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Mora is able to accurately re-assign reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads. The key idea behind Mora is to maximize read re-assignment qualities\n                      <jats:italic>while simultaneously<\/jats:italic>\n                      minimizing the difference from estimated abundance levels, allowing Mora to avoid over assigning reads to the same genomes. On simulated diverse reads, this allows Mora to achieve F1 scores comparable to other algorithms while having less runtime. However, Mora significantly outshines other algorithms on very similar reads. We show that the high penalty of over assigning reads to a common reference genome allows Mora to accurately infer correct strains for real data in the form of\n                      <jats:italic>E. coli<\/jats:italic>\n                      reads.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Mora is a fast and accurate read re-assignment algorithm that is modularized, allowing it to be incorporated into general metagenomics and genomics workflows. It is freely available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/AfZheng126\/MORA\">https:\/\/github.com\/AfZheng126\/MORA<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-024-05768-9","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T23:50:52Z","timestamp":1713829852000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mora: abundance aware metagenomic read re-assignment for disentangling similar strains"],"prefix":"10.1186","volume":"25","author":[{"given":"Andrew","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jim","family":"Shaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun William","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"issue":"12","key":"5768_CR1","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1038\/s41596-022-00738-y","volume":"17","author":"J Lu","year":"2022","unstructured":"Lu J, Rincon N, Wood DE, Breitwieser FP, Pockrandt C, Langmead B, et al. 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