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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: Nov 11, 2025

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

BugSeq: a highly accurate cloud platform for long-read metagenomic analyses.

Jeremy Fan¹, Steven Huang¹, Samuel D Chorlton²

¹BugSeq Bioinformatics Inc, Vancouver, BC, Canada.

BMC Bioinformatics

|March 26, 2021

Summary

This summary is machine-generated.

BugSeq is a new, accurate metagenomic classifier for nanopore sequencing data. It provides faster and more reliable taxonomic classification than existing tools, especially for clinical samples.

Keywords:

Long-read Metagenomics Microbiology Nanopore Sequencing Third-generation

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Targeted DNA Methylation Analysis by Next-generation Sequencing

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Bioinformatics
Genomics
Microbiology

Background:

Nanopore sequencing is increasingly used for metagenomic analysis.
Existing taxonomic classification tools struggle with speed, accuracy, or scalability for long-read data.
There is a need for efficient and accurate long-read metagenomic analysis tools.

Purpose of the Study:

To develop BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads.
To evaluate BugSeq's performance against existing tools using simulated, mock, and real-world clinical data.

Main Methods:

Development of BugSeq, a cloud-deployed metagenomic classifier.
Evaluation using simulated datasets, ZymoBIOMICS mock communities (Even and Log), and clinical samples from lower respiratory tract infections.
Comparison of BugSeq's accuracy and speed against MetaMaps, Centrifuge, and CDKAM.

Main Results:

BugSeq achieved an F1 score of 0.95 at the species level on mock communities, outperforming MetaMaps (0.89-0.94).
BugSeq demonstrated improved accuracy over Centrifuge (0.79-0.93) and CDKAM (0.91-0.94) with competitive run times.
In clinical samples, BugSeq showed greater concordance with microbiological culture and qPCR than "What's In My Pot" analysis.

Conclusions:

BugSeq offers a fast, accurate, and scalable solution for long-read metagenomic analysis.
The cloud deployment of BugSeq facilitates easy access for researchers.
BugSeq is available for non-commercial use, promoting wider adoption in the scientific community.