CanISO: a database of genomic and transcriptomic variations in domestic dog (Canis lupus familiaris).

BACKGROUND: The domestic dog, Canis lupus familiaris, is a companion animal for humans as well as an animal model in cancer research due to similar spontaneous occurrence of cancers as humans. Despite the social and biological importance of dogs, the catalogue of genomic variations and transcripts for dogs is relatively incomplete. RESULTS: We developed CanISO, a new database to hold a large collection of transcriptome profiles and genomic variations for domestic dogs. CanISO provides 87,692 novel transcript isoforms and 60,992 known isoforms from whole transcriptome sequencing of canine tumors (N = 157) and their matched normal tissues (N = 64). CanISO also provides genomic variation information for 210,444 unique germline single nucleotide polymorphisms (SNPs) from the whole exome sequencing of 183 dogs, with a query system that searches gene- and transcript-level information as well as covered SNPs. Transcriptome profiles can be compared with corresponding human transcript isoforms at a tissue level, or between sample groups to identify tumor-specific gene expression and alternative splicing patterns. CONCLUSIONS: CanISO is expected to increase understanding of the dog genome and transcriptome, as well as its functional associations with humans, such as shared/distinct mechanisms of cancer. CanISO is publicly available at https://www.kobic.re.kr/caniso/ .

Reduction of Squalene Epoxidase by Cholesterol Accumulation Accelerates Colorectal Cancer Progression and Metastasis.

BACKGROUND & AIMS: Squalene epoxidase (SQLE), a rate-limiting enzyme in cholesterol biosynthesis, is suggested as a proto-oncogene. Paradoxically, SQLE is degraded by excess cholesterol, and low SQLE is associated with aggressive colorectal cancer (CRC). Therefore, we studied the functional consequences of SQLE reduction in CRC progression. METHODS: Gene and protein expression data and clinical features of CRCs were obtained from public databases and 293 human tissues, analyzed by immunohistochemistry. In vitro studies showed underlying mechanisms of CRC progression mediated by SQLE reduction. Mice were fed a 2% high-cholesterol or a control diet before and after cecum implantation of SQLE genetic knockdown/control CRC cells. Metastatic dissemination and circulating cancer stem cells were demonstrated by in vivo tracking and flow cytometry analysis, respectively. RESULTS: In vitro studies showed that SQLE reduction helped cancer cells overcome constraints by inducing the epithelial-mesenchymal transition required to generate cancer stem cells. Surprisingly, SQLE interacted with GSK3ß and p53. Active GSK3ß contributes to the stability of SQLE, thereby increasing cell cholesterol content, whereas SQLE depletion disrupted the GSK3ß/p53 complex, resulting in a metastatic phenotype. This was confirmed in a spontaneous CRC metastasis mice model, where SQLE reduction, by a high-cholesterol regimen or genetic knockdown, strikingly promoted CRC aggressiveness through the production of migratory cancer stem cells. CONCLUSIONS: We showed that SQLE reduction caused by cholesterol accumulation aggravates CRC progression via the activation of the ß-catenin oncogenic pathway and deactivation of the p53 tumor suppressor pathway. Our findings provide new insights into the link between cholesterol and CRC, identifying SQLE as a key regulator in CRC aggressiveness and a prognostic biomarker.

3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome.

Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. '3DIV' is freely available at http://3div.kr.

iCSDB: an integrated database of CRISPR screens.

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.

ChimerDB 4.0: an updated and expanded database of fusion genes.

Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http://www.kobic.re.kr/chimerdb/.

CaPSSA: visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data.

SUMMARY: Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information. AVAILABILITY AND IMPLEMENTATION: CaPSSA (Cancer Patient Stratification and Survival Analysis) runs on a web-browser and is freely available without restrictions at http://www.kobic.re.kr/capssa/. The source code is available on https://github.com/yjjang/capssa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GEMiCCL: mining genotype and expression data of cancer cell lines with elaborate visualization.

Database URL: GEMiCCL is available at https://www.kobic.kr/GEMICCL/.

Genome analysis of Hibiscus syriacus provides insights of polyploidization and indeterminate flowering in woody plants.

Hibiscus syriacus (L.) (rose of Sharon) is one of the most widespread garden shrubs in the world. We report a draft of the H. syriacus genome comprised of a 1.75 Gb assembly that covers 92% of the genome with only 1.7% (33 Mb) gap sequences. Predicted gene modeling detected 87,603 genes, mostly supported by deep RNA sequencing data. To define gene family distribution among relatives of H. syriacus, orthologous gene sets containing 164,660 genes in 21,472 clusters were identified by OrthoMCL analysis of five plant species, including H. syriacus, Arabidopsis thaliana, Gossypium raimondii, Theobroma cacao and Amborella trichopoda. We inferred their evolutionary relationships based on divergence times among Malvaceae plant genes and found that gene families involved in flowering regulation and disease resistance were more highly divergent and expanded in H. syriacus than in its close relatives, G. raimondii (DD) and T. cacao. Clustered gene families and gene collinearity analysis revealed that two recent rounds of whole-genome duplication were followed by diploidization of the H. syriacus genome after speciation. Copy number variation and phylogenetic divergence indicates that WGDs and subsequent diploidization led to unequal duplication and deletion of flowering-related genes in H. syriacus and may affect its unique floral morphology.

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining.

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.

A high-dimensional, deep-sequencing study of lung adenocarcinoma in female never-smokers.

BACKGROUND: Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development. RESULTS: Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network. CONCLUSIONS: Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.