Discoveries in Sequencing Data Analysis

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 5112

Special Issue Editors


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Guest Editor
School of Life Sciences, Sun Yat Sen University, Guangzhou, China
Interests: bioinformatics; high-throughput sequencing

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Guest Editor
School of Life Sciences, Sun Yat Sen University, Guangzhou, China
Interests: cancer biology; bioinformatics; multi-omics; autoimmune

Special Issue Information

Dear Colleagues,

High-throughput sequencing has been widely used in functional genomics studies and has revolutionized biological sciences. It enables researchers to perform a wide range of investigations and to study biological systems at an unprecedented level, as exemplified by large international research projects, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx), which provided comprehensive multi-omics sequencing datasets for research by scientists across the world. Analysis of sequencing data converts sequence information into meaningful knowledge and insights, which involves algorithm development, annotation or cataloguing information, multi-omics data integration, biomarker and drug target discovery, and disease diagnosis and drug response prediction. Whole transcriptome sequencing (RNA-seq), for example, provides sequence information about coding and multiple noncoding forms of RNA to assess variations and gene expression levels across the entire genome. Varied information can be obtained from RNA-seq, including gene expression levels, alternative splicing (AS), alternative polyadenylation (APA), gene fusion, and RNA editing. For genome or exome sequencing, nucleotide polymorphisms and structural variations, in addition to telomere variations, can be identified to uncover driver genomic events. Furthermore, single-cell RNA or DNA sequencing (scRNA- or scDNA-seq) has dramatically improved our understanding of biology in every aspect. Novel discoveries in sequencing data analysis are critical to pinpoint the key players in pathological conditions, especially for cancer and other age-related diseases.

The aim of this Special Issue is to provide a broad and up-to-date overview of “Discoveries in Sequencing Data Analysis” to elucidate new approaches analyzing sequencing data, integrating multi-omics data, discovering biological mechanisms, and developing novel treatments or therapies for diseases. Contributions in the form of research papers and reviews from experts in the field are needed to improve our understanding of relevant biological issues.

Dr. Yuanyan Xiong
Dr. Mengbiao Guo
Guest Editors

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Keywords

  • sequencing data analysis
  • multi-omics data integration
  • biomarker identification
  • drug repurposing and drug response prediction
  • discoveries in immunology, cancer, and developmental biology

Published Papers (2 papers)

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Research

16 pages, 3164 KiB  
Article
PROZ Associated with Sorafenib Sensitivity May Serve as a Potential Target to Enhance the Efficacy of Combined Immunotherapy for Hepatocellular Carcinoma
by Yinkui Chen, Xiusheng Qiu, Donghao Wu, Xu Lu, Guanghui Li, Yongsheng Tang, Changchang Jia, Zhiyong Xiong and Tiantian Wang
Genes 2022, 13(9), 1535; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13091535 - 26 Aug 2022
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Abstract
Targeted combined immunotherapy has significantly improved the prognosis of patients with advanced hepatocellular carcinoma and has now become the primary treatment for advanced hepatocellular carcinoma. However, some patients still have poor efficacy or are resistant to treatment. The further exploration of molecular markers [...] Read more.
Targeted combined immunotherapy has significantly improved the prognosis of patients with advanced hepatocellular carcinoma and has now become the primary treatment for advanced hepatocellular carcinoma. However, some patients still have poor efficacy or are resistant to treatment. The further exploration of molecular markers related to efficacy or finding molecular targets to increase efficacy is an urgent problem that needs to be resolved. In this research, we found that PROZ was a gene related to KDR expression that had significantly low expression in cancer tissue by analyzing the differential genes of cancer tissue and adjacent tissue and the intersection of KDR-related genes in hepatocellular carcinoma. The correlation analysis of clinical data showed that the low expression of PROZ was significantly correlated with the poor prognosis of hepatocellular carcinoma, and further studies found that PROZ was closely related to the expression of p-ERK and VEGFR2 in hepatocellular carcinoma. In addition, intracellular detection also showed that the expression of p-ERK increased and VEGFR2 expression decreased after PROZ interference, and PROZ downregulation with increased p-ERK and decreased VEGFR2 was also detected in sorafenib-resistant strains. At the same time, our analysis found that PROZ was negatively correlated with genes related to immunotherapy efficacy such as CD8A, CD274 and GZMA, and was also negatively correlated with T-cell infiltration in tumor tissue. Conclusion: PROZ is a gene related to the prognosis of hepatocellular carcinoma and it is closely related to the efficacy of sorafenib and immunotherapy. It may serve as a potential molecular target to improve the efficacy of targeted combined immunotherapy. Full article
(This article belongs to the Special Issue Discoveries in Sequencing Data Analysis)
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19 pages, 22513 KiB  
Article
Illegitimate Recombination between Duplicated Genes Generated from Recursive Polyploidizations Accelerated the Divergence of the Genus Arachis
by Shaoqi Shen, Yuxian Li, Jianyu Wang, Chendan Wei, Zhenyi Wang, Weina Ge, Min Yuan, Lan Zhang, Li Wang, Sangrong Sun, Jia Teng, Qimeng Xiao, Shoutong Bao, Yishan Feng, Yan Zhang, Jiaqi Wang, Yanan Hao, Tianyu Lei and Jinpeng Wang
Genes 2021, 12(12), 1944; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12121944 - 01 Dec 2021
Cited by 2 | Viewed by 2537
Abstract
The peanut (Arachis hypogaea L.) is the leading oil and food crop among the legume family. Extensive duplicate gene pairs generated from recursive polyploidizations with high sequence similarity could result from gene conversion, caused by illegitimate DNA recombination. Here, through synteny-based comparisons [...] Read more.
The peanut (Arachis hypogaea L.) is the leading oil and food crop among the legume family. Extensive duplicate gene pairs generated from recursive polyploidizations with high sequence similarity could result from gene conversion, caused by illegitimate DNA recombination. Here, through synteny-based comparisons of two diploid and three tetraploid peanut genomes, we identified the duplicated genes generated from legume common tetraploidy (LCT) and peanut recent allo-tetraploidy (PRT) within genomes. In each peanut genome (or subgenomes), we inferred that 6.8–13.1% of LCT-related and 11.3–16.5% of PRT-related duplicates were affected by gene conversion, in which the LCT-related duplicates were the most affected by partial gene conversion, whereas the PRT-related duplicates were the most affected by whole gene conversion. Notably, we observed the conversion between duplicates as the long-lasting contribution of polyploidizations accelerated the divergence of different Arachis genomes. Moreover, we found that the converted duplicates are unevenly distributed across the chromosomes and are more often near the ends of the chromosomes in each genome. We also confirmed that well-preserved homoeologous chromosome regions may facilitate duplicates’ conversion. In addition, we found that these biological functions contain a higher number of preferentially converted genes, such as catalytic activity-related genes. We identified specific domains that are involved in converted genes, implying that conversions are associated with important traits of peanut growth and development. Full article
(This article belongs to the Special Issue Discoveries in Sequencing Data Analysis)
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