Back

CRISPR-Decryptr reveals cis-regulatory elements from noncoding perturbation screens

Rasmussen, A. W.; Äijö, T.; Gabitto, M. I.; Carriero, N.; Sanjana, N.; Skok, J.; Bonneau, R.

2020-08-14 bioinformatics
10.1101/2020.08.13.247007 bioRxiv
Show abstract

Clustered Regularly Interspace Short Palindromic Repeats (CRISPR)-Cas9 genome editing methods provide the tools necessary to examine phenotypic impacts of targeted perturbations in high-throughput screens. While these technologies have the potential to reveal functional elements with direct therapeutic applications, statistical techniques to analyze noncoding screen data remain limited. We present CRISPR-Decryptr, a computational tool for the analysis of CRISPR noncoding screens. Our method leverages experimental design: accounting for multiple conditions, controls, and replicates to infer the regulatory landscape of noncoding genomic regions. We validate our method on a variety of mutagenesis, CRISPR activation, and CRISPR interference screens, extracting new insights from previously published data.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 14%
12.6%
Similar papers in this journal
2
The CRISPR Journal
○ 39 papers in training set
Top 0.1%
10.6%
Similar papers in this journal
3
Cell Reports Methods
○ 165 papers in training set
Top 0.1%
7.9%
Similar papers in this journal
4
Nature Methods
○ 385 papers in training set
Top 2%
6.2%
Similar papers in this journal
5
Bioinformatics
◐ 1204 papers in training set
Top 4%
5.5%
Similar papers in this journal
6
Genome Biology
○ 637 papers in training set
Top 2%
5.5%
7
Nucleic Acids Research
◐ 1281 papers in training set
Top 5%
4.0%
50% of probability mass above
8
Nature Biotechnology
○ 172 papers in training set
Top 1%
4.0%
9
Scientific Reports
○ 3612 papers in training set
Top 28%
3.5%
10
Cell Genomics
○ 172 papers in training set
Top 1%
3.2%
11
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.7%
12
ACS Synthetic Biology
● 287 papers in training set
Top 1%
2.6%
13
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.6%
14
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.7%
15
BMC Genomics
○ 406 papers in training set
Top 4%
1.7%
16
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.5%
17
BMC Bioinformatics
○ 457 papers in training set
Top 4%
1.3%
18
Human Genetics and Genomics Advances
○ 84 papers in training set
Top 1%
1.3%
19
Nature Computational Science
○ 55 papers in training set
Top 1%
1.1%
20
Molecular Therapy Nucleic Acids
○ 39 papers in training set
Top 0.6%
1.1%
21
Genome Medicine
○ 183 papers in training set
Top 4%
1.0%
22
Nature Genetics
○ 286 papers in training set
Top 4%
1.0%
23
Communications Biology
○ 993 papers in training set
Top 26%
1.0%
24
iScience
○ 1154 papers in training set
Top 32%
1.0%
25
Molecular Therapy - Methods & Clinical Development
○ 38 papers in training set
Top 0.7%
0.8%
26
Genome Research
● 468 papers in training set
Top 7%
0.6%
27
Patterns
○ 78 papers in training set
Top 3%
0.6%
28
Frontiers in Genetics
○ 230 papers in training set
Top 6%
0.6%
29
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 44%
0.6%
30
PLOS Computational Biology
● 1863 papers in training set
Top 21%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.