jClust: a clustering and visualization toolbox

jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron–Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside–inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Corrigendum to “Review of trials currently testing treatment and prevention of COVID-19” [Clin Microbiol Infect 26.8 (2020) 988–998] (Clinical Microbiology and Infection (2020) 26(8) (988–998), (S1198743X20302962), (10.1016/j.cmi.2020.05.019))

The authors regret that at the “Transparency declaration” (p. 997) of the published paper, the conflicts of interest were unintentionally incomplete.The following is the correct “Transparency declaration”: For Y.Y.: Co-Chair of the Global Research Collaboration for Infectious Disease Preparedness (GloPID-R) and the coordinator of REsearch and ACTion targeting emerging infectious diseases (REACTing); Presentations at workshops and consultancy honoraria from Abbvie, Gilead, Merck, J&amp;J, and ViiV Healthcare but not since 2017. For C.S.: Consultancy and research funding, Hycor Biomedical and Thermo Fisher Scientific; Consultancy, Bencard Allergie; Research Funding, Mead Johnson Nutrition. Supported by Universities Giessen and Marburg Lung Centre (UGMLC), the German Center for Lung Research, University Hospital Giessen and Marburg research funding according to article 2, section 3 cooperation agreement, and the Deutsche Forschungsgemeinschaft-funded-SFB 1021 (C04), -KFO 309 (P10), and SK 317/1-1 (Project number 428518790) as well as by the Foundation for Pathobiochemistry and Molecular Diagnostics. For FXL: Payment for development of educational presentations, Gilead Sciences, Inc. and MSD (Merck Sharp &amp; Dohme); Travel/accommodations/meeting expenses, Astellas Pharma Inc., Eumedica Pharmaceuticals, MSD (Merck Sharp &amp; Dohme). For PCF: Research funding, Doctorate scholarship by the State Scholarships Foundation (IKY), Partnership Agreement (PA) 2014-2020, co-financed by Greece and the European Union (European Social Fund - ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014-2020”. All grants are outside the submitted work. No external funding was received for this study. The authors would like to apologise for any inconvenience caused. © 2021 European Society of Clinical Microbiology and Infectious Disease

On the linear combination of normal and Laplace random variables

Laplace distribution, linear combination of random variables, normal distribution,