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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Ribosome Profiling

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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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Apr 27, 2026

Simultaneous Mapping and Quantitation of Ribonucleotides in Human Mitochondrial DNA

Simultaneous Mapping and Quantitation of Ribonucleotides in Human Mitochondrial DNA

Published on: November 14, 2017

Comparing reference-based RNA-Seq mapping methods for non-human primate data.

Ashlee M Benjamin¹, Marshall Nichols, Thomas W Burke

¹Center for Applied Genomics, Department of Medicine, Duke University, Durham, North Carolina, USA. amb103@duke.edu.

|July 9, 2014

Summary

This summary is machine-generated.

Reference-based mapping methods are useful for RNA-Sequencing (RNA-Seq) in mammals lacking a reference genome. Careful consideration of mapping algorithm features, like allowing mismatches and gaps, improves alignment sensitivity for non-human primate data.

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Last Updated: Apr 27, 2026

Simultaneous Mapping and Quantitation of Ribonucleotides in Human Mitochondrial DNA

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Published on: November 14, 2017

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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:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing, specifically RNA-Sequencing (RNA-Seq), has revolutionized gene expression studies.
RNA-Seq is particularly valuable for organisms with incomplete or missing genome sequences.
De novo assembly methods are common for reference-free RNA-Seq, but computationally intensive for complex genomes.

Purpose of the Study:

To compare the utility of four reference-based mapping methods for non-human primate RNA-Seq data.
To evaluate mapping rates, gene detection, expression value correlations, and differential expression analysis.
To assess the effectiveness of using a human reference genome for non-human primate RNA-Seq.

Main Methods:

Utilized TopHat2 and GSNAP for mapping to the human genome.
Employed Bowtie2 and Stampy for mapping to the human genome and transcriptome.
Analyzed mapping rates, locations, detected genes, expression correlations, and differential expression.

Main Results:

Demonstrated the utility of reference-based mapping for RNA-Seq in mammals without a reference genome.
Identified critical algorithm features for sensitive alignment, including short seed sequences, mismatch allowance, and gapped alignments.
Showcased the effectiveness of these features for aligning non-human primate RNA-Seq data to a human reference.

Conclusions:

Reference-based mapping is a viable approach for RNA-Seq in organisms lacking a complete genome.
Algorithm characteristics such as seed length, mismatch tolerance, and gap allowance are crucial for successful alignment.
These findings provide guidance for selecting appropriate mapping strategies in comparative genomics and transcriptomics.