With the goal of providing a comprehensive, high-quality resource for both plant transcription factors (TFs) and their regulatory interactions with target genes, we upgraded plant TF database PlantTFDB to version 4.0 (http://planttfdb.cbi.pku.edu.cn/). In the new version, we identified 320 370 TFs from 165 species, presenting a more comprehensive genomic TF repertoires of green plants. Besides updating the pre-existing abundant functional and evolutionary annotation for identified TFs, we generated three new types of annotation which provide more directly clues to investigate functional mechanisms underlying: (i) a set of high-quality, non-redundant TF binding motifs derived from experiments; (ii) multiple types of regulatory elements identified from high-throughput sequencing data; (iii) regulatory interactions curated from literature and inferred by combining TF binding motifs and regulatory elements. In addition, we upgraded previous TF prediction server, and set up four novel tools for regulation prediction and functional enrichment analyses. Finally, we set up a novel companion portal PlantRegMap (http://plantregmap.cbi.pku.edu.cn) for users to access the regulation resource and analysis tools conveniently.

/pdf/planttfdb-4-0-toward-a-central-hub-for-transcription-factors-4brrhx6xw0.pdf

PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants.

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein
 interaction data from model organisms and humans
 (http://www.thebiogrid.org). BioGRID currently holds 347 966
 interactions (170 162 genetic, 177 804 protein) curated from both
 high-throughput data sets and individual focused studies, as derived
 from over 23 000 publications in the primary literature. Complete
 coverage of the entire literature is maintained for budding yeast
 (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe)
 and thale cress (Arabidopsis thaliana), and efforts to expand curation
 across multiple metazoan species are underway. The BioGRID houses 48
 831 human protein interactions that have been curated from 10 247
 publications. Current curation drives are focused on particular areas
 of biology to enable insights into conserved networks and pathways that
 are relevant to human health. The BioGRID 3.0 web interface contains
 new search and display features that enable rapid queries across
 multiple data types and sources. An automated Interaction Management
 System (IMS) is used to prioritize, coordinate and track curation
 across international sites and projects. BioGRID provides interaction
 data to several model organism databases, resources such as Entrez-Gene
 and other interaction meta-databases. The entire BioGRID 3.0 data
 collection may be downloaded in multiple file formats, including PSI MI
 XML. Source code for BioGRID 3.0 is freely available without any
 restrictions.

/pdf/the-biogrid-interaction-database-2011-update-4t23jtqdsl.pdf

The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

The BioGRID interaction database: 2019 update

RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new methods to handle the high error rate of long reads and offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of short-read assemblies. StringTie2 is more accurate and faster and uses less memory than all comparable short-read and long-read analysis tools.

/pdf/transcriptome-assembly-from-long-read-rna-seq-alignments-1nbtfw6qyl.pdf

Transcriptome assembly from long-read RNA-seq alignments with StringTie2

Abstract The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

/pdf/the-gene-ontology-knowledgebase-in-2023-1lnqxbzz.pdf

The Gene Ontology knowledgebase in 2023

The Arabidopsis Information Resource (TAIR, http://arabidopsisorg) is a genome database for Arabidopsis thaliana, an important reference organism for many fundamental aspects of biology as well as basic and applied plant biology research TAIR serves as a central access point for Arabidopsis data, annotates gene function and expression patterns using controlled vocabulary terms, and maintains and updates the A thaliana genome assembly and annotation TAIR also provides researchers with an extensive set of visualization and analysis tools Recent de- velopments include several new genome releases (TAIR8, TAIR9 and TAIR10) in which the A thaliana assembly was updated, pseudogenes and transposon genes were re-annotated, and new data from proteomics and next generation transcriptome sequencing were incorporated into gene models and splice variants Other highlights include progress on functional anno- tation of the genome and the release of sev- eral new tools including Textpresso for Arabidopsis which provides the capability to carry out full text searches on a large body of research literature

The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools

Symbiosis between unicellular dinoflagellates (genus Symbiodinium) and their cnidarian hosts (e.g. corals, sea anemones) is the foundation of coral reef ecosystems. Dysfunction of this symbiosis under changing environmental conditions has led to global reef decline. Little information is known about Symbiodinium gene expression and mechanisms by which light impacts host-symbiont associations. To address these issues, we generated a transcriptome from axenic Symbiodinium strain SSB01. Here we report features of the transcriptome, including occurrence and length distribution of spliced leader sequences, the functional landscape of encoded proteins and the impact of light on gene expression. Expression of many Symbiodinium genes appears to be significantly impacted by light. Transcript encoding cryptochrome 2 declined in high light while some transcripts for Regulators of Chromatin Condensation (RCC1) declined in the dark. We also identified a transcript encoding a light harvesting AcpPC protein with homology to Chlamydomonas LHCSR2. The level of this transcript increased in high light autotrophic conditions, suggesting that it is involved in photo-protection and the dissipation of excess absorbed light energy. The most extensive changes in transcript abundances occurred when the algae were transferred from low light to darkness. Interestingly, transcripts encoding several cell adhesion proteins rapidly declined following movement of cultures to the dark, which correlated with a dramatic change in cell surface morphology, likely reflecting the complexity of the extracellular matrix. Thus, light-sensitive cell adhesion proteins may play a role in establishing surface architecture, which may in turn alter interactions between the endosymbiont and its host.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tpj.12789

Symbiodiniumtranscriptome and global responses of cells to immediate changes in light intensity when grown under autotrophic or mixotrophic conditions

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The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools

Symbiodiniumtranscriptome and global responses of cells to immediate changes in light intensity when grown under autotrophic or mixotrophic conditions