{
  "abstract": "Long-read RNA-sequencing enables accurate transcript-level quantification, but prior static filtering of MAPping Quality (MAPQ) can ignore transcriptome-intrinsic ambiguity, and discarding reads on this basis distorts abundance estimates. We present CALMeR (Context-Aware Long-read MAPQ-based Rescuer), which conditions MAPQ filtering on transcript properties rather than a fixed cutoff. CALMeR fits a zero-one inflated beta regression on various transcript and read-level features to model the expected MAPQ distribution for each alignment. Reads whose MAPQ fits this context, such as low-MAPQ reads from multi-isoform genes and paralog families are retained; reads with anomalously low MAPQ are removed as likely artifacts. Retained reads can be summed per transcript or passed to any downstream quantifier. Compared with a MAPQ [≥] 1 filter and expectation-maximization-based transcript quantification methods, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations. CALMeR is available at https://github.com/sparthib/CALMeR.",
  "abstract_shown": true,
  "affiliations": [
    "Johns Hopkins Bloomberg School of Public Health"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. C. Hicks"
    },
    {
      "name": "S. Parthiban"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a16pdf3200ydt.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.26.761703",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761703v1.full",
  "id": "a16pdf3200ydt",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761703v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761703v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a16pdf3200ydt.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:20:58Z",
      "native_id": "10.64898/2026.10.26.761703",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761703v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Transcript quantification using context-aware MAPQ modeling",
  "title_text": "Transcript quantification using context-aware MAPQ modeling",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:31Z",
  "url": "https://research.draconitic.com/paper/a16pdf3200ydt",
  "venue": "bioRxiv",
  "versions": []
}