The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series
Open accessAbstract
Motivation: The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data. Results: We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor Web site ( http://bioconductor.org ). Contact: mj.nueda@ua.es or aconesa@cipf.es
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McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680
Differential expression in RNA-seq: A matter of depth
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632
Self-citationStreaming fragment assignment for real-time analysis of sequencing experiments
Roberts, A., & Pachter, L. (2013) · Nature Methods · doi:10.1038/nmeth.2251 · Cited by 1,063
GC-Content Normalization for RNA-Seq Data
Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-480 · Cited by 867
Nueda, M. J., Ferrer, A., & Conesa, A. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr042 · Cited by 72
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Kip, A. M., Soons, Z., Mohren, R., Duivenvoorden, A. A. M., Röth, A. A. J., Cillero-Pastor, B., Neumann, U. P., Dejong, C. H. C., Heeren, R. M. A., Olde Damink, S. W. M., et al. (2021) · Cell Death & Disease · doi:10.1038/s41419-020-03379-9 · Cited by 55
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Kanellis, D. C., Zisi, A., Skrott, Z., Lemmens, B., Espinoza, J. A., Kosar, M., Björkman, A., Li, X., Arampatzis, S., Bartkova, J., et al. (2023) · Cell Death & Differentiation · doi:10.1038/s41418-023-01167-4 · Cited by 27
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Metadata
- Authors
- Nueda, M. J., Tarazona, S., & Conesa, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-03
- Volume
- 30
- Issue
- 18
- Pages
- 2598-2602
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2142433339
- Cited by
- 348
- References
- 7
- FWCI
- 4.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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