Genomics, Proteomics & Bioinformatics最新文献

{"title":"TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning","authors":"Yihe Pang ,&nbsp;Bin Liu","doi":"10.1016/j.gpb.2022.10.004","DOIUrl":"10.1016/j.gpb.2022.10.004","url":null,"abstract":"<div><p><strong>Disordered flexible linkers</strong> (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs, leading to a high <strong>false</strong> <strong>positive rate</strong> (FPR) and low prediction accuracy. Previous studies have shown that DFLs are extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [<em>P</em>(<em>D</em>) &gt; 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in IDRs or prediction of DFLs in entire proteins) showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed at <span>http://bliulab.net/TransDFL/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 359-369"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10354923","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":"mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants","authors":"Shi-Yuan Tong ,&nbsp;Ke Fan ,&nbsp;Zai-Wei Zhou ,&nbsp;Lin-Yun Liu ,&nbsp;Shu-Qing Zhang ,&nbsp;Yinghui Fu ,&nbsp;Guang-Zhong Wang ,&nbsp;Ying Zhu ,&nbsp;Yong-Chun Yu","doi":"10.1016/j.gpb.2022.07.005","DOIUrl":"10.1016/j.gpb.2022.07.005","url":null,"abstract":"<div><p>Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed <strong>Pathogenicity Prediction</strong> Tool for <strong>missense variants</strong> (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at <span>http://www.mvppt.club/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 414-426"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10043480","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":"RNA2Immune: A Database of Experimentally Supported Data Linking Non-coding RNA Regulation to The Immune System","authors":"Jianjian Wang ,&nbsp;Shuang Li ,&nbsp;Tianfeng Wang ,&nbsp;Si Xu ,&nbsp;Xu Wang ,&nbsp;Xiaotong Kong ,&nbsp;Xiaoyu Lu ,&nbsp;Huixue Zhang ,&nbsp;Lifang Li ,&nbsp;Meng Feng ,&nbsp;Shangwei Ning ,&nbsp;Lihua Wang","doi":"10.1016/j.gpb.2022.05.001","DOIUrl":"10.1016/j.gpb.2022.05.001","url":null,"abstract":"<div><p>Non-coding RNAs (<strong>ncRNAs</strong>), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis, as well as <strong>vaccine</strong> responses. A repository of ncRNA–immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics and prevention for immune disorders. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, <strong>immune disease</strong>, <strong>cancer immunology</strong>, and vaccines. The current version of RNA2Immune documents 50,433 immune–ncRNA associations in 42 host species, including (1) 6690 ncRNA associations with immune functions involving 31 immune cell types; (2) 38,672 ncRNA associations with 348 immune diseases; (3) 4833 ncRNA associations with cancer immunology; and (4) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching, and downloading ncRNA–immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, how dysregulation of these ncRNAs leads to pathological consequences (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at <span>http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 283-291"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10143159","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":"CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs","authors":"Tong Tang ,&nbsp;Xingyun Liu ,&nbsp;Rongrong Wu ,&nbsp;Li Shen ,&nbsp;Shumin Ren ,&nbsp;Bairong Shen","doi":"10.1016/j.gpb.2022.10.003","DOIUrl":"10.1016/j.gpb.2022.10.003","url":null,"abstract":"<div><p>Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients. Recent studies have indicated that <strong>non-coding RNAs</strong> (ncRNAs) can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR. Herein, we developed CTRR-ncRNA, a <strong>knowledgebase</strong> of CTRR-associated ncRNAs, aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs. Compared to most of the existing cancer databases, CTRR-ncRNA is focused on the clinical characterization of cancers, including cancer subtypes, as well as survival outcomes and responses to personalized therapy of cancer patients. Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction. A user-friendly interface and several functional modules have been incorporated into the database. Based on the preliminary analysis of genotype–phenotype relationships, universal ncRNAs have been found to be potential biomarkers for CTRR. The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling. CTRR-ncRNA is freely available to the public at <span>http://ctrr.bioinf.org.cn/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 292-299"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9776047","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":"inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics","authors":"Lei Du,&nbsp;Jin Zhang,&nbsp;Ying Zhao,&nbsp;Muheng Shang,&nbsp;Lei Guo,&nbsp;Junwei Han,&nbsp;The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.gpb.2023.03.005","DOIUrl":"10.1016/j.gpb.2023.03.005","url":null,"abstract":"<div><p>Identifying <strong>genetic risk factors</strong> for Alzheimer’s disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case–control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of <strong>cross-endophenotype</strong> (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, <em>i.e.</em>, pairwise endophenotype correlation-guided MTSCCA (<em>pc</em>MTSCCA) and high-order endophenotype correlation-guided MTSCCA (<em>hoc</em>MTSCCA). <em>pc</em>MTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. <em>hoc</em>MTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared <em>pc</em>MTSCCA and <em>hoc</em>MTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using <strong>multi-omic endophenotypes</strong> and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at <span>https://ngdc.cncb.ac.cn/biocode/tools/BT007330</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 396-413"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10126781","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":"iHypoxia: An Integrative Database of Protein Expression Dynamics in Response to Hypoxia in Animals","authors":"Ze-Xian Liu ,&nbsp;Panqin Wang ,&nbsp;Qingfeng Zhang ,&nbsp;Shihua Li ,&nbsp;Yuxin Zhang ,&nbsp;Yutong Guo ,&nbsp;Chongchong Jia ,&nbsp;Tian Shao ,&nbsp;Lin Li ,&nbsp;Han Cheng ,&nbsp;Zhenlong Wang","doi":"10.1016/j.gpb.2022.12.001","DOIUrl":"10.1016/j.gpb.2022.12.001","url":null,"abstract":"<div><p>Mammals have evolved mechanisms to sense <strong>hypoxia</strong> and induce hypoxic responses. Recently, high-throughput techniques have greatly promoted global studies of protein expression changes during hypoxia and the identification of candidate genes associated with hypoxia-adaptive evolution, which have contributed to the understanding of the complex regulatory networks of hypoxia. In this study, we developed an integrated resource for the <strong>expression dynamics</strong> of proteins in response to hypoxia (iHypoxia), and this database contains 2589 expression events of 1944 proteins identified by <strong>low-throughput experiments</strong> (LTEs) and 422,553 quantitative expression events of 33,559 proteins identified by <strong>high-throughput experiments</strong> from five mammals that exhibit a response to hypoxia. Various experimental details, such as the hypoxic experimental conditions, expression patterns, and sample types, were carefully collected and integrated. Furthermore, 8788 candidate genes from diverse species inhabiting low-oxygen environments were also integrated. In addition, we conducted an orthologous search and computationally identified 394,141 proteins that may respond to hypoxia among 48 animals. An enrichment analysis of human proteins identified from LTEs shows that these proteins are enriched in certain drug targets and cancer genes. Annotation of known posttranslational modification (PTM) sites in the proteins identified by LTEs reveals that these proteins undergo extensive PTMs, particularly phosphorylation, ubiquitination, and acetylation. iHypoxia provides a convenient and user-friendly method for users to obtain hypoxia-related information of interest. We anticipate that iHypoxia, which is freely accessible at <span>https://ihypoxia.omicsbio.info</span><svg><path></path></svg>, will advance the understanding of hypoxia and serve as a valuable data resource.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 267-277"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9738781","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":"NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations","authors":"Shaojun Wang ,&nbsp;Ronghui You ,&nbsp;Yunjia Liu ,&nbsp;Yi Xiong ,&nbsp;Shanfeng Zhu","doi":"10.1016/j.gpb.2023.04.001","DOIUrl":"10.1016/j.gpb.2023.04.001","url":null,"abstract":"<div><p>As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, <strong>protein language models</strong> have been proposed to learn informative representations [<em>e.g.</em>, Evolutionary Scale Modeling (ESM)-1b embedding] from protein sequences based on self-supervision. Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at <span>https://dmiip.sjtu.edu.cn/ng3.0</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 349-358"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10021973","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":"TIGER: A Web Portal of Tumor Immunotherapy Gene Expression Resource","authors":"Zhihang Chen ,&nbsp;Ziwei Luo ,&nbsp;Di Zhang ,&nbsp;Huiqin Li ,&nbsp;Xuefei Liu ,&nbsp;Kaiyu Zhu ,&nbsp;Hongwan Zhang ,&nbsp;Zongping Wang ,&nbsp;Penghui Zhou ,&nbsp;Jian Ren ,&nbsp;An Zhao ,&nbsp;Zhixiang Zuo","doi":"10.1016/j.gpb.2022.08.004","DOIUrl":"10.1016/j.gpb.2022.08.004","url":null,"abstract":"<div><p><strong>Immunotherapy</strong> is a promising cancer treatment method; however, only a few patients benefit from it. The development of new immunotherapy strategies and effective <strong>biomarkers</strong> of response and resistance is urgently needed. Recently, high-throughput bulk and single-cell <strong>gene expression</strong> profiling technologies have generated valuable resources. However, these resources are not well organized and systematic analysis is difficult. Here, we present TIGER, a tumor immunotherapy gene expression resource, which contains bulk transcriptome data of 1508 tumor samples with clinical immunotherapy outcomes and 11,057 tumor/normal samples without clinical immunotherapy outcomes, as well as single-cell transcriptome data of 2,116,945 immune cells from 655 samples. TIGER provides many useful modules for analyzing collected and user-provided data. Using the resource in TIGER, we identified a tumor-enriched subset of CD4⁺ T cells. Patients with melanoma with a higher signature score of this subset have a significantly better response and survival under immunotherapy. We believe that TIGER will be helpful in understanding anti-tumor immunity mechanisms and discovering effective biomarkers. TIGER is freely accessible at <span>http://tiger.canceromics.org/</span><svg><path></path></svg>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 2","pages":"Pages 337-348"},"PeriodicalIF":9.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10410423","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":"Computational Methods for Single-cell DNA Methylome Analysis","authors":"Waleed Iqbal ,&nbsp;Wanding Zhou","doi":"10.1016/j.gpb.2022.05.007","DOIUrl":"10.1016/j.gpb.2022.05.007","url":null,"abstract":"<div><p>Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of <strong>computational tools</strong> developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 1","pages":"Pages 48-66"},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9939249","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":"Computational Tools and Resources for CRISPR/Cas Genome Editing","authors":"Chao Li ,&nbsp;Wen Chu ,&nbsp;Rafaqat Ali Gill ,&nbsp;Shifei Sang ,&nbsp;Yuqin Shi ,&nbsp;Xuezhi Hu ,&nbsp;Yuting Yang ,&nbsp;Qamar U. Zaman ,&nbsp;Baohong Zhang","doi":"10.1016/j.gpb.2022.02.006","DOIUrl":"10.1016/j.gpb.2022.02.006","url":null,"abstract":"<div><p>The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genome editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the <strong>efficiency and specificity</strong> of single guide RNA (<strong>sgRNA</strong>)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring <strong>algorithms</strong> and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights into the limitations of available <strong>computational tools</strong> that would help researchers of this field for further optimization. Lastly, we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas <strong>genome editing</strong>.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 1","pages":"Pages 108-126"},"PeriodicalIF":9.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9884374","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}