Viral integration detection strategies and a technical update on Virus-Clip

Oncovirus infection is crucial in human malignancies.Certain oncoviruses can lead to structural variations in the human genome known as viral genomic integration,which can contribute to tumorigenesis.Existing viral integration detection tools differ in their underlying algorithms pinpointing different aspects or features of viral integration phenomenon.We discuss about major procedures in performing viral integration detection.More importantly,we provide a technical update on Virus-Clip to facilitate its usage on the latest human genome builds(hg19 and hg38)and the adoption of multi-thread mode for faster initial read alignment.By comparing the execution of Virus-Clip using single-thread and multi-thread modes of read alignment on targeted-panel sequencing data of HBV-associated hepatocellular carcinoma patients,we demonstrate the marked improvement of multi-thread mode in terms of significantly reduced execution time,while there is negligible difference in memory usage.Taken together,with the current update of Virus-Clip,it will continue supporting the in silico detection of oncoviral integration for better understanding of various human malignancies.

Genomic integrity of human induced pluripotent stem cells:Reprogramming, differentiation and applications

Ten years after the initial generation of induced pluripotent stem cells(hiPSCs)from human tissues,their potential is no longer questioned,with over 15000 publications listed on PubMed,covering various fields of research;including disease modeling,cell therapy strategies,pharmacology/toxicology screening and 3D organoid systems.However,despite evidences that the presence of mutations in hiPSCs should be a concern,publications addressing genomic integrity of these cells represent less than 1%of the literature.After a first overview of the mutation types currently reported in hiPSCs,including karyotype abnormalities,copy number variations,single point mutation as well as uniparental disomy,this review will discuss the impact of reprogramming parameters such as starting cell type and reprogramming method on the maintenance of the cellular genomic integrity.Then,a specific focus will be placed on culture conditions and subsequent differentiation protocols and how their may also trigger genomic aberrations within the cell population of interest.Finally,in a last section,the impact of genomic alterations on the possible usages of hiPSCs and their derivatives will also be exemplified and discussed.We will also discuss which techniques or combination of techniques should be used to screen for genomic abnormalities with a particular focus on the necessary quality controls and the potential alternatives.

Databases and Web Tools for Cancer Genomics Study

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community.

BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Integrative Analysis of Genome,3D Genome,and Transcriptome Alterations of Clinical Lung Cancer Samples

Genomic studies of cancer cell alterations,such as mutations,copy number variations(CNVs),and translocations,greatly promote our understanding of the genesis and development of cancers.However,the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties.To explore the 3D genome structure in clinical lung cancer,we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer,combining with RNA sequenceing analysis.We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells(1×10^(4)),compared the genome architecture between clinical samples and cell lines of lung cancer,and identified conserved and changed spatial chromatin structures between normal and cancer samples.We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer.By integrating those different types of cancer alterations,we showed significant associations between CNVs,3D genome,and gene expression.We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures.Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.

Chinmedomics Builds a Bridge from Traditional to Modern Research of Traditional Chinese Medicine

＂Omics＂ is a new research field of integrative systems biology and bioinformatics.In the post genomic era,the core scientific problem is to study the relationship between different ＂omics＂ and functions based on bioinformatics.How to apply the omics method and technology to understand the complexity of traditional Chinese medicines（TCM）is one of the hot spots in the recent decade in China.

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.