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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: Sep 4, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

TransMeta simultaneously assembles multisample RNA-seq reads.

Ting Yu¹, Xiaoyu Zhao^1,2, Guojun Li^1,3

¹Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China.

Genome Research

|July 20, 2022

Summary

This summary is machine-generated.

TransMeta is a new algorithm for assembling RNA-seq reads into full-length transcripts. It accurately reconstructs transcriptomes from multiple samples, outperforming existing tools in precision and recall.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Transcriptomics

Background:

Assembling RNA-seq reads into full-length transcripts is essential for transcriptomic studies.
Current computational methods face challenges in accurately reconstructing transcriptomes, especially from multiple samples.

Purpose of the Study:

To introduce TransMeta, a novel algorithm for simultaneous RNA-seq read assembly from multiple samples.
To evaluate TransMeta's performance against existing popular tools for meta-assembly and individual sample assembly.

Main Methods:

Developed TransMeta based on a vector-weighted splicing graph model.
Incorporated a cosine similarity-based combing strategy and a label-setting path-searching strategy for accurate consensus transcriptome reconstruction.
Tested on simulated and real RNA-seq datasets.

Main Results:

TransMeta demonstrated superior precision and recall compared to PsiCLASS, StringTie2 (with merge mode), and Scallop (with TACO) at the meta-assembly level across various coverage thresholds.
TransMeta consistently outperformed these tools at the individual sample level as well.
The algorithm proved robust and simple to use.

Conclusions:

TransMeta offers a significant advancement in RNA-seq assembly, providing more accurate transcriptome reconstruction from multiple samples.
The novel graph model and strategies employed by TransMeta lead to improved performance over current state-of-the-art tools.
TransMeta is a valuable tool for transcriptomic studies requiring high-precision meta-assembly.