Dataset Information

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort.

ABSTRACT: Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information. Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the controlled vocabularies defined by the Gene Ontology Consortium to annotate specific DNA binding transcription factors (DbTFs) based on experimental evidence reported in literature. Our standardized protocol and workflow for annotating specific DNA binding RNA polymerase II transcription factors is designed to document high-quality and decisive evidence from valid experimental methods. Within a collaborative biocuration effort involving the user community, we are now in the process of exhaustively annotating the full repertoire of human, mouse and rat proteins that qualify as DbTFs in as much as they are experimentally documented in the biomedical literature today. The completion of this task will significantly enrich Gene Ontology-based information resources for the research community. Database URL: www.tfcheckpoint.org.

SUBMITTER: Tripathi S

PROVIDER: S-EPMC3753819 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort.

Tripathi Sushil S Christie Karen R KR Balakrishnan Rama R Huntley Rachael R Hill David P DP Thommesen Liv L Blake Judith A JA Kuiper Martin M Lægreid Astrid A

Database : the journal of biological databases and curation 20130827

Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information. Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the ...[more]

PMID: 23981286

Similar Datasets

Project description:PurposeThe curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.DesignImplementation of an AI algorithm.ParticipantsSeven-field stereoscopic images from multiple clinical trials.MethodsThe 7-field stereoscopic image protocol includes 7 pairs of images from various parts of the central retina along with images of the anterior part of the eye. All images were labeled for field number by reading center graders. The model output included classification of the retinal images into 8 field numbers. Probability scores (0-1) were generated to identify misclassified images, with 1 indicating a high probability of a correct label.Main outcome measuresAgreement of AI prediction with grader classification of field number and the use of probability scores to identify mislabeled images.ResultsThe AI model was trained and validated on 17 529 images and tested on 3004 images. The pooled agreement of field numbers between grader classification and the AI model was 88.3% (kappa, 0.87). The pooled mean probability score was 0.97 (standard deviation [SD], 0.08) for images for which the graders agreed with the AI-generated labels and 0.77 (SD, 0.19) for images for which the graders disagreed with the AI-generated labels (P < 0.0001). Using receiver operating characteristic curves, a probability score of 0.99 was identified as a cutoff for distinguishing mislabeled images. A tiered workflow using a probability score of < 0.99 as a cutoff would include 27.6% of the 3004 images for human review and reduce the error rate from 11.7% to 1.5%.ConclusionsThe implementation of AI algorithms requires measures in addition to model validation. Tools to flag potential errors in the labels generated by AI models will reduce inaccuracies, increase trust in the system, and provide data for continuous model development.

Project description:BACKGROUND: Cotton fiber is the world's leading natural fiber used in the manufacture of textiles. Gossypium is also the model plant in the study of polyploidization, evolution, cell elongation, cell wall development, and cellulose biosynthesis. G. barbadense L. is an ideal candidate for providing new genetic variations useful to improve fiber quality for its superior properties. However, little is known about fiber development mechanisms of G. barbadense and only a few molecular resources are available in GenBank. METHODOLOGY AND PRINCIPAL FINDINGS: In total, 10,979 high-quality expressed sequence tags (ESTs) were generated from a normalized fiber cDNA library of G. barbadense. The ESTs were clustered and assembled into 5852 unigenes, consisting of 1492 contigs and 4360 singletons. The blastx result showed 2165 unigenes with significant similarity to known genes and 2687 unigenes with significant similarity to genes of predicted proteins. Functional classification revealed that unigenes were abundant in the functions of binding, catalytic activity, and metabolic pathways of carbohydrate, amino acid, energy, and lipids. The function motif/domain-related cytoskeleton and redox homeostasis were enriched. Among the 5852 unigenes, 282 and 736 unigenes were identified as potential cell wall biosynthesis and transcription factors, respectively. Furthermore, the relationships among cotton species or between cotton and other model plant systems were analyzed. Some putative species-specific unigenes of G. barbadense were highlighted. CONCLUSIONS/SIGNIFICANCE: The ESTs generated in this study are from the first large-scale EST project for G. barbadense and significantly enhance the number of G. barbadense ESTs in public databases. This knowledge will contribute to cotton improvements by studying fiber development mechanisms of G. barbadense, establishing a breeding program using marker-assisted selection, and discovering candidate genes related to important agronomic traits of cotton through oligonucleotide array. Our work will also provide important resources for comparative genomics, polyploidization, and genome evolution among Gossypium species.

Dataset Information

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort.

Publications

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets