A Factor Graph Approach to Automated GO Annotation

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.Fil: Spetale, Flavio Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Krsticevic, Flavia Jorgelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Roda, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Bulacio, Pilar Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentin

Bacterial riboproteogenomics : the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome re-annotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms

Lost and found : re-searching and re-scoring proteomics data aids genome annotation and improves proteome coverage

Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation

SIFTER search: a web server for accurate phylogeny-based protein function prediction.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license

A quick guide for student-driven community genome annotation

High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the course of many years. The majority of gene models present in biological databases today have been identified in draft genome assemblies using automated annotation pipelines that are frequently based on orthologs from distantly related model organisms. Manual curation is time consuming and often requires substantial expertise, but is instrumental in improving gene model structure and identification. Manual annotation may seem to be a daunting and cost-prohibitive task for small research communities but involving undergraduates in community genome annotation consortiums can be mutually beneficial for both education and improved genomic resources. We outline a workflow for efficient manual annotation driven by a team of primarily undergraduate annotators. This model can be scaled to large teams and includes quality control processes through incremental evaluation. Moreover, it gives students an opportunity to increase their understanding of genome biology and to participate in scientific research in collaboration with peers and senior researchers at multiple institutions

Automatic annotation of bioinformatics workflows with biomedical ontologies

Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 myExperiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014 conference), 15 pages, 4 figure

A new Plasmodium vivax reference sequence with improved assembly of the subtelomeres reveals an abundance of pir genes

Plasmodium vivax is now the predominant cause of malaria in the Asia-Pacific, South America and Horn of Africa. Laboratory studies of this species are constrained by the inability to maintain the parasite in continuous ex vivo culture, but genomic approaches provide an alternative and complementary avenue to investigate the parasite's biology and epidemiology. To date, molecular studies of P. vivax have relied on the Salvador-I reference genome sequence, derived from a monkey-adapted strain from South America. However, the Salvador-I reference remains highly fragmented with over 2500 unassembled scaffolds.  Using high-depth Illumina sequence data, we assembled and annotated a new reference sequence, PvP01, sourced directly from a patient from Papua Indonesia. Draft assemblies of isolates from China (PvC01) and Thailand (PvT01) were also prepared for comparative purposes. The quality of the PvP01 assembly is improved greatly over Salvador-I, with fragmentation reduced to 226 scaffolds. Detailed manual curation has ensured highly comprehensive annotation, with functions attributed to 58% core genes in PvP01 versus 38% in Salvador-I. The assemblies of PvP01, PvC01 and PvT01 are larger than that of Salvador-I (28-30 versus 27 Mb), owing to improved assembly of the subtelomeres.  An extensive repertoire of over 1200 Plasmodium interspersed repeat (pir) genes were identified in PvP01 compared to 346 in Salvador-I, suggesting a vital role in parasite survival or development. The manually curated PvP01 reference and PvC01 and PvT01 draft assemblies are important new resources to study vivax malaria. PvP01 is maintained at GeneDB and ongoing curation will ensure continual improvements in assembly and annotation quality

REPARATION : ribosome profiling assisted (re-)annotation of bacterial genomes

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence composition and often underestimate the complexity of the proteome. We developed RibosomeE Profiling Assisted (re-)AnnotaTION (REPARATION), a de novo machine learning algorithm that takes advantage of experimental protein synthesis evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation (https://github.com/Biobix/ REPARATION). REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs and >70% of all (variants of) annotated protein coding ORFs were predicted by REPARATION to be translated. Our predictions are supported by matching mass spectrometry proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to intrinsically perform a de novo ORF delineation in bacterial genomes irrespective of the sequence features linked to open reading frames