Transcript quantification using context-aware MAPQ modeling
Stephanie C. Hicks, Sowmya Parthiban
CALMeR improves transcript quantification by context-aware MAPQ modeling, recovering more spike-in transcripts and better matching known concentrations.
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Johns Hopkins UniversityORCID 0000-0003-0314-2787Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
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Stephanie C. Hicks, Sowmya Parthiban
CALMeR improves transcript quantification by context-aware MAPQ modeling, recovering more spike-in transcripts and better matching known concentrations.
Casey J. Keuthan, Sowmya Parthiban, Yen-Yu Chang…·Donald J. Zack
The paper uses long-read RNA sequencing to study dynamic mRNA isoform changes during human retinal organoid development, revealing splicing patterns and allelic imbalance.
Sang Ho Kwon, Sowmya Parthiban, Madhavi Tippani…·Leonardo Collado‐Torres
The paper uses spatial proteogenomics to analyze gene expression changes in Alzheimer's disease brains linked to amyloid-beta and tau pathology.