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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jan 9, 2026

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.

Siyi Huang¹, Linfeng Jiang², Ming Yi¹

¹School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei, China.

Plos Computational Biology

|December 1, 2025

Summary

This summary is machine-generated.

D3Impute enhances single-cell RNA sequencing (scRNA-seq) analysis by accurately distinguishing technical zeros from biological zeros. This computational framework improves downstream analyses like cell clustering and gene expression detection.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity but is challenged by technical "zeros" masking true gene expression.
Distinguishing technical "zeros" from biological "zeros" is crucial for accurate data interpretation in scRNA-seq analysis.
Current computational methods struggle to reliably differentiate these zero types, potentially distorting biological signals.

Purpose of the Study:

To introduce D3Impute, a novel discriminative imputation framework designed to address the challenge of non-biological zeros in scRNA-seq data.
To improve the accuracy of scRNA-seq data analysis by accurately identifying and handling technical zero measurements.
To provide a robust and user-oriented solution for zero-inflated data in computational biology.

Main Methods:

Developed D3Impute, a framework incorporating distribution-aware normalization, a dual-network discriminator utilizing bulk RNA-seq data, and a density-guided imputation engine.
The dual-network discriminator leverages bulk RNA-seq as a reference to identify non-biological zeros while preserving biological zeros.
The imputation engine recovers expression values while maintaining cellular neighborhood structures.

Main Results:

D3Impute demonstrated significant improvements over 12 state-of-the-art methods across six diverse scRNA-seq datasets.
The framework consistently enhanced downstream analyses, including cell clustering, trajectory inference, and differential expression detection.
Evaluations confirmed D3Impute's robustness across varying data qualities, with guidelines for optimal application provided.

Conclusions:

D3Impute offers a robust, biologically informed solution for handling non-biological zeros in scRNA-seq data.
The framework significantly improves the accuracy and reliability of scRNA-seq data analysis.
D3Impute provides a generalizable approach for managing zero-inflated data in computational biology.