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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

When less is more: 'slicing' sequencing data improves read decoding accuracy and de novo assembly quality.

Stefano Lonardi¹, Hamid Mirebrahim¹, Steve Wanamaker¹

¹Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.

Bioinformatics (Oxford, England)

|May 22, 2015

Summary

Excessive DNA sequencing depth can hinder genome assembly and read decoding due to increased errors. A "divide and conquer" approach effectively processes large datasets, improving assembly quality for bacterial artificial chromosome (BAC) clones.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Traditional de novo genome assembly faces challenges with insufficient sequencing depth.
This study investigates the inverse problem: the impact of excessive sequencing depth on genome assembly and read decoding.

Purpose of the Study:

To analyze the effects of ultra-deep sequencing data on decoding reads to bacterial artificial chromosome (BAC) clones.
To evaluate the performance of de novo assembly of BAC clones with ultra-deep sequencing data.
To propose and validate a novel 'divide and conquer' strategy for handling large sequencing datasets.

Main Methods:

Exploration of ultra-deep sequencing data in two key areas: read decoding to BAC clones and de novo assembly of BAC clones.
Implementation of a 'divide and conquer' strategy involving data slicing, independent decoding, and result merging.
Utilizing real ultra-deep sequencing data from barley and cowpea BACs for experimental validation.

Main Results:

Ultra-deep sequencing, beyond a certain threshold, increases sequencing errors, degrading the quality of read decoding and de novo assembly.
The proposed 'divide and conquer' method significantly improves decoding and assembly quality for over 15,000 barley BACs and 4,000 cowpea BACs.
Modern de novo assemblers do not benefit from ultra-deep sequencing data, showing no improvement in performance.

Conclusions:

Excessive sequencing depth presents unique challenges in genomics, contrary to expectations with error-free data.
The 'divide and conquer' approach offers an effective solution for managing and analyzing ultra-deep sequencing data in BAC clone assembly.
Findings highlight limitations of current de novo assemblers in leveraging ultra-deep sequencing data.