Genomics & informatics最新文献

{"title":"Publication trends of somatic mutation and recombination tests research: a bibliometric analysis (1984‒2020)","authors":"Ghada Tagorti, B. Kaya","doi":"10.5808/gi.21083","DOIUrl":"https://doi.org/10.5808/gi.21083","url":null,"abstract":"Human exposure to pollutants has been on the rise. Thus, researchers have been focused on understanding the effect of these compounds on human health, especially on the genetic information by using various tests, among them the somatic mutation and recombination tests (SMARTs). It is a sensitive and accurate method applicable to genotoxicity analysis. Here, a comprehensive bibliometric analysis of SMART assays in genotoxicity studies was performed to assess publication trends of this field. Data were extracted from the Web of Science database and analyzed by the bibliometric tools HistCite, Biblioshiny (RStudio), VOSViewer, and CiteSpace. Results have shown an increase in the last 10 years in terms of publication. A total of 392 records were published in 96 sources mainly from Brazil, Spain, and Turkey. Research collaboration networks between countries and authors were performed. Based on document co-citation, five large research clusters were identified and analyzed. The youngest research frontier emphasized on nanoparticles. With this study, how research trends evolve over years was demonstrated. Thus, international collaboration could be enhanced, and a promising field could be developed.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48753660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor’s introduction to this issue (G&I 20:1, 2022)","authors":"Taesung Park","doi":"10.5808/gi.20.1.e1","DOIUrl":"https://doi.org/10.5808/gi.20.1.e1","url":null,"abstract":"2022 Korea Genome Organization This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this issue, there are two review articles, eight original articles, one genome archive, and two application notes. In this editorial, I would like to focus on the two review articles, as well as two original articles and one application note on genome-wide association studies (GWAS). Recent rapid advances in single-cell RNA sequencing have made it possible to recognize a variety of previously unidentified subpopulations and rare cell states in tumors and the immune system based on single-cell gene expression profiles. Single-cell RNA sequencing is the topic of the first review article, by Dr. Jong-Il Kim’s group (Seoul National University College of Medicine, Korea). This review addresses the current development of methods for constructing single-cell epigenomic libraries, including multi-omics tools with important elements and additional requirements for the future, focusing on DNA methylation, chromatin accessibility, and histone post-translational modification. Single-cell epigenomic libraries help to understand the principles of comprehensive gene regulation that determine cell fate through transcripts alone and the resulting output of gene expression programs. The corresponding single-cell epigenome is expected to elucidate the mechanisms involved in the origin and maintenance of a comprehensive single-cell transcriptome. This review insightfully summarizes current research trends in the field of cellular differentiation and disease development at the single-cell level, moving toward the single-cell epigenome. The second review, led by Dr. Tung (Dagon University, Myanmar), deals with recent developments in whole-genome sequencing technologies. While the analysis of whole-genome sequencing data requires highly sophisticated bioinformatics tools, many researchers do not have the bioinformatics capabilities to analyze the genomic data and are therefore unable to take maximum advantage of whole-genome sequencing. This review provides a practical guide on a set of bioinformatics tools available online to analyze whole-genome sequence data of bacterial genomes and presents a description of their web interfaces. Now, I would like to turn to three articles about GWAS. The main goal of GWAS is the identification of causal variants associated with the phenotype of interest. All GWAS introduce appropriate statistical models to explain the phenotype and then to perform statistical tests. An important challenge in this post-GWAS era is to increase statistical power by using better statistical models and tests, and to investigate the causal effects between modifiable risk factors and the phenotypes via Mendelian randomization (MR). The first article, t","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49173955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental development of the epigenomic library construction method to elucidate the epigenetic diversity and causal relationship between epigenome and transcriptome at a single-cell level","authors":"Kyunghyuk Park, Min Chul Jeon, Bokyung Kim, Bukyoung Cha, Jong-Il Kim","doi":"10.5808/gi.21078","DOIUrl":"https://doi.org/10.5808/gi.21078","url":null,"abstract":"The method of single-cell RNA sequencing has been rapidly developed, and numerous experiments have been conducted over the past decade. Their results allow us to recognize various subpopulations and rare cell states in tissues, tumors, and immune systems that are previously unidentified, and guide us to understand fundamental biological processes that determine cell identity based on single-cell gene expression profiles. However, it is still challenging to understand the principle of comprehensive gene regulation that determines the cell fate only with transcriptome, a consequential output of the gene expression program. To elucidate the mechanisms related to the origin and maintenance of comprehensive single-cell transcriptome, we require a corresponding single-cell epigenome, which is a differentiated information of each cell with an identical genome. This review deals with the current development of single-cell epigenomic library construction methods, including multi-omics tools with crucial factors and additional requirements in the future focusing on DNA methylation, chromatin accessibility, and histone post-translational modifications. The study of cellular differentiation and the disease occurrence at a single-cell level has taken the first step with single-cell transcriptome and is now taking the next step with single-cell epigenome.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44772237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the copy-neutral loss of heterozygosity identified from whole-exome sequencing data using three different tools","authors":"Gang-Taik Lee, Y. Chung","doi":"10.5808/gi.21066","DOIUrl":"https://doi.org/10.5808/gi.21066","url":null,"abstract":"Loss of heterozygosity (LOH) is a genomic aberration. In some cases, LOH can be generated without changing the copy number, which is called copy-neutral LOH (CN-LOH). CN-LOH frequently occurs in various human diseases, including cancer. However, the biological and clinical implications of CN-LOH for human diseases have not been well studied. In this study, we compared the performance of CN-LOH determination using three commonly used tools. For an objective comparison, we analyzed CN-LOH profiles from single-nucleotide polymorphism array data from 10 colon adenocarcinoma patients, which were used as the reference for comparison with the CN-LOHs obtained through whole-exome sequencing (WES) data of the same patients using three different analysis tools (FACETS, Nexus, and Sequenza). The majority of the CN-LOHs identified from the WES data were consistent with the reference data. However, some of the CN-LOHs identified from the WES data were not consistent between the three tools, and the consistency with the reference CN-LOH profile was also different. The Jaccard index of the CN-LOHs using FACETS (0.84 ± 0.29; mean value, 0.73) was significantly higher than that of Nexus (0.55 ± 0.29; mean value, 0.50; p = 0.02) or Sequenza (0 ± 0.41; mean value, 0.34; p = 0.04). FACETS showed the highest area under the curve value. Taken together, of the three CN-LOH analysis tools, FACETS showed the best performance in identifying CN-LOHs from The Cancer Genome Atlas colon adenocarcinoma WES data. Our results will be helpful in exploring the biological or clinical implications of CN-LOH for human diseases.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41856398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Presentation of potential genes and deleterious variants associated with non-syndromic hearing loss: a computational approach","authors":"Manisha Ray, S. Rath, S. Sarkar, M. Sable","doi":"10.5808/gi.21070","DOIUrl":"https://doi.org/10.5808/gi.21070","url":null,"abstract":"Non-syndromic hearing loss (NSHL) is a common hereditary disorder. Both clinical and genetic heterogeneity has created many obstacles to understanding the causes of NSHL. The present study has attempted to ravel the genetic aetiology in NSHL progression and to screen out potential target genes using computational approaches. The reported NSHL target genes (2009‒2020) have been studied by analyzing different biochemical and signaling pathways, interpretation of their functional association network, and discovery of important regulatory interactions with three previously established miRNAs in the human inner ear as well as in NSHL such as miR-183, miR-182, and miR-96. This study has identified SMAD4 and SNAI2 as the most putative target genes of NSHL. But pathogenic and deleterious non-synonymous single nucleotide polymorphisms discovered within SMAD4 is anticipated to have an impact on NSHL progression. Additionally, the identified deleterious variants in the functional domains of SMAD4 added a supportive clue for further study. Thus, the identified deleterious variant i.e., rs377767367 (G491V) in SMAD4 needs further clinical validation. The present outcomes would provide insights into the genetics of NSHL progression.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46711857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole-genome sequence analysis through online web interfaces: a review","authors":"A. Gunasekara, L. Rajapaksha, T. Tung","doi":"10.5808/gi.20038","DOIUrl":"https://doi.org/10.5808/gi.20038","url":null,"abstract":"The recent development of whole-genome sequencing technologies paved the way for understanding the genomes of microorganisms. Every whole-genome sequencing (WGS) project requires a considerable cost and a massive effort to address the questions at hand. The final step of WGS is data analysis. The analysis of whole-genome sequence is dependent on highly sophisticated bioinformatics tools that the research personal have to buy. However, many laboratories and research institutions do not have the bioinformatics capabilities to analyze the genomic data and therefore, are unable to take maximum advantage of whole-genome sequencing. In this aspect, this study provides a guide for research personals on a set of bioinformatics tools available online that can be used to analyze whole-genome sequence data of bacterial genomes. The web interfaces described here have many advantages and, in most cases exempting the need for costly analysis tools and intensive computing resources.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48200666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies","authors":"Wonil Chung, Youngkwan Cho","doi":"10.5808/gi.21080","DOIUrl":"https://doi.org/10.5808/gi.21080","url":null,"abstract":"Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41453021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TcellInflamedDetector: an R package to distinguish T cell inflamed tumor types from non–T cell inflamed tumor types","authors":"San-Duk Yang, Hyun-Seok Park","doi":"10.5808/gi.22005","DOIUrl":"https://doi.org/10.5808/gi.22005","url":null,"abstract":"A major issue in the use of immune checkpoint inhibitors is their lack of efficacy in many patients. Previous studies have reported that the T cell inflamed signature can help predict the response to immunotherapy. Thus, many studies have investigated mechanisms of immunotherapy resistance by defining the tumor microenvironment based on T cell inflamed and non–T cell inflamed subsets. Although methods of calculating T cell inflamed subsets have been developed, valid screening tools for distinguishing T cell inflamed from non–T cell inflamed subsets using gene expression data are still needed, since general researchers who are unfamiliar with the details of the equations can experience difficulties using extant scoring formulas to conduct analyses. Thus, we introduce TcellInflamedDetector, an R package for distinguishing T cell inflamed from non–T cell inflamed samples using cancer gene expression data via bulk RNA sequencing.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45472943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hypothetical protein predicted to be tumor suppressor: a protein functional analysis","authors":"M. Kader, Akash Ahammed, Md. Sharif Khan, Sheikh Abdullah Al Ashik, M. Islam, Mohammad Uzzal Hossain","doi":"10.21203/rs.3.rs-602073/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-602073/v1","url":null,"abstract":"Litorilituus sediminis is a Gram-negative, aerobic, novel bacterium under the family of Colwelliaceae, has a stunning hypothetical protein containing domain called von Hippel-Lindau that has significant tumor suppressor activity. Therefore, this study was designed to elucidate the structure and function of the biologically important hypothetical protein EMK97_00595 (QBG34344.1) using several bioinformatics tools. The functional annotation exposed that the hypothetical protein is an extracellular secretory soluble signal peptide and contains the von Hippel-Lindau (VHL; VHL beta) domain that has a significant role in tumor suppression. This domain is conserved throughout evolution, as its homologs are available in various types of the organism like mammals, insects, and nematode. The gene product of VHL has a critical regulatory activity in the ubiquitous oxygen-sensing pathway. This domain has a significant role in inhibiting cell proliferation, angiogenesis progression, kidney cancer, breast cancer, and colon cancer. At last, the current study depicts that the annotated hypothetical protein is linked with tumor suppressor activity which might be of great interest to future research in the higher organism.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48638779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation-based and feature-driven mutation signature analyses to identify genetic features associated with DNA mutagenic processes in cancer genomes","authors":"H. Jeong, Jinseon Yoo, Hyunwoo J. Kim, Tae-Min Kim","doi":"10.1101/2020.01.06.895698","DOIUrl":"https://doi.org/10.1101/2020.01.06.895698","url":null,"abstract":"Mutation signatures represent unique sequence footprints of somatic mutations resulting from specific DNA mutagenic and repair processes; however, their causal associations and potential utility for genome research remain largely unknown. In this study, we performed PanCancer-scale correlative analyses to identify the genomic features associated with tumor mutation burdens (TMB) and individual mutation signatures. We observed that TMB was correlated with tumor purity, ploidy, and the level of aneuploidy, as well as with the expression of cell proliferation-related genes representing genomic covariates in evaluating TMB. Correlative analyses of mutation signature levels with genes belonging to DNA damage-repair processes revealed that deficiencies of NHEJ1 and ALKBH3 may elevate TMB levels in cancer genomes accompanying APOBEC overactivity and DNA mismatch repair deficiency, respectively. We further employed a strategy to identify feature-driven, de novo mutation signatures and demonstrated they can be reconstructed using known causal features such as APOBEC overexpression, MLH1 underexpression, POLE mutations, and the level of homologous recombination deficiency. We further demonstrated, that tumor hypoxia-related mutation signatures are similar to those associated with APOBEC suggesting that APOBEC-related mutagenic activity mediates hypoxia-related mutational consequences in cancer genomes, and also, that mutation signatures can be further used to predict hypoxic tumors. Taken together, our study advances mutation signature-level mechanistic insights in cancer genomes, extending categories of cancer-relevant mutation signatures and their potential biological implications. Author summary Mutation signature analysis is powerful in deciphering the causative mutagenic events and their contributions in individual cancer genomes, but the causal relationship of individual mutation signatures are still largely unknown. PanCancer-scaled correlative analysis revealed mutation resource candidates in cancer genomes such as NHEJ1 and ALKBH3 deficiencies that may facilitate the accumulation of mutations in the setting of APOBEC overactivity and DNA mismatch repair deficiency, respectively. A feature-driven mutation discovery approach was employed to identify the mutation signatures representing homologous recombination deficiency and tumor hypoxia, the extent of which may serve as mutation-based phenotypic measures, previously estimated by DNA copy number alterations and mRNA expression signatures, respectively. Our study advances our understanding into the mechanistic insights of mutation signatures and proposes a method to utilize somatic mutations as a molecular proxy in terms of mutation signatures.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48497540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}