ArticleJournal of the American Medical Informatics Association : JAMIA
HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression.Microorganisms · 2023Article
- The bird's-eye view: A data-driven approach to understanding patient journeys from claims data.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Tackling the Challenges of FASTQ Referential Compression.Bioinformatics and biology insights · 2019Article
- A privacy-preserving solution for compressed storage and selective retrieval of genomic data.Genome research · 2016Article
- Data-dependent bucketing improves reference-free compression of sequencing reads.Bioinformatics (Oxford, England) · 2015Article
- Reference-based compression of short-read sequences using path encoding.Bioinformatics (Oxford, England) · 2015Article
- Light-weight reference-based compression of FASTQ data.BMC bioinformatics · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
objectiveShort-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data.
methodsWe developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values.
resultsThe proposed method produced a compression ratio in the range 0.5-0.65, which corresponds to 35-50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license. LIMITATION: Our method requires having different reference genomes and prolongs the execution time for additional alignments.
conclusionsThe proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.