Cargando…
Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens
BACKGROUND: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019185/ https://www.ncbi.nlm.nih.gov/pubmed/36937794 |
_version_ | 1784907974201835520 |
---|---|
author | Zhao, Yueshan Zhang, Min Yang, Da |
author_facet | Zhao, Yueshan Zhang, Min Yang, Da |
author_sort | Zhao, Yueshan |
collection | PubMed |
description | BACKGROUND: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. RESULTS: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens. CONCLUSION: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods. |
format | Online Article Text |
id | pubmed-10019185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-100191852023-03-16 Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens Zhao, Yueshan Zhang, Min Yang, Da Quant Biol Article BACKGROUND: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. RESULTS: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens. CONCLUSION: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods. 2022-12 /pmc/articles/PMC10019185/ /pubmed/36937794 Text en https://creativecommons.org/licenses/by/4.0/OPEN ACCESS: This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhao, Yueshan Zhang, Min Yang, Da Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title | Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title_full | Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title_fullStr | Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title_full_unstemmed | Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title_short | Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens |
title_sort | bioinformatics approaches to analyzing crispr screen data: from dropout screens to single-cell crispr screens |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019185/ https://www.ncbi.nlm.nih.gov/pubmed/36937794 |
work_keys_str_mv | AT zhaoyueshan bioinformaticsapproachestoanalyzingcrisprscreendatafromdropoutscreenstosinglecellcrisprscreens AT zhangmin bioinformaticsapproachestoanalyzingcrisprscreendatafromdropoutscreenstosinglecellcrisprscreens AT yangda bioinformaticsapproachestoanalyzingcrisprscreendatafromdropoutscreenstosinglecellcrisprscreens |