hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets

TL;DR: The Animal Transcription Factor Database (AnimalTFDB) was updated to version 4.0 with up-to-date data and functions and refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl.

TL;DR: In this paper , the authors investigated the oncogenic roles and biogenesis of circBCAR3 in esophageal carcinogenesis using loss-of-function assays and showed that splicing factor quaking (QKI) is a positive regulator of circRNAs via targeting the introns flanking the hsa_circ_0007624-formed exons in BCAR3 premRNA.

TL;DR: In this article , the authors investigated the oncogenic roles and biogenesis of circBCAR3 in esophageal carcinogenesis using loss-of-function assays and showed that splicing factor quaking (QKI) is a positive regulator of circRNAs via targeting the introns flanking the hsa_circ_0007624-formed exons in BCAR3 premRNA.

TL;DR: The TFLink gateway is introduced, which uniquely provides experimentally explored and highly accurate information on transcription factor–target gene interactions, nucleotide sequences and genomic locations of transcription factor binding sites for human and six model organisms by integrating 10 resources.

TL;DR: The idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task is explored.

TL;DR: In this article, the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors were mapped for a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time.

TL;DR: This analysis suggests that the binding of ESR1, RARA, and RARG has both activating and repressive effects, and a curated database of 88 nuclear receptor cistrome data sets and other associated high-throughput data sets constitutes a valuable resource for the nuclear receptor and cancer community.

TL;DR: The TF and miRNA co-regulatory networks for each stage were constructed by combining their FFLs and revealed the key regulators in each stage, for example, MYC, STAT5A, PAX5 and miR-17 ~ 92 in the transition of pro-B cells into pre-B Cells.