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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.
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: May 24, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study.

Qiong-Yi Zhao¹, Yi Wang, Yi-Meng Kong

¹Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China.

BMC Bioinformatics

|March 1, 2012

Summary

This summary is machine-generated.

This study evaluated transcriptome assembly tools and strategies for RNA sequencing data. The multiple k-mer (MK) approach generally outperformed single k-mer (SK) methods, with Trinity showing good performance but high resource use.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput RNA sequencing (RNA-Seq) is crucial for transcriptome study.
De novo transcript assembly is vital for organisms lacking a reference genome.
Optimal selection of assembly tools and strategies for RNA-Seq data remains unclear.

Purpose of the Study:

To analyze the performance of different transcriptome assembly programs.
To investigate the impact of variables like k-mer values and data properties on assembly outcomes.
To provide guidelines for effective de novo transcript reconstruction.

Main Methods:

Evaluated seven program conditions: four single k-mer (SK) assemblers (SOAPdenovo, ABySS, Oases, Trinity) and three multiple k-mer (MK) methods (SOAPdenovo-MK, trans-ABySS, Oases-MK).
Assessed factors including k-mer values, genome complexity, coverage depth, and read directionality.
Compared assembly performance across different transcript expression levels.

Main Results:

Multiple k-mer (MK) strategies demonstrated effectiveness across various expression levels.
Trinity (SK) showed strong performance but required the longest runtime.
Oases consumed the most memory; SOAPdenovo was fastest but yielded poor full-length CDS reconstruction; ABySS offered a balance of resource usage and assembly quality.

Conclusions:

Compared publicly available transcriptome assemblers and identified key factors influencing de novo assembly.
Proposed practical guidelines for transcript reconstruction using short-read RNA-Seq data.
Demonstrated improved de novo assembly of *C. sinensis* transcriptome through optimized methods.