Publications by authors named "Dimitri Guala"

9 Publications

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FunCoup 5: Functional Association Networks in All Domains of Life, Supporting Directed Links and Tissue-Specificity.

J Mol Biol 2021 05 2;433(11):166835. Epub 2021 Feb 2.

Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden. Electronic address:

FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.
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http://dx.doi.org/10.1016/j.jmb.2021.166835DOI Listing
May 2021

Genome-wide functional association networks: background, data & state-of-the-art resources.

Brief Bioinform 2020 07;21(4):1224-1237

Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.

The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
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http://dx.doi.org/10.1093/bib/bbz064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373183PMC
July 2020

RebiQoL: A randomized trial of telemedicine patient support program for health-related quality of life and adherence in people with MS treated with Rebif.

PLoS One 2019 5;14(7):e0218453. Epub 2019 Jul 5.

Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

RebiQoL was a phase IV multicenter randomized study to assess the impact of a telemedicine patient support program (MSP) on health-related quality of life (HRQoL) in patients with relapsing-remitting MS (RRMS) being administered with Rebif with the RebiSmart device. The primary endpoint was to assess the impact of MSP compared to patients only receiving technical support for RebiSmart on HRQoL at 12 months, using the psychological part of Multiple Sclerosis Impact Scale (MSIS-29), in patients administered with Rebif. A total of 97 patients diagnosed with RRMS were screened for participation in the study of which 3 patients did not fulfill the eligibility criteria and 1 patient withdrew consent. Of the 93 randomized patients, 46 were randomized to MSP and 47 to Technical support only. The demographic characteristics of the patients were well-balanced in the two arms. There were no statistical differences (linear mixed model) in any of the primary (difference of 0.48, 95% CI: -8.30-9.25, p = 0.91) or secondary outcomes (p>0.05). Although the study was slightly underpowered, there was a trend towards better adherence in the MSP group (OR 3.5, 95% CI 0.85-14.40, p = 0.08) although not statistically significant. No unexpected adverse events occurred. This study did not show a statistically significant effect of the particular form of teleintervention used in this study on HRQoL as compared to pure technical support, for MS patients already receiving Rebif with the RebiSmart device. Trial Registration: ClinicalTrials.gov: NCT01791244.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218453PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611587PMC
February 2020

Experimental validation of predicted cancer genes using FRET.

Methods Appl Fluoresc 2018 Apr 25;6(3):035007. Epub 2018 Apr 25.

Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.

Huge amounts of data are generated in genome wide experiments, designed to investigate diseases with complex genetic causes. Follow up of all potential leads produced by such experiments is currently cost prohibitive and time consuming. Gene prioritization tools alleviate these constraints by directing further experimental efforts towards the most promising candidate targets. Recently a gene prioritization tool called MaxLink was shown to outperform other widely used state-of-the-art prioritization tools in a large scale in silico benchmark. An experimental validation of predictions made by MaxLink has however been lacking. In this study we used Fluorescence Resonance Energy Transfer, an established experimental technique for detection of protein-protein interactions, to validate potential cancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink for selection of new targets in the battle with polygenic diseases.
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http://dx.doi.org/10.1088/2050-6120/aab932DOI Listing
April 2018

FunCoup 4: new species, data, and visualization.

Nucleic Acids Res 2018 01;46(D1):D601-D607

Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.

This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.
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http://dx.doi.org/10.1093/nar/gkx1138DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755233PMC
January 2018

A large-scale benchmark of gene prioritization methods.

Sci Rep 2017 04 21;7:46598. Epub 2017 Apr 21.

Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.

In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.
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http://dx.doi.org/10.1038/srep46598DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5399445PMC
April 2017

A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation.

Nucleic Acids Res 2017 01 22;45(2):e8. Epub 2016 Sep 22.

Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.

Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods. BinoX is available at http://sonnhammer.org/BinoX.
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http://dx.doi.org/10.1093/nar/gkw849DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314790PMC
January 2017

MaxLink: network-based prioritization of genes tightly linked to a disease seed set.

Bioinformatics 2014 Sep 20;30(18):2689-90. Epub 2014 May 20.

Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-17121 Solna, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, SE-11321, Sweden and Swedish eScience Research Center, SE-10450 Stockholm, Sweden Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-17121 Solna, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, SE-11321, Sweden and Swedish eScience Research Center, SE-10450 Stockholm, Sweden Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-17121 Solna, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, SE-11321, Sweden and Swedish eScience Research Center, SE-10450 Stockholm, Sweden.

Unlabelled: MaxLink, a guilt-by-association network search algorithm, has been made available as a web resource and a stand-alone version. Based on a user-supplied list of query genes, MaxLink identifies and ranks genes that are tightly linked to the query list. This functionality can be used to predict potential disease genes from an initial set of genes with known association to a disease. The original algorithm, used to identify and rank novel genes potentially involved in cancer, has been updated to use a more statistically sound method for selection of candidate genes and made applicable to other areas than cancer. The algorithm has also been made faster by re-implementation in C++, and the Web site uses FunCoup 3.0 as the underlying network.

Availability And Implementation: MaxLink is freely available at http://maxlink.sbc.su.se both as a web service and a stand-alone application for download.
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http://dx.doi.org/10.1093/bioinformatics/btu344DOI Listing
September 2014

Canonical insertion-deletion markers for rapid DNA typing of Francisella tularensis.

Emerg Infect Dis 2007 Nov;13(11):1725-32

Swedish Defence Research Agency, Umeå, Sweden.

To develop effective and accurate typing of strains of Francisella tularensis, a potent human pathogen and a putative bioterrorist agent, we combined analysis of insertion-deletion (indel) markers with multiple-locus variable-number tandem repeat analysis (MLVA). From 5 representative F. tularensis genome sequences, 38 indel markers with canonical properties, i.e., capable of sorting strains into major genetic groups, were selected. To avoid markers with a propensity for homoplasy, we used only those indels with 2 allelic variants and devoid of substantial sequence repeats. MLVA included sequences with much diversity in copy number of tandem repeats. The combined procedure allowed subspecies division, delineation of clades A.I and A.II of subspecies tularensis, differentiation of Japanese strains from other strains of subspecies holarctica, and high-resolution strain typing. The procedure uses limited amounts of killed bacterial preparations and, because only 1 single analytic method is needed, is time- and cost-effective.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874433PMC
http://dx.doi.org/10.3201/eid1311.070603DOI Listing
November 2007