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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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![Figure 6 Yeast dataset: Proportion of DE genes vs. GC-content. Here, a gene is declared DE between the three growth conditions if its nominal unadjusted p-value from the negative binomial LRT is below the threshold of 10-5 (corresponding to a nominal Bonferroni family wise error rate of 0.057 and Benjamini & Hochberg [37] false discovery rate of 4.22 × 10-5). There is a clear trend towards more detected differential expression at higher GC-content with all within-lane normalization procedures but the full-quantile.](/paper-figures/133c4095c1b84462ae53508ba5e7f521/gc-content-normalization-for-rna-seq-data-figure-6.jpg)
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“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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 the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
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
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
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
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
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
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
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
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
Cited by
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DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
Xiong, B., Yang, Y., Fineis, F., & Wang, J.-P. (2019) · Genome biology · doi:10.1186/s13059-019-1682-7 · Cited by 46
Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology
Hardcastle, T. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv569 · Cited by 44
Fischer, S., Dethlefsen, S., Klockgether, J., & Tümmler, B. (2020) · mSystems · doi:10.1128/msystems.01007-20 · Cited by 44
Isensee, J., Carsten, W., Buschow, R., Weißmann, R., Kuss, A. W., & Hucho, T. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115731 · Cited by 44
Nätt, D., Johansson, I., Faresjö, T., Ludvigsson, J., & Thorsell, A. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0123-z · Cited by 43
Impaired response of the bronchial epithelium to inflammation characterizes severe equine asthma
Tessier, L., Côté, O., Clark, M. E., Viel, L., Diaz-Méndez, A., Anders, S., & Bienzle, D. (2017) · BMC Genomics · doi:10.1186/s12864-017-4107-6 · Cited by 43
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis
Cava, C., Bertoli, G. R., Colaprico, A., Olsen, C., Bontempi, G., & Castiglioni, I. (2018) · BMC Genomics · doi:10.1186/s12864-017-4423-x · Cited by 43
Peacock, H. M., Tabibian, A., Criem, N., Caolo, V., Hamard, L., Deryckere, A., Haefliger, J., Kwak, B. R., Zwijsen, A., & Jones, E. A. V. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.313122 · Cited by 46
Data-based RNA-seq simulations by binomial thinning
Gerard, D. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3450-9 · Cited by 43
Yang, C., & Wei, H. (2014) · Molecular Plant · doi:10.1016/j.molp.2014.11.012 · Cited by 41
Raman, A. T., Pohodich, A. E., Wan, Y., Yalamanchili, H. K., Lowry, W. E., Zoghbi, H. Y., & Liu, Z. (2018) · Nature Communications · doi:10.1038/s41467-018-05627-1 · Cited by 42
Fanidis, D., & Moulos, P. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa156 · Cited by 41
Adaptation to Varying Salinity in Halomonas elongata: Much More Than Ectoine Accumulation
Hobmeier, K., Cantone, M., Nguyen, Q. A., Pflüger‐Grau, K., Kremling, A., Kunte, H., Pfeiffer, F., & Marı́n-Sanguino, A. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.846677 · Cited by 40
Hu, G., Dong, X., Gong, S., Song, Y., Hutchins, A. P., & Yao, H. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa705 · Cited by 40
Dong, K., Chen, W., Pan, X., Wang, H., Sun, Y., Qian, C., Chen, W., Wang, C., Fu, Y., & Cui, X. (2022) · BMC Cancer · doi:10.1186/s12885-022-09251-7 · Cited by 40
Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation
Vanrobaeys, Y. S., Mukherjee, U., Langmack, L., Beyer, S. E., Bahl, E., Lin, L., Michaelson, J. J., Abel, T. G., & Chatterjee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-41715-7 · Cited by 39
Microbial Community Composition Impacts Pathogen Iron Availability during Polymicrobial Infection
Stacy, A., Abraham, N., Jorth, P. A., & Whiteley, M. (2016) · PLoS Pathogens · doi:10.1371/journal.ppat.1006084 · Cited by 43
Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma
Wrzeszczyński, K. O., Frank, M. O., Koyama, T., Rhrissorrakrai, K., Robine, N., Utro, F., Emde, A., Chen, B., Arora, K. S., Shah, M. J., et al. (2017) · Neurology Genetics · doi:10.1212/nxg.0000000000000164 · Cited by 39
Maltese, M., Martella, G., Imbriani, P., Schuermans, J., Billion, K., Sciamanna, G., Farook, F., Ponterio, G., Tassone, A., Santoro, M., et al. (2017) · Neurobiology of Disease · doi:10.1016/j.nbd.2017.08.007 · Cited by 39
Bush, S. J., Freem, L. J., MacCallum, A. J., O’Dell, J., Wu, C., Afrasiabi, C., Psifidi, A., Stevens, M. P., Smith, J., SUMMERS, K. M., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-4972-7 · Cited by 40
Inferring Evolutionary Histories of Pathway Regulation from Transcriptional Profiling Data
Schraiber, J. G., Mostovoy, Y., Hsu, T. Y., & Brem, R. B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003255 · Cited by 40
Noviello, T., Giacomo, A. M. D., Caruso, F. P., Covre, A., Mortarini, R., Scala, G., Costa, M. C., Coral, S., Fridman, W. H., Sautès‐Fridman, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40994-4 · Cited by 44
Characterization of Cytochrome P450s with Key Roles in Determining Herbicide Selectivity in Maize
Brazier‐Hicks, M., Ortega, S. F., Watson, P., Rougemont, B., Cohn, J., Dale, R. P., Hawkes, T. R., Goldberg-Cavalleri, A., Onkokesung, N., & Edwards, R. (2022) · ACS Omega · doi:10.1021/acsomega.2c01705 · Cited by 38
Houser, J. R., Barnhart, C. S., Boutz, D. R., Carroll, S. M., Dasgupta, A., Michener, J. K., Needham, B. D., Papoulas, O., Sridhara, V., Sydykova, D. K., et al. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004400 · Cited by 38
Pandey, R. S., Graham, L., Uyar, A., Preuss, C., Howell, G. R., & Carter, G. W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0351-3 · Cited by 37
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37
Li, S., Dong, X., & Su, Z. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-520 · Cited by 40
Grabek, K. R., Behn, C. D., Barsh, G. S., Hesselberth, J. R., & Martin, S. L. (2015) · eLife · doi:10.7554/elife.04517 · Cited by 37
Tsementzi, D., Poretsky, R. S., Rodriguez‐R, L. M., Luo, C., & Konstantinidis, K. T. (2014) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12180 · Cited by 40
Zermiani, M., Zonin, E., Nonis, A., Begheldo, M., Ceccato, L., Vezzaro, A., Baldan, B., Trentin, A., Masi, A., Pegoraro, M., et al. (2015) · Journal of Experimental Botany · doi:10.1093/jxb/erv422 · Cited by 37
Zhernakova, D. V., de Klerk, E., Westra, H.-J., Mastrokolias, A., Amini, S., Ariyürek, Y., Jansen, R., Penninx, B. W. J. H., Hottenga, J., Willemsen, G. A. H. M., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003594 · Cited by 38
Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data
Cleynen, A., Koskas, M., Lebarbier, É., Rigaill, G. J., & Robin, S. S. (2014) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-9-6 · Cited by 41
Single-Cell Transcriptome Study as Big Data
Yu, P., & Lin, W. (2016) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2016.01.005 · Cited by 36
Senescence induction universally activates transposable element expression
Colombo, A., Elias, H. K., & Ramsingh, G. (2018) · Cell Cycle · doi:10.1080/15384101.2018.1502576 · Cited by 37
Tapia-Carrillo, D., Tovar, H., Velazquez-Caldelas, T. E., & Lemus, E. H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01180 · Cited by 36
Thomson-Luque, R., Votborg-Novél, L., Ndovie, W., Andrade, C. M., Niangaly, M., Attipa, C., Lima, N. F., Coulibaly, D. M., Doumtabé, D., Guindo, B., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25062-z · Cited by 36
Yang, Y., Shi, L., Fu, X., Ma, G., Yang, Z., Li, Y., Zhou, Y., Yuan, L., Xia, Y., Zhong, X., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001682 · Cited by 36
Ferté, C., Trister, A. D., Huang, E. S., Bot, B. M., Guinney, J., Commo, F., Sieberts, S. K., André, F., Besse, B., Soria, J., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3937 · Cited by 36
Obp56hModulates Mating Behavior inDrosophila melanogaster
Shorter, J. R., Dembeck, L. M., Everett, L. J., Morozova, T. V., Arya, G. H., Turlapati, L., Armour, G. S., Schal, C., Mackay, T. F. C., & Anholt, R. R. H. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.034595 · Cited by 36
Song, M. J., & Zhong, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa613 · Cited by 36
Durán, R. C., Wei, H., & Wu, J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10364-5 · Cited by 37
Deng, T., Pang, C., Lu, X., Zhu, P., Duan, A., Tan, Z., Huang, J. H., Li, H., Chen, M., & Liang, X. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147132 · Cited by 35
A network of CLAVATA receptors buffers auxin-dependent meristem maintenance
John, A., Sarkel, E., Jones, D. S., Soyars, C. L., & Nimchuk, Z. L. (2023) · Nature Plants · doi:10.1038/s41477-023-01485-y · Cited by 35
Clarifying Omics Concepts, Challenges, and Opportunities for Prunus Breeding in the Postgenomic Era
Martínez-Gómez, P., Sánchez‐Pérez, R., & Rubio, M. (2012) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2011.0133 · Cited by 35
Delepine, C., Li, K., Shih, J., Gaudeaux, P., & Sur, M. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1982-22.2023 · Cited by 35
Schall, K. A., Thornton, M. E., Isani, M. A., Holoyda, K. A., Hou, X., Lien, C., Grubbs, B. H., & Grikscheit, T. C. (2017) · BMC Genomics · doi:10.1186/s12864-016-3433-4 · Cited by 34
Patchett, A. L., Coorens, T. H. H., Darby, J. M., Wilson, R., McKay, M. J., Kamath, K. S., Rubin, A. F., Wakefield, M. J., McIntosh, L., Mangiola, S., et al. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-019-03259-2 · Cited by 34
Tait, S., Baldassarre, A., Masotti, A., Calura, E., Martini, P. G. V., Varı́, R., Scazzocchio, B., Gessani, S., & Cornò, M. D. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01089 · Cited by 34
TNFAIP6 defines the MSC subpopulation with enhanced immune suppression activities
Li, L., Yang, L., Chen, X., Chen, X., Diao, L., Zeng, Y., & Xu, J. (2022) · Stem Cell Research & Therapy · doi:10.1186/s13287-022-03176-5 · Cited by 34
Optimal Scaling of Digital Transcriptomes
Glusman, G., Caballero-Pérez, J., Robinson, M., Kutlu, B., & Hood, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077885 · Cited by 33
Zoppoli, P., Calice, G., Laurino, S., Ruggieri, V., Rocca, F. L., Torre, G. L., Ciuffi, M., Amendola, E., Vita, F. D., Petrillo, A., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8050662 · Cited by 33
Interoperable RNA-Seq analysis in the cloud
Lachmann, A., Clarke, D. J. B., Torre, D., Xie, Z., & Ma’ayan, A. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2020.194521 · Cited by 33
Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo
Hum, N. R., Sebastian, A., Gilmore, S. F., He, W., Martin, K. A., Hinckley, A., Dubbin, K., Moya, M. L., Wheeler, E. K., Coleman, M. A., et al. (2020) · Cancers · doi:10.3390/cancers12030690 · Cited by 34
A fully defined 3D matrix for ex vivo expansion of human colonic organoids from biopsy tissue
Bergenheim, F. H., Fregni, G., Buchanan, C. F., Riis, L. B., Heulot, M., Touati, J., Seidelin, J. B., Rizzi, S. C., & Nielsen, O. H. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.120248 · Cited by 33
Kakati, T., Bhattacharyya, D. K., Kalita, J. K., & Norden‐Krichmar, T. M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-021-04527-4 · Cited by 33
Antigen reactivity defines tissue-resident memory and exhausted T cells in tumors
Burn, T. N., Schröder, J., Gandolfo, L. C., Osman, M., Wainwright, E. N., Lam, E. Y. N., McDonald, K. M., Evans, R., Li, S., Rawlinson, D., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02347-9 · Cited by 36
Non-Pleiotropic Coupling of Daily and Seasonal Temporal Isolation in the European Corn Borer
Levy, R., Kozak, G. M., & Dopman, E. B. (2018) · Genes · doi:10.3390/genes9040180 · Cited by 31
George, N. I., & Chang, C. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-92 · Cited by 31
Huang, H.-C. R., Niu, Y., & Qin, L. (2015) · Cancer Informatics · doi:10.4137/cin.s21631 · Cited by 31
The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer
Nygaard, M. M., Terkelsen, T., Olsen, A. V., Sora, V., Viloria, J. S., Rizza, F., Bergstrand-Poulsen, S., Marco, M. D., Vistesen, M. V., Tiberti, M., et al. (2016) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2016.00078 · Cited by 31
Endoplasmic reticulum chaperone genes encode effectors of long-term memory
Chatterjee, S., Bahl, E., Mukherjee, U., Walsh, E. N., Shetty, M. S., Yan, A. L., Vanrobaeys, Y. S., Lederman, J. D., Giese, K.-P., Michaelson, J. J., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm6063 · Cited by 35
Emmrich, S., Trapp, A., Zakusilo, F. T., Straight, M. E., Ying, K., Tyshkovskiy, A., Mariotti, M., Gray, S., Zhang, Z., Drage, M. G., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021109694 · Cited by 32
Li, H., Gao, X., Li, X., Chen, Q., Dong, J., & Zhao, W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083530 · Cited by 31
Wieck, M. M., Schlieve, C. R., Thornton, M. E., Fowler, K. L., Isani, M. A., Grant, C. N., Hilton, A. E., Hou, X., Grubbs, B. H., Frey, M. R., et al. (2017) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2016.12.008 · Cited by 31
Comparison of alternative approaches for analysing multi-level RNA-seq data
Mohorianu, I. I., Bretman, A. J., Smith, D. T., Fowler, E. K., Dalmay, T., & Chapman, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182694 · Cited by 32
Bi, Y., & Davuluri, R. V. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-262 · Cited by 30
RNA-Seq optimization with eQTL gold standards
Ellis, S. E., Gupta, S., Ashar, F. N., Bader, J. S., West, A. B., & Arking, D. E. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-892 · Cited by 30
Chiam, K., Mayne, G. C., Watson, D. I., Woodman, R. J., Bright, T., Michael, M., Karapetis, C. S., Irvine, T., Phillips, W. A., Hummel, R., et al. (2018) · Annals of Surgical Oncology · doi:10.1245/s10434-018-6626-z · Cited by 30
Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts
Babarinde, I. A., Li, Y., & Hutchins, A. P. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.04.012 · Cited by 30
The CBP KIX domain regulates long-term memory and circadian activity
Chatterjee, S., Angelakos, C. C., Bahl, E., Hawk, J. D., Gaine, M. E., Poplawski, S. G., Schneider‐Anthony, A., Yadav, M., Porcari, G. S., Cassel, J., et al. (2020) · BMC Biology · doi:10.1186/s12915-020-00886-1 · Cited by 34
Hu, D. G., Marri, S., Mackenzie, P. I., Hulin, J.-A., McKinnon, R. A., & Meech, R. (2021) · Cancers · doi:10.3390/cancers13174491 · Cited by 30
Application of third-generation sequencing in cancer research
Chen, Z., & He, X. (2021) · Medical Review · doi:10.1515/mr-2021-0013 · Cited by 35
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MacKnight, N. J., Dimos, B., Beavers, K. M., Muller, E. M., Brandt, M. E., & Mydlarz, L. D. (2022) · Science Advances · doi:10.1126/sciadv.abo6153 · Cited by 31
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Bilyk, O., Brötz, E., Tokovenko, B. T., Bechthold, A., Paululat, T., & Luzhetskyy, A. N. (2015) · ACS Chemical Biology · doi:10.1021/acschembio.5b00669 · Cited by 30
Unveiling the role of osteosarcoma-derived secretome in premetastatic lung remodelling
Almeida, S. F. F., Santos, L., Sampaio-Ribeiro, G., Ferreira, H. R. S., Lima, N. R., Caetano, R., Abreu, M., Zuzarte, M., Ribeiro, A. S., Paiva, A. A., et al. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02886-9 · Cited by 29
Prevention, diagnosis and treatment of high‐throughput sequencing data pathologies
Zhou, X., & Rokas, A. (2014) · Molecular Ecology · doi:10.1111/mec.12680 · Cited by 33
Systematic comparison of RNA-Seq normalization methods using measurement error models
Sun, Z., & Zhu, Y. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts497 · Cited by 29
Perrin, H. J., Currin, K. W., Vadlamudi, S., Pandey, G. K., Ng, K. K. H., Wabitsch, M., Laakso, M., Love, M. I., & Mohlke, K. L. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009865 · Cited by 29
Benraiss, A., Mariani, J. N., Tate, A., Madsen, P. M., Clark, K. M., Welle, K. A., Solly, R., Capellano, L., Bentley, K. L. D. M., Chandler-Militello, D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2022.111291 · Cited by 31
Alaimo, A., Genovesi, S., Annesi, N., Felice, D. D., Subedi, S., Macchia, A., Manna, F. L., Ciani, Y., Vannuccini, F., Mugoni, V., et al. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00040-5 · Cited by 28
A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials
Wang, D., Liu, Y., Zhang, Y., Chen, Q., Han, Y., Hou, W., Liu, C., Yu, Y., Li, Z., Li, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50420-y · Cited by 28
Zou, F., Sun, W., Crowley, J. J., Zhabotynsky, V., Sullivan, P. F., & de Villena, F. P. (2014) · Genetics · doi:10.1534/genetics.113.160119 · Cited by 28
iSeqQC: a tool for expression-based quality control in RNA sequencing
Kumar, G., Ertel, A., Feldman, G. J., Kupper, J., & Fortina, P. M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3399-8 · Cited by 28
Specific targeting of IL-1β activity to CD8+ T cells allows for safe use as a vaccine adjuvant
Eeckhout, B. V. D., Hoecke, L. V., Burg, E., Lint, S. V., Peelman, F., Kley, N., Uzé, G., Saelens, X., Tavernier, J. H., & Gerlo, S. (2020) · npj Vaccines · doi:10.1038/s41541-020-00211-5 · Cited by 28
Anichini, A., Molla, A., Nicolini, G., Perotti, V., Sgambelluri, F., Covre, A., Fazio, C., Lofiego, M. F., Giacomo, A. M. D., Coral, S., et al. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02529-5 · Cited by 30
Fuller, Z. L., Koury, S. A., Leonard, C. J., Young, R. E., Ikegami, K., Westlake, J. D., Richards, S., Schaeffer, S. W., & Phadnis, N. (2020) · Genetics · doi:10.1534/genetics.120.303460 · Cited by 28
Venit, T., Sapkota, O., Abdrabou, W. S., Palanikumar, L., Pasricha, R., Mahmood, S. R., Said, N. H. E., Sherif, S., Thomas, S. A., Abdelrazig, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42093-w · Cited by 27
Al‐Wathiqui, N., Lewis, S. M., & Dopman, E. B. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-189 · Cited by 27
Visualization methods for differential expression analysis
Rutter, L. A., Lauter, A. N. M., Graham, M. A., & Cook, D. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2968-1 · Cited by 30
The Bone-Forming Properties of Periosteum-Derived Cells Differ Between Harvest Sites
Groeneveldt, L. C., Herpelinck, T., Maréchal, M., Politis, C., van IJcken, W. F. J., Huylebroeck, D., Geris, L., Mulugeta, E., & Luyten, F. P. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.554984 · Cited by 27
TraV: A Genome Context Sensitive Transcriptome Browser
Dietrich, S., Wiegand, S., & Liesegang, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093677 · Cited by 28
Transcriptomic Profiling Using Next Generation Sequencing - Advances, Advantages, and Challenges
Anamika, K., Verma, S. P., Jere, A., & Desai, A. (2016) · InTech eBooks · doi:10.5772/61789 · Cited by 32
Bioinformatic analysis of bacteria and host cell dual RNA-sequencing experiments
Marsh, J. W., Hayward, R. J., Shetty, A. C., Mahurkar, A., Humphrys, M. S., & Myers, G. S. A. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx043 · Cited by 29
Struyfs, C., Cools, T. L., Cremer, K. D., Sampaio‐Marques, B., Ludovico, P., Wasko, B. M., Kaeberlein, M. R., Cammue, B. P. A., & Thevissen, K. (2020) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2020.183255 · Cited by 26
Excess Lipin enzyme activity contributes to TOR1A recessive disease and DYT-TOR1A dystonia
Cascalho, A. C. C., Foroozandeh, J., Hennebel, L., Swerts, J., Klein, C., Rous, S., Gonzalez, B. D., Pisani, A., Meringolo, M., Gallego, S. F., et al. (2020) · Brain · doi:10.1093/brain/awaa139 · Cited by 26
Kumar, M., & Papaleo, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-71527-4 · Cited by 26
Molecular characterization of 7 new established cell lines from high grade serous ovarian cancer
Kreuzinger, C., Gamperl, M., Wolf, A., Heinze, G., Geroldinger, A., Lambrechts, D., Boeckx, B., Smeets, D., Horvat, R., Aust, S., et al. (2015) · Cancer Letters · doi:10.1016/j.canlet.2015.03.040 · Cited by 26
Cong, P., Xiao, S., Chen, Y., Wang, L., Gao, J., Li, M., He, Z., Guo, Y., Zhao, G., Zhang, X., et al. (2014) · Molecular Biology Reports · doi:10.1007/s11033-014-3460-7 · Cited by 26
Filtration and Normalization of Sequencing Read Data in Whole-Metagenome Shotgun Samples
Chouvarine, P., Wiehlmann, L., Losada, P. M., DeLuca, D. S., & Tümmler, B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165015 · Cited by 26
Placental lncRNA expression associated with placental cadmium concentrations and birth weight
Hussey, M. R., Burt, A., Deyssenroth, M. A., Jackson, B. P., Hao, K., Peng, S., Chen, J., Marsit, C. J., & Everson, T. M. (2020) · Current Zoology · doi:10.1093/eep/dvaa003 · Cited by 27
Metadata
- 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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