2) the expression levels of regulators such as
transcription factors in the experiment (``parents''). Thus our learning task
integrates two qualitatively different data sources: genome-wide cDNA
microarray data across multiple perturbation and mutant experiments along with
motif profile data from regulatory sequences. We convert the regression task of
predicting real-valued gene expression measurement to a classification task of
predicting +1 and -1 labels, corresponding to up- and down-regulation beyond
the levels of biological and measurement noise in microarray measurements. The
learning algorithm employed is boosting with a margin-based generalization of
decision trees, alternating decision trees. This large-margin classifier is
sufficiently flexible to allow complex logical functions, yet sufficiently
simple to give insight into the combinatorial mechanisms of gene regulation. We
observe encouraging prediction accuracy on experiments based on the Gasch S.
cerevisiae dataset, and we show that we can accurately predict up- and
down-regulation on held-out experiments. Our method thus provides predictive
hypotheses, suggests biological experiments, and provides interpretable insight
into the structure of genetic regulatory networks.Comment: 8 pages, 4 figures, presented at Twelfth International Conference on
Intelligent Systems for Molecular Biology (ISMB 2004), supplemental website:
http://www.cs.columbia.edu/compbio/geneclas
Predicting Combinatorial Binding of Transcription Factors to Regulatory Elements in the Human Genome by Association Rule Mining
Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cisregulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment. Results: Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature. Conclusion: Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription factor.NIAAA Alcohol Training GrantNational Science FoundationCellular and Molecular Biolog
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