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The thresholding problem is studied in the context of graph theoretical analysis of gene co-expression data. A number of thresholding methodologies are described, implemented, and tested over a large collection of graphs derived from real high-throughput biological data. Comparative results are presented and discussed.
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Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
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In this study, more than one hundred thousand Escherichia coli and Shigella genomes were examined and classified. This is, to our knowledge, the largest E. coli genome dataset analyzed to date. A Mash-based analysis of a cleaned set of 10,667 E. coli genomes from GenBank revealed 14 distinct phylogroups. A representative genome or medoid identified for each phylogroup was used as a proxy to classify 95,525 unassembled genomes from the Sequence Read Archive (SRA). We find that most of the sequenced E. coli genomes belong to four phylogroups (A, C, B1 and E2(O157)). Authenticity of the 14 phylogroups is supported by several different lines of evidence: phylogroup-specific core genes, a phylogenetic tree constructed with 2613 single copy core genes, and differences in the rates of gene gain/loss/duplication. The methodology used in this work is able to reproduce known phylogroups, as well as to identify previously uncharacterized phylogroups in E. coli species.
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Escherichia coli/clasificación , Escherichia coli/genética , Genoma Bacteriano , Biología Computacional/métodos , Proteínas de Escherichia coli/genética , Especiación Genética , Genómica/métodos , Filogenia , Análisis de Secuencia de ADN , Shigella/clasificación , Shigella/genéticaRESUMEN
Various patterns of multi-phenotype associations (MPAs) exist in the results of Genome Wide Association Studies (GWAS) involving different topologies of single nucleotide polymorphism (SNP)-phenotype associations. These can provide interesting information about the different impacts of a gene on closely related phenotypes or disparate phenotypes (pleiotropy). In this work we present MPA Decomposition, a new network-based approach which decomposes the results of a multi-phenotype GWAS study into three bipartite networks, which, when used together, unravel the multi-phenotype signatures of genes on a genome-wide scale. The decomposition involves the construction of a phenotype powerset space, and subsequent mapping of genes into this new space. Clustering of genes in this powerset space groups genes based on their detailed MPA signatures. We show that this method allows us to find multiple different MPA and pleiotropic signatures within individual genes and to classify and cluster genes based on these SNP-phenotype association topologies. We demonstrate the use of this approach on a GWAS analysis of a large population of 882 Populus trichocarpa genotypes using untargeted metabolomics phenotypes. This method should prove invaluable in the interpretation of large GWAS datasets and aid in future synthetic biology efforts designed to optimize phenotypes of interest.