Moving Just Enough Deep Sequencing Data to Get the Job Done

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput DNA sequencing generates massive datasets, leading to high storage and transfer costs.
• These costs can limit data processing capabilities, especially for institutions with limited resources.
• Processing only essential data subsets is a potential solution to mitigate these costs.

Purpose of the Study:

• To investigate the feasibility of processing partial DNA sequence datasets.
• To determine if data reduction strategies can maintain biological information integrity.
• To assess the impact of processing data subsets on RNA-Seq analysis and transfer times.

Main Methods:

• Utilized four high-throughput DNA sequence datasets from two species with varying sequencing depths.
• Employed an RNA-Seq workflow to evaluate the effect of partial dataset processing on transcript detection.
• Determined a data cutoff point based on transcript detection sufficiency.
• Physically transferred minimal partial datasets and compared transfer times with full datasets.

Main Results:

• Processing partial datasets in an RNA-Seq workflow demonstrated the ability to detect RNA transcripts.
• A reduction of approximately 25% in total transfer time was observed when transferring minimal partial datasets compared to full datasets.
• The study successfully identified a data subset that sufficiently detects the biological signal.

Conclusions:

• Transferring minimal partial DNA sequence datasets is a viable strategy to reduce data transfer times.
• This method can accelerate analysis pipelines for large sequencing datasets.
• Optimizing data transfer through subsetting can make genomic data processing more accessible.