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In Vitro Selection of Aptamers to Differentiate Infectious from Non-Infectious Viruses

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A new efficient referential genome compression technique for FastQ files.

Sanjeev Kumar¹, Mukund Pratap Singh², Soumya Ranjan Nayak³

¹United University, Prayagraj, Uttar Pradesh, 211012, India.

Functional & Integrative Genomics

|November 10, 2023

Summary

This summary is machine-generated.

A new method, RBFQC, efficiently compresses FastQ files using a reference genome. This novel approach significantly improves compression ratios and speeds up data handling for next-generation sequencing (NGS) data.

Keywords:

Compression Decompression FastQ Identifiers Quality scores

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Genomic data generation is rapidly increasing in healthcare and research.
Existing compression methods are inefficient for specialized genomic data formats like FastQ.
Efficient compression is crucial for managing storage, transmission, and processing costs.

Purpose of the Study:

To develop a novel, high-performance compression technique for FastQ files.
To reduce the storage and transmission burden of large-scale genomic datasets.
To improve the speed and efficiency of genomic data processing.

Main Methods:

A reference genome-based compression strategy for FastQ files.
Splitting FastQ files into identifier, sequence, and quality score streams.
Implementing a lightweight mapping mechanism for sequence stream compression.

Main Results:

RBFQC demonstrates superior compression ratios compared to GZIP (80-140%).
RBFQC offers significant speed improvements (10-25%) over existing domain-specific methods.
The method maintains data quality without sacrificing compression efficiency.

Conclusions:

RBFQC provides an effective solution for compressing FastQ genomic data.
The technique offers substantial benefits in terms of storage, speed, and cost reduction.
This advancement is vital for handling the growing volume of next-generation sequencing data.