{"title":"Whole-genome Duplication Reshaped Adaptive Evolution in A Relict Plant Species, Cyclocarya paliurus.","authors":"Yinquan Qu, Xulan Shang, Ziyan Zeng, Yanhao Yu, Guoliang Bian, Wenling Wang, Li Liu, Li Tian, Shengcheng Zhang, Qian Wang, Dejin Xie, Xuequn Chen, Zhenyang Liao, Yibin Wang, Jian Qin, Wanxia Yang, Caowen Sun, Xiangxiang Fu, Xingtan Zhang, Shengzuo Fang","doi":"10.1016/j.gpb.2023.02.001","DOIUrl":"10.1016/j.gpb.2023.02.001","url":null,"abstract":"<p><p>Cyclocarya paliurus is a relict plant species that survived the last glacial period and shows a population expansion recently. Its leaves have been traditionally used to treat obesity and diabetes with the well-known active ingredient cyclocaric acid B. Here, we presented three C. paliurus genomes from two diploids with different flower morphs and one haplotype-resolved tetraploid assembly. Comparative genomic analysis revealed two rounds of recent whole-genome duplication events and identified 691 genes with dosage effects that likely contribute to adaptive evolution through enhanced photosynthesis and increased accumulation of triterpenoids. Resequencing analysis of 45 C. paliurus individuals uncovered two bottlenecks, consistent with the known events of environmental changes, and many selectively swept genes involved in critical biological functions, including plant defense and secondary metabolite biosynthesis. We also proposed the biosynthesis pathway of cyclocaric acid B based on multi-omics data and identified key genes, in particular gibberellin-related genes, associated with the heterodichogamy in C. paliurus species. Our study sheds light on evolutionary history of C. paliurus and provides genomic resources to study the medicinal herbs.</p>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" ","pages":"455-469"},"PeriodicalIF":11.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10695746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fang-Yuan Shi, Yu Wang, Dong Huang, Yu Liang, Nan Liang, Xiao-Wei Chen, Ge Gao
{"title":"Computational Assessment of the Expression-modulating Potential for Non-coding Variants.","authors":"Fang-Yuan Shi, Yu Wang, Dong Huang, Yu Liang, Nan Liang, Xiao-Wei Chen, Ge Gao","doi":"10.1016/j.gpb.2021.10.003","DOIUrl":"10.1016/j.gpb.2021.10.003","url":null,"abstract":"<p><p>Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.</p>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" ","pages":"662-673"},"PeriodicalIF":11.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39574450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Morphine Re-arranges Chromatin Spatial Architecture of Primate Cortical Neurons.","authors":"Liang Wang, Xiaojie Wang, Chunqi Liu, Wei Xu, Weihong Kuang, Qian Bu, Hongchun Li, Ying Zhao, Linhong Jiang, Yaxing Chen, Feng Qin, Shu Li, Qinfan Wei, Xiaocong Liu, Bin Liu, Yuanyuan Chen, Yanping Dai, Hongbo Wang, Jingwei Tian, Gang Cao, Yinglan Zhao, Xiaobo Cen","doi":"10.1016/j.gpb.2023.03.003","DOIUrl":"10.1016/j.gpb.2023.03.003","url":null,"abstract":"<p><p>The expression of linear DNA sequence is precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neurons is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the effects of morphine on the 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes from the RNA sequencing data were mapped to the specific TAD boundaries or differential loops, and were further validated for changed expression. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated with morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans.</p>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" ","pages":"551-572"},"PeriodicalIF":11.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9544973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Zhang, Jingwen Zhang, Wei Zhang, Mohan Wang, Shuangqi Wang, Yao Xu, Lun Zhao, Xingwang Li, Guoliang Li
{"title":"Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes.","authors":"Yan Zhang, Jingwen Zhang, Wei Zhang, Mohan Wang, Shuangqi Wang, Yao Xu, Lun Zhao, Xingwang Li, Guoliang Li","doi":"10.1016/j.gpb.2023.01.004","DOIUrl":"10.1016/j.gpb.2023.01.004","url":null,"abstract":"<p><p>Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.</p>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" ","pages":"573-588"},"PeriodicalIF":11.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10615752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.","authors":"David Earl Hostallero, Lixuan Wei, Liewei Wang, Junmei Cairns, Amin Emad","doi":"10.1016/j.gpb.2023.01.006","DOIUrl":"10.1016/j.gpb.2023.01.006","url":null,"abstract":"<p><p>Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.</p>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" ","pages":"535-550"},"PeriodicalIF":11.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10695748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guangsheng Pei , Fangfang Yan , Lukas M. Simon , Yulin Dai , Peilin Jia , Zhongming Zhao
{"title":"deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues","authors":"Guangsheng Pei , Fangfang Yan , Lukas M. Simon , Yulin Dai , Peilin Jia , Zhongming Zhao","doi":"10.1016/j.gpb.2022.04.001","DOIUrl":"10.1016/j.gpb.2022.04.001","url":null,"abstract":"<div><p>Single-cell RNA sequencing (<strong>scRNA-seq</strong>) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on <em>a priori</em> knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas.<!--> <!-->Here, we present decoding Cell type Specificity (<em>deCS</em>), an automatic <strong>cell type annotation</strong> method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used <em>deCS</em> to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, <em>deCS</em> significantly reduced computation time and increased accuracy. <em>deCS</em> can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of <em>deCS</em> to identify <strong>trait–cell type associations</strong> in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for <em>deCS</em>, including source code, user manual, demo data, and tutorials, are freely available at <span>https://github.com/bsml320/deCS</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 370-384"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10059212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongqiu Li , Yiheng Hu , Xuelian Ma , Lingling Da , Jiajie She , Yue Liu , Xin Yi , Yaxin Cao , Wenying Xu , Yuannian Jiao , Zhen Su
{"title":"WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors","authors":"Zhongqiu Li , Yiheng Hu , Xuelian Ma , Lingling Da , Jiajie She , Yue Liu , Xin Yi , Yaxin Cao , Wenying Xu , Yuannian Jiao , Zhen Su","doi":"10.1016/j.gpb.2022.04.007","DOIUrl":"10.1016/j.gpb.2022.04.007","url":null,"abstract":"<div><p>Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed <strong>co-expression networks</strong> for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. <strong>Functional annotations</strong> like pathways, gene families, protein–protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of <em>WHEAT ABERRANT PANICLE ORGANIZATION 1</em> (<em>WAPO1</em>) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of <em>WAPO-A1</em>, suggesting that neofunctionalization of the <em>WAPO-A1</em> gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at <span>http://bioinformatics.cpolar.cn/WheatCENet</span><svg><path></path></svg> and <span>http://bioinformatics.cau.edu.cn/WheatCENet</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 324-336"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9779875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ye Chen , Yuyan Wang , Ping Zhou , Hao Huang , Rui Li , Zhen Zeng , Zifeng Cui , Rui Tian , Zhuang Jin , Jiashuo Liu , Zhaoyue Huang , Lifang Li , Zheying Huang , Xun Tian , Meiying Yu , Zheng Hu
{"title":"VIS Atlas: A Database of Virus Integration Sites in Human Genome from NGS Data to Explore Integration Patterns","authors":"Ye Chen , Yuyan Wang , Ping Zhou , Hao Huang , Rui Li , Zhen Zeng , Zifeng Cui , Rui Tian , Zhuang Jin , Jiashuo Liu , Zhaoyue Huang , Lifang Li , Zheying Huang , Xun Tian , Meiying Yu , Zheng Hu","doi":"10.1016/j.gpb.2023.02.005","DOIUrl":"10.1016/j.gpb.2023.02.005","url":null,"abstract":"<div><p>Integration of oncogenic <strong>DNA viruses</strong> into the human genome is a key step in most virus-induced carcinogenesis. Here, we constructed a <strong>virus integration site</strong> (VIS) Atlas database, an extensive collection of integration breakpoints for three most prevalent oncoviruses, human papillomavirus, hepatitis B virus, and Epstein–Barr virus based on the <strong>next-generation sequencing</strong> (NGS) data, literature, and experimental data. There are 63,179 breakpoints and 47,411 junctional sequences with full annotations deposited in the VIS Atlas database, comprising 47 <strong>virus genotypes</strong> and 17 disease types. The VIS Atlas database provides (1) a genome browser for NGS breakpoint quality check, visualization of VISs, and the local genomic context; (2) a novel platform to discover <strong>integration patterns</strong>; and (3) a statistics interface for a comprehensive investigation of genotype-specific integration features. Data collected in the VIS Atlas aid to provide insights into virus pathogenic mechanisms and the development of novel antitumor drugs. The VIS Atlas database is available at <span>https://www.vis-atlas.tech/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 300-310"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10149144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants","authors":"Hao Lu, Luyu Ma, Cheng Quan, Lei Li, Yiming Lu, Gangqiao Zhou, Chenggang Zhang","doi":"10.1016/j.gpb.2021.08.011","DOIUrl":"10.1016/j.gpb.2021.08.011","url":null,"abstract":"<div><p>Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking. Here, we introduce a deep neural network (DNN)-based computational framework, RegVar, which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes. We show that by robustly learning the genomic characteristics of massive variant–gene expression associations in a variety of human tissues, RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a web server at <span>https://regvar.omic.tech/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 385-395"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39866008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ncFO: A Comprehensive Resource of Curated and Predicted ncRNAs Associated with Ferroptosis","authors":"Shunheng Zhou , Yu’e Huang , Jiani Xing , Xu Zhou , Sina Chen , Jiahao Chen , Lihong Wang , Wei Jiang","doi":"10.1016/j.gpb.2022.09.004","DOIUrl":"10.1016/j.gpb.2022.09.004","url":null,"abstract":"<div><p><strong>Ferroptosis</strong> is a form of regulated cell death driven by the accumulation of lipid hydroperoxides. Regulation of ferroptosis might be beneficial to <strong>cancer</strong> treatment. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play critical roles in regulating ferroptosis. Herein, we developed ncFO, the ncRNA–ferroptosis association database, to document the manually curated and predicted ncRNAs that are associated with ferroptosis. Collectively, ncFO contains 90 experimentally verified entries, including 46 <strong>microRNAs</strong> (miRNAs), 21 <strong>long non-coding RNAs</strong> (lncRNAs), and 17 <strong>circular RNAs</strong> (circRNAs). In addition, ncFO also incorporates two online prediction tools based on the regulation and co-expression of ncRNA and ferroptosis genes. Using default parameters, we obtained 3260 predicted entries, including 598 miRNAs and 178 lncRNAs, by regulation, as well as 2,592,661 predicted entries, including 967 miRNAs and 9632 lncRNAs, by ncRNA–ferroptosis gene co-expression in more than 8000 samples across 20 cancer types. The detailed information of each entry includes ncRNA name, disease, species, tissue, target, regulation, publication time, and PubMed identifier. ncFO also provides survival analysis and differential expression analysis for ncRNAs. In summary, ncFO offers a user-friendly platform to search and predict ferroptosis-associated ncRNAs, which might facilitate research on ferroptosis and discover potential targets for cancer treatment. ncFO can be accessed at <span>http://www.jianglab.cn/ncFO/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 278-282"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}