Full Spectrum CRISPR Analysis: Rapidly Verify On-Target and Off-Target Edits with PED
Published in Bioengineering & Biotechnology, Cancer, and Genetics & Genomics
My previous blog post introduced bidirectional alignment algorithm PED (Polymorphic Edge Detection), a method that aligns a reference genome with Next-Generation Sequencing (NGS) reads from both directions and detects the edges of genome portions where the mutation has occurred.
A key advantage of this approach is its ability to identify large deletion mutations often missed by other programs. Furthermore, it can detect various other mutations, including single-base substitutions, insertions, translocations, and inversions.
This blog post demonstrates how PED can be used for sequence analysis of organisms that have undergone genome editing with CRISPR/Cas9.
To illustrate, I began by searching for "CRISPR" on NCBI's Sequence Read Archive (SRA) to find relevant sequence data. I found a dataset from Umeå University titled "Genotyping of C. elegans mutants - CRISPR/Cas9 of all GPCR and neuropeptide genes" and downloaded it from NCBI.
The sequence data for a specific sample, ERR11472167, was downloaded using the fastq-dump command from the SRA Toolkit provided by NCBI:
fastq-dump --split-files ERR11472167
After saving the downloaded files to the ERR11472167/read directory, the PED program was run using the following command:
perl ped.pl target=ERR11472167,ref=WBcel235
Here, WBcel235 refers to the reference genome sequence for the nematode C. elegans. Subsequently, the snpEff program was used to identify the affected genes and the types of mutations, generating a list of these findings (Figure 1).
According to NCBI's BioSample database, the ERR11472167 sample was reported to have intended mutations in genes WBGene00005318 and WBGene00005319. As highlighted in red in Figure 1, the PED program confirmed mutations in the targeted genes (smg-10/WBGene00005318 and dsh-2/WBGene00000102), demonstrating its ability to verify successful genome editing.
Importantly, PED analysis also revealed numerous off-target mutations in unintended genomic locations. A total of sixty-two off-target mutations were identified in this specific C. elegans line.
Similarly, for sample ERR11472179, the SRA database indicated that gene WBGene00005641 (the sro-1 gene) was the target for genome editing.
As shown in Figure 2 (with sro-1/WBGene00005641 highlighted in red), PED confirmed a frameshift mutation in the sro-1 gene. However, it also detected 60 additional off-target mutations in this sample.
These examples demonstrate that the PED program is a valuable tool not only for verifying intended edits but also for comprehensively checking for off-target mutations in genome-edited organisms. We encourage researchers to try PED for their analyses.
References
Miyao, A., Kiyomiya, J.S., Iida, K. et al. Polymorphic edge detection (PED): two efficient methods of polymorphism detection from next-generation sequencing data. BMC Bioinformatics 20, 362 (2019). https://doi.org/10.1186/s12859-019-2955-6
https://github.com/akiomiyao/ped
Cingolani, P., Platts, A., Wang, leL., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly, 6(2), 80–92. https://doi.org/10.4161/fly.19695
Follow the Topic
-
BMC Bioinformatics
This is an open access, peer-reviewed journal that considers articles describing novel computational algorithms and software, models and tools, including statistical methods, machine learning and artificial intelligence, as well as systems biology.
Related Collections
With Collections, you can get published faster and increase your visibility.
Extracellular vesicle research
BMC Bioinformatics is welcoming submissions to our Collection on Extracellular vesicles research.
BMC Bioinformatics is welcoming submissions to our Collection on Extracellular vesicles research. Extracellular vesicles (EVs) are are small lipid bilayer-delimited particles released by cells that play crucial roles in intercellular communication and various physiological processes. The study of EVs has gained significant attention due to their potential as biomarkers for disease diagnosis, therapeutic targets and drug delivery systems. Advanced bioinformatics tools are essential for analyzing EV data, identifying EV-associated molecules, and understanding their biological functions.
This Collection welcomes submissions on the development of new computational and/or statistical approaches for the study of extracellular vesicles. We encourage contributions that highlight innovative methods for detecting and characterizing EVs and elucidating the molecular mechanisms underlying EV biogenesis and function.
All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.
Publishing Model: Open Access
Deadline: Sep 30, 2026
Bioinformatics and ecology
BMC Bioinformatics is calling for submissions to our Collection on Bioinformatics and ecology.
Ecology has become a data-heavy science. Genomic technologies and computational methods have changed what ecologists can ask and answer, and bioinformatics tools are now central to making sense of the large, messy datasets these studies produce.
This matters for some of the most pressing environmental problems we face, including climate change, habitat loss, non-indigenous species, and biodiversity crisis. Advances in DNA sequencing, comprehensive reference databases, and computational analysis now let researchers characterise microbial and macrobial communities and assess their role in ecosystem function with a level of detail that was not possible a decade ago. In conservation genomics, the same tools support practical decisions about managing endangered species and restoring habitats.
There is plenty of room for the field to develop further. As machine learning methods mature, they should improve predictions of species distributions and how ecosystems respond to environmental pressure. Better taxonomic profiling and genome assembly will also sharpen our understanding of microbial ecology and what it tells us about ecosystem health.
This Collection brings together research that pairs bioinformatics approaches with ecological questions. We are particularly interested in environmental DNA (eDNA) analysis, metagenomics, and the use of machine learning to understand community structure and species distribution. We welcome contributions that include:
- Ecosystem monitoring using bioinformatics
- Metagenomics in microbial ecology
- Applications of machine learning in ecological studies
- Environmental DNA as a tool for biodiversity assessment
- Conservation genomics and species management
- Metabarcoding approaches for large-scale biodiversity studies
- Bioinformatic pipelines and tools for ecological genomics
This Collection supports and amplifies research related to SDG6: Clean Water and Sanitation, SDG 13: Climate Action, SDG 14: Life Below Water, and SDG 15: Life on Land.
All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.
Publishing Model: Open Access
Deadline: Apr 30, 2027