Search

In this work, a modular coupling approach for particle methods with the FEM (finite element method) is presented. The proposed coupled strategy takes advantage from the ability of particle methods of dealing with large displacements and deformations, especially when solving complex fluid–structure and solid–structure interaction problems. The coupling between the FEM and particle methods is done using a co-simulation approach implemented in the open-source Kratos Multiphysics framework. The particle methods considered in this work are the DEM (discrete element method) and the PFEM (particle finite element method). The Lagrangian description of the PFEM is well suited for modeling fluids undergoing large deformations and free-surface motions, and the DEM can be used to simulate rocks, debris and other solid objects. To accelerate the convergence of the coupled strategy, a block Gauss–Seidel algorithm with Aitken relaxation is used. Several numerical examples, with an emphasis on natural hazards, are presented to test and validate the proposed coupled method., Peer Reviewed, Postprint (published version)

Summary Transcription factors regulate gene expression by binding to specific short DNA sequences of 5-20 bp to regulate the rate of transcription of genetic information from DNA to messenger RNA. We present PWMScan, a fast web-based tool to scan server-resident genomes for matches to a user-supplied PWM or transcription factor binding site model from a public database. Availability and implementation The web server and source code are available at http://ccg.vital-it.ch/pwmscan and https://sourceforge.net/projects/pwmscan, respectively. Supplementary information Supplementary data are available at Bioinformatics online.

This article presents a staggered approach to couple the interaction of very flexible tension structures with large deformations, described with the finite element method (FEM), and objects undergoing large, complex, and arbitrary motions discretized with particle methods, in this case the discrete element method (DEM). The quantitative solution quality and convergence rate of this partitioned approach is highly time step dependent. Thus, the strong coupling approach is presented here, where the convergence is achieved in an iterative manner within each time step. This approach helps increase the time step size significantly, decreases the overall computational costs, and improves the numerical stability. Moreover, the proposed algorithm enables the application of two independent, standalone codes for simulating DEM and structural FEM as blackbox solvers. Systematic evaluations of the newly proposed iterative coupling scheme with respect to accuracy, robustness, and efficiency as well as cross comparisons between strong and weak FEM-DEM coupling approaches are performed. Additionally, the approach is validated against the rest position of an impacting object, and further examples with objects impacting highly flexible protection structures are presented. Here, the protection nets are described with nonlinear structural finite elements and the impacting objects as DEM elements. To allow the interested reader to independently reproduce the results, detailed code and algorithm descriptions are included in the appendix., Peer Reviewed, Postprint (published version)

Within the past two decades, mass movements hazards involving fast and large soil deformation have increased significantly in frequency and magnitude due to their strong relation to climate changes and global warming. These phenomena often bring along rocks, debris, and heavy materials that can extensively damage and destroy the landscape and infrastructures, causing devastating economic loss, and often, human casualties. The risk of future disasters continues to escalate with the increase of real estate development in suburban areas, including mountainous regions. Further assessment and prediction on such disasters and their countermeasures are, therefore, in high economic demands. One of the most intuitive ways is to install protective structures in mountain slopes and valleys that can hold the materials brought by the moving landslides. While the current state of the art of landslide prediction using numerical methods has been mainly dominated by the development of advanced geomechanical models suited for different types of soil materials, e.g. multi-phase unsaturated soil model, this study focuses more on the interaction of such phenomena with the installed protective structures. Here, an implicit formulation of material point method (MPM) is implemented to model the landslides considering finite strain assumption. Furthermore, a staggered coupling scheme with traditional Finite Element Method (FEM) is proposed to simulate accurately and robustly the dynamic force and displacement coupling of soil-structure interaction (SSI). All developments of the method are implemented within the Kratos-Multiphysics framework [1] and available under the BSD license (https://github.com/KratosMultiphysics/Kratos/wiki). In the future works, more adequate consideration of coupling scheme and material models considering damage and fracture will be investigated before conducting a real-scale landslide simulation.

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window

We present an update of EPDNew (http://epd.vital-it.ch), a recently introduced new part of the Eukaryotic Promoter Database (EPD) which has been described in more detail in a previous NAR Database Issue. EPD is an old database of experimentally characterized eukaryotic POL II promoters, which are conceptually defined as transcription initiation sites or regions. EPDnew is a collection of automatically compiled, organism-specific promoter lists complementing the old corpus of manually compiled promoter entries of EPD. This new part is exclusively derived from next generation sequencing data from high-throughput promoter mapping experiments. We report on the recent growth of EPDnew, its extension to additional model organisms and its improved integration with other bioinformatics resources developed by our group, in particular the Signal Search Analysis and ChIP-Seq web servers

The PROSITE database consists of biologically significant patterns and profiles formulated in such a way that with appropriate computational tools it can help to determine to which known family of protein (if any) a new sequence belongs, or which known domain(s) it contains

Signal search analysis is a general method to discover and characterize sequence motifs that are positionally correlated with a functional site (e.g. a transcription or translation start site). The method has played an instrumental role in the analysis of eukaryotic promoter elements. The signal search analysis server provides access to four different computer programs as well as to a large number of precompiled functional site collections. The programs offered allow: (i) the identification of non-random sequence regions under evolutionary constraint; (ii) the detection of consensus sequence-based motifs that are over- or under-represented at a particular distance from a functional site; (iii) the analysis of the positional distribution of a consensus sequence- or weight matrix-based sequence motif around a functional site; and (iv) the optimization of a weight matrix description of a locally over-represented sequence motif. These programs can be accessed at: http://www.isrec.isb-sib.ch/ssa

Motivation: Genomic context analysis, also known as phylogenetic profiling, is widely used to infer functional interactions between proteins but rarely applied to non-coding cis-regulatory DNA elements. We were wondering whether this approach could provide insights about utlraconserved non-coding elements (UCNEs). These elements are organized as large clusters, so-called gene regulatory blocks (GRBs) around key developmental genes. Their molecular functions and the reasons for their high degree of conservation remain enigmatic. Results: In a special setting of genomic context analysis, we analyzed the fate of GRBs after a whole-genome duplication event in five fish genomes. We found that in most cases all UCNEs were retained together as a single block, whereas the corresponding target genes were often retained in two copies, one completely devoid of UCNEs. This ‘winner-takes-all' pattern suggests that UCNEs of a GRB function in a highly cooperative manner. We propose that the multitude of interactions between UCNEs is the reason for their extreme sequence conservation. Supplementary information: Supplementary data are available at Bioinformatics online and at http://ccg.vital-it.ch/ucne

A comparative study of the compositional properties of various protein sets from both cellular and viral organisms is presented. Invariants and contrasts of amino acid usages have been discerned for different protein function classes and for different species using robust statistical methods based on quantile distributions and stochastic ordering relationships. In addition, a quantitative criterion to assess amino acid compositional extremes relative to a reference protein set is proposed and applied. Invariants of amino acid usage relate mainly to the central range of quantile distributions, whereas contrasts occur mainly in the tails of the distributions, especially contrasts between eukaryote and prokaryote species. Influences from genomic constraint are evident, for example, in the arginine:lysine ratios and the usage frequencies of residues encoded by G + C-rich versus A + T-rich codon types. The structurally similar amino acids, glutamate versus aspartate and phenylalanine versus tyrosine, show stochastic dominance relationships for most species protein sets favoring glutamate and phenylalanine respectively. The quantile distribution of hydrophobic amino acid usages in prokaryote data dominates the corresponding quantile distribution in human data. In contrast, glutamate, cysteine, proline and serine usages in human proteins dominate the corresponding quantile distributions in Escherichia coli. E.coli dominates human in the use of basic residues, but no dominance ordering applies to acidic residues. The discussion centers on commonalities and anomalies of the amino acid compositional spectrum in relation to species, function, cellular localization, biochemical and steric attributes, complexity of the amino acid biosynthetic pathway, amino acid relative abundances and founder effects

PROSITE [Bairoch and Bucher (1994) Nucleic Acids Res., 22, 3583-3589; Hofmann et al. (1999) Nucleic Acids Res., 27, 215-219] is a method of identifying the functions of uncharacterized proteins translated from genomic or cDNA sequences. The PROSITE database (http://www.expasy.org/prosite/) consists of biologically significant patterns and profiles designed in such a way that with appropriate computational tools it can rapidly and reliably help to determine to which known family of proteins (if any) a new sequence belongs, or which known domain(s) it contains

The Eukaryotic Promoter Database (EPD) is an annotated non-redundant collection of eukaryotic POL II promoters, for which the transcription start site has been determined experimentally. Access to promoter sequences is provided by pointers to positions in nucleotide sequence entries. The annotation part of an entry includes description of the initiation site mapping data, cross-references to other databases, and bibliographic references. EPD is structured in a way that facilitates dynamic extraction of biologically meaningful promoter subsets for comparative sequence analysis. Recent efforts have focused on exhaustive crossreferencing to the EMBL nucleotide sequence database, and on the improvement of the WWW-based user interfaces and data retrieval mechanisms. EPD can be accessed at http://www.epd.isb-sib.ch

Summary: Centrifuge is a user-friendly system to simultaneously access Arabidopsis gene annotations and intra- and inter-organism sequence comparison data. The tool allows rapid retrieval of user-selected data for each annotated Arabidopsis gene providing, in any combination, data on the following features: predicted protein properties such as mass, pI, cellular location and transmembrane domains; SWISS-PROT annotations; Interpro domains; Gene Ontology records; verified transcription; BLAST matches to the proteomes of A.thaliana, Oryza sativa (rice), Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. The tool lends itself particularly well to the rapid analysis of contigs or of tens or hundreds of genes identified by high-throughput gene expression experiments. In these cases, a summary table of principal predicted protein features for all genes is given followed by more detailed reports for each individual gene. Centrifuge can also be used for single gene analysis or in a word search mode. Availability: http://centrifuge.unil.ch/ Contact: edward.farmer@unil.ch

The CleanEx expression database (http://www.cleanex.isb-sib.ch) provides access to public gene expression data via unique gene names as well as via experiments biomedical characteristics. To reach this, a dual annotation of both sequences and experiments has been generated. First, the system links official gene symbols to any kind of sequences used for gene expression measurements (cDNA, Affymetrix, oligonucleotide arrays, SAGE or MPSS tags, Expressed Sequence Tags or other mRNA sequences, etc.). For the biomedical annotation, we re-annotate each experiment from the CleanEx database with the MeSH (Medical Subject Headings) terms, primarily used by NLM (National Library of Medicine) for indexing articles for the MEDLINE/PubMED database. This annotation allows a fast and easy retrieval of expression data with common biological or medical features. The numerical data can then be exported as matrix-like tab-delimited text files. Data can be extracted from either one dataset or from heterogeneous datasets

The function of DNA-binding proteins is controlled not just by their abundance, but mainly at the level of their activity in terms of their interactions with DNA and protein targets. Moreover, the affinity of such transcription factors to their target sequences is often controlled by co-factors and/or modifications that are not easily assessed from biological samples. Here, we describe a scalable method for monitoring protein-DNA interactions on a microarray surface. This approach was designed to determine the DNA-binding activity of proteins in crude cell extracts, complementing conventional expression profiling arrays. Enzymatic labeling of DNA enables direct normalization of the protein binding to the microarray, allowing the estimation of relative binding affinities. Using DNA sequences covering a range of affinities, we show that the new microarray-based method yields binding strength estimates similar to low-throughput gel mobility-shift assays. The microarray is also of high sensitivity, as it allows the detection of a rare DNA-binding protein from breast cancer cells, the human tumor suppressor AP-2. This approach thus mediates precise and robust assessment of the activity of DNA-binding proteins and takes present DNA-binding assays to a high throughput level

Motivation: We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms. Results: We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant improvements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters. Availability and implementation: The software and code are available in the supplementary material. Contact: Philipp.Bucher@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years

The PROSITE database (http://www.expasy.ch/sprot/prosite.html) consists of biologically significant patterns and profiles formulated in such a way that with appropriate computational tools it can help to determine to which known family of protein (if any) a new sequence belongs, or which known domain(s) it contains

The Eukaryotic Promoter Database (EPD) is an annotated non-redundant collection of experimentally characterised eukaryotic POL II promoters. The underlying definition of a promoter is that of a transcription initiation site. All information presented in EPD results from an independent evaluation of primary experimental data shown in the biological literature. Sequences flanking transcription initiation sites are indirectly given by pointers to EMBL sequences. The annotation part of a promoter entry includes description of the promoter-defining evidence, cross-references to other databases, and bibliographic references. Being designed as a resource for comparative sequence analysis, EPD is structured in a way that facilitates dynamic extraction of biologically meaningful promoter subsets. The database is available through the World Wide Web at URL http://cmpteam4.unil.ch