A method for benchmarking genetic screens reveals a predominant mitochondrial bias
Abstract We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measur...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021-05-01
|
| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.202010013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850109773902512128 |
|---|---|
| author | Mahfuzur Rahman Maximilian Billmann Michael Costanzo Michael Aregger Amy H Y Tong Katherine Chan Henry N Ward Kevin R Brown Brenda J Andrews Charles Boone Jason Moffat Chad L Myers |
| author_facet | Mahfuzur Rahman Maximilian Billmann Michael Costanzo Michael Aregger Amy H Y Tong Katherine Chan Henry N Ward Kevin R Brown Brenda J Andrews Charles Boone Jason Moffat Chad L Myers |
| author_sort | Mahfuzur Rahman |
| collection | DOAJ |
| description | Abstract We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them. |
| format | Article |
| id | doaj-art-b77f0c4de34540ad85f7efa1a9046e4b |
| institution | OA Journals |
| issn | 1744-4292 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-b77f0c4de34540ad85f7efa1a9046e4b2025-08-20T02:37:58ZengSpringer NatureMolecular Systems Biology1744-42922021-05-0117511210.15252/msb.202010013A method for benchmarking genetic screens reveals a predominant mitochondrial biasMahfuzur Rahman0Maximilian Billmann1Michael Costanzo2Michael Aregger3Amy H Y Tong4Katherine Chan5Henry N Ward6Kevin R Brown7Brenda J Andrews8Charles Boone9Jason Moffat10Chad L Myers11Department of Computer Science and Engineering, University of Minnesota – Twin CitiesDepartment of Computer Science and Engineering, University of Minnesota – Twin CitiesDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoBioinformatics and Computational Biology Graduate Program, University of Minnesota – Twin CitiesDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoDonnelly Centre, University of TorontoDepartment of Computer Science and Engineering, University of Minnesota – Twin CitiesAbstract We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.https://doi.org/10.15252/msb.202010013computational evaluationCRISPR screenselectron transport chain |
| spellingShingle | Mahfuzur Rahman Maximilian Billmann Michael Costanzo Michael Aregger Amy H Y Tong Katherine Chan Henry N Ward Kevin R Brown Brenda J Andrews Charles Boone Jason Moffat Chad L Myers A method for benchmarking genetic screens reveals a predominant mitochondrial bias Molecular Systems Biology computational evaluation CRISPR screens electron transport chain |
| title | A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| title_full | A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| title_fullStr | A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| title_full_unstemmed | A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| title_short | A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| title_sort | method for benchmarking genetic screens reveals a predominant mitochondrial bias |
| topic | computational evaluation CRISPR screens electron transport chain |
| url | https://doi.org/10.15252/msb.202010013 |
| work_keys_str_mv | AT mahfuzurrahman amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT maximilianbillmann amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT michaelcostanzo amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT michaelaregger amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT amyhytong amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT katherinechan amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT henrynward amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT kevinrbrown amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT brendajandrews amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT charlesboone amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT jasonmoffat amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT chadlmyers amethodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT mahfuzurrahman methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT maximilianbillmann methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT michaelcostanzo methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT michaelaregger methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT amyhytong methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT katherinechan methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT henrynward methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT kevinrbrown methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT brendajandrews methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT charlesboone methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT jasonmoffat methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias AT chadlmyers methodforbenchmarkinggeneticscreensrevealsapredominantmitochondrialbias |