MotifCut: regulatory motifs finding with maximum density subgraphs

Fratkin, Eugene; Naughton, Brian; Brutlag, Douglas L.; Batzoglou, Serafim

doi:10.1093/bioinformatics/btl243

Cited by 147 publications

(83 citation statements)

References 34 publications

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“…RepeatNet finds such dense subgraphs of the repeat graph with a heuristic that selects the vertex with the highest degree, and other vertices that share an edge with this selected vertex. Alternatively, a maximum density subgraph algorithm can be used (Fratkin et al 2006) Fig. S1).…”

Section: Resultsmentioning

confidence: 99%

Genome-wide characterization of centromeric satellites from multiple mammalian genomes

Alkan¹,

Cardone²,

Catacchio³

et al. 2010

Genome Res.

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Despite its importance in cell biology and evolution, the centromere has remained the final frontier in genome assembly and annotation due to its complex repeat structure. However, isolation and characterization of the centromeric repeats from newly sequenced species are necessary for a complete understanding of genome evolution and function. In recent years, various genomes have been sequenced, but the characterization of the corresponding centromeric DNA has lagged behind. Here, we present a computational method (RepeatNet) to systematically identify higher-order repeat structures from unassembled whole-genome shotgun sequence and test whether these sequence elements correspond to functional centromeric sequences. We analyzed genome datasets from six species of mammals representing the diversity of the mammalian lineage, namely, horse, dog, elephant, armadillo, opossum, and platypus. We define candidate monomer satellite repeats and demonstrate centromeric localization for five of the six genomes. Our analysis revealed the greatest diversity of centromeric sequences in horse and dog in contrast to elephant and armadillo, which showed high-centromeric sequence homogeneity. We could not isolate centromeric sequences within the platypus genome, suggesting that centromeres in platypus are not enriched in satellite DNA. Our method can be applied to the characterization of thousands of other vertebrate genomes anticipated for sequencing in the near future, providing an important tool for annotation of centromeres.[Supplemental material is available online at http://www.genome.org. The RepeatNet algorithm is freely available at

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Section: Resultsmentioning

confidence: 99%

Genome-wide characterization of centromeric satellites from multiple mammalian genomes

Alkan¹,

Cardone²,

Catacchio³

et al. 2010

Genome Res.

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“…In bioinformatics dense subgraphs are used for detecting protein complexes in protein-protein interaction networks [9] and for finding regulatory motifs in DNA [24]. They are also used for detecting link spam in Web graphs [26], graph compression [17] and mining micro-blogging streams [6].…”

Section: Introductionmentioning

confidence: 99%

The K-clique Densest Subgraph Problem

Tsourakakis

2015

Proceedings of the 24th International Conference on World Wide Web

216

160

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Numerous graph mining applications rely on detecting subgraphs which are large near-cliques. Since formulations that are geared towards finding large near-cliques are NP-hard and frequently inapproximable due to connections with the Maximum Clique problem, the poly-time solvable densest subgraph problem which maximizes the average degree over all possible subgraphs "lies at the core of large scale data mining" [10]. However, frequently the densest subgraph problem fails in detecting large near-cliques in networks.In this work, we introduce the k-clique densest subgraph problem, k ≥ 2. This generalizes the well studied densest subgraph problem which is obtained as a special case for k = 2. For k = 3 we obtain a novel formulation which we refer to as the triangle densest subgraph problem: given a graph G(V, E), find a subset of vertices S * such that, where t(S) is the number of triangles induced by the set S.On the theory side, we prove that for any k constant, there exist an exact polynomial time algorithm for the kclique densest subgraph problem. Furthermore, we propose an efficient 1 k -approximation algorithm which generalizes the greedy peeling algorithm of Asahiro and Charikar [8,18] for k = 2. Finally, we show how to implement efficiently this peeling framework on MapReduce for any k ≥ 3, generalizing the work of Bahmani, Kumar and Vassilvitskii for the case k = 2 [10]. On the empirical side, our two main findings are that (i) the triangle densest subgraph is consistently closer to being a large near-clique compared to the densest subgraph and (ii) the peeling approximation algorithms for both k = 2 and k = 3 achieve on real-world networks approximation ratios closer to 1 rather than the pessimistic 1 kguarantee. An interesting consequence of our work is that triangle counting, a well-studied computational problem in the context of social network analysis can be used to detect large near-cliques. Finally, we evaluate our proposed method on a popular graph mining application.

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“…Doing so, we weight the edges by the fraction of the Gibbs sampling predictions in which two nucleotides co-occur ( Figure 1C). Once a BSG is constructed, it remains to identify the subgraph of densely connected nucleotides corresponding to TF binding sites [26]. Various graph properties and clustering techniques may be useful to identify such clusters.…”

Section: Resultsmentioning

confidence: 99%

Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites

2005

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Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP-chip experiments. Results from these experiments are publicly available at http://cagt10.bu. edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of transcription factor binding specificities.

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MotifCut: regulatory motifs finding with maximum density subgraphs

Abstract: MotifCut server and other materials can be found at motifcut.stanford.edu.

Cited by 147 publications

References 34 publications

Genome-wide characterization of centromeric satellites from multiple mammalian genomes

Genome-wide characterization of centromeric satellites from multiple mammalian genomes

The K-clique Densest Subgraph Problem

Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites

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