Supplementary data are available at Bioinformatics online.

The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

Increasing evidence has demonstrated that small nucleolar RNAs (snoRNAs) play important roles in tumorigenesis. We systematically investigated the expression landscape and clinical relevance of snoRNAs in >10,000 samples across 31 cancer types from The Cancer Genome Atlas. We observed overall elevated expression of snoRNAs and their ribonucleoproteins in multiple cancer types. We showed complex regulation of snoRNA expression by their host genes, copy number variation, and DNA methylation. Unsupervised clustering revealed that the snoRNA expression subtype is highly concordant with other molecular/clinical subtypes. We further identified 46 clinically relevant snoRNAs and experimentally demonstrated functional roles of SNORD46 in promoting cell proliferation, migration, and invasion. We developed a user-friendly data portal, SNORic, to benefit the research community. Our study highlights the significant roles of snoRNAs in the development and implementation of biomarkers or therapeutic targets for cancer and provides a valuable resource for cancer research.

Cancer chronotherapy, treatment at specific times during circadian rhythms, endeavors to optimize anti-tumor effects and to lower toxicity. However, comprehensive characterization of clock genes and their clinical relevance in cancer is lacking. We systematically characterized the alterations of clock genes across 32 cancer types by analyzing data from The Cancer Genome Atlas, Cancer Therapeutics Response Portal, and The Genomics of Drug Sensitivity in Cancer databases. Expression alterations of clock genes are associated with key oncogenic pathways, patient survival, tumor stage, and subtype in multiple cancer types. Correlations between expression of clock genes and of other genes in the genome were altered in cancerous versus normal tissues. We identified interactions between clock genes and clinically actionable genes by analyzing co-expression, protein-protein interaction, and chromatin immunoprecipitation sequencing data and also found that clock gene expression is correlated to anti-cancer drug sensitivity in cancer cell lines. Our study provides a comprehensive analysis of the circadian clock across different cancer types and highlights potential clinical utility of cancer chronotherapy.

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.