Quality of computationally inferred gene ontology annotations

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation

MOTIVATION

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.

RESULTS

We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.

This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jiyuc/de-inconsistency.

Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data

BACKGROUND

Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals.

METHODS

To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration.

RESULTS

There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10^-3).

CONCLUSIONS

Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals.

Exploring automatic inconsistency detection for literature-based gene ontology annotation

Motivation

Literature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection.

Results

We have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records. Major challenges resulting from such inconsistencies in the context of several specific application settings are reported. This is the first study to introduce automatic approaches that are designed to address the challenges in current GOA quality assurance workflows. The data underlying this article are available in Github at https://github.com/jiyuc/AutoGOAConsistency.

Automatic consistency assurance for literature-based gene ontology annotation

BACKGROUND

Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated.

RESULTS

In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions.

CONCLUSIONS

Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain

Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.

PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/photomod.

Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

MOTIVATION

Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases.

RESULTS

We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are 'Inferred from Biological aspect of Ancestor (IBA)' which is in contradiction with previous observations that attributed misannotations mainly to 'Inferred from Sequence or structural Similarity (ISS)', probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies.

AVAILABILITY AND IMPLEMENTATION

https://zhanglab.ccmb.med.umich.edu/RAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Automatic Gene Function Prediction in the 2020's

The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.

Genes that are involved in the same biological process tend to co-evolve. This property is exploited by the technique of phylogenetic profiling, which identifies co-evolving (and therefore likely functionally related) genes through patterns of correlated gene retention and loss in evolution and across species. However, conventional methods to computing and clustering these correlated genes do not scale with increasing numbers of genomes. HogProf is a novel phylogenetic profiling tool built on probabilistic data structures. It allows the user to construct searchable databases containing the evolutionary history of hundreds of thousands of protein families. Such fast detection of coevolution takes advantage of the rapidly increasing amount of genomic data publicly available, and can uncover unknown biological networks and guide in-vivo research and experimentation. We have applied our tool to describe the biological networks underpinning sexual reproduction in eukaryotes.

Benchmarking gene ontology function predictions using negative annotations

MOTIVATION

With the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations.

RESULTS

This article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments.

AVAILABILITY AND IMPLEMENTATION

All data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction

MOTIVATION

The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The 'ortholog conjecture' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions.

RESULTS

We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy.

AVAILABILITY AND IMPLEMENTATION

https://github.com/predragradivojac/oc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Photosynthetic protein classification using genome neighborhood-based machine learning feature

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore, expected that applying a computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their conserved neighboring genes observed by 'Phylo score', indicating their functions could be inferred from the genome neighborhood profile. Therefore, we created a new method for extracting patterns based on the genome neighborhood network (GNN) and applied them for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 87% in the classification of photosynthetic proteins and also showed better performance (Mathew's correlation coefficient = 0.718) than other available tools including the sequence similarity search (0.447) and ML-based method (0.361). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins compared to the other methods. Our classifier is available at http://bicep2.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod.

Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

Extinction of chromosomes due to specialization is a universal occurrence

The human X and Y chromosomes evolved from a pair of autosomes approximately 180 million years ago. Despite their shared evolutionary origin, extensive genetic decay has resulted in the human Y chromosome losing 97% of its ancestral genes while gene content and order remain highly conserved on the X chromosome. Five 'stratification' events, most likely inversions, reduced the Y chromosome's ability to recombine with the X chromosome across the majority of its length and subjected its genes to the erosive forces associated with reduced recombination. The remaining functional genes are ubiquitously expressed, functionally coherent, dosage-sensitive genes, or have evolved male-specific functionality. It is unknown, however, whether functional specialization is a degenerative phenomenon unique to sex chromosomes, or if it conveys a potential selective advantage aside from sexual antagonism. We examined the evolution of mammalian orthologs to determine if the selective forces that led to the degeneration of the Y chromosome are unique in the genome. The results of our study suggest these forces are not exclusive to the Y chromosome, and chromosomal degeneration may have occurred throughout our evolutionary history. The reduction of recombination could additionally result in rapid fixation through isolation of specialized functions resulting in a cost-benefit relationship during times of intense selective pressure.

Wild Dictyostelium discoideum social amoebae show plastic responses to the presence of nonrelatives during multicellular development

When multiple strains of microbes form social groups, such as the multicellular fruiting bodies of Dictyostelium discoideum, conflict can arise regarding cell fate. Both fixed and plastic differences among strains can contribute to cell fate, and plastic responses may be particularly important if social environments frequently change. We used RNA-sequencing and photographic time series analysis to detect possible conflict-induced plastic differences between wild D. discoideum aggregates formed by single strains compared with mixed pairs of strains (chimeras). We found one hundred and two differentially expressed genes that were enriched for biological processes including cytoskeleton organization and cyclic AMP response (up-regulated in chimeras), and DNA replication and cell cycle (down-regulated in chimeras). In addition, our data indicate that in reference to a time series of multicellular development in the laboratory strain AX4, chimeras may be slightly behind clonal aggregates in their development. Finally, phenotypic analysis supported slower splitting of aggregates and a nonsignificant trend for larger group sizes in chimeras. The transcriptomic comparison and phenotypic analyses support discoordination among aggregate group members due to social conflict. These results are consistent with previously observed factors that affect cell fate decision in D. discoideum and provide evidence for plasticity in cAMP signaling and phenotypic coordination during development in response to social conflict in D. discoideum and similar microbial social groups.

Handling Noise in Protein Interaction Networks

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

A transcriptional response of Clostridium beijerinckii NRRL B-598 to a butanol shock

BACKGROUND

One of the main obstacles preventing solventogenic clostridia from achieving higher yields in biofuel production is the toxicity of produced solvents. Unfortunately, regulatory mechanisms responsible for the shock response are poorly described on the transcriptomic level. Although the strain Clostridium beijerinckii NRRL B-598, a promising butanol producer, has been studied under different conditions in the past, its transcriptional response to a shock caused by butanol in the cultivation medium remains unknown.

RESULTS

In this paper, we present a transcriptional response of the strain during a butanol challenge, caused by the addition of butanol to the cultivation medium at the very end of the acidogenic phase, using RNA-Seq. We resequenced and reassembled the genome sequence of the strain and prepared novel genome and gene ontology annotation to provide the most accurate results. When compared to samples under standard cultivation conditions, samples gathered during butanol shock represented a well-distinguished group. Using reference samples gathered directly before the addition of butanol, we identified genes that were differentially expressed in butanol challenge samples. We determined clusters of 293 down-regulated and 301 up-regulated genes whose expression was affected by the cultivation conditions. Enriched term "RNA binding" among down-regulated genes corresponded to the downturn of translation and the cluster contained a group of small acid-soluble spore proteins. This explained phenotype of the culture that had not sporulated. On the other hand, up-regulated genes were characterized by the term "protein binding" which corresponded to activation of heat-shock proteins that were identified within this cluster.

CONCLUSIONS

We provided an overall transcriptional response of the strain C. beijerinckii NRRL B-598 to butanol shock, supplemented by auxiliary technologies, including high-pressure liquid chromatography and flow cytometry, to capture the corresponding phenotypic response. We identified genes whose regulation was affected by the addition of butanol to the cultivation medium and inferred related molecular functions that were significantly influenced. Additionally, using high-quality genome assembly and custom-made gene ontology annotation, we demonstrated that this settled terminology, widely used for the analysis of model organisms, could also be applied to non-model organisms and for research in the field of biofuels.

SamPler - a novel method for selecting parameters for gene functional annotation routines

BACKGROUND

As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins.

RESULTS

Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm's parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters.

CONCLUSIONS

The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Motivation

Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.

Results

PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.

Availability and implementation

https://github.com/ima23/maxent-ppi

Supplementary information

Supplementary data are available at Bioinformatics online.

A New Microbial Pathway for Organophosphonate Degradation Catalyzed by Two Previously Misannotated Non-Heme-Iron Oxygenases

The assignment of biochemical functions to hypothetical proteins is challenged by functional diversification within many protein structural superfamilies. This diversification, which is particularly common for metalloenzymes, renders functional annotations that are founded solely on sequence and domain similarities unreliable and often erroneous. Definitive biochemical characterization to delineate functional subgroups within these superfamilies will aid in improving bioinformatic approaches for functional annotation. We describe here the structural and functional characterization of two non-heme-iron oxygenases, TmpA and TmpB, which are encoded by a genomically clustered pair of genes found in more than 350 species of bacteria. TmpA and TmpB are functional homologues of a pair of enzymes (PhnY and PhnZ) that degrade 2-aminoethylphosphonate but instead act on its naturally occurring, quaternary ammonium analogue, 2-(trimethylammonio)ethylphosphonate (TMAEP). TmpA, an iron(II)- and 2-(oxo)glutarate-dependent oxygenase misannotated as a γ-butyrobetaine (γbb) hydroxylase, shows no activity toward γbb but efficiently hydroxylates TMAEP. The product, ( R)-1-hydroxy-2-(trimethylammonio)ethylphosphonate [( R)-OH-TMAEP], then serves as the substrate for the second enzyme, TmpB. By contrast to its purported phosphohydrolytic activity, TmpB is an HD-domain oxygenase that uses a mixed-valent diiron cofactor to enact oxidative cleavage of the C-P bond of its substrate, yielding glycine betaine and phosphate. The high specificities of TmpA and TmpB for their N-trimethylated substrates suggest that they have evolved specifically to degrade TMAEP, which was not previously known to be subject to microbial catabolism. This study thus adds to the growing list of known pathways through which microbes break down organophosphonates to harvest phosphorus, carbon, and nitrogen in nutrient-limited niches.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

Gene ontology improves template selection in comparative protein docking

Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains-biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.

Prioritising candidate genes causing QTL using hierarchical orthologous groups

Motivation

A key goal in plant biotechnology applications is the identification of genes associated to particular phenotypic traits (for example: yield, fruit size, root length). Quantitative Trait Loci (QTL) studies identify genomic regions associated with a trait of interest. However, to infer potential causal genes in these regions, each of which can contain hundreds of genes, these data are usually intersected with prior functional knowledge of the genes. This process is however laborious, particularly if the experiment is performed in a non-model species, and the statistical significance of the inferred candidates is typically unknown.

Results

This paper introduces QTLSearch, a method and software tool to search for candidate causal genes in QTL studies by combining Gene Ontology annotations across many species, leveraging hierarchical orthologous groups. The usefulness of this approach is demonstrated by re-analysing two metabolic QTL studies: one in Arabidopsis thaliana, the other in Oryza sativa subsp. indica. Even after controlling for statistical significance, QTLSearch inferred potential causal genes for more QTL than BLAST-based functional propagation against UniProtKB/Swiss-Prot, and for more QTL than in the original studies.

Availability and implementation

QTLSearch is distributed under the LGPLv3 license. It is available to install from the Python Package Index (as qtlsearch), with the source available from https://bitbucket.org/alex-warwickvesztrocy/qtlsearch.

Supplementary information

Supplementary data are available at Bioinformatics online.

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

BACKGROUND

The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner.

RESULTS

We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models.

CONCLUSIONS

In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

Mapping genes for calcium signaling and their associated human genetic disorders

Motivation

Signal transduction via calcium ions (Ca²⁺) represents a fundamental signaling pathway in all eukaryotic cells. A large portion of the human genome encodes proteins used to assemble signaling systems that can transduce signals with diverse spatial and temporal dynamics.

Results

Here, we provide a map of all of the genes involved in Ca²⁺ signaling and link these genes to human genetic disorders. Using Gene Ontology terms and genome databases, 1805 genes were identified as regulators or targets of intracellular Ca²⁺ signals. Associating these 1805 genes with human genetic disorders uncovered 1470 diseases with mutated ‘Ca²⁺ genes’. A network with scale-free properties appeared when the Ca²⁺ genes were mapped to their associated genetic disorders.

Availability and Implementation

The Ca²⁺ genome database is freely available at http://cagedb.uhlenlab.org and will foster studies of gene functions and genetic disorders associated with Ca²⁺ signaling.

Supplementary information

Supplementary data are available at Bioinformatics online.

Investigations on factors influencing HPO-based semantic similarity calculation

Background

Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with complex phenotypes and high genetic heterogeneity. Recently, calculating Human Phenotype Ontology (HPO)-based phenotype semantic similarity has contributed a lot for completing disease diagnosis. However, factors which affect the accuracy of HPO-based semantic similarity have not been evaluated systematically.

Results

In this study, we proposed a new framework called HPOFactor to evaluate these factors. Our model includes four components: (1) the size of annotation set, (2) the evidence code of annotations, (3) the quality of annotations and (4) the coverage of annotations respectively.

Conclusions

HPOFactor analyzes the four factors systematically based on two kinds of experiments: causative gene prediction and disease prediction. Furthermore, semantic similarity measurement could be designed based on the characteristic of these factors.

NoGOA: predicting noisy GO annotations using evidences and sparse representation

BACKGROUND

Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem.

RESULTS

We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction.

CONCLUSIONS

The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .

Assessment of genome annotation using gene function similarity within the gene neighborhood

BACKGROUND

Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments.

RESULTS

We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria.

CONCLUSIONS

Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations.

Down-regulation of aminopeptidase N and ABC transporter subfamily G transcripts in Cry1Ab and Cry1Ac resistant Asian corn borer, Ostrinia furnacalis (Lepidoptera: Crambidae)

The Asian corn borer (ACB), Ostrinia furnacalis (Lepidoptera: Crambidae), is a highly destructive pest of cultivated maize throughout East Asia. Bacillus thuringiensis (Bt) crystalline protein (Cry) toxins cause mortality by a mechanism involving pore formation or signal transduction following toxin binding to receptors along the midgut lumen of susceptible insects, but this mechanism and mutations therein that lead to resistance are not fully understood. In the current study, quantitative comparisons were made among midgut expressed transcripts from O. furnacalis susceptible (ACB-BtS) and laboratory selected strains resistant to Cry1Ab (ACB-AbR) and Cry1Ac toxins (ACB-AcR) when feeding on non-Bt diet. From a combined de novo transcriptome assembly of 83,370 transcripts, ORFs of ≥ 100 amino acids were predicted and annotated for 28,940 unique isoforms derived from 12,288 transcripts. Transcriptome-wide expression estimated from RNA-seq read depths predicted significant down-regulation of transcripts for previously known Bt resistance genes, aminopeptidase N1 (apn1) and apn3, as well as a putative ATP binding cassette transporter group G (abcg) gene in both ACB-AbR and -AcR (log2[fold-change] ≥ 1.36; P < 0.0001). The transcripts that were most highly differentially regulated in both ACB-AbR and -AcR compared to ACB-BtS (log2[fold-change] ≥ 2.0; P < 0.0001) included up- and down-regulation of serine proteases, storage proteins and cytochrome P450 monooxygenases, as well as up-regulation of genes with predicted transport function. This study predicted the significant down-regulation of transcripts for previously known Bt resistance genes, aminopeptidase N1 (apn1) and apn3, as well as abccg gene in both ACB-AbR and -AcR. These data are important for the understanding of systemic differences between Bt resistant and susceptible genotypes.

The natural history of molecular functions inferred from an extensive phylogenomic analysis of gene ontology data

The origin and natural history of molecular functions hold the key to the emergence of cellular organization and modern biochemistry. Here we use a genomic census of Gene Ontology (GO) terms to reconstruct phylogenies at the three highest (1, 2 and 3) and the lowest (terminal) levels of the hierarchy of molecular functions, which reflect the broadest and the most specific GO definitions, respectively. These phylogenies define evolutionary timelines of functional innovation. We analyzed 249 free-living organisms comprising the three superkingdoms of life, Archaea, Bacteria, and Eukarya. Phylogenies indicate catalytic, binding and transport functions were the oldest, suggesting a 'metabolism-first' origin scenario for biochemistry. Metabolism made use of increasingly complicated organic chemistry. Primordial features of ancient molecular functions and functional recruitments were further distilled by studying the oldest child terms of the oldest level 1 GO definitions. Network analyses showed the existence of an hourglass pattern of enzyme recruitment in the molecular functions of the directed acyclic graph of molecular functions. Older high-level molecular functions were thoroughly recruited at younger lower levels, while very young high-level functions were used throughout the timeline. This pattern repeated in every one of the three mappings, which gave a criss-cross pattern. The timelines and their mappings were remarkable. They revealed the progressive evolutionary development of functional toolkits, starting with the early rise of metabolic activities, followed chronologically by the rise of macromolecular biosynthesis, the establishment of controlled interactions with the environment and self, adaptation to oxygen, and enzyme coordinated regulation, and ending with the rise of structural and cellular complexity. This historical account holds important clues for dissection of the emergence of biomcomplexity and life.

Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework

Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usually quantified by combining the GO hierarchy with an annotation corpus that links genes and gene products to GO terms. One large group of algorithms involves calculation of GO term semantic similarity (SS) between all the terms annotating the two proteins, followed by a second step, described as “mixing strategy”, which involves combining the SS values to yield the final FS value. Due to the variability of protein annotation caused e.g. by annotation bias, this value cannot be reliably compared on an absolute scale. We therefore introduce a similarity z-score that takes into account the FS background distribution of each protein. For a selection of popular SS measures and mixing strategies we demonstrate moderate accuracy improvement when using z-scores in a benchmark that aims to separate orthologous cases from random gene pairs and discuss in this context the impact of annotation corpus choice. The approach has been implemented in Frela, a fast high-throughput public web server for protein FS calculation and interpretation.

Evaluating Computational Gene Ontology Annotations

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Membrane Proteomic Insights into the Physiology and Taxonomy of an Oleaginous Green Microalga

Ettlia oleoabundans is a nonsequenced oleaginous green microalga. Despite the significant biotechnological interest in producing value-added compounds from the acyl lipids of this microalga, a basic understanding of the physiology and biochemistry of oleaginous microalgae is lacking, especially under nitrogen deprivation conditions known to trigger lipid accumulation. Using an RNA sequencing-based proteomics approach together with manual annotation, we are able to provide, to our knowledge, the first membrane proteome of an oleaginous microalga. This approach allowed the identification of novel proteins in E. oleoabundans, including two photoprotection-related proteins, Photosystem II Subunit S and Maintenance of Photosystem II under High Light1, which were considered exclusive to higher photosynthetic organisms, as well as Retinitis Pigmentosa Type 2-Clathrin Light Chain, a membrane protein with a novel domain architecture. Free-flow zonal electrophoresis of microalgal membranes coupled to liquid chromatography-tandem mass spectrometry proved to be a useful technique for determining the intracellular location of proteins of interest. Carbon-flow compartmentalization in E. oleoabundans was modeled using this information. Molecular phylogenetic analyses of protein markers and 18S ribosomal DNA support the reclassification of E. oleoabundans within the trebouxiophycean microalgae, rather than with the Chlorophyceae class, in which it is currently classified, indicating that it may not be closely related to the model green alga Chlamydomonas reinhardtii A detailed survey of biological processes taking place in the membranes of nitrogen-deprived E. oleoabundans, including lipid metabolism, provides insights into the basic biology of this nonmodel organism.

Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.

Evaluating Functional Annotations of Enzymes Using the Gene Ontology

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

Gene Ontology: Pitfalls, Biases, and Remedies

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

Semantic Similarity in the Gene Ontology

Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS measures based on GO has grown considerably, and their application ranges from functional coherence evaluation, protein interaction prediction, and disease gene prioritization.Understanding how SS measures work, what issues can affect their performance and how they compare to each other in different evaluation settings is crucial to gain a comprehensive view of this area and choose the most appropriate approaches for a given application.In this chapter, we provide a guide to understanding and selecting SS measures for biomedical researchers. We present a straightforward categorization of SS measures and describe the main strategies they employ. We discuss the intrinsic and external issues that affect their performance, and how these can be addressed. We summarize comparative assessment studies, highlighting the top measures in different settings, and compare different implementation strategies and their use. Finally, we discuss some of the extant challenges and opportunities, namely the increased semantic complexity of GO and the need for fast and efficient computation, pointing the way towards the future generation of SS measures.

Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution

BACKGROUND

High-density marker panels and/or whole-genome sequencing, coupled with advanced phenotyping pipelines and sophisticated statistical methods, have dramatically increased our ability to generate lists of candidate genes or regions that are putatively associated with phenotypes or processes of interest. However, the speed with which we can validate genes, or even make reasonable biological interpretations about the principles underlying them, has not kept pace. A promising approach that runs parallel to explicitly validating individual genes is analyzing a set of genes together and assessing the biological similarities among them. This is often achieved via gene ontology analysis, a powerful tool that involves evaluating publicly available gene annotations. However, additional resources such as Medical Subject Headings (MeSH) can also be used to evaluate sets of genes to make biological interpretations.

RESULTS

In this manuscript, we describe utilizing MeSH terms to make biological interpretations in maize. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be directly mapped to genes to develop gene annotations. Once mapped, these terms can be evaluated for enrichment in sets of genes or similarity between gene sets to provide biological insights. Here, we implement MeSH analyses in five maize datasets to demonstrate how MeSH can be leveraged by the maize and broader crop-genomics community.

CONCLUSIONS

We demonstrate that MeSH terms can be effectively leveraged to generate hypotheses and make biological interpretations in maize, and we provide a pipeline that enables the use of MeSH terms in other plant species.

Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function

Schizophrenia is a severe and highly heritable psychiatric disorder affecting approximately 1% of the population. Genome-wide association studies have identified 108 independent genetic loci with genome-wide significance but their functional importance has yet to be elucidated. Here, we develop a novel strategy based on network analysis of protein-protein interactions (PPI) to infer biological function associated with variants most strongly linked to illness risk. We show that the schizophrenia loci are strongly linked to synaptic transmission (P _FWE < .001) and ion transmembrane transport (P _FWE = .03), but not to ontological categories previously found to be shared across psychiatric illnesses. We demonstrate that brain expression of risk-linked genes within the identified processes is strongly modulated during birth and identify a set of synaptic genes consistently changed across multiple brain regions of adult schizophrenia patients. These results suggest synaptic function as a developmentally determined schizophrenia process supported by the illness's most associated genetic variants and their PPI networks. The implicated genes may be valuable targets for mechanistic experiments and future drug development approaches.

NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.

Eliciting the Functional Taxonomy from protein annotations and taxa

The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules.

Extensive complementarity between gene function prediction methods

MOTIVATION

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions.

RESULTS

Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

AVAILABILITY AND IMPLEMENTATION

The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online.

NegGOA: negative GO annotations selection using ontology structure

MOTIVATION

Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space.

RESULTS

In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods.

AVAILABILITY AND IMPLEMENTATION

The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa

CONTACT

gxyu@swu.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Coordinated Action of Biological Processes during Embryogenesis Can Cause Genome-Wide Linkage Disequilibrium in the Human Genome and Influence Age-Related Phenotypes

A role of non-Mendelian inheritance in genetics of complex, age-related traits is becoming increasingly recognized. Recently, we reported on two inheritable clusters of SNPs in extensive genome-wide linkage disequilibrium (LD) in the Framingham Heart Study (FHS), which were associated with the phenotype of premature death. Here we address biologically-related properties of these two clusters. These clusters have been unlikely selected randomly because they are functionally and structurally different from matched sets of randomly selected SNPs. For example, SNPs in LD from each cluster are highly significantly enriched in genes (p=7.1×10-22 and p=5.8×10-18), in general, and in short genes (p=1.4×10-47 and p=4.6×10-7), in particular. Mapping of SNPs in LD to genes resulted in two, partly overlapping, networks of 1764 and 4806 genes. Both these networks were gene enriched in developmental processes and in biological processes tightly linked with development including biological adhesion, cellular component organization, locomotion, localization, signaling, (p<10-4, q<10-4 for each category). Thorough analysis suggests connections of these genetic networks with different stages of embryogenesis and highlights biological interlink of specific processes enriched for genes from these networks. The results suggest that coordinated action of biological processes during embryogenesis may generate genome-wide networks of genetic variants, which may influence complex age-related phenotypes characterizing health span and lifespan.

GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations

The removal of annotation from biological databases is often perceived as an indicator of erroneous annotation. As a corollary, annotation stability is considered to be a measure of reliability. However, diverse data-driven events can affect the stability of annotations in both primary protein sequence databases and the protein family databases that are built upon the sequence databases and used to help annotate them. Here, we describe some of these events and their consequences for the InterPro database, and demonstrate that annotation removal or reassignment is not always linked to incorrect annotation by the curator.

Database URL:http://www.ebi.ac.uk/interpro

Extending gene ontology with gene association networks

MOTIVATION

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO.

RESULTS

We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature.

AVAILABILITY AND IMPLEMENTATION

Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender

CONTACT

jinchen@msu.edu or ydwang@hit.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GOTA: GO term annotation of biomedical literature

Background

Functional annotation of genes and gene products is a major challenge in the post-genomic era. Nowadays, gene function curation is largely based on manual assignment of Gene Ontology (GO) annotations to genes by using published literature. The annotation task is extremely time-consuming, therefore there is an increasing interest in automated tools that can assist human experts.

Results

Here we introduce GOTA, a GO term annotator for biomedical literature. The proposed approach makes use only of information that is readily available from public repositories and it is easily expandable to handle novel sources of information. We assess the classification capabilities of GOTA on a large benchmark set of publications. The overall performances are encouraging in comparison to the state of the art in multi-label classification over large taxonomies. Furthermore, the experimental tests provide some interesting insights into the potential improvement of automated annotation tools.

Conclusions

GOTA implements a flexible and expandable model for GO annotation of biomedical literature. The current version of the GOTA tool is freely available at http://gota.apice.unibo.it.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0777-8) contains supplementary material, which is available to authorized users.