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Brucellosis is an economically important zoonotic disease affecting humans, livestock, and wildlife health globally and especially in Africa. Brucella abortus and B. melitensis have been isolated from human, livestock (cattle and goat), and wildlife (sable) in South Africa (SA) but with little knowledge of the population genomic structure of this pathogen in SA. As whole genome sequencing can assist to differentiate and trace the origin of outbreaks of Brucella spp. strains, the whole genomes of retrospective isolates (n = 19) from previous studies were sequenced. Sequences were analysed using average nucleotide identity (ANI), pangenomics, and whole genome single nucleotide polymorphism (wgSNP) to trace the geographical origin of cases of brucellosis circulating in human, cattle, goats, and sable from different provinces in SA. Pangenomics analysis of B. melitensis (n = 69) and B. abortus (n = 56) was conducted with 19 strains that included B. abortus from cattle (n = 3) and B. melitensis from a human (n = 1), cattle (n = 1), goat (n = 1), Rev1 vaccine strain (n = 1), and sable (n = 12). Pangenomics analysis of B. melitensis genomes, highlighted shared genes, that include 10 hypothetical proteins and genes that encodes for acetyl-coenzyme A synthetase (acs), and acylamidase (aam) amongst the sable genomes. The wgSNP analysis confirmed the B. melitensis isolated from human was more closely related to the goat from the Western Cape Province from the same outbreak than the B. melitensis cattle sample from different cases in the Gauteng Province. The B. melitensis sable strains could be distinguished from the African lineage, constituting their own African sub-clade. The sequenced B. abortus strains clustered in the C2 lineage that is closely related to the isolates from Mozambique and Zimbabwe. This study identified genetically diverse Brucella spp. among various hosts in SA. This study expands the limited known knowledge regarding the presence of B. melitensis in livestock and humans in SA, further building a foundation for future research on the distribution of the Brucella spp. worldwide and its evolutionary background. [ABSTRACT FROM AUTHOR]

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Background: Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. Results: We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. Conclusions: eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar. biocomp.unibo.it. [ABSTRACT FROM AUTHOR]

Genome annotation is one of the most important issues in the genomic era. The exponential growth rate of newly sequenced genomes and proteomes urges the development of fast and reliable annotation methods, suited to exploit all the information available in curated databases of protein sequences and structures. To this aim we developed BAR+, the Bologna Annotation Resource.1 The basic notion is that sequences with high identity value to a counterpart can inherit the same function/s and structure, if available. As a case study we describe how the ATP-binding domain of the ABC transporters can be found and modeled in over 30,000 new sequences not annotated before. We also mapped into BAR+ all the ABC transporters listed in the Transporter Classification DataBase2 and found that within our environment annotation could be extended to another 256,866 sequences. [ABSTRACT FROM AUTHOR]

Background: In the genomic era a key issue is protein annotation, namely how to endow protein sequences, upon translation from the corresponding genes, with structural and functional features. Routinely this operation is electronically done by deriving and integrating information from previous knowledge. The reference database for protein sequences is UniProtKB divided into two sections, UniProtKB/TrEMBL which is automatically annotated and not reviewed and UniProtKB/Swiss-Prot which is manually annotated and reviewed. The annotation process is essentially based on sequence similarity search. The question therefore arises as to which extent annotation based on transfer by inheritance is valuable and specifically if it is possible to statistically validate inherited features when little homology exists among the target sequence and its template(s). Results: In this paper we address the problem of annotating protein sequences in a statistically validated manner considering as a reference annotation resource UniProtKB. The test case is the set of 48,298 proteins recently released by the Critical Assessment of Function Annotations (CAFA) organization. We show that we can transfer after validation, Gene Ontology (GO) terms of the three main categories and Pfam domains to about 68% and 72% of the sequences, respectively. This is possible after alignment of the CAFA sequences towards BAR+, our annotation resource that allows discriminating among statistically validated and not statistically validated annotation. By comparing with a direct UniProtKB annotation, we find that besides validating annotation of some 78% of the CAFA set, we assign new and statistically validated annotation to 14.8% of the sequences and find new structural templates for about 25% of the chains, half of which share less than 30% sequence identity to the corresponding template/s. Conclusion: Inheritance of annotation by transfer generally requires a careful selection of the identity value among the target and the template in order to transfer structural and/or functional features. Here we prove that even distantly remote homologs can be safely endowed with structural templates and GO and/or Pfam terms provided that annotation is done within clusters collecting cluster-related protein sequences and where a statistical validation of the shared structural and functional features is possible. [ABSTRACT FROM AUTHOR]

Given the relevance of the pig proteome in different studies, including human complex maladies, a statistical validation of the annotation is required for a better understanding of the role of specific genes and proteins in the complex networks underlying biological processes in the animal. Presently, approximately 80% of the pig proteome is still poorly annotated, and the existence of protein sequences is routinely inferred automatically by sequence alignment towards preexisting sequences. In this article, we introduce SUS-BAR, a database that derives information mainly from UniProt Knowledgebase and that includes 26 206 pig protein sequences. In SUS-BAR, 16 675 of the pig protein sequences are endowed with statistically validated functional and structural annotation. Our statistical validation is determined by adopting a cluster-centric annotation procedure that allows transfer of different types of annotation, including structure and function. Each sequence in the database can be associated with a set of statistically validated Gene Ontologies (GOs) of the three main subontologies (Molecular Function, Biological Process and Cellular Component), with Pfam functional domains, and when possible, with a cluster Hidden Markov Model that allows modelling the 3D structure of the protein. A database search allows some statistics demonstrating the enrichment in both GO and Pfam annotations of the pig proteins as compared with UniProt Knowledgebase annotation. Searching in SUS-BAR allows retrieval of the pig protein annotation for further analysis. The search is also possible on the basis of specific GO terms and this allows retrieval of all the pig sequences participating into a given biological process, after annotation with our system. Alternatively, the search is possible on the basis of structural information, allowing retrieval of all the pig sequences with the same structural characteristics. [ABSTRACT FROM AUTHOR]

Background: Magnesium research is increasing in molecular medicine due to the relevance of this ion in several important biological processes and associated molecular pathogeneses. It is still difficult to predict from the protein covalent structure whether a human chain is or not involved in magnesium binding. This is mainly due to little information on the structural characteristics of magnesium binding sites in proteins and protein complexes. Magnesium binding features, differently from those of other divalent cations such as calcium and zinc, are elusive. Here we address a question that is relevant in protein annotation: how many human proteins can bind Mg2+? Our analysis is performed taking advantage of the recently implemented Bologna Annotation Resource (BAR-PLUS), a non hierarchical clustering method that relies on the pair wise sequence comparison of about 14 millions proteins from over 300.000 species and their grouping into clusters where annotation can safely be inherited after statistical validation. Results: After cluster assignment of the latest version of the human proteome, the total number of human proteins for which we can assign putative Mg binding sites is 3,751. Among these proteins, 2,688 inherit annotation directly from human templates and 1,063 inherit annotation from templates of other organisms. Protein structures are highly conserved inside a given cluster. Transfer of structural properties is possible after alignment of a given sequence with the protein structures that characterise a given cluster as obtained with a Hidden Markov Model (HMM) based procedure. Interestingly a set of 370 human sequences inherit Mg2+ binding sites from templates sharing less than 30% sequence identity with the template. Conclusion: We describe and deliver the "human magnesome", a set of proteins of the human proteome that inherit putative binding of magnesium ions. With our BAR-hMG, 251 clusters including 1,341 magnesium binding protein structures corresponding to 387 sequences are sufficient to annotate some 13,689 residues in 3,751 human sequences as "magnesium binding". Protein structures act therefore as three dimensional seeds for structural and functional annotation of human sequences. The data base collects specifically all the human proteins that can be annotated according to our procedure as "magnesium binding", the corresponding structures and BAR+ clusters from where they derive the annotation (http://bar.biocomp.unibo.it/mg). [ABSTRACT FROM AUTHOR]

Motivation: The next-generation sequencing era requires reliable, fast and efficient approaches for the accurate annotation of the ever-increasing number of biological sequences and their variations. Transfer of annotation upon similarity search is a standard approach. The procedure of all-against-all protein comparison is a preliminary step of different available methods that annotate sequences based on information already present in databases. Given the actual volume of sequences, methods are necessary to pre-process data to reduce the time of sequence comparison. Results: We present an algorithm that optimizes the partition of a large volume of sequences (the whole database) into sets where sequence length values (in residues) are constrained depending on a bounded minimal and expected alignment coverage. The idea is to optimally group protein sequences according to their length, and then computing the all-against-all sequence alignments among sequences that fall in a selected length range. We describe a mathematically optimal solution and we show that our method leads to a 5-fold speed-up in real world cases. [ABSTRACT FROM AUTHOR]

The availability of whole genome sequences in public databases permits genome-wide comparative studies of various bacterial species. Whole genome sequence-single nucleotide polymorphisms (WGS-SNP) analysis has been used in recent studies and allows the discrimination of various Brucella species and strains. In the present study, 13 Brucella spp. strains from cattle of various locations in provinces of South Africa were typed and discriminated. WGS-SNP analysis indicated a maximum pairwise distance ranging from 4 to 77 single nucleotide polymorphisms (SNPs) between the South African Brucella abortus virulent field strains. Moreover, it was shown that the South African B. abortus strains grouped closely to B. abortus strains from Mozambique and Zimbabwe, as well as other Eurasian countries, such as Portugal and India. WGS-SNP analysis of South African B. abortus strains demonstrated that the same genotype circulated in one farm (Farm 1), whereas another farm (Farm 2) in the same province had two different genotypes. This indicated that brucellosis in South Africa spreads within the herd on some farms, whereas the introduction of infected animals is the mode of transmission on other farms. Three B. abortus vaccine S19 strains isolated from tissue and aborted material were identical, even though they originated from different herds and regions of South Africa. This might be due to the incorrect vaccination of animals older than the recommended age of 4–8 months or might be a problem associated with vaccine production. [ABSTRACT FROM AUTHOR]