The Computational Biology Lab (LBC) focuses on comparative and functional genomics of pathogens. Within these general areas of work, the aim of our research is to understand the genetic basis of complex phenotypes in bacteria and the evolution of multigene families in worms. In addition, we invest time in the development of bioinformatics tools and strategies for the application of deep sequencing and genome analysis.
The Computational Biology Lab (LBC in Spanish) was established as an independent group in May 2016 and is part of the Biotechnology Department at the School of Medicine, Universidad de la República - Uruguay.
LBC’s main objective is to understand the basis of differential pathogenesis in bacteria and helminth using omic data as the main input. We have also invested some time in the development of bioinformatics tools and strategies for the analysis of sequenced data and to support other groups of researchers who need to apply deep sequencing or massive data analysis.
Our teams is currently formed by young research assistants, graduate students, and one postdoc. During these years many fruitful national and international collaborations were built, as a result, our group is participating in diverse and exciting research projects.
One of the main goals of comparative genomics is the elucidation of the genetic basis of the phenotypic differences among species and strains. The genomic approach is of particular importance when analyzing complex phenotypes that depend on a combination of several genetic elements, such as pathogenicity, virulence, or symbiosis. The genomic variability may include several levels (rearrangements, presence/absence of genes, indels, or single nucleotide variants). We study these genomic traits and their evolutionary dynamics in pathogenic and nonpathogenic bacteria, including clinical and environmental isolates of Salmonella, Acinetobacter, and Shewanella, among others.
The study of genomes confirmed that duplication is a powerful evolutionary mechanism, generating raw material for the acquisition of new functions in the cell. In many cases, the increase of copies in a family of genes has proven to be the result of an adaptive process. We have studied the evolution of gene families in Platyhelminthes and other phyla. Studies mainly include phylogenetic analyzes, gene structure identification, protein 3D prediction, and molecular distance estimations.
Transcriptomic analysis, the analysis of the complete set of RNA transcripts, is nowadays the most commonly used approach in functional genomics. In our lab we apply RNA-seq to study the genome response to different conditions in a wide variety of organisms. Thus, we aim to identify novel genetic elements or interactions that contribute to produce the complex phenotype that characterize the pathogen when going through critical states relevant to its pathogenesis.
The increasing pollution in the world is a serious problem with severe long-term consequences. The main sources of human-caused water pollution include mining, industry, livestock, and agriculture. In particular, the treatment and disposal of industrial waste requires complex processing that raises the costs of the final products.
Parasitic flatworms generally have complex cycles involving various hosts, including humans and livestock species, and therefore have a great impact on human and animal health. Examples of species of this group of relevance are Echinococcus granulosus, Schistosoma mansoni, and Fasciola hepatica, among many others.
Salmonella enterica, a pathogen of birds and mammals, is distributed worldwide and has a considerable impact on human and animal health, being the main causal agent of foodborne infections. The different serotypes within the species show important differences in epidemic potential, virulence, and pathogenicity.
Due to genetic changes, certain strains of E. coli have moved from commensals to pathogens. According to the pathogenic mechanisms involved, different pathotypes are recognized, one of them is STEC, a pathotype that produces Shiga toxins (Stxs).
Background Spliced Leader trans-splicing is an important mechanism for the maturation of mRNAs in several lineages of eukaryotes, including several groups of parasites of great medical and economic importance. Nevertheless, its study across the tree of life is severely hindered by the problem of identifying the SL sequences that are being trans-spliced.
Results In this paper we present SLFinder, a four-step pipeline meant to identify de novo candidate SL sequences making very few assumptions regarding the SL sequence properties. The pipeline takes transcriptomic de novo assemblies and a reference genome as input and allows the user intervention on several points to account for unexpected features of the dataset. The strategy and its implementation were tested on real RNAseq data from species with and without SL Trans-Splicing.
Conclusions SLFinder is capable to identify SL candidates with good precision in a reasonable amount of time. It is especially suitable for species with unknown SL sequences, generating candidate sequences for further refining and experimental validation.