Vincent Miele

DNA physical properties determine nucleosome occupancy from yeast to fly

Nucleosome positioning plays an essential role in cellular processes by modulating accessibility of DNA to proteins. Here, using only sequence-dependent DNA flexibility and intrinsic curvature, we predict the nucleosome occupancy along the genomes of Saccharomyces cerevisiae and Drosophila melanogaster and demonstrate the predictive power and universality of our model through its correlation with experimentally determined nucleosome occupancy data. In yeast promoter regions, the computed average nucleosome occupancy closely superimposes with experimental data, exhibiting a <200 bp region unfavourable for nucleosome formation bordered by regions that facilitate nucleosome formation. In the fly, our model faithfully predicts promoter strength as encoded in distinct chromatin architectures characteristic of strongly and weakly expressed genes. We also predict that nucleosomes are repositioned by active mechanisms at the majority of fly promoters. Our model uses only basic physical properties to describe the wrapping of DNA around the histone core, yet it captures a substantial part of chromatins structural complexity, thus leading to a much better prediction of nucleosome occupancy than methods based merely on periodic curved DNA motifs. Our results indicate that the physical properties of the DNA chain, and not just the regulatory factors and chromatin-modifying enzymes, play key roles in eukaryotic transcription.

Ultra-fast sequence clustering from similarity networks with SiLiX

BackgroundThe number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time.ResultsWe present the software package SiLiX that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity.ConclusionsComparing state-of-the-art software, SiLiX presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. SiLiX is freely available at http://lbbe.univ-lyon1.fr/SiLiX.

Toward community standards in the quest for orthologs

The identification of orthologs—genes pairs descended from a common ancestor through speciation, rather than duplication—has emerged as an essential component of many bioinformatics applications, ranging from the annotation of new genomes to experimental target prioritization. Yet, the development and application of orthology inference methods is hampered by the lack of consensus on source proteomes, file formats and benchmarks. The second ‘Quest for Orthologs’ meeting brought together stakeholders from various communities to address these challenges. We report on achievements and outcomes of this meeting, focusing on topics of particular relevance to the research community at large. The Quest for Orthologs consortium is an open community that welcomes contributions from all researchers interested in orthology research and applications. Contact: dessimoz@ebi.ac.uk

Fast online graph clustering via Erdős-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erdos-Renyi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.

Deciphering the connectivity structure of biological networks using MixNet

BackgroundAs biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the networks topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.ResultsWe present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.ConclusionWe show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience.

Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities’ response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., “multiplex networks”), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full “entangled bank” of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.

High-quality sequence clustering guided by network topology and multiple alignment likelihood

MOTIVATION Proteins can be naturally classified into families of homologous sequences that derive from a common ancestor. The comparison of homologous sequences and the analysis of their phylogenetic relationships provide useful information regarding the function and evolution of genes. One important difficulty of clustering methods is to distinguish highly divergent homologous sequences from sequences that only share partial homology due to evolution by protein domain rearrangements. Existing clustering methods require parameters that have to be set a priori. Given the variability in the evolution pattern among proteins, these parameters cannot be optimal for all gene families. RESULTS We propose a strategy that aims at clustering sequences homologous over their entire length, and that takes into account the pattern of substitution specific to each gene family. Sequences are first all compared with each other and clustered into pre-families, based on pairwise similarity criteria, with permissive parameters to optimize sensitivity. Pre-families are then divided into homogeneous clusters, based on the topology of the similarity network. Finally, clusters are progressively merged into families, for which we compute multiple alignments, and we use a model selection technique to find the optimal tradeoff between the number of families and multiple alignment likelihood. To evaluate this method, called HiFiX, we analyzed simulated sequences and manually curated datasets. These tests showed that HiFiX is the only method robust to both sequence divergence and domain rearrangements. HiFiX is fast enough to be used on very large datasets. AVAILABILITY AND IMPLEMENTATION The Python software HiFiX is freely available at http://lbbe.univ-lyon1.fr/hifix.

Strategies for online inference of model-based clustering in large and growing networks

In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.

Fruiting Strategies of Perennial Plants: A Resource Budget Model to Couple Mast Seeding to Pollination Efficiency and Resource Allocation Strategies

Masting, a breeding strategy common in perennial plants, is defined by seed production that is highly variable over years and synchronized at the population level. Resource budget models (RBMs) proposed that masting relies on two processes: (i) the depletion of plant reserves following high fruiting levels, which leads to marked temporal fluctuations in fruiting; and (ii) outcross pollination that synchronizes seed crops among neighboring trees. We revisited the RBM approach to examine the extent to which masting could be impacted by the degree of pollination efficiency, by taking into account various logistic relationships between pollination success and pollen availability. To link masting to other reproductive traits, we split the reserve depletion coefficient into three biological parameters related to resource allocation strategies for flowering and fruiting. While outcross pollination is considered to be the key mechanism that synchronizes fruiting in RBMs, our model counterintuitively showed that intense masting should arise under low-efficiency pollination. When pollination is very efficient, medium-level masting may occur, provided that the costs of female flowering (relative to pollen production) and of fruiting (maximum fruit set and fruit size) are both very high. Our work highlights the powerful framework of RBMs, which include explicit biological parameters, to link fruiting dynamics to various reproductive traits and to provide new insights into the reproductive strategies of perennial plants.

DNA Physical Properties and Nucleosome Positions Are Major Determinants of HIV-1 Integrase Selectivity

Retroviral integrases (INs) catalyse the integration of the reverse transcribed viral DNA into the host cell genome. This process is selective, and chromatin has been proposed to be a major factor regulating this step in the viral life cycle. However, the precise underlying mechanisms are still under investigation. We have developed a new in vitro integration assay using physiologically-relevant, reconstituted genomic acceptor chromatin and high-throughput determination of nucleosome positions and integration sites, in parallel. A quantitative analysis of the resulting data reveals a chromatin-dependent redistribution of the integration sites and establishes a link between integration sites and nucleosome positions. The co-activator LEDGF/p75 enhanced integration but did not modify the integration sites under these conditions. We also conducted an in cellulo genome-wide comparative study of nucleosome positions and human immunodeficiency virus type-1 (HIV-1) integration sites identified experimentally in vivo. These studies confirm a preferential integration in nucleosome-covered regions. Using a DNA mechanical energy model, we show that the physical properties of DNA probed by IN binding are important in determining IN selectivity. These novel in vitro and in vivo approaches confirm that IN has a preference for integration into a nucleosome, and suggest the existence of two levels of IN selectivity. The first depends on the physical properties of the target DNA and notably, the energy required to fit DNA into the IN catalytic pocket. The second depends on the DNA deformation associated with DNA wrapping around a nucleosome. Taken together, these results indicate that HIV-1 IN is a shape-readout DNA binding protein.