The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The barcode, UMI, set format and BUStools
Open accessAbstract
Summary: We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific and robust workflows for single-cell RNA-seq analysis. Availability and implementation: http://BUStools.github.io/ and http://pachterlab.github.io/kallisto/singlecell.html. Supplementary information: Supplementary data are available at Bioinformatics online.
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Cites
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Self-citationDroplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Single-cell RNA-seq: advances and future challenges
Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
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COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
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Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529
Shahan, R., Hsu, C.-W., Nolan, T. M., Cole, B. J., Taylor, I. W., Greenstreet, L., Zhang, S., Afanassiev, A., Vlot, A. H. C., Schiebinger, G., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.01.008 · Cited by 363
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186
Self-citationComputational approaches and challenges in spatial transcriptomics
Fang, S., Chen, B., Zhang, Y., Sun, H., Liu, L., Liu, S., Li, Y., & Xu, X. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.10.001 · Cited by 159
Metadata
- Authors
- Melsted, P., Ntranos, V., & Pachter, L.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05-09
- Volume
- 35
- Issue
- 21
- Pages
- 4472-4473
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2946682337
- Cited by
- 165
- References
- 5
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- green
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