VICTOR: A visual analytics web application for comparing cluster sets

Karatzas, Evangelos; Gkonta, Maria; Hotova, Joana; Baltoumas, Fotis A.; Kontou, Panagiota I.; Bobotsis, Christopher J.; Bagos, Pantelis G.; Pavlopoulos, Georgios A.

doi:10.1016/j.compbiomed.2021.104557

Cited by 6 publications

(4 citation statements)

References 78 publications

(93 reference statements)

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“…Once an SSN has been created, one can apply a graph-based clustering algorithm to group proteins into families. Despite the great variety of graph-based clustering algorithms available today ( Xu and Wunsch, 2005 ; Brohée and van Helden, 2006 ; Moschopoulos et al, 2011 ; Koutrouli et al, 2020b ; Karatzas et al, 2021b ), only a few can cope with networks of millions of nodes and edges. For example, SPICi ( Jiang and Singh, 2010 ) is a fast, local clustering algorithm that detects densely connected communities within a network.…”

Section: Sequence Clustering Strategiesmentioning

confidence: 99%

“…with an abundance of visualization options (e.g., bar charts, heat maps, Sankey plots, interaction networks) and statistical metrics (mutual information, adjusted rand index, etc. ) ( Karatzas et al, 2021b ). Krona is a frequently used, web-based interactive metagenome visualization platform.…”

Section: Visualization Of Metagenomic Data At a Raw And Family Levelmentioning

confidence: 99%

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Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters

Baltoumas

Karatzas²,

Páez-Espino³

et al. 2023

Front. Bioinform.

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Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

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Section: Sequence Clustering Strategiesmentioning

confidence: 99%

Section: Visualization Of Metagenomic Data At a Raw And Family Levelmentioning

confidence: 99%

Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters

Baltoumas

Karatzas²,

Páez-Espino³

et al. 2023

Front. Bioinform.

Self Cite

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“…In addition to the aforementioned visualizers, several complementary tools have been implemented for network topological analysis and cluster set comparisons. Typical examples are the Network Analyzer [291], ZoomOut [292], Network Analysis Toolkit (NEAT) [293], NAP [294,295], VICTOR [296] and the Stanford Network Analysis Project (SNAP) [297]. Back-end libraries for network storage and analysis which are worth mentioning are igraph [298], NetworkX [299], GraphViz and Graph-tool [300].…”

Section: Data Visualizationmentioning

confidence: 99%

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

et al. 2021

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Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

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“…This website provides visualizations of various clustering algorithms to aid in cluster selection. 59 …”

Section: Clusteringmentioning

confidence: 99%

Connecting the dots: The boons and banes of network modeling

Climer

2021

Patterns

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Summary Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.

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VICTOR: A visual analytics web application for comparing cluster sets

Cited by 6 publications

References 78 publications

Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters

Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Connecting the dots: The boons and banes of network modeling

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