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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Amaranth: Enhanced Single-Cell Transcript Assembly via Discriminative Modeling of UMI Reads and Internal Reads.

Xiaofei Carl Zang^1,2, Tasfia Zahin³, Irtesam Mahmud Khan³

¹Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA.

Biorxiv : the Preprint Server for Biology

|December 8, 2025

Summary

This summary is machine-generated.

A new computational tool, Amaranth, accurately reconstructs full-length transcripts from single-cell RNA sequencing data by distinguishing between UMI-linked and internal reads. This advancement improves isoform-level analysis in single-cell transcriptomics.

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Last Updated: Jan 9, 2026

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Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) enables transcriptome profiling at cellular resolution.
Accurate reconstruction of full-length transcripts from scRNA-seq data remains a challenge.
Emerging scRNA-seq protocols generate reads spanning entire transcripts for isoform analysis.

Purpose of the Study:

To address the challenge of accurate full-length transcript reconstruction in single-cell RNA sequencing.
To develop a novel assembler that leverages distinct properties of different read types.
To improve isoform-level expression analysis in single-cell transcriptomics.

Main Methods:

Identified distinct biological and statistical properties of UMI-linked and internal reads in scRNA-seq data.
Developed discriminative modeling approaches to enhance assembly accuracy.
Created Amaranth, a single-cell assembler with new heuristics for UMI-linked and internal reads.
Developed Amaranth-meta for integrated cell assembly.

Main Results:

Demonstrated that discriminative modeling of UMI and internal reads significantly increases assembly accuracy.
Amaranth accurately assigns strandness, refines splicing graphs, and determines transcript start/end sites.
Amaranth and Amaranth-meta outperformed state-of-the-art assemblers on Smart-seq3 datasets.
Achieved substantial improvements in individual cell and meta-assembly.

Conclusions:

Amaranth provides a significant advancement for isoform-level analysis in single-cell transcriptomics.
The developed heuristics effectively address distinct biases in scRNA-seq reads.
This work facilitates more detailed cellular resolution studies through improved transcript reconstruction.