TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

doi:10.1093/bioinformatics/btab342

. 2021 Oct 25;37(20):3650-3651.

doi: 10.1093/bioinformatics/btab342.

TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

Ales Varabyou^{1

2}, Geo Pertea³, Christopher Pockrandt^{1

4}, Mihaela Pertea^{1

2

4}

Affiliations

¹ Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA.
² Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211, USA.
³ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins University, Baltimore, MD 21211, USA.
⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.

PMID: 33964128
PMCID: PMC8545345
DOI: 10.1093/bioinformatics/btab342

TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

Ales Varabyou et al. Bioinformatics. 2021.

. 2021 Oct 25;37(20):3650-3651.

doi: 10.1093/bioinformatics/btab342.

Authors

Ales Varabyou^{1

2}, Geo Pertea³, Christopher Pockrandt^{1

4}, Mihaela Pertea^{1

2

4}

Affiliations

¹ Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA.
² Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211, USA.
³ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins University, Baltimore, MD 21211, USA.
⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.

PMID: 33964128
PMCID: PMC8545345
DOI: 10.1093/bioinformatics/btab342

Abstract

Summary: Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input.

Availability and implementation: TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush).

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

**Fig. 1.**
Overview of the workflow of TieBrush and illustration of the summarized data produced by TieCov. (a) Redundant reads from two samples (left and right diagonals) are identified by TieBrush to produce a collapsed representation of 10 reads in 5 records. (b) Comparison of transcription of the NEFL gene in heart (top) and brain (bottom) tissues from GTEx. Each tissue is represented by three tracks produced by TieCov: read coverage (top), percent of samples containing the reads (middle) and splice junctions (bottom). The plot illustrates the higher prevalence and expression of the gene in brain tissue. (c) Comparison of transcription of the SLC25A3 gene in heart (top) and brain (bottom) tissues from GTEx. While the gene is expressed in both tissues, coverage data clearly indicate an exon switch where the third and fourth exons are expressed at dramatically different levels in the two tissues

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References

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[1] Li H. et al.; 1000 Genome Project Data Processing Subgroup. (2009) The sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078–2079. - PMC - PubMed

[2] Li H. et al.; 1000 Genome Project Data Processing Subgroup. (2009) The sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078–2079. - PMC - PubMed

[3] Lonsdale J. et al. (2013) The genotype-tissue expression (GTEx) project. Nat. Genet., 45, 580–585. - PMC - PubMed

[4] Lonsdale J. et al. (2013) The genotype-tissue expression (GTEx) project. Nat. Genet., 45, 580–585. - PMC - PubMed

[5] Pedersen B.S. et al. (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics, 34, 867–868. - PMC - PubMed

[6] Pedersen B.S. et al. (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics, 34, 867–868. - PMC - PubMed

[7] Quinlan A.R. et al. (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26, 841–842. - PMC - PubMed

[8] Quinlan A.R. et al. (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26, 841–842. - PMC - PubMed

[9] Rosenbloom K.R. et al. (2015) The UCSC genome browser database: 2015 update. Nucleic Acids Res., 43, D670–D681. - PMC - PubMed

[10] Rosenbloom K.R. et al. (2015) The UCSC genome browser database: 2015 update. Nucleic Acids Res., 43, D670–D681. - PMC - PubMed

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TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

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TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

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Abstract

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