{"doi":"10.1093/nar/gkaa1156","title":"Deploying MMEJ using MENdel in precision gene editing applications for gene therapy and functional genomics","abstract":"Gene-editing experiments commonly elicit the error-prone non-homologous end joining for DNA double-strand break (DSB) repair. Microhomology-mediated end joining (MMEJ) can generate more predictable outcomes for functional genomic and somatic therapeutic applications. We compared three DSB repair prediction algorithms - MENTHU, inDelphi, and Lindel - in identifying MMEJ-repaired, homogeneous genotypes (PreMAs) in an independent dataset of 5,885 distinct Cas9-mediated mouse embryonic stem cell DSB repair events. MENTHU correctly identified 46% of all PreMAs available, a ∼2- and ∼60-fold sensitivity increase compared to inDelphi and Lindel, respectively. In contrast, only Lindel correctly predicted predominant single-base insertions. We report the new algorithm MENdel, a combination of MENTHU and Lindel, that achieves the most predictive coverage of homogeneous out-of-frame mutations in this large dataset. We then estimated the frequency of Cas9-targetable homogeneous frameshift-inducing DSBs in vertebrate coding regions for gene discovery using MENdel. 47 out of 54 genes (87%) contained at least one early frameshift-inducing DSB and 49 out of 54 (91%) did so when also considering Cas12a-mediated deletions. We suggest that the use of MENdel helps researchers use MMEJ at scale for reverse genetics screenings and with sufficient intra-gene density rates to be viable for nearly all loss-of-function based gene editing therapeutic applications.","journal":"Nucleic Acids Research","year":2020,"id":70786,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9528,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":374339,"name":"Parnal Joshi","orcid":"0000-0001-6689-3858","position":1,"is_corresponding":false},{"id":16317,"name":"Iddo Friedberg","orcid":"0000-0002-1789-8000","position":2,"is_corresponding":false},{"id":253785,"name":"Armando Manduca","orcid":"0000-0002-9411-5671","position":3,"is_corresponding":false},{"id":254326,"name":"Stephen C. Ekker","orcid":"0000-0003-0726-4212","position":4,"is_corresponding":false},{"id":374338,"name":"Gabriel Martínez‐Gálvez","orcid":"0000-0002-4392-2596","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-18T21:43:32.585486Z","pmid":"33305328","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}