ArticlePloS one2013
Compression of structured high-throughput sequencing data.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- Tackling the Challenges of FASTQ Referential Compression.Bioinformatics and biology insights · 2019Article
- TRCMGene: A two-step referential compression method for the efficient storage of genetic data.PloS one · 2018Article
- Tacrolimus rescues the signaling and gene expression signature of endothelial ALK1 loss-of-function and improves HHT vascular pathology.Human molecular genetics · 2017Article
- Computational biology: deep learning.Emerging topics in life sciences · 2017Article
- A mouse model of hereditary hemorrhagic telangiectasia generated by transmammary-delivered immunoblocking of BMP9 and BMP10.Scientific reports · 2016Article
- Boiler: lossy compression of RNA-seq alignments using coverage vectors.Nucleic acids research · 2016Article
- Exome Sequencing and Prediction of Long-Term Kidney Allograft Function.PLoS computational biology · 2016Article
- Novel bioinformatic developments for exome sequencing.Human genetics · 2016Review
- 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
- Aligned genomic data compression via improved modeling.Journal of bioinformatics and computational biology · 2014Article
- Article
- Compression of structured high-throughput sequencing data.PloS one · 2013Article
- GobyWeb: simplified management and analysis of gene expression and DNA methylation sequencing data.PloS one · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.
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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.