A relational database that offers, in an organized and computable form, updated knowledge on transcriptional regulation in Escherichia coli K-12. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Major changes to the overall navigation and structure of the main pages have been made, offering more structured access to the data, based on the two dominant types of users: biologists, usually conducting individual search queries, and those interested in data collections.
Provides data on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes. TRANSCompel contains data on eukaryotic transcription factors experimentally proven to act together in a synergistic or antagonistic manner.
A database of functional and evolutionary study of plant transcription factors. With the version 4.0, PlantTFDB offers 320 370 TFs from 165 species. Three types of annotation 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 highthroughput sequencing data and (iii) regulatory interactions curated from literature and inferred by combining TF binding motifs and regulatory elements.
A toolkit for the study of transcriptional regulation in plants. PlantRegMap provides a comprehensive, high-quality resource of plant transcription factors, regulatory elements and interactions between them. It contains sets of high-quality transcription factor binding motifs for 157 plant species, multiple types of regulatory elements derived from experiments, and genome-wide transcriptional regulatory interactions. In addition, multiple online tools are set up for transcription factor prediction, transcription factor binding site prediction, regulation prediction and functional enrichment analyses.
Covers motifs found in plant cis-acting regulatory DNA elements. PLACE contains some motifs in non-plant cis-elements in the hope that assist researchers in finding plant homologues. It offers functions allowing keyword search, signal scan search, or homology search in FASTA format. This database furnishes a brief definition and description of each motif, and relevant literature with PubMed ID numbers and GenBank accession numbers.
Provides data on enhancer control by transcription factors (TFs) and cofactors. Factors is a database that includes almost 470 TFs and more than 330 transcriptional cofactors (chromatin remodelers and modifiers and components of the basal transcription machinery) targeted to 24 contexts. Users can search for a specific protein or perform an advanced search with different features such as ortholog factors or cofactors.
Integrates several open access repositories of curated cis-elements, DNA motifs, and TFs into a unique repository. footprintDB systematically annotates the binding interfaces of the TFs by exploiting protein–DNA complexes deposited in the Protein Data Bank. Each entry in footprintDB is thus a DNA motif linked to the protein sequence of the TF(s) known to recognize it, and in most cases, the set of predicted interface residues involved in specific recognition.
Provides information about gene regulation in prokaryotes. PRODORIC is a database that gathers DNA binding sites for prokaryotic transcription factors. This repository includes entries generated by manually screening the literature, as well as transcription factor binding site (TFBS) detected by diverse high-throughput techniques. The database provides a basis for the prediction of gene regulatory networks (GRNs). The web application Virtual Footprint, for recognizing DNA patterns in prokaryotic genomes, is also available, but only the most essential options are offered.
It was originally constructed as a collection of uniquely determined transcriptional start sites (TSSs) in humans and some other species in 2002. Since then, DBTSS has been regularly updated and in recent updates epigenetic information has also been incorporated because such information is useful for characterizing the biological relevance of these TSSs/downstream genes. DBTSS is no longer a mere storage site for TSS information but has evolved into an integrative platform of a variety of genome activity data.
Provides information about transcription factor binding sites, as well as composite relationships within transcription factors. ooTFD based on data structures derived from the tables of the original database, now referred to as relational Transcription Factors Database (rTFD), which gathers information about the polypeptide interactions comprising and defining the properties of transcription factors.
Provides a set of hierarchical multi-layered concept of transcriptional regulation. CoryneRegNet consists of an ontology-based data warehouse. It employs a modular data processing pipeline that can recognize clusters of homologous proteins, match binding site motifs, determine operons and display special networks and graphs. This platform is useful for large-scale analysis of transcriptional regulation of gene expression in corynebacterial microorganisms.
A database on composite elements. TRANSCompel focuses on so-called composite elements, consisting of two (or more) neighboring binding sites, characterized by synergistic or antagonistic effects between the two transcription factors binding to them. It contains, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based.
Gathers data on more than 1700 transcriptions factors (TFs) related to Arabidopsis thaliana. AtTFDB is classified according to 50 families based on the presence of conserved domains. Searches can be made by browsing these families or by submitting a locus ID or a gene name. It displays a chart that give access to gene synonyms, nucleotide and protein sequences, information on related regulatory sub-network and links towards external pages such as The Arabidopsis Information Resource (TAIR). The complete database can be downloaded in zip format.
Provides a computational system adapted to genome annotation. rSNP_Guide includes both sequences of known Transcription Factors (TFs)-sites different types and available experimental data on alterations in binding of mutated DNA to unknown TFs. This resource focuses on the annotation of potential TF sites using models of experimentally characterized altered TF sites derived by rSNP_Guide system for recognizing TFs relevant to the known TF site.
A comprehensive resource for the analysis of Prokaryotic Two-Component Systems (TCSs). TCSs are comprised of a receptor histidine kinase (HK) and a partner response regulator (RR) and control important prokaryotic behaviors. The latest incarnation of P2CS includes 164 651 TCS proteins, from 2758 sequenced prokaryotic genomes. Several important new features have been added to P2CS since it was last described. Users can search P2CS via BLAST, adding hits to their cart, and homologous proteins can be aligned using MUSCLE and viewed using Jalview within P2CS. P2CS also provides phylogenetic trees based on the conserved signaling domains of the RRs and HKs from entire genomes.
Represents an integrated collaborative effort in neuroscience and psychiatry to collectively analyze genomic regulatory elements in a large cohort of well-curated human brains. Key goals of the PsychENCODE project are to provide an enhanced framework of regulatory genomic elements (promoters, enhancers, silencers and insulators), catalog epigenetic modifications and quantify coding and non-coding RNA and protein expression in tissue-and cell-type-specific samples from healthy (neurotypical) control and disease-affected post-mortem human brains.
Provides a portal dealing with Arabidopsis genes regulatory information. AGRIS is a repository composed of three mains panels (i) AtcisDB, that compiles upstream regions of annotated Arabidopsis genes; (ii) AtTFDB, that displays information on transcription factors (TFs); and (iii) GRG-X that allows users to browse among direct interactions between TFs and target genes. In addition, the platform provides links towards external resources.
Provides information about pathologically relevant mutations in transcription factor (TF) genes or in their binding sites (TFBS). PathoDB is a module of TRANSFAC, a database of TF and their DNA-binding sites and profiles. The database contains more than 10 530 transcription factor mutations and 19 mutated binding sites. It is searchable by diagnose methods, genotypes, mutated factors or sites and by phenotypes.
A database of predicted transcription factors in completely sequenced genomes. The predicted transcription factors all contain assignments to sequence specific DNA-binding domain families. The predictions are based on domain assignments from the SUPERFAMILY and Pfam hidden Markov model libraries.
A data warehouse that addresses one of the important challenges of modern biology: how to integrate and make use of the diversity and volume of current biological data. Its main focus is genomic and proteomics data for Drosophila and other insects. FlyMine provides web access to integrated data at a number of different levels, from simple browsing to construction of complex queries, which can be executed on either single items or lists.
Simplifies the studies on insulators and their roles in demarcating functional genomic domains. CTCFBSDB is an online database that includes almost 15 million experimentally determined CTCF binding sites across several species. This repository contains several features: (1) inclusion of genomic topological domains defined using Hi-C data; (2) identification of CTCF-binding sequences that overlap a given CTCF-binding sequence; (3) inclusion of occupancy data; (4) classification of motif match type; and (5) integration with Genome Browser.
A comprehensive database including classification and annotation of genome-wide transcription factors (TFs), transcription co-factors and chromatin remodeling factors in 65 animal genomes. The TFs are further classified into 70 families based on their DNA-binding domain (DBD).
Enables to explore the transcriptional regulatory networks of noncoding RNAs (ncRNAs) and protein-coding genes (PCGs). ChIPBase is an open database that integrates many ChIPseq peak datasets of trans-acting factors, including transcription factors (TFs), transcription cofactors (TCFs), chromatin-remodeling factors (CRFs), other DNA-binding proteins and histone modifications. The database consists of nine web-based modules and tools.
A recently introduced new part of the Eukaryotic Promoter Database (EPD) which has been described in more detail in a previous NAR Database Issue. EPD is an old database of experimentally characterized eukaryotic POL II promoters, which are conceptually defined as transcription initiation sites or regions. EPDnew is a collection of automatically compiled, organism-specific promoter lists complementing the old corpus of manually compiled promoter entries of EPD. This part is exclusively derived from next generation sequencing data from high-throughput promoter mapping experiments.
Aims at classifying eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs). For this, a classification schema comprising four generic levels (superclass, class, family and subfamily) was defined that could accommodate all known DNA-binding human TFs. TFClass is freely available through a web interface and for download in OBO format.
Provides data of computationally predicted regulatory interactions within the genomes of several organisms of this group. Tractor_DB contains orthology relationships between gene pairs that are constructed with the bidirectional best hits (BBH) methodology. It permits the user to directly retrieve the information regarding the conservation of regulatory interactions within a given regulon from a map that contains all known Escherichia coli transcription factors (TFs) and the regulatory interactions that interconnect them.
A database for human transcription co-factors and transcription factor interacting proteins. The content of the database has significantly expanded, and is enriched with information from Gene Ontology, biological pathways, diseases and molecular signatures. TcoF-DB enables the exploration of information on TcoFs and allows investigations into their influence on transcriptional regulation in humans and mice.
Provides a comprehensive parts list of functional elements in the human genome. ENCODE is an online resource that includes elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active. This corpus of data provides an astounding resource for annotation, curation and functional characterization in the human and mouse genomes in a large variety of sample types.
A comprehensive and consistently annotated knowledgebase of all published or public ChIP-seq and DNase-seq data in mouse and human. In total, there are 2711 ChIP-Seq datasets for transcription and chromatin regulators, 2355 for histone modifications and variants, 412 DNase-Seq and 996 control datasets. Among transcription and chromatin regulators, POLR2A, CTCF, ESR1, RELA and EP300 are the most often profiled ChIP-Seq factors. For histone marks, H3K4me3, H3K27me3, H3K4me1, H3K36me3 and H3K9me3 ChIP-Seq are the most common, which together accounts for over 70% of all of the histone ChIP-Seq data.
Compiles data relative to putatively complete sets of transcription factors (TFs) and transcriptional regulators (TRs) from 19 plant species. PInTFDB is a repository with the aim of determining and recording plant genes involved in transcriptional control. It provides information about the different regulatory proteins and their classification into families, sequence alignments, as well as literature references and links to other databases.
A curated catalog of mouse and human transcription factors (TF) based on a reliable core collection of annotations obtained by expert review of the scientific literature. Annotated genes are assigned to a functional category and confidence level. For protein families linked to DNA-binding, genes are also associated with a structural classification system.
Offers promoter data collecting procedure and specific features of plant promoter sequences. PlantProm DB serves as a learning set in developing plant promoter prediction programs. It provides information on plant promoters with experimentally known transcription start site (TSS): (i) DNA sequence of the promoter region, (ii) Nucleotide Frequency Matrices (NFM) for canonical promoter elements, (iii) taxonomic and promoter type classification of promoters.
Collects information about transcription factors (TFs) in the fruit fly Drosophila melanogaster. FlyTF provides results from the curation of over 1052 candidate TFs selected for the presence of a canonical DNA-binding domain. The database allows users to search for a single gene of interest, upload extensive gene lists, from which the genes encoding TFs will be recognised and marked and make lists of TFs fulfilling specific criteria.
Informs user about sequenced bacterial genomes and plasmids. WebGeSTer DB consists of all types of intrinsic terminators identified in about 1000 bacterial chromosomes and more than 700 plasmids available at the NCBI database. This database provides user several whole-genome terminator maps.
A proteome database of eukaryotic transcription factors based upon predicted motifs by TranScout and data sources such as InterPro and Gene Ontology Annotation. Extensive and diverse information for each database entry, different analyses considering TranScout classification and similarity relationships are offered for research on transcription factors or gene expression.
Facilitates the search and application of gene expression data for generating new hypotheses on transcriptional regulatory programs under diverse biological and clinical contexts. DPRP contains about 980 gene expression data sets, which include about 29 750 samples. Users can search a disease concept to discover all related gene expression data sets, choose the interested data set and then select a subset pair within the data set for transcription factor (TF) regulatory program analysis.
Gives access to Drosophila melanogaster 5’-end mRNA tags at different developmental states. MachiBase is designed to assist fly biologists in their analyses of gene expression and in placing expression data in the context of functional genomics through genomic orientation. Users can access information on differentially expressed genes by either inputting the gene name as a keyword or selecting a chromosomal location. The database can assist biologists in explaining transcriptional initiation mechanisms by combining additional information on chromatin structure and DNA methylation.
Compiles information about interactions between DNA methylation and transcription factors (TFs). MeDReaders is a manually curated database that assembles more than 750 methylated DNA–TF interactions extracted from both published literatures and the ENCODE database. Users can search by TF gene names, Ensembl gene ids, Refseq gene ids and binding DNA sequences as well as download the TF motifs and DNA binding sequences in different DNA methylation status.
Enables an interactive global view of genomic scale regulatory networks. YEASTRACT integrates several experimentally validated transcriptional regulatory data for published S. cerevisiae. This resource contains more than 41 000 regulatory associations based on DNA binding evidence and about 172 000 on expression evidence. It holds analysis tools for investigation of the transcriptional regulation of genes involved in a particular biological response.
A database of DNA binding specificities for Drosophila transcription factors (TFs) primarily determined using the bacterial one-hybrid system. FlyFactorSurvey provides community access to over 400 recognition motifs and position weight matrices for over 200 TFs, including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within our database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. Together, this database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.
Provides a database of abiotic stress responsive genes. STIFDB is a resource that analyses promoters of abiotic stress responsive genes for potential stress-specific transcription factor binding sites. This resource can provide insights into the regulation of these stress responsive genes by upstream transcription factors. It also offers clues towards stress signal that affects the transcription of this gene, which might offer clarity about signal specific regulation.
Dr. Yashwanth Subbannayya obtained his M.Sc. degree in Medical Biochemistry from Manipal University. He qualified the competitive CSIR-UGC National Eligibility Test and joined the Institute of Bioinformatics, Bangalore as a UGC Junior Research Fellow. As part of his Ph.D. work, he studied the molecular mechanisms of gastric cancer in clinical specimens using quantitative proteomic technologies. This study, the results of which were published in Cancer Biology and Therapy, yielded a novel therapeutic target for gastric cancer- CAMKK2. Further, he also studied the serum proteome of gastric cancer patients and developed assays for potential markers using the revolutionary multiple reaction monitoring approach. The results of this study were published in Journal of Proteomics. In addition to his research work, he also trained extensively in sample preparation for mass spectrometry, fractionation techniques and gained expertise in quantitative proteomic techniques and data analysis. In addition, he also trained extensively in various validation platforms including immunohistochemsitry, multiple reaction monitoring and Western blot. He has also worked as a curator for several biological databases including NetPath, Human Protein Reference Database (HPRD) and Breast cancer database. His work in various research projects have yielded him 23 publications either as lead author or co-author in peer reviewed journals. He is a reviewer for the journal Proteomics.
Dr. Yashwanth Subbannayya joined the YU-IOB Center for Systems Biology and Molecular Medicine in June, 2015. During the initial period, his job consisted of assisting other personnel of the university in the establishment of YU-IOB Center for Systems Biology and Molecular Medicine. He was also involved in training of Ph.D. students in biological aspects. After the establishment of the center, he trained in cell culture techniques and metabolomics analysis. At YU-IOB CSBMM, he is studying the molecular mechanisms in various cancers including oral cancer. In addition, he is studying the molecular mechanisms as well as the metabolic constituents of traditional medicine formulations using mass spectrometry technologies. In June 2016, he convened the national symposium “Genomics in clinical practice: Future of precision medicine” held at Yenepoya University on June 1 and 2, 2016. The resource persons included 16 individuals from various academic organizations as well as industry. The symposium was attended by 218 participants from 24 institutions around India. He is a member of the Scientific Review Board of Yenepoya Research Centre where he facilitates timely scientific review of research projects.