Abstract
Large-scale mining and analysis of bacterial datasets contribute to the comprehensive characterization of complex microbial dynamics within a microbiome and among different bacterial strains, e.g., during disease outbreaks. The study of large-scale bacterial evolutionary dynamics poses many challenges. These include data-mining steps, such as gene annotation, ortholog detection, sequence alignment and phylogeny reconstruction. These steps require the use of multiple bioinformatics tools and ad-hoc programming scripts, making the entire process cumbersome, tedious and error-prone due to manual handling. This motivated us to develop the M1CR0B1AL1Z3R web server, a ‘one-stop shop’ for conducting microbial genomics data analyses via a simple graphical user interface. Some of the features implemented in M1CR0B1AL1Z3R are: (i) extracting putative open reading frames and comparative genomics analysis of gene content; (ii) extracting orthologous sets and analyzing their size distribution; (iii) analyzing gene presence–absence patterns; (iv) reconstructing a phylogenetic tree based on the extracted orthologous set; (v) inferring GC-content variation among lineages. M1CR0B1AL1Z3R facilitates the mining and analysis of dozens of bacterial genomes using advanced techniques, with the click of a button. M1CR0B1AL1Z3R is freely available at https://microbializer.tau.ac.il/.
Highlights
In a typical microbial genomics study, a few dozen bacterial samples are sequenced using generation sequencing technologies, with each sample representing a different bacterial species, strain or isolate
Typical research challenges are: (i) inferring the core genome and pangenome [1]; (ii) reconstructing the evolutionary history of the analyzed samples as a phylogenetic tree [2]; (iii) analyzing the variation in GC content among samples [3]; (iv) analyzing the gene gain and loss dynamics, which is often an indication of the intensity of horizontal gene transfer [4]; (v) detecting genes that are likely to have experienced positive selection [5,6,7]
The above computations require the use of multiple bioinformatics tools and ad-hoc programming scripts to handle information flow among the various programs, which in turn necessitates a dedicated bioinformatician to conduct such analyses
Summary
In a typical microbial genomics study, a few dozen bacterial samples are sequenced using generation sequencing technologies, with each sample representing a different bacterial species, strain or isolate. The Pan-X web server provides ready-made examples of different microbial datasets [8].
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