{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:05:33Z","timestamp":1779876333209,"version":"3.53.1"},"reference-count":29,"publisher":"SAGE Publications","issue":"5-6","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>\n                    RNA editing is a post-transcriptional modification that alters single-nucleotide sites within RNA strands, thus diversifying transcriptomes and proteomes and modulating gene expression. While better characterized in eukaryotes and in a few microbes, the study of RNA editing in entire microbiomes remains unexplored. Recent studies have demonstrated that A-to-I RNA editing contributes to bacterial adaptation and pathogenicity. Previously, we developed M\n                    <jats:sc>eta<\/jats:sc>\n                    E\n                    <jats:sc>dit<\/jats:sc>\n                    , a reference-based computational pipeline to detect RNA edit sites in microbiomes. While M\n                    <jats:sc>eta<\/jats:sc>\n                    E\n                    <jats:sc>dit<\/jats:sc>\n                    successfully identified RNA edit sites in\n                    <jats:italic toggle=\"yes\">Escherichia coli<\/jats:italic>\n                    within the context of the human gut microbiome, including previously reported loci, it relied primarily on aligning reads to reference genomes of target bacteria. This dependence on reference genomes introduced potential biases, as editing can only be identified in reference genomes, while editing in novel microbial strains missing from the reference databases could be overlooked. Even for reference genomes, the search for edit sites is inefficient since it would have to be conducted one reference genome at a time.\n                  <\/jats:p>\n                  <jats:p>\n                    Here, we introduce ME\n                    <jats:sc>ditome<\/jats:sc>\n                    , employing\n                    <jats:italic toggle=\"yes\">de novo<\/jats:italic>\n                    assembly to overcome these limitations. This crucial change enables the detection of RNA edit sites across all microbial organisms in the microbiome, including novel bacterial strains for which comprehensive reference genomes are unavailable. Using sequencing data from the Integrative Human Microbiome Project, ME\n                    <jats:sc>ditome<\/jats:sc>\n                    identified 2,295 unique RNA editing sites across diverse bacterial taxa. Several of these overlaps with previously identified edits in\n                    <jats:italic toggle=\"yes\">E. coli<\/jats:italic>\n                    detected by MetaEdit in\n                    <jats:italic toggle=\"yes\">hok\/gef<\/jats:italic>\n                    gene family and arginine-associated genes, providing\n                    <jats:italic toggle=\"yes\">in silico<\/jats:italic>\n                    validation of accuracy. We observed taxon-specific editing patterns and gene-level differential editing associated with inflammatory bowel disease, highlighting RNA editing as a potential regulatory mechanism influencing microbial adaptation and host\u2013microbe interactions.\n                  <\/jats:p>","DOI":"10.1177\/15578666261428562","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:55:41Z","timestamp":1774263341000},"page":"643-659","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["ME\n                    <scp>ditome<\/scp>\n                    : Computational Detection of RNA Edit Sites Using\n                    <i>de Novo<\/i>\n                    Assembly in Microbiomes"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1645-0463","authenticated-orcid":false,"given":"Arpit","family":"Mehta","sequence":"first","affiliation":[{"name":"Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitalii","family":"Stebliankin","sequence":"additional","affiliation":[{"name":"Euleris LLC, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalai","family":"Mathee","sequence":"additional","affiliation":[{"name":"Euleris LLC, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giri","family":"Narasimhan","sequence":"additional","affiliation":[{"name":"Euleris LLC, Miami, Florida, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"e_1_3_3_2_1","article-title":"FastQC: A quality control tool for high throughput sequence data","author":"Andrews S","year":"2010","unstructured":"Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinformatics 2010 http:\/\/www.bioinformatics.babraham.ac.uk\/projects\/fastqc\/","journal-title":"Babraham Bioinformatics"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.222760.117"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.2951204"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msab293"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-019-2071-4"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1123061"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp163"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.genet.34.1.499"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1085"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.87702"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/mst010"},{"key":"e_1_3_3_14_1","article-title":"Trim Galore: A wrapper tool around Cutadapt and FastQC","author":"Krueger F","year":"2012","unstructured":"Krueger F. Trim Galore: A wrapper tool around Cutadapt and FastQC. Babraham Bioinformatics 2012 https:\/\/www.bioinformatics.babraham.ac.uk\/projects\/trim_galore\/","journal-title":"Babraham Bioinformatics"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1923"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv033"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1237-9"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.3109\/10409238.2012.714350"},{"key":"e_1_3_3_19_1","unstructured":"Mehta A. MEditome: A computational framework for microbial RNA editing discovery and analysis. Version 1.0. GitHub repository 2025. Available from: https:\/\/github.com\/ameht014\/MEditome"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-02489-3_12"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2016.08.007"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1008740"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt287"},{"key":"e_1_3_3_24_1","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gks1219","article-title":"The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools","volume":"41","author":"Quast C","year":"2013","unstructured":"Quast C, , Pruesse E, , Yilmaz P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res 2013;41(Database issue):D590\u2013D596.","journal-title":"Nucleic Acids Res"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2013-14-5-r51"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0092-4"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13742-016-0143-4"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.87.12.4576"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/emboj\/cdf362"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx473"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261428562","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15578666261428562","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261428562","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T09:46:35Z","timestamp":1779875195000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15578666261428562"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":29,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2026,6,1]]}},"alternative-id":["10.1177\/15578666261428562"],"URL":"https:\/\/doi.org\/10.1177\/15578666261428562","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,23]]}}}