Genomics, proteomics & bioinformatics最新文献_第5页

BSAlign: A Library for Nucleotide Sequence Alignment. BSAlign：核苷酸序列比对库。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae025

Haojing Shao, Jue Ruan

{"title":"BSAlign: A Library for Nucleotide Sequence Alignment.","authors":"Haojing Shao, Jue Ruan","doi":"10.1093/gpbjnl/qzae025","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae025","url":null,"abstract":"Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic programming (DP) algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: redesigning data structures [e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations], increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing search space (e.g., banded DP). However, no methods combine all these three aspects to build an ultra-fast algorithm. In this study, we developed a Banded Striped Aligner (BSAlign) library that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded DP. We applied our new acceleration design on both regular and edit distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD-based implementations for regular pairwise alignment, and 1.5-fold to 4-fold speed-up in edit distance-based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142116457","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}

引用次数: 0

DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps. DiffGR：从 Hi-C 接触图中检测差异交互基因组区域。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae028

Huiling Liu, Wenxiu Ma

{"title":"DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps.","authors":"Huiling Liu, Wenxiu Ma","doi":"10.1093/gpbjnl/qzae028","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae028","url":null,"abstract":"Recent advances in high-throughput chromosome conformation capture (Hi-C) techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains (TADs) are still lacking. Here, we proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets, and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods. The DiffGR R package is published under the GNU General Public License (GPL) ≥ 2 license and is publicly available at https://github.com/wmalab/DiffGR.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121427","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}

引用次数: 0

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer. 小细胞肺癌预后和治疗方案的蛋白质组学分层

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae033

Zitian Huo, Yaqi Duan, Dongdong Zhan, Xizhen Xu, Nairen Zheng, Jing Cai, Ruifang Sun, Jianping Wang, Fang Cheng, Zhan Gao, Caixia Xu, Wanlin Liu, Yuting Dong, Sailong Ma, Qian Zhang, Yiyun Zheng, Liping Lou, Dong Kuang, Qian Chu, Jun Qin, Guoping Wang, Yi Wang

{"title":"Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer.","authors":"Zitian Huo, Yaqi Duan, Dongdong Zhan, Xizhen Xu, Nairen Zheng, Jing Cai, Ruifang Sun, Jianping Wang, Fang Cheng, Zhan Gao, Caixia Xu, Wanlin Liu, Yuting Dong, Sailong Ma, Qian Zhang, Yiyun Zheng, Liping Lou, Dong Kuang, Qian Chu, Jun Qin, Guoping Wang, Yi Wang","doi":"10.1093/gpbjnl/qzae033","DOIUrl":"10.1093/gpbjnl/qzae033","url":null,"abstract":"Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data. 单细胞 ATAC-seq 数据基因组评分基准算法。

Genomics, proteomics & bioinformatics Pub Date : 2024-07-03 DOI: 10.1093/gpbjnl/qzae014

Xi Wang, Qiwei Lian, Haoyu Dong, Shuo Xu, Yaru Su, Xiaohui Wu

{"title":"Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.","authors":"Xi Wang, Qiwei Lian, Haoyu Dong, Shuo Xu, Yaru Su, Xiaohui Wu","doi":"10.1093/gpbjnl/qzae014","DOIUrl":"10.1093/gpbjnl/qzae014","url":null,"abstract":"Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reprogramming of RNA m6A Modification Is Required for Acute Myeloid Leukemia Development. 急性髓性白血病的发展需要对 RNA m6A 修饰进行重编程。

Genomics, proteomics & bioinformatics Pub Date : 2024-06-24 DOI: 10.1093/gpbjnl/qzae049

Weidong Liu, Yuhua Wang, Shuxin Yao, Guoqiang Han, Jin Hu, Rong Yin, Fuling Zhou, Ying Cheng, Haojian Zhang

{"title":"Reprogramming of RNA m6A Modification Is Required for Acute Myeloid Leukemia Development.","authors":"Weidong Liu, Yuhua Wang, Shuxin Yao, Guoqiang Han, Jin Hu, Rong Yin, Fuling Zhou, Ying Cheng, Haojian Zhang","doi":"10.1093/gpbjnl/qzae049","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae049","url":null,"abstract":"Hematopoietic homeostasis is maintained by hematopoietic stem cells (HSCs), and it is tightly controlled at multiple levels to sustain the self-renewal capacity and differentiation potential of HSCs. Dysregulation of self-renewal and differentiation of HSCs leads to the development of hematologic diseases, including acute myeloid leukemia (AML). Thus, understanding the underlying mechanisms of HSC maintenance and the development of hematologic malignancies is one of the fundamental scientific endeavors in stem cell biology. N 6-methyladenosine (m6A) is a common modification in mammalian messenger RNAs (mRNAs) and plays important roles in various biological processes. In this study, we performed a comparative analysis of the dynamics of the RNA m6A methylome of hematopoietic stem and progenitor cells (HSPCs) and leukemia-initiating cells (LICs) in AML. We found that RNA m6A modification regulates the transformation of long-term HSCs into short-term HSCs and determines the lineage commitment of HSCs. Interestingly, m6A modification leads to reprogramming that promotes cellular transformation during AML development, and LIC-specific m6A targets are recognized by different m6A readers. Moreover, the very long chain fatty acid transporter ATP-binding cassette subfamily D member 2 (ABCD2) is a key factor that promotes AML development, and deletion of ABCD2 damages clonogenic ability, inhibits proliferation, and promotes apoptosis of human leukemia cells. This study provides a comprehensive understanding of the role of m6A in regulating cell state transition in normal hematopoiesis and leukemogenesis, and identifies ABCD2 as a key factor in AML development.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447874","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}

引用次数: 0

The Bioinformatic Applications of Hi-C and Linked Reads. Hi-C 和关联读数的生物信息学应用

Genomics, proteomics & bioinformatics Pub Date : 2024-06-21 DOI: 10.1093/gpbjnl/qzae048

Libo Jiang, Michael A Quail, Jack Fraser-Govi, Haipeng Wang, Xuequn Shi, Karen Oliver, Esther Mellado Gomez, Fengtang Yang, Zemin Ning

{"title":"The Bioinformatic Applications of Hi-C and Linked Reads.","authors":"Libo Jiang, Michael A Quail, Jack Fraser-Govi, Haipeng Wang, Xuequn Shi, Karen Oliver, Esther Mellado Gomez, Fengtang Yang, Zemin Ning","doi":"10.1093/gpbjnl/qzae048","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae048","url":null,"abstract":"Long-range sequencing grants insight into additional genetic information beyond that which can be accessed by both short reads and modern long-read technology. Several new sequencing technologies are available for long-range datasets such as \"Hi-C\" and \"Linked Reads\" with high-throughput and high-resolution genome analysis, and are rapidly advancing the field of genome assembly, genome scaffolding, and more comprehensive variant identification. In this article, we focused on five major long-range sequencing technologies: high-throughput chromosome conformation capture (Hi-C), 10x Genomics Linked Reads, haplotagging, transposase enzyme linked long-read sequencing (TELL-seq), and single tube long fragment read (stLFR). We detailed the mechanisms and data products of the five platforms, introduced several of the most important applications, evaluated the quality of sequencing data from different platforms, and discussed the currently available bioinformatics tools. We hope this work will benefit the selection of appropriate long-range technology for specific biological studies.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141437947","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}

引用次数: 0

Deep Amplicon Sequencing Reveals Culture Selection of Mycobacterium Tuberculosis by Clinical Samples. 深度扩增子测序揭示了临床样本对结核分枝杆菌培养的选择。

Genomics, proteomics & bioinformatics Pub Date : 2024-06-13 DOI: 10.1093/gpbjnl/qzae046

Jiuxin Qu, Wanfei Liu, Shuyan Chen, Chi Wu, Wenjie Lai, Rui Qin, Feidi Ye, Yuanchun Li, Liang Fu, Guofang Deng, Lei Liu, Qiang Lin, Peng Cui

{"title":"Deep Amplicon Sequencing Reveals Culture Selection of Mycobacterium Tuberculosis by Clinical Samples.","authors":"Jiuxin Qu, Wanfei Liu, Shuyan Chen, Chi Wu, Wenjie Lai, Rui Qin, Feidi Ye, Yuanchun Li, Liang Fu, Guofang Deng, Lei Liu, Qiang Lin, Peng Cui","doi":"10.1093/gpbjnl/qzae046","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae046","url":null,"abstract":"The commonly-used drug susceptibility testing (DST) relies on bacterial culture and faces shortcomings such as long turnaround time and clone/subclone selection. We developed a targeted deep amplification sequencing (DAS) method directly applied to clinical specimens. In this DAS panel, we examined 941 drug-resistant mutations associated with 20 anti-tuberculosis drugs with an initial amount of 4 pg DNA and reduced clinical testing time from 20 days to two days. A prospective study was conducted using 115 clinical specimens mainly with Xpert® Mycobacterium tuberculosis/rifampicin (Xpert MTB/RIF) assay positive to evaluate drug-resistant mutation detection. DAS was performed on culture-free specimens, while culture-dependent isolates were used for phenotypic DST, DAS, and whole-genome sequencing (WGS). For in silico molecular DST, our result based on DAS panel revealed the similar accuracy to three published reports based on WGS. For 82 isolates, application of DAS showed better sensitivity (93.03% vs. 92.16%), specificity (96.10% vs. 95.02%), and accuracy (91.33% vs. 90.62%) than Mykrobe software using WGS. Compared to culture-dependent WGS, culture-free DAS provides a full picture of sequence variation at population level, exhibiting in detail the gain-and-loss variants caused by bacterial culture. Our study performs a systematic verification of the advantages of DAS in clinical applications and comprehensively illustrates the discrepancy in Mycobacterium tuberculosis before and after culture.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319301","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}

引用次数: 0

Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level. Scm6A：单细胞水平 m6A 修饰定量的快速低成本方法。

Genomics, proteomics & bioinformatics Pub Date : 2024-06-07 DOI: 10.1093/gpbjnl/qzae039

Yueqi Li, Jingyi Li, Wenxing Li, Shuaiyi Liang, Wudi Wei, Jiemei Chu, Jingzhen Lai, Yao Lin, Hubin Chen, Jinming Su, Xiaopeng Hu, Gang Wang, Jun Meng, Junjun Jiang, Li Ye, Sanqi An

{"title":"Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level.","authors":"Yueqi Li, Jingyi Li, Wenxing Li, Shuaiyi Liang, Wudi Wei, Jiemei Chu, Jingzhen Lai, Yao Lin, Hubin Chen, Jinming Su, Xiaopeng Hu, Gang Wang, Jun Meng, Junjun Jiang, Li Ye, Sanqi An","doi":"10.1093/gpbjnl/qzae039","DOIUrl":"10.1093/gpbjnl/qzae039","url":null,"abstract":"It is widely accepted that N6-methyladenosine (m6A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduced Single-cell m6A Analysis (Scm6A), a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we applied a winscore-based m6A calculation method to conduct N6-methyladenosine sequencing (m6A-seq) analysis on CD4+ and CD8+ T-cells isolated through magnetic-activated cell sorting (MACS). Subsequently, we employed Scm6A for analysis on the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with m6A quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6A's reliability. Additionally, we performed single-cell level m6A analysis on lung cancer tissues as well as blood samples from the coronavirus disease 2019 (COVID-19) patients, and demonstrated the landscape and regulatory mechanisms of m6A in different T-cell subtypes from these diseases. In summary, our work has yielded a novel, dependable, and accurate method for single-cell m6A detection. We are confident that Scm6A have broad applications in the realm of m6A-related research.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484017","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}

引用次数: 0

Acknowledgments to Reviewers 2023. 鸣谢审稿人 2023.

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae038

引用次数: 0

RNase P: Beyond Precursor tRNA Processing. RNase P：超越前体 tRNA 处理。

Genomics, proteomics & bioinformatics Pub Date : 2024-05-09 DOI: 10.1093/gpbjnl/qzae016

Peipei Wang, Juntao Lin, Xiangyang Zheng, Xingzhi Xu

引用次数: 0