Evidence map›Paper›PMID 34718995›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Computational Approaches for Designing Highly Specific and Efficient sgRNAs.

Jaspreet Kaur Dhanjal, Dhvani Vora, Navaneethan Radhakrishnan, Durai Sundar

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Jaspreet Kaur DhanjalDepartment of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
Dhvani VoraDepartment of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
Navaneethan RadhakrishnanDepartment of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
Durai SundarDepartment of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India. sundar@dbeb.iitd.ac.in.
Indian Institute of Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The easily programmable CRISPR/Cas9 system has found applications in biomedical research as well as microbial and crop applications, due to its ability to create site-specific edits. This powerful and flexible system has also been modified to enable inducible gene regulation, epigenome modifications and high-throughput screens. Designing efficient and specific guides for the nuclease is a key step and also a major challenge in effective application. This chapter describes rules for sgRNA design and important features to consider while touching upon bioinformatics advances in predicting efficient guides. Computational tools that suggest improved guides, depending on application, or predict off-targets have also been mentioned and compared.

Indexed as

Computational BiologyCRISPR-Cas SystemsEndonucleasesRNA, Guide, CRISPR-Cas SystemsEndonucleasesRNA, Guide, CRISPR-Cas SystemsCRISPR/Cas9KnockinKnockoutOff-targetsgRNATarget

Identifiers

PMID34718995
OpenAlexW3210461221

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.