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Neumann B.,MitoCheck Project Group | Walter T.,MitoCheck Project Group | Heriche J.-K.,Wellcome Trust Sanger Institute | Heriche J.-K.,MitoCheck Project Group | And 30 more authors.
Nature | Year: 2010

Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the ∼21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community. © 2010 Macmillan Publishers Limited. All rights reserved.


Powell S.,Computational Biology Unit | Forslund K.,Computational Biology Unit | Szklarczyk D.,Swiss Institute of Bioinformatics | Trachana K.,Institute for Systems Biology | And 12 more authors.
Nucleic Acids Research | Year: 2014

With the increasing availability of various 'omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download. © 2013 The Author(s). Published by Oxford University Press.


Barsnes H.,University of Bergen | Barsnes H.,Computational Biology Unit | Eidhammer I.,University of Bergen | Martens L.,VIB | Martens L.,Ghent University
Proteomics | Year: 2011

Understanding the fragmentation process in MS/MS experiments is vital when trying to validate the results of such experiments, and one way of improving our understanding is to analyze existing data. We here present our findings from an analysis of a large and diverse data set of MS/MS-based peptide identifications, in which each peptide has been identified from multiple spectra, recorded on two commonly used types of electrospray instruments. By analyzing these data we were able to study fragmentation variability on three levels: (i) variation in detection rates and intensities for fragment ions from the same peptide sequence measured multiple times on a single instrument; (ii) consistency of rank-based fragmentation patterns; and (iii) a set of general observations on fragment ion occurrence in MS/MS experiments, regardless of sequence. Our results confirm that substantial variation can be found at all levels, even when high-quality identifications are used and the experimental conditions as well as the peptide sequences are kept constant. Finally, we discuss the observed variability in light of ongoing efforts to create spectral libraries and predictive software for target selection in targeted proteomics. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Barsnes H.,University of Bergen | Barsnes H.,Computational Biology Unit | Vaudel M.,Leibniz Institute for Analytical Sciences | Colaert N.,VIB | And 7 more authors.
BMC Bioinformatics | Year: 2011

Background: The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.Results: In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development.Conclusions: As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics. © 2011 Barsnes et al; licensee BioMed Central Ltd.


PubMed | University of Trento, CNR Institute of Neuroscience, University of Rome La Sapienza, Computational Biology Unit and 2 more.
Type: Journal Article | Journal: Nature microbiology | Year: 2016

Microbial epidemiology and population genomics have previously been carried out near-exclusively for organisms grown in vitro. Metagenomics helps to overcome this limitation, but it is still challenging to achieve strain-level characterization of microorganisms from culture-independent data with sufficient resolution for epidemiological modelling. Here, we have developed multiple complementary approaches that can be combined to profile and track individual microbial strains. To specifically profile highly recombinant neisseriae from oral metagenomes, we integrated four metagenomic analysis techniques: single nucleotide polymorphisms in the clades core genome, DNA uptake sequence signatures, metagenomic multilocus sequence typing and strain-specific marker genes. We applied these tools to 520 oral metagenomes from the Human Microbiome Project, finding evidence of site tropism and temporal intra-subject strain retention. Although the opportunistic pathogen Neisseria meningitidis is enriched for colonization in the throat, N. flavescens and N. subflava populate the tongue dorsum, and N. sicca, N. mucosa and N. elongata the gingival plaque. The buccal mucosa appeared as an intermediate ecological niche between the plaque and the tongue. The resulting approaches to metagenomic strain profiling are generalizable and can be extended to other organisms and microbiomes across environments.


Fuglebakk E.,University of Bergen | Fuglebakk E.,Computational Biology Unit | Reuter N.,University of Bergen | Reuter N.,Computational Biology Unit | And 2 more authors.
Journal of Chemical Theory and Computation | Year: 2013

Elastic network models (ENMs) are valuable tools for investigating collective motions of proteins, and a rich variety of simple models have been proposed over the past decade. A good representation of the collective motions requires a good approximation of the covariances between the fluctuations of the individual atoms. Nevertheless, most studies have validated such models only by the magnitudes of the single-atom fluctuations they predict. In the present study, we have quantified the agreement between the covariance structure predicted by molecular dynamics (MD) simulations and those predicted by a representative selection of proposed coarse-grained ENMs. We then contrast this approach with the comparison to MD-predicted atomic fluctuations and comparison to crystallographic B-factors. While all the ENMs yield approximations to the MD-predicted covariance structure, we report large and consistent differences between proposed models. We also find that the ability of the ENMs to predict atomic fluctuations is correlated with their ability to capture the covariance structure. In contrast, we find that the models that agree best with B-factors model collective motions less reliably and recommend against using B-factors as a benchmark. © 2013 American Chemical Society.


Mitternacht S.,Computational Biology Unit | Mitternacht S.,University of Bergen | Berezovsky I.N.,Computational Biology Unit
Protein Engineering, Design and Selection | Year: 2011

An important aspect of understanding protein allostery, and of artificial effector design, is the characterization and prediction of substrate-and effector-binding sites. To find binding sites in allosteric enzymes, many of which are oligomeric with allosteric sites at domain interfaces, we devise a local centrality measure for residue interaction graphs, which behaves well for both small/monomeric and large/multimeric proteins. The measure is purely structure based and has a clear geometrical interpretation and no free parameters. It is not biased towards typically catalytic residues, a property that is crucial when looking for non-catalytic effector sites, which are potent drug targets. © The Author 2011. Published by Oxford University Press. All rights reserved.


Goncearenco A.,Computational Biology Unit | Goncearenco A.,University of Bergen | Berezovsky I.N.,Computational Biology Unit
Bioinformatics | Year: 2011

Motivation: Earlier studies of protein structure revealed closed loops with a characteristic size 25-30 residues and ring-like shape as a basic universal structural element of globular proteins. Elementary functional loops (EFLs) have specific signatures and provide functional residues important for binding/activation and principal chemical transformation steps of the enzymatic reaction. The goal of this work is to show how these functional loops evolved from pre-domain peptides and to find a set of prototypes from which the EFLs of contemporary proteins originated. Results: This article describes a computational method for deriving prototypes of EFLs based on the sequences of complete genomes. The procedure comprises the iterative derivation of sequence profiles followed by their hierarchical clustering. The scoring function takes into account information content on profile positions, thus preserving the signature. The statistical significance of scores is evaluated from the empirical distribution of scores of the background model. A set of prototypes of EFLs from archaeal proteomes is derived. This set delineates evolutionary connections between major functions and illuminates how folds and functions emerged in pre-domain evolution as a combination of prototypes. © The Author(s) 2010.


Broemstrup T.,University of Bergen | Broemstrup T.,Computational Biology Unit | Reuter N.,Computational Biology Unit | Reuter N.,University of Bergen
Physical Chemistry Chemical Physics | Year: 2010

Proteinase 3 (PR3) is a serine protease of the neutrophils whose membrane expression is relevant in a number of inflammatory pathologies. It has been shown to strongly interact with reconstituted bilayers containing dimyristoylphosphatidylcholine (DMPC), dimyristoylphosphatidylglycerol (DMPG) or mixtures of both phospholipids. Here we present the results of molecular dynamics simulations of PR3 anchored at three different phospholipid bilayers: DMPC, DMPG and an equimolar mixture of DMPC/DMPG. We present for the first time a detailed model of membrane-bound PR3. A thorough inventory of the interaction between the lipids and the enzyme reveals three types of interactions contributing to the anchorage of PR3. Basic residues (R177, R186A, R186B, K187 and R222) interact via hydrogen bonds with the lipid headgroups to stabilize PR3 at the interfacial membrane region. Hydrophobic amino acids (V163, F165, F166, I217, L223, and F224) insert into the hydrophobic core below the carbonyl groups of the bilayers and six aromatic amino acids (F165, F192, F215, W218, F224, and F227) contribute electrostatic interaction via cation-π interactions with the choline groups of DMPC. PR3 presents all the characteristics of a peripheral membrane protein with an ability to bind negative phospholipids. Although the catalytic triad remains unperturbed by the presence of the membrane, the ligand binding sites are located in close proximity to the membrane and amino acids K99 and I217 interact significantly with the lipids. We expect the binding of long ligands to be modified by the presence of the lipids. © 2010 the Owner Societies.


PubMed | University of Trento and Computational Biology Unit
Type: | Journal: Nucleic acids research | Year: 2016

Metagenomic characterization of microbial communities has the potential to become a tool to identify pathogens in human samples. However, software tools able to extract strain-level typing information from metagenomic data are needed. Low-throughput molecular typing schema such as Multilocus Sequence Typing (MLST) are still widely used and provide a wealth of strain-level information that is currently not exploited by metagenomic methods. We introduce MetaMLST, a software tool that reconstructs the MLST loci of microorganisms present in microbial communities from metagenomic data. Tested on synthetic and spiked-in real metagenomes, the pipeline was able to reconstruct the MLST sequences with >98.5% accuracy at coverages as low as 1. On real samples, the pipeline showed higher sensitivity than assembly-based approaches and it proved successful in identifying strains in epidemic outbreaks as well as in intestinal, skin and gastrointestinal microbiome samples.

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