Designing Deep Sequencing Experiments
Author Information
Author(s): Bashir Ali, Vikas Bansal, Vineet Bafna
Primary Institution: UC San Diego
Hypothesis
How can we optimize the design of deep sequencing experiments to detect structural variations and estimate transcript abundance?
Conclusion
The study provides a framework for designing sequencing experiments that improves the detection of structural variations and the estimation of transcript abundance.
Supporting Evidence
- Optimal detection of structural variations is achieved using a mix of two insert library lengths.
- A small pilot study showed that 1 million reads can predict the expression probability distribution of low expressed genes with over 95% accuracy.
- The study provides software tools to help design sequencing experiments.
Takeaway
This study helps scientists figure out the best ways to use DNA sequencing to find changes in genes and measure how much of each gene is present.
Methodology
The study analyzes various sequencing technologies and provides guidelines for optimal insert lengths and sequencing depth.
Potential Biases
Potential biases in transcript sampling due to library preparation methods are acknowledged.
Limitations
The analysis assumes constant insert lengths and does not address all potential biases in transcript sampling.
Digital Object Identifier (DOI)
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