Taking U out, with two nucleases?

BACKGROUND: REX1 and REX2 are protein components of the RNA editing complex (the editosome) and function as exouridylylases. The exact roles of REX1 and REX2 in the editosome are unclear and the consequences of the presence of two related proteins are not fully understood. Here, a variety of computational studies were performed to enhance understanding of the structure and function of REX proteins in Trypanosoma and Leishmania species. RESULTS: Sequence analysis and homology modeling of the Endonuclease/Exonuclease/Phosphatase (EEP) domain at the C-terminus of REX1 and REX2 highlights a common active site shared by all EEP domains. Phylogenetic analysis indicates that REX proteins contain a distinct subfamily of EEP domains. Inspection of three-dimensional models of the EEP domain in Trypanosoma brucei REX1 and REX2, and Leishmania major REX1 suggests variations of previously characterized key residues likely to be important in catalysis and determining substrate specificity. CONCLUSION: We have identified features of the REX EEP domain that distinguish it from other family members and hence subfamily specific determinants of catalysis and substrate binding. The results provide specific guidance for experimental investigations about the role(s) of REX proteins in RNA editing

Comparative analysis of the kinomes of three pathogenic trypanosomatids: Leishmania major, Trypanosoma brucei and Trypanosoma cruzi

BACKGROUND: The trypanosomatids Leishmania major, Trypanosoma brucei and Trypanosoma cruzi cause some of the most debilitating diseases of humankind: cutaneous leishmaniasis, African sleeping sickness, and Chagas disease. These protozoa possess complex life cycles that involve development in mammalian and insect hosts, and a tightly coordinated cell cycle ensures propagation of the highly polarized cells. However, the ways in which the parasites respond to their environment and coordinate intracellular processes are poorly understood. As a part of an effort to understand parasite signaling functions, we report the results of a genome-wide analysis of protein kinases (PKs) of these three trypanosomatids. RESULTS: Bioinformatic searches of the trypanosomatid genomes for eukaryotic PKs (ePKs) and atypical PKs (aPKs) revealed a total of 176 PKs in T. brucei, 190 in T. cruzi and 199 in L. major, most of which are orthologous across the three species. This is approximately 30% of the number in the human host and double that of the malaria parasite, Plasmodium falciparum. The representation of various groups of ePKs differs significantly as compared to humans: trypanosomatids lack receptor-linked tyrosine and tyrosine kinase-like kinases, although they do possess dual-specificity kinases. A relative expansion of the CMGC, STE and NEK groups has occurred. A large number of unique ePKs show no strong affinity to any known group. The trypanosomatids possess few ePKs with predicted transmembrane domains, suggesting that receptor ePKs are rare. Accessory Pfam domains, which are frequently present in human ePKs, are uncommon in trypanosomatid ePKs. CONCLUSION: Trypanosomatids possess a large set of PKs, comprising approximately 2% of each genome, suggesting a key role for phosphorylation in parasite biology. Whilst it was possible to place most of the trypanosomatid ePKs into the seven established groups using bioinformatic analyses, it has not been possible to ascribe function based solely on sequence similarity. Hence the connection of stimuli to protein phosphorylation networks remains enigmatic. The presence of numerous PKs with significant sequence similarity to known drug targets, as well as a large number of unusual kinases that might represent novel targets, strongly argue for functional analysis of these molecules

Rat Strain Ontology: structured controlled vocabulary designed to facilitate access to strain data at RGD

BACKGROUND: The Rat Genome Database (RGD) ( http://rgd.mcw.edu/) is the premier site for comprehensive data on the different strains of the laboratory rat (Rattus norvegicus). The strain data are collected from various publications, direct submissions from individual researchers, and rat providers worldwide. Rat strain, substrain designation and nomenclature follow the Guidelines for Nomenclature of Mouse and Rat Strains, instituted by the International Committee on Standardized Genetic Nomenclature for Mice. While symbols and names aid in identifying strains correctly, the flat nature of this information prohibits easy search and retrieval, as well as other data mining functions. In order to improve these functionalities, particularly in ontology-based tools, the Rat Strain Ontology (RS) was developed. RESULTS: The Rat Strain Ontology (RS) reflects the breeding history, parental background, and genetic manipulation of rat strains. This controlled vocabulary organizes strains by type: inbred, outbred, chromosome altered, congenic, mutant and so on. In addition, under the chromosome altered category, strains are organized by chromosome, and further by type of manipulations, such as mutant or congenic. This allows users to easily retrieve strains of interest with modifications in specific genomic regions. The ontology was developed using the Open Biological and Biomedical Ontology (OBO) file format, and is organized on the Directed Acyclic Graph (DAG) structure. Rat Strain Ontology IDs are included as part of the strain report (RS: ######). CONCLUSIONS: As rat researchers are often unaware of the number of substrains or altered strains within a breeding line, this vocabulary now provides an easy way to retrieve all substrains and accompanying information. Its usefulness is particularly evident in tools such as the PhenoMiner at RGD, where users can now easily retrieve phenotype measurement data for related strains, strains with similar backgrounds or those with similar introgressed regions. This controlled vocabulary also allows better retrieval and filtering for QTLs and in genomic tools such as the GViewer. The Rat Strain Ontology has been incorporated into the RGD Ontology Browser ( http://rgd.mcw.edu/rgdweb/ontology/view.html?acc_id=RS:0000457#s) and is available through the National Center for Biomedical Ontology ( http://bioportal.bioontology.org/ontologies/1150) or the RGD ftp site ( ftp://rgd.mcw.edu/pub/ontology/rat_strain/)

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets

SURVEY AND SUMMARY: Comparative analysis of editosome proteins in trypanosomatids

Detailed comparisons of 16 editosome proteins from Trypanosoma brucei, Trypanosoma cruzi and Leishmania major identified protein motifs associated with catalysis and protein or nucleic acid interactions that suggest their functions in RNA editing. Five related proteins with RNase III-like motifs also contain a U1-like zinc finger and either dsRBM or Pumilio motifs. These proteins may provide the endoribonuclease function in editing. Two other related proteins, at least one of which is associated with U-specific 3′ exonuclease activity, contain two putative nuclease motifs. Thus, editosomes contain a plethora of nucleases or proteins presumably derived from nucleases. Five additional related proteins, three of which have zinc fingers, each contain a motif associated with an OB fold; the TUTases have C-terminal folds reminiscent of RNA binding motifs, thus indicating the presence of numerous nucleic acid and/or protein binding domains, as do the two RNA ligases and a RNA helicase, which provide for additional catalytic steps in editing. These data indicate that trypanosomatid RNA editing is orchestrated by a variety of domains for catalysis, molecular interaction and structure. These domains are generally conserved within other protein families, but some are found in novel combinations in the editosome proteins

Ten simple rules for using public biological data for your research.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility