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Polygraph: Automatically generating signatures for polymorphic worms
- In Proceedings of the IEEE Symposium on Security and Privacy
, 2005
"... It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match poly ..."
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Cited by 275 (17 self)
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It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives. 1.
Genome-wide analysis of transcription factor binding sites based on Chip-Seq data
- Nat Methods
, 2008
"... Molecular interactions between protein complexes and DNA carry out essential gene regulatory functions. Uncovering such interactions by means of chromatin-immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) has recently become the focus of intense interest. We here introduce Qu ..."
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Cited by 193 (3 self)
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Molecular interactions between protein complexes and DNA carry out essential gene regulatory functions. Uncovering such interactions by means of chromatin-immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) has recently become the focus of intense interest. We here introduce QuEST (Quantitative Enrichment of Sequence Tags), a powerful statistical framework based on the Kernel Density Estimation approach, which utilizes ChIP-Seq data to determine positions where protein complexes come into contact with DNA. Using QuEST, we discovered several thousand binding sites for the human transcription factors SRF, GABP and NRSF at an average resolution of about 20 base-pairs. MEME-based motif analyses on the QuEST-identified sequences revealed DNA binding by cofactors of SRF, providing evidence that cofactor binding specificity can be obtained from ChIP-Seq data. By combining QuEST analyses with gene ontology (GO) annotations and expression data, we illustrate how general functions of transcription factors can be inferred.
Additivity in protein–DNA interactions: how good an approximation is it
- Nucleic Acids Res
, 2002
"... Man and Stormo and Bulyk et al. recently presented their results on the study of the DNA binding af®nity of proteins. In both of these studies the main conclusion is that the additivity assumption, usually applied in methods to search for binding sites, is not true. In the ®rst study, the analysis o ..."
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Cited by 162 (24 self)
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Man and Stormo and Bulyk et al. recently presented their results on the study of the DNA binding af®nity of proteins. In both of these studies the main conclusion is that the additivity assumption, usually applied in methods to search for binding sites, is not true. In the ®rst study, the analysis of binding af®nity data from the Mnt repressor protein bound to all possible DNA (sub)targets at positions 16 and 17 of the binding site, showed that those positions are not independent. In the second study, the authors analysed DNA binding af®nity data of the wild-type mouse EGR1 protein and four variants differing on the middle ®nger. The binding af®nity of these proteins was measured to all 64 possible trinucleotide (sub)targets of the middle ®nger using microarray technology. The analysis of the measurements also showed interdependence among the positions in the DNA target. In the present report, we review the data of both studies and we reanalyse them using various statistical methods, including a comparison with a multiple regression approach. We conclude that despite the fact that the additivity assumption does not ®t the data perfectly, in most cases it provides a very good approximation of the true nature of the speci®c protein±DNA interactions. Therefore, additive models can be very useful for the discovery and prediction of binding sites in genomic DNA.
Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes
- Nucleic Acids Res
, 2004
"... One of the greatest challenges that modern molecular biology is facing is the understanding of the complex mechanisms regulating gene expression. A fundamental step in this process requires the characterization of regulatory motifs playing key roles in the regulation of gene expression at transcript ..."
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Cited by 145 (2 self)
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One of the greatest challenges that modern molecular biology is facing is the understanding of the complex mechanisms regulating gene expression. A fundamental step in this process requires the characterization of regulatory motifs playing key roles in the regulation of gene expression at transcriptional and post-transcriptional levels. In particular, transcription is modulated by the interaction of transcription factors with their corresponding binding sites. Weeder Web is a web interface to Weeder, an algorithm for the automatic discovery of conserved motifs in a set of related regulatory DNA sequences. The motifs found are in turn likely to be instances of binding sites for some transcription factor. Other than providing access to the program, the interface has been designed so to make usage of the program itself as simple as possible, and to require very little prior knowledge about the length and the conservation of the motifs to be found. In fact, the interface automatically starts different runs of the program, each one with different parameters, and provides the user with an overall summary of the results as well as some ‘advice ’ on which motifs look more interesting according to their statistical significance and some simple considerations. The web interface is available at the address www.pesolelab.it by following the ‘Tools ’ link.
Exploring the Conditional Coregulation of Yeast Gene Expression Through Fuzzy K-Means Clustering
, 2002
"... Background: Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. Many proteins serve different roles depending on the demands of the organism, and therefore the corresponding genes are often coexpressed with different groups o ..."
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Cited by 137 (0 self)
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Background: Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. Many proteins serve different roles depending on the demands of the organism, and therefore the corresponding genes are often coexpressed with different groups of genes under different situations. This poses a challenge in analyzing wholegenome expression data, because many genes will be similarly expressed to multiple, distinct groups of genes. Because most commonly used analytical methods cannot appropriately represent these relationships, the connections between conditionally coregulated genes are often missed.
Comining phylogenetic data with co-regulated genes to identify regulatory motif
- BIOINFORMATICS
, 2003
"... Motivation: Discovery of regulatory motifs in unaligned DNA sequences remains a fundamental problem in computational biology. Two categories of algorithms have been developed to identify common motifs from a set of DNA sequences. The first can be called a ‘multiple genes, single species’approach. It ..."
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Cited by 136 (11 self)
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Motivation: Discovery of regulatory motifs in unaligned DNA sequences remains a fundamental problem in computational biology. Two categories of algorithms have been developed to identify common motifs from a set of DNA sequences. The first can be called a ‘multiple genes, single species’approach. It proposes that a degenerate motif is embedded in some or all of the otherwise unrelated input sequences and tries to describe a consensus motif and identify its occurrences. It is often used for co-regulated genes identified through experimental approaches. The second approach can be called ‘single gene, multiple species’. It requires orthologous input sequences and tries to identify unusually well conserved regions by phylogen-etic footprinting. Both approaches perform well, but each has some limitations. It is tempting to combine the knowledge of co-regulation among different genes and conservation among orthologous genes to improve our ability to identify motifs. Results: Based on the Consensus algorithm previously established by our group, we introduce a new algorithm called PhyloCon (Phylogenetic Consensus) that takes into account both conservation among orthologous genes and co-regulation of genes within a species. This algorithm first aligns conserved regions of orthologous sequences into multiple sequence alignments, or profiles, then compares profiles representing non-orthologous sequences. Motifs emerge as common regions in these profiles. Here we present a novel statistic to compare profiles of DNA sequences and a greedy approach to search for common subprofiles. We demonstrate that PhyloCon performs well on both synthetic and biological data. Availability: Software available upon request from the authors.
Hmmstr: a hidden markov model for local sequence-structure correlations in proteins
- Journal of Molecular Biology
, 2000
"... *Corresponding authors ..."
Genome-wide discovery of transcriptional modules from DNA sequence and gene expression
- Bioinformatics
, 2003
"... In this paper, we describe an approach for understanding
transcriptional regulation from both gene expression and
promoter sequence data. We aim to identify transcriptional
modules—sets of genes that are co-regulated in a set
of experiments, through a common motif profile. Using
the EM algorithm, o ..."
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Cited by 122 (7 self)
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In this paper, we describe an approach for understanding
transcriptional regulation from both gene expression and
promoter sequence data. We aim to identify transcriptional
modules—sets of genes that are co-regulated in a set
of experiments, through a common motif profile. Using
the EM algorithm, our approach refines both the module
assignment and the motif profile so as to best explain
the expression data as a function of transcriptional motifs.
It also dynamically adds and deletes motifs, as required
to provide a genome-wide explanation of the expression
data. We evaluate the method on two Saccharomyces
cerevisiae gene expression data sets, showing that our
approach is better than a standard one at recovering
known motifs and at generating biologically coherent
modules. We also combine our results with binding
localization data to obtain regulatory relationships with
known transcription factors, and show that many of the
inferred relationships have support in the literature.
PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny
- PLoS Comput Biol 2005
"... 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 anal ..."
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Cited by 118 (5 self)
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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 Markovchain 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 motiffinding
Modeling Dependencies in Protein-DNA Binding Sites
, 2003
"... The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation ..."
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Cited by 117 (2 self)
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The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation of transcription factor binding sites is aposition specific score matrix (PSSM). This representation makes the strong assumption that binding site positions are independent of each other. In this work, we explore Bayesian network representations of binding sites that provide different tradeoffs between complexity (number of parameters) and the richness of dependencies between positions. We develop the formal machinery for learning such models from data and for estimating the statistical significance of putative binding sites. We then evaluate the ramifications of these richer representations in characterizing binding site motifs and predicting their genomic locations. We show that these richer representations improve over the PSSM model in both tasks.
