bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/103267

UMI-Reducer: Collapsing duplicate sequencing reads via Unique Molecular Identifiers

Mangul, S.·Van Driesche, S.·Martin, L. S.·Martin, K. C.·Eskin, E.
View original source ↗Export metadata ↓

Abstract

Short Structured AbstractO_ST_ABSSummaryC_ST_ABSEvery sequencing library contains duplicate reads. While many duplicates arise during polymerase chain reaction (PCR), some duplicates derive from multiple identical fragments of mRNA present in the original lysate (termed \"biological duplicates\"). Unique Molecular Identifiers (UMIs) are random oligonucleotide sequences that allow differentiation between technical and biological duplicates. Here we report the development of UMI-Reducer, a new computational tool for processing and differentiating PCR duplicates from biological duplicates. UMI-Reducer uses UMIs and the mapping position of the read to identify and collapse reads that are technical duplicates. Remaining true biological reads are further used for bias-free estimate of mRNA abundance in the original lysate. This strategy is of particular use for libraries made from low amounts of starting material, which typically require additional cycles of PCR and therefore are most prone to PCR duplicate bias.\n\nAvailability and ImplementationThe UMI-Reducer is an open source Python software and is freely available for non-commercial use (GPL-3.0) at https://sergheimangul.wordpress.com/umi-reducer/. Documentation and tutorials are available at https://github.com/smangul1/UMI-Reducer/wiki/.\n\nContactsmangul@ucla.edu, SVanDriesche@mednet.ucla.edu\n\nSupplementary informationFlowchart of Library Construction

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mangul, S., Van Driesche, S., Martin, L. S., Martin, K. C., Eskin, E.. 2017-01-25. UMI-Reducer: Collapsing duplicate sequencing reads via Unique Molecular Identifiers. https://doi.org/10.1101/103267

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

Genetic perturbation responses capture the functional consequences of gene-level interventions, providing functional information for drug-response prediction beyond baseline gene expression. Within the same cellular context, genetic and chemical perturbations engage shared regulatory networks, providing a biological basis for transferring response information between perturbation modalities. Here we introduce GRAFT, a framework that combines genetic-to-chemical transfer with cross-context prediction to infer drug-induced transcriptional responses without drug measurements in the target context. Across benchmarked LINCS and external contexts, GRAFT improved response correlation, directional agreement and magnitude reconstruction over competing methods, particularly for drugs with context-dependent responses. GRAFT-Adapt further refines predictions of unmeasured responses using a small number of drug measurements from the target context. GRAFT-Atlas spans 13 cellular contexts and 7,856 drug catalog entries, comprising 102,128 predictions for drug-context combinations. The atlas captured recognizable pharmacological neighborhoods, shared functional programs, context-dependent transcriptional changes and structural-functional associations supported by molecular docking. Across ten iPSC lines from distinct donors, GRAFT identified candidate inter-donor differences, with functional support for selected candidates from measured genetic perturbation responses. Together, GRAFT enables perturbation-informed prediction and comparison of drug responses across cellular contexts.

bioinformatics↗
bioRxiv2026

Pycea: a scverse toolkit for single-cell lineage tracing analysis

Lineage tracing enables reconstruction of cellular histories in development, regeneration, and disease, but integrating lineage data into single-cell analysis workflows remains challenging. We introduce Pycea, a Python toolkit for lineage analysis and visualization, and TreeData, an extension of AnnData that stores lineage trees alongside cellular measurements. TreeData is compatible with Scanpy and other scverse tools, enabling integrated analysis without converting between formats. Pycea provides scalable methods for tree visualization, clade identification, distance comparisons, ancestral state inference, and lineage statistics. This software is open source and available at https://github.com/YosefLab/pycea.

bioinformatics↗
bioRxiv2026

Transcript quantification using context-aware MAPQ modeling

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.

bioinformatics↗