Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model

The Arabidopsis Information Resource in 2024

Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a "gold standard" functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access, and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resource's role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used gene ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function and (2) the set of "unknown" genes. Currently, 74% of the proteome has been annotated to at least one gene ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries.

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists' work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Using the Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes

The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs, gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of web-based search and display tools. This article primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genomes. Additionally, we describe how members of the community can share data via JBrowse and the Generic Online Annotation Submission Tool (GOAT) in order to make their published research more accessible and visible. © 2022 Wiley Periodicals LLC. Basic Protocol 1: TAIR homepage, sitemap, and navigation Basic Protocol 2: Finding comprehensive information about Arabidopsis genes Basic Protocol 3: Using the Arabidopsis genome browser: JBrowse Basic Protocol 4: Using the Gene Ontology annotations for gene discovery and gene function analysis Basic Protocol 5: Using gene lists to download bulk datasets Basic Protocol 6: Using TAIR's analysis tools to find short sequences and motifs Basic Protocol 7: Using the TAIR generic online annotation tool (GOAT) to submit functional annotations for Arabidopsis (or any other species) genes Basic Protocol 8: Using PhyloGenes to visualize gene families and predict functions Basic Protocol 9: Using TAIR to browse Arabidopsis literature Basic Protocol 10: Using the synteny viewer to find and display syntenic regions from Arabidopsis and other plant species.

New reasons for biologists to write with a formal language

Current biological writing is afflicted by the use of ambiguous names, convoluted sentences, vague statements and narrative-fitted storylines. This represents a challenge for biological research in general and in particular for fields such as biological database curation and text mining, which have been tasked to cope with exponentially growing content. Improving the quality of biological writing by encouraging unambiguity and precision would foster expository discipline and machine reasoning. More specifically, the routine inclusion of formal languages in biological writing would improve our ability to describe, compile and model biology.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions’ strength distribution, the challenges to studying these interactions’ strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases

Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

The increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

Ewé: a web-based ethnobotanical database for storing and analysing data

Ethnobotanical databases serve as repositories of traditional knowledge (TK), either at international or local scales. By documenting plant species with traditional use, and most importantly, the applications and modes of use of such species, ethnobotanical databases play a role in the conservation of TK and also provide access to information that could improve hypothesis generation and testing in ethnobotanical studies. Brazil has a rich medicinal flora and a rich cultural landscape. Nevertheless, cultural change and ecological degradation can lead to loss of TK. Here, we present an online database developed with open-source tools with a capacity to include all medicinal flora of Brazil. We present test data for the Leguminosae comprising a total of 2078 records, referred to here as use reports, including data compiled from literature and herbarium sources. Unlike existing databases, Ewé provides tools for the visualization of large datasets, facilitating hypothesis generation and meta-analyses. The Ewé database is currently available at www.ewedb.com.

Current status of the multinational Arabidopsis community

The multinational Arabidopsis research community is highly collaborative and over the past thirty years these activities have been documented by the Multinational Arabidopsis Steering Committee (MASC). Here, we (a) highlight recent research advances made with the reference plant Arabidopsis thaliana; (b) provide summaries from recent reports submitted by MASC subcommittees, projects and resources associated with MASC and from MASC country representatives; and (c) initiate a call for ideas and foci for the "fourth decadal roadmap," which will advise and coordinate the global activities of the Arabidopsis research community.

COPO: a metadata platform for brokering FAIR data in the life sciences

Scientific innovation is increasingly reliant on data and computational resources. Much of today’s life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data. COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share them with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible, building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.

Wikidata as a knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

Tripal v3: an ontology-based toolkit for construction of FAIR biological community databases

Community biological databases provide an important online resource for both public and private data, analysis tools and community engagement. These sites house genomic, transcriptomic, genetic, breeding and ancillary data for specific species, families or clades. Due to the complexity and increasing quantities of these data, construction of online resources is increasingly difficult especially with limited funding and access to technical expertise. Furthermore, online repositories are expected to promote FAIR data principles (findable, accessible, interoperable and reusable) that presents additional challenges. The open-source Tripal database toolkit seeks to mitigate these challenges by creating both the software and an interactive community of developers for construction of online community databases. Additionally, through coordinated, distributed co-development, Tripal sites encourage community-wide sustainability. Here, we report the release of Tripal version 3 that improves data accessibility and data sharing through systematic use of controlled vocabularies (CVs). Tripal uses the community-developed Chado database as a default data store, but now provides tools to support other data stores, while ensuring that CVs remain the central organizational structure for the data. A new site developer can use Tripal to develop a basic site with little to no programming, with the ability to integrate other data types using extension modules and the Tripal application programming interface. A thorough online User’s Guide and Developer’s Handbook are available at http://tripal.info, providing download, installation and step-by-step setup instructions.

Double triage to identify poorly annotated genes in maize: The missing link in community curation

The sophistication of gene prediction algorithms and the abundance of RNA-based evidence for the maize genome may suggest that manual curation of gene models is no longer necessary. However, quality metrics generated by the MAKER-P gene annotation pipeline identified 17,225 of 130,330 (13%) protein-coding transcripts in the B73 Reference Genome V4 gene set with models of low concordance to available biological evidence. Working with eight graduate students, we used the Apollo annotation editor to curate 86 transcript models flagged by quality metrics and a complimentary method using the Gramene gene tree visualizer. All of the triaged models had significant errors-including missing or extra exons, non-canonical splice sites, and incorrect UTRs. A correct transcript model existed for about 60% of genes (or transcripts) flagged by quality metrics; we attribute this to the convention of elevating the transcript with the longest coding sequence (CDS) to the canonical, or first, position. The remaining 40% of flagged genes resulted in novel annotations and represent a manual curation space of about 10% of the maize genome (~4,000 protein-coding genes). MAKER-P metrics have a specificity of 100%, and a sensitivity of 85%; the gene tree visualizer has a specificity of 100%. Together with the Apollo graphical editor, our double triage provides an infrastructure to support the community curation of eukaryotic genomes by scientists, students, and potentially even citizen scientists.

The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation

Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering.

PlaSMo stands for ‘Plant Systems-biology Modelling’ and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting until 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative, public repository with secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in a similar situation.

We share our reflections on the sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.

Comparative Transcriptome Analysis of Pinus densiflora Following Inoculation with Pathogenic (Bursaphelenchus xylophilus) or Non-pathogenic Nematodes (B. thailandae)

Pinus densiflora (Korean red pine) is a species of evergreen conifer that is distributed in Korea, Japan, and China, and of economic, scientific, and ecological importance. Korean red pines suffer from pine wilt disease (PWD) caused by Bursaphelenchus xylophilus, the pinewood nematode (PWN). To facilitate diagnosis and prevention of PWD, studies have been conducted on the PWN and its beetle vectors. However, transcriptional responses of P. densiflora to PWN have received less attention. Here, we inoculated Korean red pines with pathogenic B. xylophilus, or non-pathogenic B. thailandae, and collected cambium layers 4 weeks after inoculation for RNA sequencing analysis. We obtained 72,864 unigenes with an average length of 869 bp (N50 = 1,403) from a Trinity assembly, and identified 991 differentially expressed genes (DEGs). Biological processes related to phenylpropanoid biosynthesis, flavonoid biosynthesis, oxidation–reduction, and plant-type hypersensitive response were significantly enriched in DEGs found in trees inoculated with B. xylophilus. Several transcription factor families were found to be involved in the response to B. xylophilus inoculation. Our study provides the first evidence of transcriptomic differences in Korean red pines inoculated with B. xylophilus and B. thailandae, and might facilitate early diagnosis of PWD and selection of PWD-tolerant Korean red pines.

agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update

The agriGO platform, which has been serving the scientific community for >10 years, specifically focuses on gene ontology (GO) enrichment analyses of plant and agricultural species. We continuously maintain and update the databases and accommodate the various requests of our global users. Here, we present our updated agriGO that has a largely expanded number of supporting species (394) and datatypes (865). In addition, a larger number of species have been classified into groups covering crops, vegetables, fish, birds and insects closely related to the agricultural community. We further improved the computational efficiency, including the batch analysis and P-value distribution (PVD), and the user-friendliness of the web pages. More visualization features were added to the platform, including SEACOMPARE (cross comparison of singular enrichment analysis), direct acyclic graph (DAG) and Scatter Plots, which can be merged by choosing any significant GO term. The updated platform agriGO v2.0 is now publicly accessible at http://systemsbiology.cau.edu.cn/agriGOv2/.

AppleMDO: A Multi-Dimensional Omics Database for Apple Co-Expression Networks and Chromatin States

As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO (http://bioinformatics.cau.edu.cn/AppleMDO/) was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

Heavy metal rich stone-processing wastewater inhibits the growth and development of plants

Large amounts of wastewater are generated from stone processing, which are toxic and cause serious environmental and health risks. To quantify the content of stone processing wastewater and estimate its effects on plant growth, we collected water samples from sewage outfall of four stone processing factories and nearby water bodies. The concentration of potential toxic metals were much higher in the wastewater than background controls. Wastewater inhibited plant primary root elongation, lateral root formation, and growth of aerial part. Seedlings treated with the effluents were unhealthy with deep purple leaves and usually died before flowering. Chlorophyll a/b contents and chloroplast number were reduced in those abnormal mesophyll cells. Transcriptional levels were decreased for chloroplast formation genes, but increased for those participated in chloroplast degradation and catabolism. Six out of nine tested senescence-associated genes were up-regulated. Furthermore, our results show that endogenous toxic metal levels indeed increased after wastewater treatment. Altogether, these results indicated that the potential toxic metals rich wastewater had significant inhibition on plant growth and led to senescence-associated program cell death, which could be helpful for the government and enterprises to understand the environmental risks and formulate reasonable wastewater emission standards for the stone processing industry.

Dynamic modeling of transcriptional gene regulatory network uncovers distinct pathways during the onset of Arabidopsis leaf senescence

Age-dependent senescence is a multifaceted and highly coordinated developmental phase in the life of plants that is manifested with genetic, biochemical and phenotypic continuum. Thus, elucidating the dynamic network modeling and simulation of molecular events, in particular gene regulatory network during the onset of senescence is essential. Here, we constructed a computational pipeline that integrates senescence-related co-expression networks with transcription factor (TF)-promoter relationships and microRNA (miR)-target interactions. Network structural and functional analyses revealed important nodes within each module of these co-expression networks. Subsequently, we inferred significant dynamic transcriptional regulatory models in leaf senescence using time-course gene expression datasets. Dynamic simulations and predictive network perturbation analyses followed by experimental dataset illustrated the kinetic relationships among TFs and their downstream targets. In conclusion, our network science framework discovers cohorts of TFs and their paths with previously unrecognized roles in leaf senescence and provides a comprehensive landscape of dynamic transcriptional circuitry.

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Funding knowledgebases: Towards a sustainable funding model for the UniProt use case

Millions of life scientists across the world rely on bioinformatics data resources for their research projects. Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases. Despite the increasing need for such highly accurate and reliable sources of scientific information, most of them do not have secured funding over the near future and often depend on short-term grants that are much shorter than their planning horizon. Additionally, they are often evaluated as research projects rather than as research infrastructure components.

In this work, twelve funding models for data resources are described and applied on the case study of the Universal Protein Resource (UniProt), a key resource for protein sequences and functional information knowledge. We show that most of the models present inconsistencies with open access or equity policies, and that while some models do not allow to cover the total costs, they could potentially be used as a complementary income source.

We propose the Infrastructure Model as a sustainable and equitable model for all core data resources in the life sciences. With this model, funding agencies would set aside a fixed percentage of their research grant volumes, which would subsequently be redistributed to core data resources according to well-defined selection criteria. This model, compatible with the principles of open science, is in agreement with several international initiatives such as the Human Frontiers Science Program Organisation (HFSPO) and the OECD Global Science Forum (GSF) project. Here, we have estimated that less than 1% of the total amount dedicated to research grants in the life sciences would be sufficient to cover the costs of the core data resources worldwide, including both knowledgebases and deposition databases.

ThaleMine: A Warehouse for Arabidopsis Data Integration and Discovery

ThaleMine (https://apps.araport.org/thalemine/) is a comprehensive data warehouse that integrates a wide array of genomic information of the model plant Arabidopsis thaliana. The data collection currently includes the latest structural and functional annotation from the Araport11 update, the Col-0 genome sequence, RNA-seq and array expression, co-expression, protein interactions, homologs, pathways, publications, alleles, germplasm and phenotypes. The data are collected from a wide variety of public resources. Users can browse gene-specific data through Gene Report pages, identify and create gene lists based on experiments or indexed keywords, and run GO enrichment analysis to investigate the biological significance of selected gene sets. Developed by the Arabidopsis Information Portal project (Araport, https://www.araport.org/), ThaleMine uses the InterMine software framework, which builds well-structured data, and provides powerful data query and analysis functionality. The warehoused data can be accessed by users via graphical interfaces, as well as programmatically via web-services. Here we describe recent developments in ThaleMine including new features and extensions, and discuss future improvements. InterMine has been broadly adopted by the model organism research community including nematode, rat, mouse, zebrafish, budding yeast, the modENCODE project, as well as being used for human data. ThaleMine is the first InterMine developed for a plant model. As additional new plant InterMines are developed by the legume and other plant research communities, the potential of cross-organism integrative data analysis will be further enabled.

Crowd-sourcing and author submission as alternatives to professional curation

Can we decrease the costs of database curation by crowd-sourcing curation work or by offloading curation to publication authors? This perspective considers the significant experience accumulated by the bioinformatics community with these two alternatives to professional curation in the last 20 years; that experience should be carefully considered when formulating new strategies for biological databases. The vast weight of empirical evidence to date suggests that crowd-sourced curation is not a successful model for biological databases. Multiple approaches to crowd-sourced curation have been attempted by multiple groups, and extremely low participation rates by ‘the crowd’ are the overwhelming outcome. The author-curation model shows more promise for boosting curator efficiency. However, its limitations include that the quality of author-submitted annotations is uncertain, the response rate is low (but significant), and to date author curation has involved relatively simple forms of annotation involving one or a few types of data. Furthermore, shifting curation to authors may simply redistribute costs rather than decreasing costs; author curation may in fact increase costs because of the overhead involved in having every curating author learn what professional curators know: curation conventions, curation software and curation procedures.

AtPID: a genome-scale resource for genotype-phenotype associations in Arabidopsis

AtPID (Arabidopsis thalianaProtein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

ccNET: Database of co-expression networks with functional modules for diploid and polyploid Gossypium

Plant genera with both diploid and polyploid species are a common evolutionary occurrence. Polyploids, especially allopolyploids such as cotton and wheat, are a great model system for heterosis research. Here, we have integrated genome sequences and transcriptome data of Gossypium species to construct co-expression networks and identified functional modules from different cotton species, including 1155 and 1884 modules in G. arboreum and G. hirsutum, respectively. We overlayed the gene expression results onto the co-expression network. We further provided network comparison analysis for orthologous genes across the diploid and allotetraploid Gossypium. We also constructed miRNA-target networks and predicted PPI networks for both cotton species. Furthermore, we integrated in-house ChIP-seq data of histone modification (H3K4me3) together with cis-element analysis and gene sets enrichment analysis tools for studying possible gene regulatory mechanism in Gossypium species. Finally, we have constructed an online ccNET database (http://structuralbiology.cau.edu.cn/gossypium) for comparative gene functional analyses at a multi-dimensional network and epigenomic level across diploid and polyploid Gossypium species. The ccNET database will be beneficial for community to yield novel insights into gene/module functions during cotton development and stress response, and might be useful for studying conservation and diversity in other polyploid plants, such as T. aestivum and Brassica napus.