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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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ARSDA: A New Approach for Storing, Transmitting and Analyzing Transcriptomic Data.

¹Department of Biology, University of Ottawa, Ontario K1N 6N5, Canada xxia@uottawa.ca.

G3 (Bethesda, Md.)

|October 29, 2017

Summary

This summary is machine-generated.

High-throughput sequencing (HTS) data analysis faces challenges with large file sizes and read allocation. A new FASTA+ format and ARSDA software address these by reducing storage needs and improving gene expression analysis.

Keywords:

ARSDA novel storage solution quantifying expression of paralogous genes sequence format transcriptomics

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

High-throughput sequencing (HTS) data analysis is hindered by massive file sizes and difficulties in allocating reads to paralogous genes.
Current storage, transmission, and analysis methods for HTS data are inefficient due to redundant read storage.

Purpose of the Study:

To introduce a novel data compression format (FASTA+) for HTS data, reducing file sizes without information loss.
To present a new method and associated software (ARSDA) for accurate read allocation to paralogous genes.
To demonstrate the efficiency gains in storage, transmission, and downstream analysis time using the proposed methods.

Main Methods:

Developed a FASTA+ format to store identical reads as a single entry with copy counts (SeqID_NumCopy).
Created ARSDA software implementing the FASTA+ format and a new read allocation algorithm.
Applied ARSDA to HTS files for model species and compared gene expression results with Cufflinks.

Main Results:

The FASTA+ format significantly reduces HTS file sizes and transmission bandwidth requirements.
ARSDA software accelerates downstream data analysis by processing unique reads only once.
ARSDA provides robust gene expression characterization, comparable or superior to existing tools like Cufflinks.

Conclusions:

The FASTA+ format and ARSDA software offer a significant improvement in HTS data management and analysis efficiency.
ARSDA facilitates more accurate and faster gene expression studies by addressing key challenges in HTS data analysis.
ARSDA is freely available for multiple operating systems, promoting wider adoption in the research community.