NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data
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BACKGROUND: RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models. Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models. The method has advantages in handling different read distributions, but there hasn't been an efficient program to implement this algorithm. RESULTS: We developed an efficient implementation of the algorithm in the program NURD. It uses a binary interval search algorithm. The program can correct both the global tendency of sequencing bias in the data and local sequencing bias specific to each gene. The correction makes the isoform expression estimation more reliable under various read distributions. And the implementation is computationally efficient in both the memory cost and running time and can be readily scaled up for huge datasets. CONCLUSION: NURD is an efficient and reliable tool for estimating the isoform expression level. Given the reads mapping result and gene annotation file, NURD will output the expression estimation result. The package is freely available for academic use at http://bioinfo.au.tsinghua.edu.cn/software/NURD/.
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NURD provides an efficient way of estimating transcript expression from single-end reads with a low memory and computing cost.
“NURD [44] provides an efficient way of estimating transcript expression from SE reads with a low memory and computing cost.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [44]
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601 · see citing works
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179 · see citing works
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
The Art of Computer Programming
Knuth, D. E. (1968) · Cited by 16,097 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
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 · see citing works
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
The Art of Computer Programming: Volume 3: Sorting and Searching
Knuth, D. E. (1973) · Cited by 3,955 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
RNA-Seq gene expression estimation with read mapping uncertainty
Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp692 · Cited by 1,198 · see citing works
Streaming 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 · see citing works
Statistical inferences for isoform expression in RNA-Seq
Jiang, H., & Wong, W. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp113 · Cited by 453 · see citing works
Modelling and simulating generic RNA-Seq experiments with the flux simulator
Griebel, T., Zacher, B., Ribeca, P., Raineri, E., Lacroix, V., Guigó, R., & Sammeth, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks666 · Cited by 278 · see citing works
Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq
Wu, Z.-P., Wang, X., & Zhang, X.-G. (2011) · Bioinform. · doi:10.1093/bioinformatics/btq696 · Cited by 108 · see citing works
Self-citationSayed, A. H. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009218146.013 · Cited by 8 · see citing works
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A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Self-citationRNA-Seq Gene Profiling - A Systematic Empirical Comparison
Fonseca, N. A., Marioni, J. C., & Brāzma, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107026 · Cited by 99 · see citing works
Feng, H., Zhang, X., & Zhang, C. (2015) · Nature Communications · doi:10.1038/ncomms8816 · Cited by 61 · see citing works
Self-citationiRAP - an integrated RNA-seq Analysis PipelinePreprint
Fonseca, N. A., Petryszak, R., Marioni, J. C., & Brāzma, A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/005991 · Cited by 33 · see citing works
Petralia, F., Wang, L., Peng, J., Yan, A., Zhu, J., & Wang, P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty280 · Cited by 29 · see citing works
Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data
Feng, H., Li, T., & Zhang, X. (2018) · BMC Genomics · doi:10.1186/s12864-018-4925-1 · Cited by 21 · see citing works
Self-citationSuo, C., Calza, S., Salim, A., & Yudi Pawitan, Y. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt704 · Cited by 19 · see citing works
Pesson, M., Eymin, B., de la Grange, P., Simon, B., & Corcos, L. (2014) · Molecular Cancer · doi:10.1186/1476-4598-13-9 · Cited by 12 · see citing works
Guan, L., Yang, Q., Gu, M., Chen, L., & Zhang, X. (2014) · Quantitative Biology · doi:10.1007/s40484-014-0031-9 · Cited by 9 · see citing works
Self-citationWang, X., & Cairns, M. J. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801105-8.00006-0 · Cited by 6 · see citing works
Prakash, C., & von Haeseler, A. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0096 · Cited by 2 · see citing works
Oh, S. (2015) · Communications for Statistical Applications and Methods · doi:10.5351/csam.2015.22.2.181 · Cited by 1 · see citing works
Oh, S., & Kim, C. S. (2016) · Journal of the Korean Data and Information Science Society · doi:10.7465/jkdi.2016.27.1.225
- Authors
- Ma, X., & Zhang, X.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Zhang, Xuegong
- ORCID
- Zhang, Xuegong
- Venue
- BMC Bioinformatics
- Published
- 2013-07-10
- Volume
- 14
- Issue
- 1
- Pages
- 220-220
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3717102
- OpenAlex
- W2139903782
- Cited by
- 14
- References
- 15
- Citations by year
- 2018: 2 · 2016: 3 · 2015: 2 · 2014: 5 · 2013: 2
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2012CB316504, 2012AA020401)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- alternative splicing, nonparametric models, read distribution, gene expression estimation, computational efficiency
- MeSH terms
- Algorithms, Software, Alternative Splicing, Sequence Analysis, RNA, Gene Expression Profiling, RNA Isoforms
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