Expert curation in UniProtKB: a case study on dealing with conflicting and erroneous data

Proteomic analysis and lethality of the venom of Aegaeobuthus nigrocinctus, a scorpion of medical significance in the Middle East

The scorpion Aegaeobuthus nigrocinctus inhabits areas in Turkey and the Levant region of the Middle East where severe/lethal envenomings have been reported. Previous research indicated its extreme venom lethality to vertebrates and distinct envenomation syndrome. We report on the composition of A. nigrocinctus venom from Lebanese specimens using nESI-MS/MS, MALDI-TOF MS, SDS-PAGE and RP-HPLC. Venom lethality in mice was also assessed (LD50 = 1.05 (0.19-1.91) mg/kg, i.p), confirming A. nigrocinctus venom toxicity from Levantine populations. Forty-seven peaks were resolved using RP-HPLC, 25 of which eluted between 20 and 40 % acetonitrile. In reducing SDS-PAGE, most predominant components were <10 kDa, with minor components at higher molecular masses of 19.6, 26.1, 46.3 and 57.7 kDa. MALDI-TOF venom fingerprinting detected 20 components within the 1,000-12,000 m/z range. Whole venom 'shotgun' bottom-up nLC-MS/MS approach, combined with in-gel tryptic digestion of SDS-PAGE bands, identified at least 67 different components belonging to 15 venom families, with ion channel-active components (K+ toxins (23); Na+ toxins (20); Cl- toxins (2)) being predominant. The sequence of a peptide (named α-KTx9.13) ortholog to Leiurus hebraeus putative α-KTx9.3 toxin was fully determined, which exhibited 81-96 % identity to other members of the α-KTx9 subfamily targeting Kv1.x and Ca2+-activated K+ channels. Chlorotoxin-like peptides were also identified. Our study underscores the medical significance of A. nigrocinctus in the region and reveals the potential value of its venom components as lead templates for biomedical applications. Future work should address whether available antivenoms in the Middle East are effective against A. nigrocinctus envenoming in the Levant area.

Venomics of Leiurus abdullahbayrami, the most lethal scorpion in the Levant region of the Middle East

The scorpion Leiurus abdullahbayrami has been associated with severe/lethal envenomings throughout the Levant region of the Middle East, encompassing Turkey, Syria, and Lebanon, and only scarce information is available on its venom composition, activity, and antigenicity. We report on the composition of L. abdullahbayrami venom collected from Lebanese specimens using nESI-MS/MS, MALDI-TOF MS, SDS-PAGE and RP-HPLC. Venom lethality, through LD50 determination in mice (intraperitoneal), was also assessed (0.75 (0.16-1.09) mg/kg), confirming L.abdullahbayrami venom vertebrate toxicity. Fifty-four peaks were detected using RP-HPLC, half of which eluted in the gradient region between 20 and 40% acetonitrile. In reducing SDS-PAGE, most predominant components were <10 kDa, with minor components at higher molecular masses of 24.4, 43.1, and 48.9 kDa. Venom mass fingerprint by MALDI-TOF detected 21 components within the 1000-12,000 m/z range. Whole venom 'shotgun' bottom-up nLC-MS/MS approach, combined with in-gel tryptic digestion of SDS-PAGE bands, identified at least 113 different components belonging to 15 venom families and uncharacterized proteins, with ion channel-active components (K+ channel toxins (28); Na+ channel toxins (42); Cl- channel toxins (4); Ca+2 toxins (2)) being predominant. A single match for a L. adbullahbayrami NaTx was found in the UniProt database with other congeneric species, toxin h3.1 from Leiurus hebraeus, suggesting this might be an indication of venom divergence within Leiurus, eventhough this warrants further investigation involving venom proteomics and transcriptomics of relevant species. Considering such potential interspecific venom variation, future work should address whether preparation of a specific anti-L. abdullahbayrami antivenom is justified.

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network

Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Automated assessment of biological database assertions using the scientific literature

BACKGROUND

The large biological databases such as GenBank contain vast numbers of records, the content of which is substantively based on external resources, including published literature. Manual curation is used to establish whether the literature and the records are indeed consistent. We explore in this paper an automated method for assessing the consistency of biological assertions, to assist biocurators, which we call BARC, Biocuration tool for Assessment of Relation Consistency. In this method a biological assertion is represented as a relation between two objects (for example, a gene and a disease); we then use our novel set-based relevance algorithm SaBRA to retrieve pertinent literature, and apply a classifier to estimate the likelihood that this relation (assertion) is correct.

RESULTS

Our experiments on assessing gene-disease relations and protein-protein interactions using the PubMed Central collection show that BARC can be effective at assisting curators to perform data cleansing. Specifically, the results obtained showed that BARC substantially outperforms the best baselines, with an improvement of F-measure of 3.5% and 13%, respectively, on gene-disease relations and protein-protein interactions. We have additionally carried out a feature analysis that showed that all feature types are informative, as are all fields of the documents.

CONCLUSIONS

BARC provides a clear benefit for the biocuration community, as there are no prior automated tools for identifying inconsistent assertions in large-scale biological databases.

Functional annotation of orthologs in metagenomes: a case study of genes for the transformation of oceanic dimethylsulfoniopropionate

Dimethylsulfoniopropionate (DMSP) is produced mainly by phytoplankton and bacteria. It is relatively abundant and ubiquitous in the marine environment, where bacterioplankton make use of it readily as both carbon and sulfur sources. In one transformation pathway, part of the molecule becomes dimethylsulfide (DMS), which escapes into the atmosphere and plays an important role in the sulfur exchange between oceans and atmosphere. Through its other dominant catabolic pathway, bacteria are able to use it as sulfur source. During the past few years, a number of genes involved in its transformation have been characterized. Identifying genes in taxonomic groups not amenable to conventional methods of cultivation is challenging. Indeed, functional annotation of genes in environmental studies is not straightforward, considering that particular taxa are not well represented in the available sequence databases. Furthermore, many genes belong to families of paralogs with similar sequences but perhaps different functions. In this study, we develop in silico approaches to infer protein function of an environmentally important gene (dmdA) that carries out the first step in the sulfur assimilation from DMSP. The method combines a set of tools to annotate a targeted gene in genome databases and metagenome assemblies. The method will be useful to identify genes that carry out key biochemical processes in the environment.

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor.

Database URL: http://biomodels.caltech.edu

Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.

Scaling up data curation using deep learning: An application to literature triage in genomic variation resources

Manually curating biomedical knowledge from publications is necessary to build a knowledge based service that provides highly precise and organized information to users. The process of retrieving relevant publications for curation, which is also known as document triage, is usually carried out by querying and reading articles in PubMed. However, this query-based method often obtains unsatisfactory precision and recall on the retrieved results, and it is difficult to manually generate optimal queries. To address this, we propose a machine-learning assisted triage method. We collect previously curated publications from two databases UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog, and used them as a gold-standard dataset for training deep learning models based on convolutional neural networks. We then use the trained models to classify and rank new publications for curation. For evaluation, we apply our method to the real-world manual curation process of UniProtKB/Swiss-Prot and the GWAS Catalog. We demonstrate that our machine-assisted triage method outperforms the current query-based triage methods, improves efficiency, and enriches curated content. Our method achieves a precision 1.81 and 2.99 times higher than that obtained by the current query-based triage methods of UniProtKB/Swiss-Prot and the GWAS Catalog, respectively, without compromising recall. In fact, our method retrieves many additional relevant publications that the query-based method of UniProtKB/Swiss-Prot could not find. As these results show, our machine learning-based method can make the triage process more efficient and is being implemented in production so that human curators can focus on more challenging tasks to improve the quality of knowledge bases.

As the volume of literature on genomic variants continues to grow at an increasing rate, it is becoming more difficult for a curator of a variant knowledge base to keep up with and curate all the published papers. Here, we suggest a deep learning-based literature triage method for genomic variation resources. Our method achieves state-of-the-art performance on the triage task. Moreover, our model does not require any laborious preprocessing or feature engineering steps, which are required for traditional machine learning triage methods. We applied our method to the literature triage process of UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog for genomic variation by collaborating with the database curators. Both the manual curation teams confirmed that our method achieved higher precision than their previous query-based triage methods without compromising recall. Both results show that our method is more efficient and can replace the traditional query-based triage methods of manually curated databases. Our method can give human curators more time to focus on more challenging tasks such as actual curation as well as the discovery of novel papers/experimental techniques to consider for inclusion.

On expert curation and scalability: UniProtKB/Swiss-Prot as a case study

Motivation

Biological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, their ability to keep up with the growth of biomedical literature is under scrutiny. Using UniProtKB/Swiss-Prot as a case study, we address this concern via multiple literature triage approaches.

Results

With the assistance of the PubTator text-mining tool, we tagged more than 10 000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture as demonstrated by the fact that 8000–10 000 papers are curated in UniProt each year while curators evaluate 50 000–70 000 papers per year. We show that 90% of the papers in PubMed are out of the scope of UniProt, that a maximum of 2–3% of the papers indexed in PubMed each year are relevant for UniProt curation, and that, despite appearances, expert curation in UniProt is scalable.

Availability and implementation

UniProt is freely available at http://www.uniprot.org/.

Supplementary information

Supplementary data are available at Bioinformatics online.

eGenPub, a text mining system for extending computationally mapped bibliography for UniProt Knowledgebase by capturing centrality

UniProt Knowledgebase (UniProtKB) is a publicly available database with access to a vast amount of protein sequence and functional information. To widen the scope of the publications associated with a protein entry, UniProt has introduced the computationally mapped additional bibliography section, which includes literature collected from external sources. In this article, we describe a text mining system, eGenPub, which selects articles that are 'about' specific proteins and allows automatic identification of additional bibliography for given UniProt protein entries. Focusing on plant proteins initially, eGenPub utilizes a gene normalization tool called pGenN, and a trained support vector machine model, which achieves a precision of 95.3%, to predict whether an article, based on its abstract, should be linked to a given UniProt entry. We have conducted a full-scale PubMed processing using eGenPub for eight common plant species. Altogether, 9025 articles are identified as relevant bibliography for 4752 UniProt entries, among which 5252 are additional papers not in the existing publication section. These newly computationally mapped additional bibliography via eGenPub is being integrated in the UniProt production pipeline, and can be accessed via the UniProtKB protein entry publication view.

Deep learning of mutation-gene-drug relations from the literature

Background

Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature.

Results

Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks.

Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively.

Conclusion

We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2029-1) contains supplementary material, which is available to authorized users.

Strategies to improve usability and preserve accuracy in biological sequence databases

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

From the research laboratory to the database: the Caenorhabditis elegans kinome in UniProtKB

Protein kinases form one of the largest protein families and are found in all species, from viruses to humans. They catalyze the reversible phosphorylation of proteins, often modifying their activity and localization. They are implicated in virtually all cellular processes and are one of the most intensively studied protein families. In recent years, they have become key therapeutic targets in drug development as natural mutations affecting kinase genes are the cause of many diseases. The vast amount of data contained in the primary literature and across a variety of biological data collections highlights the need for a repository where this information is stored in a concise and easily accessible manner. The UniProt Knowledgebase meets this need by providing the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequence and functional information. Here, we describe the expert curation process for kinases, focusing on the Caenorhabditis elegans kinome. The C. elegans kinome is composed of 438 kinases and almost half of them have been functionally characterized, highlighting that C. elegans is a valuable and versatile model organism to understand the role of kinases in biological processes.

Automated detection of records in biological sequence databases that are inconsistent with the literature

We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious". Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available. Overall, the obtained results show promise for the development of a new kind of approach to detecting low-quality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature.

Duplicates, redundancies and inconsistencies in the primary nucleotide databases: a descriptive study

GenBank, the EMBL European Nucleotide Archive and the DNA DataBank of Japan, known collectively as the International Nucleotide Sequence Database Collaboration or INSDC, are the three most significant nucleotide sequence databases. Their records are derived from laboratory work undertaken by different individuals, by different teams, with a range of technologies and assumptions and over a period of decades. As a consequence, they contain a great many duplicates, redundancies and inconsistencies, but neither the prevalence nor the characteristics of various types of duplicates have been rigorously assessed. Existing duplicate detection methods in bioinformatics only address specific duplicate types, with inconsistent assumptions; and the impact of duplicates in bioinformatics databases has not been carefully assessed, making it difficult to judge the value of such methods. Our goal is to assess the scale, kinds and impact of duplicates in bioinformatics databases, through a retrospective analysis of merged groups in INSDC databases. Our outcomes are threefold: (1) We analyse a benchmark dataset consisting of duplicates manually identified in INSDC—a dataset of 67 888 merged groups with 111 823 duplicate pairs across 21 organisms from INSDC databases – in terms of the prevalence, types and impacts of duplicates. (2) We categorize duplicates at both sequence and annotation level, with supporting quantitative statistics, showing that different organisms have different prevalence of distinct kinds of duplicate. (3) We show that the presence of duplicates has practical impact via a simple case study on duplicates, in terms of GC content and melting temperature. We demonstrate that duplicates not only introduce redundancy, but can lead to inconsistent results for certain tasks. Our findings lead to a better understanding of the problem of duplication in biological databases.

Database URL: the merged records are available at https://cloudstor.aarnet.edu.au/plus/index.php/s/Xef2fvsebBEAv9w

Benchmarks for measurement of duplicate detection methods in nucleotide databases

Duplication of information in databases is a major data quality challenge. The presence of duplicates, implying either redundancy or inconsistency, can have a range of impacts on the quality of analyses that use the data. To provide a sound basis for research on this issue in databases of nucleotide sequences, we have developed new, large-scale validated collections of duplicates, which can be used to test the effectiveness of duplicate detection methods. Previous collections were either designed primarily to test efficiency, or contained only a limited number of duplicates of limited kinds. To date, duplicate detection methods have been evaluated on separate, inconsistent benchmarks, leading to results that cannot be compared and, due to limitations of the benchmarks, of questionable generality. In this study, we present three nucleotide sequence database benchmarks, based on information drawn from a range of resources, including information derived from mapping to two data sections within the UniProt Knowledgebase (UniProtKB), UniProtKB/Swiss-Prot and UniProtKB/TrEMBL. Each benchmark has distinct characteristics. We quantify these characteristics and argue for their complementary value in evaluation. The benchmarks collectively contain a vast number of validated biological duplicates; the largest has nearly half a billion duplicate pairs (although this is probably only a tiny fraction of the total that is present). They are also the first benchmarks targeting the primary nucleotide databases. The records include the 21 most heavily studied organisms in molecular biology research. Our quantitative analysis shows that duplicates in the different benchmarks, and in different organisms, have different characteristics. It is thus unreliable to evaluate duplicate detection methods against any single benchmark. For example, the benchmark derived from UniProtKB/Swiss-Prot mappings identifies more diverse types of duplicates, showing the importance of expert curation, but is limited to coding sequences. Overall, these benchmarks form a resource that we believe will be of great value for development and evaluation of the duplicate detection or record linkage methods that are required to help maintain these essential resources.

DATABASE URL

: https://bitbucket.org/biodbqual/benchmarks.

UniProt: the universal protein knowledgebase

The UniProt knowledgebase is a large resource of protein sequences and associated detailed annotation. The database contains over 60 million sequences, of which over half a million sequences have been curated by experts who critically review experimental and predicted data for each protein. The remainder are automatically annotated based on rule systems that rely on the expert curated knowledge. Since our last update in 2014, we have more than doubled the number of reference proteomes to 5631, giving a greater coverage of taxonomic diversity. We implemented a pipeline to remove redundant highly similar proteomes that were causing excessive redundancy in UniProt. The initial run of this pipeline reduced the number of sequences in UniProt by 47 million. For our users interested in the accessory proteomes, we have made available sets of pan proteome sequences that cover the diversity of sequences for each species that is found in its strains and sub-strains. To help interpretation of genomic variants, we provide tracks of detailed protein information for the major genome browsers. We provide a SPARQL endpoint that allows complex queries of the more than 22 billion triples of data in UniProt (http://sparql.uniprot.org/). UniProt resources can be accessed via the website at http://www.uniprot.org/.

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.

Text Mining to Support Gene Ontology Curation and Vice Versa

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

TOMATOMICS: A Web Database for Integrated Omics Information in Tomato

Solanum lycopersicum (tomato) is an important agronomic crop and a major model fruit-producing plant. To facilitate basic and applied research, comprehensive experimental resources and omics information on tomato are available following their development. Mutant lines and cDNA clones from a dwarf cultivar, Micro-Tom, are two of these genetic resources. Large-scale sequencing data for ESTs and full-length cDNAs from Micro-Tom continue to be gathered. In conjunction with information on the reference genome sequence of another cultivar, Heinz 1706, the Micro-Tom experimental resources have facilitated comprehensive functional analyses. To enhance the efficiency of acquiring omics information for tomato biology, we have integrated the information on the Micro-Tom experimental resources and the Heinz 1706 genome sequence. We have also inferred gene structure by comparison of sequences between the genome of Heinz 1706 and the transcriptome, which are comprised of Micro-Tom full-length cDNAs and Heinz 1706 RNA-seq data stored in the KaFTom and Sequence Read Archive databases. In order to provide large-scale omics information with streamlined connectivity we have developed and maintain a web database TOMATOMICS (http://bioinf.mind.meiji.ac.jp/tomatomics/). In TOMATOMICS, access to the information on the cDNA clone resources, full-length mRNA sequences, gene structures, expression profiles and functional annotations of genes is available through search functions and the genome browser, which has an intuitive graphical interface.

Best Practices in Manual Annotation with the Gene Ontology

The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientific literature is large and selection of appropriate GO terms can be challenging. Both these issues are compounded by the fact that our understanding of biology is still incomplete; hence it is important to appreciate that GO is inherently an evolving model. In this chapter, we describe how biocurators create GO annotations from experimental findings from research articles. We describe the current best practices for high-quality literature curation and how GO curators succeed in modeling biology using a relatively simple framework. We also highlight a number of difficulties when translating experimental assays into GO annotations.

UniProt: the universal protein knowledgebase

The UniProt knowledgebase is a large resource of protein sequences and associated detailed annotation. The database contains over 60 million sequences, of which over half a million sequences have been curated by experts who critically review experimental and predicted data for each protein. The remainder are automatically annotated based on rule systems that rely on the expert curated knowledge. Since our last update in 2014, we have more than doubled the number of reference proteomes to 5631, giving a greater coverage of taxonomic diversity. We implemented a pipeline to remove redundant highly similar proteomes that were causing excessive redundancy in UniProt. The initial run of this pipeline reduced the number of sequences in UniProt by 47 million. For our users interested in the accessory proteomes, we have made available sets of pan proteome sequences that cover the diversity of sequences for each species that is found in its strains and sub-strains. To help interpretation of genomic variants, we provide tracks of detailed protein information for the major genome browsers. We provide a SPARQL endpoint that allows complex queries of the more than 22 billion triples of data in UniProt (http://sparql.uniprot.org/). UniProt resources can be accessed via the website at http://www.uniprot.org/.

Methyl transfer by substrate signaling from a knotted protein fold

Proteins with knotted configurations, in comparison with unknotted proteins, are restricted in conformational space. Little is known regarding whether knotted proteins have sufficient dynamics to communicate between spatially separated substrate-binding sites. TrmD is a bacterial methyltransferase that uses a knotted protein fold to catalyze methyl transfer from S-adenosyl methionine (AdoMet) to G37-tRNA. The product, m1G37-tRNA, is essential for life and maintains protein-synthesis reading frames. Using an integrated approach of structural, kinetic, and computational analysis, we show that the structurally constrained TrmD knot is required for its catalytic activity. Unexpectedly, the TrmD knot undergoes complex internal movements that respond to AdoMet binding and signaling. Most of the signaling propagates the free energy of AdoMet binding, thereby stabilizing tRNA binding and allowing assembly of the active site. This work demonstrates new principles of knots as organized structures that capture the free energies of substrate binding and facilitate catalysis.