Transcript quantification using context-aware MAPQ modeling

CALMeR rescues low-MAPQ long-read alignments by modeling expected MAPQ from transcript and read context, improving spike-in transcript recovery versus fixed MAPQ filtering.

• Why it matters: Static MAPQ cutoffs can discard reads that are intrinsically ambiguous in the transcriptome, distorting transcript abundance estimates.
• What they did: CALMeR fits a zero-one inflated beta regression using transcript and read-level features to predict the MAPQ distribution per alignment, retaining reads that fit and removing anomalously low MAPQ reads.
• The result: Compared with a MAPQ ≥ 1 filter and expectation-maximization-based quantification, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations.

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