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

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Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph.

Gaëtan Benoit¹, Claire Lemaitre², Dominique Lavenier³

¹INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. gaetan.benoit@inria.fr.

BMC Bioinformatics

|September 16, 2015

Summary

A new reference-free compression method, LEON, significantly reduces data size for high-throughput sequencing (HTS) data. This novel approach achieves higher compression ratios than existing tools, aiding data storage and transmission for genomics research.

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing (HTS) generates massive data volumes, posing storage and transmission challenges.
General-purpose compression tools like gzip are insufficient for efficient HTS data management.

Purpose of the Study:

To develop a novel, reference-free compression method for HTS data.
To improve data storage and transmission efficiency for sequencing datasets.

Main Methods:

Implemented a reference-free compression algorithm (LEON) using de Bruijn graph principles.
Constructed a de novo probabilistic de Bruijn graph from sequencing reads.
Encoded reads as paths in the graph and lossily compressed quality scores.

Main Results:

LEON achieved higher compression ratios across diverse sequencing datasets (whole genome, exome, RNA-seq, metagenomics).
Demonstrated a >20x file size reduction on a C. elegans whole genome dataset.
Maintained data integrity for downstream analyses despite lossy quality score compression.

Conclusions:

LEON offers superior compression performance compared to state-of-the-art methods for HTS data.
The open-source LEON software provides a valuable tool for managing large sequencing datasets.
LEON effectively addresses the data storage and transmission concerns in modern genomics.