ArticleNucleic acids research2014
POGO-DB--a database of pairwise-comparisons of genomes and conserved orthologous genes.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 24 citations in OpenAlex.
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- Bias at the third nucleotide of codon pairs in virus and host genomes.Scientific reports · 2022Article
- Multiplexed detection of bacterial nucleic acids using Cas13 in droplet microarrays.PNAS nexus · 2022Article
- Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.BMC bioinformatics · 2020Article
- 16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses.PLoS computational biology · 2019Article
- Horizontally Acquired Genes Are Often Shared between Closely Related Bacterial Species.Frontiers in microbiology · 2017Article
- Bacterial intra-species gene loss occurs in a largely clocklike manner mostly within a pool of less conserved and constrained genes.Scientific reports · 2016Article
- Article
- Databases for Microbiologists.Journal of bacteriology · 2015Review
- Gene Loss Dominates As a Source of Genetic Variation within Clonal Pathogenic Bacterial Species.Genome biology and evolution · 2015Article
- Prokaryotic nucleotide composition is shaped by both phylogeny and the environment.Genome biology and evolution · 2015Article
- A New Comparative-Genomics Approach for Defining Phenotype-Specific Indicators Reveals Specific Genetic Markers in Predatory Bacteria.PloS one · 2015Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
POGO-DB (http://pogo.ece.drexel.edu/) provides an easy platform for comparative microbial genomics. POGO-DB allows users to compare genomes using pre-computed metrics that were derived from extensive computationally intensive BLAST comparisons of >2000 microbes. These metrics include (i) average protein sequence identity across all orthologs shared by two genomes, (ii) genomic fluidity (a measure of gene content dissimilarity), (iii) number of 'orthologs' shared between two genomes, (iv) pairwise identity of the 16S ribosomal RNA genes and (v) pairwise identity of an additional 73 marker genes present in >90% prokaryotes. Users can visualize these metrics against each other in a 2D plot for exploratory analysis of genome similarity and of how different aspects of genome similarity relate to each other. The results of these comparisons are fully downloadable. In addition, users can download raw BLAST results for all or user-selected comparisons. Therefore, we provide users with full flexibility to carry out their own downstream analyses, by creating easy access to data that would normally require heavy computational resources to generate. POGO-DB should prove highly useful for researchers interested in comparative microbiology and benefit the microbiome/metagenomic communities by providing the information needed to select suitable phylogenetic marker genes within particular lineages.
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