GC-Content Normalization for RNA-Seq Data
Open accessAbstract
Background: Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. Results: We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Conclusions: Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.
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Batch effects can be minimized by appropriate experimental design or removed by batch-correction methods such as COMBAT or ARSyN.
“These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
Filtering out lowly expressed genes before differential expression analysis reduces the severity of multiple testing correction and may improve power.
“Filtering out genes that are expressed at low levels prior to differential expression analysis reduces the severity of the correction and may improve the power of detection [20].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
NOISeq and NOISeqBIO avoid false positives more efficiently at some cost to sensitivity and perform well with different numbers of replicates.
“NOISeq and NOISeqBIO (the adaptation of NOISeq for biological replication) are more efficient in avoiding false positive calls at the cost of some sensitivity but perform well with different numbers of replicates [10, 20, 212].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
SAMseq controls FDR well but has acceptable sensitivity only when the number of replicates is relatively high, at least 10.
“SAMseq performs well in terms of FDR but presents an acceptable sensitivity when the number of replicates is relatively high, at least 10 [20, 55, 209].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
In large-scale RNA-seq studies, commonly used normalization methods (cqn, EDASeq, RUV2, sva, PEER) differ in how well they remove systematic biases, depending on sample complexity and initial data quality.
“commonly used methods fornormalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
Applying GC bias correction tools (cqn, EDASeq) to individual sites or restricting read counts to 3′-UTRs did not reduce inter-site false positive DEGs and often increased them.
“Notably, neither applying GC bias correction tools (cqn14 and EDASeq15) to individual sites nor changing read counts to only use 3′-UTRs was effective at decreasing the number of inter-site false positive DEGs (Fig. 1b and Supplementary Fig. 3); in most cases, these methods actually increased the number of false positives.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
GC content bias correction packages cqn and EDASeq could not remove the false positives, likely because GC content bias is not the only source of bias in gene expression data.
“The application of GC content bias correction packages including cqn14 and EDASeq15 could not remove these false positives, likely because GC content bias is not the only source that contributes to bias in gene expression data.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
The findings confirm GC-content and insert size as recognized RNA-seq latent variables and identify gene coverage variation and error rate as additional contributions to technical variation.
“This confirms the impact of two already recognized RNA-seq latent variables, GC-content and insert size15,22, and it also identifies two more relevant contributions to technical variation, gene coverage variation and error rate.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · 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
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · 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
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107 · see citing works
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,740 · see citing works
Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722 · see citing works
Self-citationAccurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · 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
The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing
Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., & Snyder, M. (2008) · Science · doi:10.1126/science.1158441 · Cited by 2,479 · see citing works
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126 · see citing works
Improving RNA-Seq expression estimates by correcting for fragment bias
Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011) · Genome Biology · doi:10.1186/gb-2011-12-3-r22 · Cited by 1,725 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
Self-citationA rapid and simple method for preparation of RNA from Saccharomyces cerevisiae
Schmitt, M. E., Brown, T. A., & Trumpower, B. L. (1990) · Nucleic Acids Research · doi:10.1093/nar/18.10.3091 · Cited by 1,344 · see citing works
Understanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256 · see citing works
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088 · see citing works
Transcriptome analysis by strand-specific sequencing of complementary DNA
Parkhomchuk, D., Borodina, T. A., Amstislavskiy, V. S., Banaru, M., Hallen, L., Krobitsch, S., Lehrach, H. R., & Солдатов, А. В. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp596 · Cited by 934 · see citing works
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.2307/2289282 · Cited by 854 · see citing works
Biases in Illumina transcriptome sequencing caused by random hexamer priming
Hansen, K. D., Brenner, S. E., & Dudoit, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq224 · Cited by 713 · see citing works
Self-citationSynthetic spike-in standards for RNA-seq experiments
Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653 · see citing works
Sensitive and accurate detection of copy number variants using read depth of coveragePreprint
Yoon, S. C., Xuan, Z., Makarov, V., Ye, K. Q., & Sebat, J. L. (2009) · Genome Research · doi:10.1101/gr.092981.109 · Cited by 619 · see citing works
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works
Transcript length bias in RNA-seq data confounds systems biology
Oshlack, A., & Wakefield, M. J. (2009) · Biology Direct · doi:10.1186/1745-6150-4-14 · Cited by 497 · see citing works
Boeva, V., Zinovyev, A. Y., Kevin Bleakley, A., Vert, J., Janoueix‐Lerosey, I., Delattre, O., & Barillot, E. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq635 · Cited by 267 · see citing works
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193 · see citing works
Bias detection and correction in RNA-Sequencing data
Zheng, W., Chung, L. M., & Zhao, H. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-290 · Cited by 161 · see citing works
The Bayes/Non-Bayes Compromise: A Brief Review
Good, I. J. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10475256 · Cited by 140 · see citing works
Impact of Chromatin Structures on DNA Processing for Genomic Analyses
Teytelman, L., Özaydın, B., Zill, O. A., Lefrançois, P., Snyder, M. P., Rine, J., & Eisen, M. B. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006700 · Cited by 116 · see citing works
Polygenic and directional regulatory evolution across pathways in Saccharomyces
Bullard, J. H., Mostovoy, Y., Dudoit, S., & Brem, R. B. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912959107 · Cited by 87 · see citing works
Self-citationManiar, J. M., & Fire, A. Z. (2011) · Current Biology · doi:10.1016/j.cub.2011.02.019 · Cited by 80 · see citing works
The Bayes/Non-Bayes Compromise: A Brief Review
Good, I. J. (1992) · Journal of the American Statistical Association · doi:10.2307/2290192 · Cited by 21 · see citing works
Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads
Martin, J., Bruno, V. M., Fang, Z., Meng, X., Blow, M., Zhang, T., Sherlock, G., Snyder, M., & Wang, Z. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-663
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., Sabedot, T. S., Malta, T. M., Pagnotta, S. M., Castiglioni, I., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1507 · Cited by 4,133 · see citing works
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
Normalization of RNA-seq data using factor analysis of control genes or samples
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 1,986 · see citing works
Self-citationRNA sequencing: the teenage years
Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 1,903 · see citing works
Dillies, M., Rau, A., Aubert, J., Hennequet‐Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estellé, J., et al. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs046 · Cited by 1,281 · see citing works
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M. J., Smyth, G. K., Huber, W. K., & Robinson, M. D. (2013) · Nature Protocols · doi:10.1038/nprot.2013.099 · Cited by 1,129 · see citing works
Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., Carré, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.041 · Cited by 958 · see citing works
Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells
Zhao, S., Fung‐Leung, W., Bittner, A., Ngo, K., & Liu, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0078644 · Cited by 945 · see citing works
Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf018 · Cited by 898 · see citing works
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
Lohse, M., Bolger, A., Nagel, A., Fernie, A. R., Lunn, J. E., Stitt, M., & Usadel, B. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks540 · Cited by 778 · see citing works
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
Tarazona, S., Furió‐Tarí, P., Turrà, D., Pietro, A. D., Nueda, M. J., Ferrer, A., & Conesa, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv711 · Cited by 757 · see citing works
variancePartition: interpreting drivers of variation in complex gene expression studies
Hoffman, G. E., & Schadt, E. E. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1323-z · Cited by 752 · see citing works
A general and flexible method for signal extraction from single-cell RNA-seq data
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718 · see citing works
Self-citationRevealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Comprehensive analysis of normal adjacent to tumor transcriptomes
Aran, D., Camarda, R., Odegaard, J. I., Paik, H., Oskotsky, B., Krings, G., Goga, A., Sirota, M., & Butte, A. J. (2017) · Nature Communications · doi:10.1038/s41467-017-01027-z · Cited by 619 · see citing works
Computational analysis of bacterial RNA-Seq data
McClure, R., Balasubramanian, D., Sun, Y., Bobrovskyy, M., Sumby, P., Genco, C. A., Vanderpool, C. K., & Tjaden, B. C. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt444 · Cited by 584 · see citing works
Systems vaccinology of the BNT162b2 mRNA vaccine in humans
Arunachalam, P. S., Scott, M. K. D., Hagan, T., Li, C., Feng, Y., Wimmers, F., Grigoryan, L., Trisal, M., Edara, V. V., Lai, L., et al. (2021) · Nature · doi:10.1038/s41586-021-03791-x · Cited by 539 · see citing works
Mounir, M., Lucchetta, M., Silva, T. C., Olsen, C., Bontempi, G., Chen, X., Noushmehr, H., Colaprico, A., & Papaleo, E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006701 · Cited by 521 · see citing works
Gupta, S., Ellis, S. E., Ashar, F. N., Moes, A., Bader, J. S., Zhan, J., West, A. B., & Arking, D. E. (2014) · Nature Communications · doi:10.1038/ncomms6748 · Cited by 504 · see citing works
“…the gene count data was normalized for within and between lane biases (e.g. GC content) and sequencing depth by methods implemented in Conditional Quantile Normalization 45 (CQN) and Exploratory Data Analysis and Normalization for RNA-Seq (EDASeq) 46 , using the default settings for each method.”
A new view of transcriptome complexity and regulation through the lens of local splicing variations
Vaquero-Garcia, J., Barrera, A., Gazzara, M. R., González-Vallinas, J., Lahens, N. F., Hogenesch, J. B., Lynch, K. W., & Barash, Y. (2016) · eLife · doi:10.7554/elife.11752 · Cited by 488 · see citing works
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 437 · see citing works
Generation of a Broadly Useful Model for COVID-19 Pathogenesis, Vaccination, and Treatment
Sun, J., Zhuang, Z., Zheng, J. H., Li, K., Wong, R. L.-Y., Liu, D., Huang, J., He, J., Zhu, A., Zhao, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.010 · Cited by 434 · see citing works
Necroptosis microenvironment directs lineage commitment in liver cancer
Seehawer, M., Heinzmann, F., D’Artista, L., Harbig, J., Roux, P.-F., Hoenicke, L., Dang, H., Klotz, S., Robinson, L., Doré, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0519-y · Cited by 406 · see citing works
RNA-Seq workflow: gene-level exploratory analysis and differential expression
Love, M. I., Anders, S., Kim, V., & Huber, W. K. (2015) · F1000Research · doi:10.12688/f1000research.7035.1 · Cited by 397 · see citing works
Genetic improvement of tomato by targeted control of fruit softening
Uluışık, S., Chapman, N. H., Smith, R. A., Poole, M., Adams, G. G., Gillis, R. B., Besong, T. M. D., Sheldon, J. M., Stiegelmeyer, S. M., Pérez-Fons, L., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3602 · Cited by 383 · see citing works
Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions
Evans, C., Hardin, J., & Stoebel, D. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx008 · Cited by 368 · see citing works
Garofano, L., Migliozzi, S., Oh, Y. T., D’Angelo, F., Najac, R. D., Ko, A., Frangaj, B., Caruso, F. P., Yu, K., Yuan, J., et al. (2021) · Nature Cancer · doi:10.1038/s43018-020-00159-4 · Cited by 363 · see citing works
Transcriptomics in the RNA-seq era
McGettigan, P. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2012.12.008 · Cited by 360 · see citing works
Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series
Nueda, M. J., Tarazona, S., & Conesa, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu333 · Cited by 348 · see citing works
Atlas of clinically distinct cell states and ecosystems across human solid tumors
Luca, B. A., Steen, C. B., Matusiak, M., Azizi, A., Varma, S., Zhu, C., Przybył, J., Espín-Pérez, A., Diehn, M., Alizadeh, A. A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.014 · Cited by 344 · see citing works
Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
Best, M. G., Sol, N., Veld, S. G. J. G. I. ‘., Vancura, A., Muller, M., Niemeijer, A.-L. N., Fejes, A. V., Tjon‐Kon‐Fat, L., Veld, A. E. H. I. ‘., Leurs, C. E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.004 · Cited by 339 · see citing works
Multifunctional role of the transcription factor Blimp-1 in coordinating plasma cell differentiation
Minnich, M., Tagoh, H., Bönelt, P., Axelsson, E., Fischer, M., Cebolla, B., Tarakhovsky, A., Nutt, S. L., Jaritz, M., & Busslinger, M. (2016) · Nature Immunology · doi:10.1038/ni.3349 · Cited by 318 · see citing works
<i>Polyester</i> : simulating RNA-seq datasets with differential transcript expression
Frazee, A. C., Jaffe, A. E., Langmead, B., & Leek, J. T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv272 · Cited by 313 · see citing works
TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages
Silva, T. C., Colaprico, A., Olsen, C., D’Angelo, F., Bontempi, G., Ceccarelli, M., & Noushmehr, H. (2016) · F1000Research · doi:10.12688/f1000research.8923.1 · Cited by 295 · see citing works
Franzén, O., Ermel, R., Cohain, A., Akers, N. K., Di Narzo, A., Talukdar, H. A., Foroughi-Asl, H., Giambartolomei, C., Fullard, J. F., Sukhavasi, K., et al. (2016) · Science · doi:10.1126/science.aad6970 · Cited by 288 · see citing works
On the length, weight and GC content of the human genome
Piovesan, A., Pelleri, M. C., Antonaros, F., Strippoli, P., Caracausi, M., & Vitale, L. (2019) · BMC Research Notes · doi:10.1186/s13104-019-4137-z · Cited by 283 · see citing works
Lehmann, B. D., Colaprico, A., Silva, T. C., Chen, J., An, H., Ban, Y. J., Huang, H., Wang, L. H.-C., James, J. L., Balko, J. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26502-6 · Cited by 283 · see citing works
SCnorm: robust normalization of single-cell RNA-seq data
Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282 · see citing works
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 277 · see citing works
A systematic evaluation of single cell RNA-seq analysis pipelines
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265 · see citing works
Data-based filtering for replicated high-throughput transcriptome sequencing experiments
Rau, A., Gallopin, M., Celeux, G., & Jaffrézic, F. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt350 · Cited by 262 · see citing works
Human iPSC Glial Mouse Chimeras Reveal Glial Contributions to Schizophrenia
Windrem, M. S., Osipovitch, M., Liu, Z., Bates, J., Chandler-Militello, D., Zou, L., Munir, J., Schanz, S. J., McCoy, K., Miller, R. H., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.06.012 · Cited by 252 · see citing works
Chromatin Accessibility Dynamics during iPSC Reprogramming
Li, D., Liu, J., Yang, X., Zhou, C., Guo, J., Wu, C., Qin, Y., Guo, L., He, J., Yu, S., et al. (2017) · Cell Stem Cell · doi:10.1016/j.stem.2017.10.012 · Cited by 251 · see citing works
Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories
The GEUVADIS Consortium, 't Hoen, P. A. C., Friedländer, M. R., Almlöf, J., Sammeth, M., Pulyakhina, I., Anvar, S. Y., Laros, J. F. J., Buermans, H. P. J., Karlberg, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2702 · Cited by 250 · see citing works
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Wu, H., Wang, C., & Wu, Z. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs033 · Cited by 241 · see citing works
Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240 · see citing works
MNase titration reveals differences between nucleosome occupancy and chromatin accessibility
Mieczkowski, J., Cook, A., Bowman, S., Mueller, B., Alver, B. H., Kundu, S., Deaton, A. M., Urban, J. A., Larschan, E., Park, P. J., et al. (2016) · Nature Communications · doi:10.1038/ncomms11485 · Cited by 235 · see citing works
Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis
Finotello, F., & Camillo, B. D. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu035 · Cited by 234 · see citing works
Nicolás, C., Martin-Bertelsen, T., Floudas, D., Bentzer, J., Smits, M. M., Johansson, T., Troein, C., Persson, P., & Tunlid, A. (2018) · The ISME Journal · doi:10.1038/s41396-018-0331-6 · Cited by 220 · see citing works
Trimming of sequence reads alters RNA-Seq gene expression estimates
Williams, C., Baccarella, A., Parrish, J. Z., & Kim, C. C. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0956-2 · Cited by 217 · see citing works
Carpinelli, E. C., Telatin, A., Vitulo, N., Forcato, C., D’Angelo, M., Schiavon, R., Vezzi, A., Giacometti, G. M., Morosinotto, T., & Valle, G. (2013) · Molecular Plant · doi:10.1093/mp/sst120 · Cited by 213 · see citing works
The molecular landscape of glioma in patients with Neurofibromatosis 1
D’Angelo, F., Ceccarelli, M., Tala, Garofano, L., Zhang, J., Frattini, V., Caruso, F. P., Lewis, G., Alfaro-Munoz, K. D., Bauchet, L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0263-8 · Cited by 213 · see citing works
Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-484 · Cited by 198 · see citing works
He, J., Babarinde, I. A., Sun, L., Xu, S., Chen, R., Shi, J., Wei, Y., Li, Y., Ma, G., Zhuang, Q., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21808-x · Cited by 197 · see citing works
Romero, I. G., Pavlovic, B. J., Hernando-Herraez, I., Zhou, X., Ward, M. C., Banovich, N. E., Kagan, C. L., Burnett, J. E., Huang, C. H., Mitrano, A., et al. (2015) · eLife · doi:10.7554/elife.07103 · Cited by 194 · see citing works
A metabolic function of FGFR3-TACC3 gene fusions in cancer
Frattini, V., Pagnotta, S. M., Tala, Fan, J. J., Russo, M. V., Lee, S. B., Garofano, L., Zhang, J., Shi, P., Lewis, G., et al. (2018) · Nature · doi:10.1038/nature25171 · Cited by 189 · see citing works
Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186 · see citing works
HTR7 Mediates Serotonergic Acute and Chronic Itch
Morita, T., McClain, S. P., Batia, L. M., Pellegrino, M., Wilson, S. R., Kienzler, M. A., Lyman, K. A., Olsen, A. S. B., Wong, J. F., Stucky, C. L., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.044 · Cited by 186 · see citing works
Genomic landscape of carcinogen-induced and genetically induced mouse skin squamous cell carcinoma
Nassar, D., Latil, M., Boeckx, B., Lambrechts, D., & Blanpain, C. (2015) · Nature Medicine · doi:10.1038/nm.3878 · Cited by 185 · see citing works
Detecting and correcting systematic variation in large-scale RNA sequencing data
Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., Phan, J., Wu, P.-Y., Wang, M., Wang, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3000 · Cited by 175 · see citing works
Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling
Giraldez, M. D., Spengler, R. M., Etheridge, A., Godoy, P. M., Barczak, A. J., Srinivasan, S., Hoff, P. L. D., Tanrıverdi, K., Courtright-Lim, A., Lu, S., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4183 · Cited by 175 · see citing works
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173 · see citing works
Self-citationCieslik, M. P., Chugh, R. K., Wu, Y.-M., Wu, M., Brennan, C. A., Lonigro, R. J., Su, F., Wang, R., Siddiqui, J., Mehra, R., et al. (2015) · Genome Research · doi:10.1101/gr.189621.115 · Cited by 172 · see citing works
Cobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
Trehalose 6-Phosphate Regulates Photosynthesis and Assimilate Partitioning in Reproductive Tissue
Oszvald, M., Primavesi, L. F., Griffiths, C. A., Cohn, J., Basu, S. S., Nuccio, M. L., & Paul, M. J. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.01673 · Cited by 165 · see citing works
De Smedt, L., Palmans, S., Andel, D., Govaere, O., Boeckx, B., Smeets, D., Galle, E., Wouters, J., Barras, D., Suffiotti, M., et al. (2017) · British Journal of Cancer · doi:10.1038/bjc.2016.382 · Cited by 163 · see citing works
RNA-seq and microarray complement each other in transcriptome profiling
Kogenaru, S., Yan, Q., Guo, Y., & Wang, N. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-629 · Cited by 157 · see citing works
Global analysis of mutations driving microevolution of a heterozygous diploid fungal pathogen
Ene, I. V., Farrer, R. A., Hirakawa, M. P., Agwamba, K., Cuomo, C. A., & Bennett, R. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806002115 · Cited by 155 · see citing works
Developmental chromatin programs determine oncogenic competence in melanoma
Baggiolini, A., Callahan, S. J., Montal, E. D., Weiss, J. M., Trieu, T., Tagore, M. M., Tischfield, S. E., Walsh, R. M., Suresh, S., Fan, Y., et al. (2021) · Science · doi:10.1126/science.abc1048 · Cited by 150 · see citing works
Gene expression based inference of cancer drug sensitivity
Chawla, S., Rockstroh, A., Lehman, M., Ratther, E., Jain, A., Anand, A., Gupta, A., Bhattacharya, N., Poonia, S., Rai, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33291-z · Cited by 150 · see citing works
RNA-Seq and molecular docking reveal multi-level pesticide resistance in the bed bug
Mamidala, P., Wijeratne, A., Wijeratne, S., Kornacker, K., Sudhamalla, B., Rivera-Vega, L. J., Hoelmer, A., Meulia, T., Jones, S. C., & Mittapalli, O. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-6 · Cited by 148 · see citing works
Enhancer Architecture and Essential Core Regulatory Circuitry of Chronic Lymphocytic Leukemia
Ott, C. J., Alexander Joel Federation, Schwartz, L. S., Kasar, S. N., Klitgaard, J. L., Lenci, R., Li, Q., Lawlor, M. A., Fernandes, S. M., Souza, A., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.11.001 · Cited by 148 · see citing works
RNA fate determination through cotranscriptional adenosine methylation and microprocessor binding
Knuckles, P., Carl, S. H., Musheev, M. U., Niehrs, C., Wenger, A., & Bühler, M. (2017) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3419 · Cited by 147 · see citing works
RNA-seq data science: From raw data to effective interpretation
Deshpande, D., Chhugani, K., Chang, Y., Karlsberg, A., Loeffler, C., Zhang, J.-Y., Muszyńska, A., Munteanu, V., Yang, H., Rotman, J., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.997383 · Cited by 121 · see citing works
Atak, Z. K., Gianfelici, V., Hulselmans, G. J., Keersmaecker, K. D., Devasia, A. G., Geerdens, E., Mentens, N., Chiaretti, S., Durinck, K., Uyttebroeck, A., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003997 · Cited by 146 · see citing works
Bunnik, E. M., Chung, D.-W. D., Hamilton, M., Ponts, N., Saraf, A., Prudhomme, J. G., Florens, L. A., & Roch, K. G. L. (2013) · Genome biology · doi:10.1186/gb-2013-14-11-r128 · Cited by 146 · see citing works
Kühl, I., Miranda, M., Posse, V., Milenkovic, D., Mourier, A., Siira, S. J., Bonekamp, N. A., Neumann, U., Filipovska, A., Polosa, P. L., et al. (2016) · Science Advances · doi:10.1126/sciadv.1600963 · Cited by 146 · see citing works
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K. M., Soneson, C., Tiberi, S., Clement, L., Love, M., Patro, R., & Robinson, M. D. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 119 · see citing works
Macrophages are metabolically heterogeneous within the tumor microenvironment
Geeraerts, X., Fernández-García, J., Hartmann, F. J., de Goede, K. E., Martens, L., Elkrim, Y., Debraekeleer, A., Stijlemans, B., Vandekeere, A., Rinaldi, G., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110171 · Cited by 144 · see citing works
Detection and localization of early- and late-stage cancers using platelet RNA
Veld, S. G. J. G. I. ‘., Arkani, M., Post, E., Antunes‐Ferreira, M., D’Ambrosi, S., Vessies, D. C. L., Vermunt, L., Vancura, A., Muller, M., Niemeijer, A.-L. N., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.08.006 · Cited by 142 · see citing works
He, J., Fu, X., Zhang, M., He, F., Li, W., Abdul, M. M., Zhou, J., Sun, L., Chang, C., Li, Y., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-08006-y · Cited by 141 · see citing works
Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works
Acidophilic green algal genome provides insights into adaptation to an acidic environment
Hirooka, S., Hirose, Y., Kanesaki, Y., Higuchi, S., Fujiwara, T., Onuma, R., Era, A., Ohbayashi, R., Uzuka, A., Nozaki, H., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707072114 · Cited by 137 · see citing works
Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia
Swarup, V., Hinz, F. I., Rexach, J. E., Noguchi, K.-I., Toyoshiba, H., Oda, A., Hirai, K., Sarkar, A., Seyfried, N. T., Cheng, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0223-3 · Cited by 136 · see citing works
Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments
Maza, É., Frasse, P., Senin, P., Bouzayen, M., & Zouine, M. (2013) · Communicative & Integrative Biology · doi:10.4161/cib.25849 · Cited by 136 · see citing works
Alboresi, A., Perin, G., Vitulo, N., Diretto, G., Block, M. A., Jouhet, J., Meneghesso, A., Valle, G., Giuliano, G., Maréchal, É., et al. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.00599 · Cited by 129 · see citing works
Li, Q., Hutchins, A. P., Chen, Y., Li, S., Shan, Y., Liao, B., Zheng, D., Shi, X., Li, Y., Chan, W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15166 · Cited by 129 · see citing works
Impulse model-based differential expression analysis of time course sequencing data
Fischer, D. S., Theis, F. J., & Yosef, N. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky675 · Cited by 126 · see citing works
Aging is associated with a systemic length-associated transcriptome imbalance
Stoeger, T., Grant, R. A., McQuattie‐Pimentel, A. C., Anekalla, K. R., Liu, S. S., Navarro, H. T., Singer, B. D., Abdala‐Valencia, H., Schwake, M., Tétreault, M., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00317-6 · Cited by 120 · see citing works
Gene-by-environment interactions in urban populations modulate risk phenotypes
Favé, M.-J., Lamaze, F. C., Soave, D. M., Hodgkinson, A., Gauvin, H., Bruat, V., Grenier, J., Gbeha, E., Skead, K., Smargiassi, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03202-2 · Cited by 119 · see citing works
A new normalization for Nanostring nCounter gene expression data
Molania, R., Gagnon-Bartsch, J. A., Dobrovic, A., & Speed, T. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz433 · Cited by 119 · see citing works
Probabilistic error correction for RNA sequencing
Le, H.-S., Schulz, M. H., McCauley, B. S., Hinman, V. F., & Bar‐Joseph, Z. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt215 · Cited by 117 · see citing works
Multi-perspective quality control of Illumina RNA sequencing data analysis
Sheng, Q., Vickers, K. C., Zhao, S., Wang, J., Samuels, D. C., Koues, O. I., Shyr, Y., & Guo, Y. P. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw035 · Cited by 116 · see citing works
Predictive models of subcellular localization of long RNAs
Zuckerman, B. M., & Ulitsky, I. (2019) · RNA · doi:10.1261/rna.068288.118 · Cited by 115 · see citing works
RNA-Seq Perspectives to Improve Clinical Diagnosis
Marco-Puche, G., Lois, S., Benitez, J. J., & Triviño, J. C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01152 · Cited by 115 · see citing works
A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development
Modzelewski, A. J., Shao, W., Chen, J., Lee, A., Qi, X., Noon, M., Tjokro, K., Sales, G., Biton, A., Anand, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.021 · Cited by 115 · see citing works
Self-citationCoNekT: an open-source framework for comparative genomic and transcriptomic network analyses
Proost, S., & Mutwil, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky336 · Cited by 113 · see citing works
Wilson, J. A. C., Prow, N. A., Schroder, W. A., Ellis, J. J., Cumming, H. E., Gearing, L. J., Poo, Y. S., Taylor, A., Hertzog, P. J., Giallonardo, F. D., et al. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006155 · Cited by 111 · see citing works
Engelholm, L. H., Riaz, A., Serra, D., Dagnæs‐Hansen, F., Johansen, J. V., Santoni‐Rugiu, E., Hansen, S. H., Niola, F., & Frödin, M. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.09.008 · Cited by 109 · see citing works
- Authors
- Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S.
- Affiliations
- University of Padua (IT)
- Stanford University (US)
- University of California, Berkeley (US)
- Corresponding author
- Dudoit, Sandrine
- Venue
- BMC Bioinformatics
- Published
- 2011-12
- Volume
- 12
- Issue
- 1
- Pages
- 480-480
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3315510
- OpenAlex
- W2168342464
- Cited by
- 867
- References
- 35
- Citations by year
- 2026: 29 · 2025: 58 · 2024: 83 · 2023: 80 · 2022: 89 · 2021: 97 · 2020: 74 · 2019: 55 · 2018: 60 · 2017: 49 · 2016: 59 · 2015: 38 · 2014: 48 · 2013: 30 · 2012: 18
- FWCI
- 12.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Università degli Studi di Padova
- National Institutes of Health
- National Human Genome Research Institute (R01 HG03468, R01HG003468)
- National Institute of Allergy and Infectious Diseases (R01AI077737)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, differential gene expression, gene expression bias, transcriptome sequencing
- MeSH terms
- Base Composition, Saccharomyces cerevisiae, Sequence Analysis, RNA, Gene Expression Profiling, Transcriptome
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