6,940 research outputs found
Gcn4p and novel upstream activating sequences regulate targets of the unfolded protein response.
Eukaryotic cells respond to accumulation of unfolded proteins in the endoplasmic reticulum (ER) by activating the unfolded protein response (UPR), a signal transduction pathway that communicates between the ER and the nucleus. In yeast, a large set of UPR target genes has been experimentally determined, but the previously characterized unfolded protein response element (UPRE), an upstream activating sequence (UAS) found in the promoter of the UPR target gene KAR2, cannot account for the transcriptional regulation of most genes in this set. To address this puzzle, we analyzed the promoters of UPR target genes computationally, identifying as candidate UASs short sequences that are statistically overrepresented. We tested the most promising of these candidate UASs for biological activity, and identified two novel UPREs, which are necessary and sufficient for UPR activation of promoters. A genetic screen for activators of the novel motifs revealed that the transcription factor Gcn4p plays an essential and previously unrecognized role in the UPR: Gcn4p and its activator Gcn2p are required for induction of a majority of UPR target genes during ER stress. Both Hac1p and Gcn4p bind target gene promoters to stimulate transcriptional induction. Regulation of Gcn4p levels in response to changing physiological conditions may function as an additional means to modulate the UPR. The discovery of a role for Gcn4p in the yeast UPR reveals an additional level of complexity and demonstrates a surprising conservation of the signaling circuit between yeast and metazoan cells
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Identifying DNA motifs based on match and mismatch alignment information
The conventional way of identifying DNA motifs, solely based on match
alignment information, is susceptible to a high number of spurious sites. A
novel scoring system has been introduced by taking both match and mismatch
alignment information into account. The mismatch alignment information is
useful to remove spurious sites encountered in DNA motif searching. As an
example, a correct TATA box site in Homo sapiens H4/g gene has successfully
been identified based on match and mismatch alignment information
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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions
Spectral Sequence Motif Discovery
Sequence discovery tools play a central role in several fields of
computational biology. In the framework of Transcription Factor binding
studies, motif finding algorithms of increasingly high performance are required
to process the big datasets produced by new high-throughput sequencing
technologies. Most existing algorithms are computationally demanding and often
cannot support the large size of new experimental data. We present a new motif
discovery algorithm that is built on a recent machine learning technique,
referred to as Method of Moments. Based on spectral decompositions, this method
is robust under model misspecification and is not prone to locally optimal
solutions. We obtain an algorithm that is extremely fast and designed for the
analysis of big sequencing data. In a few minutes, we can process datasets of
hundreds of thousand sequences and extract motif profiles that match those
computed by various state-of-the-art algorithms.Comment: 20 pages, 3 figures, 1 tabl
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 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 weight matrices (WMs) when these WMs are unknown. Our analysis suggests
that the set of all putative sites for a single genome (e.g. E. 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.Comment: 27 pages including 9 figures and 3 table
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