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Updated: May 24, 2026
09:06
High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
Published on: October 5, 2018
Rapid identification of non-human sequences in high-throughput sequencing datasets
Aparna Bhaduri1, Kun Qu, Carolyn S Lee
1Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA 94304, USA. abhaduri@stanford.edu
Bioinformatics (Oxford, England)
|March 2, 2012
Summary
Rapid Identification of Non-human Sequences (RINS) is a workflow for detecting pathogens in sequencing data. It accurately identifies viral sequences in under two hours using custom reference genomes.
Area of Science:
- Bioinformatics
- Genomics
- Pathogen Detection
Background:
- Deep sequencing generates vast amounts of data, necessitating efficient methods for identifying non-human sequences.
- Existing workflows may lack the flexibility to incorporate custom reference genomes for targeted pathogen detection.
Purpose of the Study:
- To introduce Rapid Identification of Non-human Sequences (RINS), a novel workflow for pathogen detection.
- To enable rapid and accurate identification of non-human sequences within large sequencing datasets using user-defined reference genomes.
Main Methods:
- RINS employs an intersection-based approach for sequence identification.
- The workflow utilizes user-provided custom reference genome sets.
- It is compatible with standard bioinformatics alignment and assembly programs.
Main Results:
- RINS successfully identified a known virus in the SRR73726 dataset in under 2 hours.
- The workflow accurately identifies sequencing reads from intact or mutated non-human genomes.
- RINS robustly generates contigs from identified non-human sequences.
Conclusions:
- RINS provides a rapid and accurate method for identifying non-human sequences in deep sequencing data.
- The workflow's compatibility with custom reference genomes enhances its utility for diverse pathogen detection scenarios.
- RINS is a valuable tool for researchers in bioinformatics and infectious disease surveillance.
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