Core transcription regulatory circuitry (CRC) is comprised of a small group of self-regulated transcription factors (TFs) and their interconnected regulatory loops. Studies from embryonic stem cells and other cellular models have revealed the elementary roles of CRCs in transcriptional control of cell identity and cellular fate. Systematic identification and subsequent archiving of CRCs across diverse cell types and tissues are needed to explore both cell/tissue type-specific and disease-associated transcriptional networks. Here, we present a comprehensive and interactive database (dbCoRC, http://dbcorc.cam-su.org) of CRC models which are computationally inferred from mapping of super-enhancer and prediction of TF binding sites. The current version of dbCoRC contains CRC models for 188 human and 50 murine cell lines/tissue samples. In companion with CRC models, this database also provides: (i) super enhancer, typical enhancer, and H3K27ac landscape for individual samples, (ii) putative binding sites of each core TF across the super-enhancer regions within CRC and (iii) expression of each core TF in normal or cancer cells/tissues. The dbCoRC will serve as a valuable resource for the scientific community to explore transcriptional control and regulatory circuitries in biological processes related to, but not limited to lineage specification, tissue homeostasis and tumorigenesis.

Background The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively. Results We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV). Conclusions AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation.

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In cancer, extrachromosomal circular DNA (ecDNA), or megabase-pair amplified circular DNA, plays an essential role in intercellular heterogeneity and tumor cell revolution because of its non-Mendelian inheritance. We developed circlehunter (https://github.com/suda-huanglab/circlehunter), a tool for identifying ecDNA from ATAC-Seq data using the enhanced chromatin accessibility of ecDNA. Using simulated data, we showed that circlehunter has an F1 score of 0.93 at 30× local depth and read lengths as short as 35 bp. Based on 1312 ecDNAs predicted from 94 publicly available datasets of ATAC-Seq assays, we found 37 oncogenes contained in these ecDNAs with amplification characteristics. In small cell lung cancer cell lines, ecDNA containing MYC leads to amplification of MYC and cis-regulates the expression of NEUROD1, resulting in an expression pattern consistent with the NEUROD1 high expression subtype and sensitive to Aurora kinase inhibitors. This showcases that circlehunter could serve as a valuable pipeline for the investigation of tumorigenesis.