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Differential expression in RNA-seq: A matter of depth
Open accessAbstract
Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach—NOISeq—that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.
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In the citing paper
“Previous studies demonstrated that inferences about DE are generally more accurate for highly expressed genes19,20.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [20]
Experimental — accuracy not yet measuredIn the citing paper
“Experimental results suggest that deep sequencing improves quantification and identification but might also result in the detection of transcriptional noise and off-target transcripts [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Saturation curves can be used to assess the improvement in transcriptome coverage to be expected at a given sequencing depth [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“For example, rRNA and small RNAs should not be present in regular polyA longRNA preparations [10, 19].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“In such cases, simpler methods based on the Poisson distribution, such as DEGseq [62], or on empirical distributions (NOISeq [10]) can be an alternative”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
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 [10]
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Citations
Cites
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
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
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
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
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
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
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
The Transcriptional Landscape of the Mammalian Genome
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M. C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C. A., et al. (2005) · Science · doi:10.1126/science.1112014 · Cited by 3,492
Cited by
Sorted by citations
Items 801–900 of 1,000
Lin, Y., Balakrishnan, C. N., & Clayton, D. F. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00409 · Cited by 15
Lo, W., & Kuo, C. (2017) · Genome Biology and Evolution · doi:10.1093/gbe/evx244 · Cited by 15
Lyu, S., Gao, L., Zhang, R., Zhang, C., & Hou, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00963 · Cited by 15
Yu, S., Zhang, X., Huang, L.-B., Lyu, Y.-P., Zhang, Y., Yao, Z., Zhang, X., Yuan, J., & Hu, Y. (2021) · BMC Genomics · doi:10.1186/s12864-021-07594-2 · Cited by 15
Spinelli, R., Pirola, A., Redaelli, S., Sharma, N. D., Raman, H., Valletta, S., Magistroni, V., Piazza, R., & Gambacorti-Passerini, C. B. (2013) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.23 · Cited by 15
Smith, G., Macias-Muñoz, A., & Briscoe, A. D. (2016) · Genome Biology and Evolution · doi:10.1093/gbe/evw180 · Cited by 16
Local sequence and sequencing depth dependent accuracy of RNA-seq reads
Cai, G., Liang, S., Zheng, X., & Xiao, F. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1780-z · Cited by 13
Liu, J., Xing, J., Fang, J., Ai, P., & Cheng, Y. (2018) · Scientia Horticulturae · doi:10.1016/j.scienta.2018.02.003 · Cited by 15
Identification of genes related to chlamydospore formation in Clonostachys rosea 67‐1
Sun, Z., Zhang, J., Sun, M., & Li, S. (2018) · MicrobiologyOpen · doi:10.1002/mbo3.624 · Cited by 15
Genomic landscape and mutational impacts of recurrently mutated genes in cancers
Liu, B., Hu, F., Zhang, Q., Hu, H., Zheng, Y., Tang, Q., & Guo, A. (2018) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.458 · Cited by 16
Zhao, H., Chen, M., Valdés, A., Lind, S. B., & Pettersson, U. (2019) · BMC Microbiology · doi:10.1186/s12866-018-1375-5 · Cited by 14
Xu, X., Li, X., Wang, F., Han, K., Liu, Z., Fan, L., Hua, H., Cai, W., & Yao, Y. (2019) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2019.109735 · Cited by 15
Li, W.-M., Dita, M. A., Rouard, M., Wu, W., Roux, N., Xie, J., & Ge, X. (2020) · Functional & Integrative Genomics · doi:10.1007/s10142-020-00734-z · Cited by 15
Sfikakis, P. P., Verrou, K., Tsitsilonis, O., Παρασκευής, Δ., Kastritis, E., Lianidou, E. S., Μουτσάτσου, Π., Terpos, E., Trougakos, I. P., Chini, V., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.746203 · Cited by 14
The impact of substrate stiffness on morphological, transcriptional and functional aspects in RPE
Wolfram, L., Gimpel, C., Schwämmle, M. E., Clark, S. J., Böhringer, D., & Schlunck, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-56661-7 · Cited by 15
Rahimian, M., & Panahi, B. (2024) · Journal of Clinical Virology Plus · doi:10.1016/j.jcvp.2024.100194 · Cited by 14
Unraveling potential gene biomarkers for dengue infection through RNA sequencing
Suppiah, J., Sani, S. S. M., Hassan, S. S., Nadzar, N. I. F., Ibrahim, N. ‘., Thayan, R., & Zain, R. M. (2024) · Virus Genes · doi:10.1007/s11262-024-02114-2 · Cited by 14
Next generation quantitative genetics in plants
Jiménez‐Gómez, J. M. (2011) · Frontiers in Plant Science · doi:10.3389/fpls.2011.00077 · Cited by 16
Sen, S. K., Barb, J. J., Cherukuri, P. F., Accame, D. S., Elkahloun, A. G., Singh, L. N., Lee-Lin, S.-Q., Program, N. C. S., Kolodgie, F. D., Cheng, Q., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-198 · Cited by 12
Luo, H., Li, J., Chia, B. K. H., Robson, P., & Nagarajan, N. (2014) · Genome biology · doi:10.1186/s13059-014-0527-7 · Cited by 14
The construction of intrahepatic cholangiocarcinoma model in zebrafish
Wang, J., Leng, X., Wang, G., Wan, X., & Cao, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-13815-0 · Cited by 13
Ionizing Radiation Deregulates the MicroRNA Expression Profile in Differentiated Thyroid Cells
Penha, R. C. C., Pellecchia, S., Pacelli, R., Pinto, L. F. R., & Fusco, A. (2018) · Thyroid · doi:10.1089/thy.2017.0458 · Cited by 14
Lozoya, O. A., Santos, J. H., & Woychik, R. P. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00176 · Cited by 14
Transcriptomic study of the mechanism of anoikis resistance in head and neck squamous carcinoma
Guo, C., Xu, L., Li, H., Wang, W., Guo, J., Jia, M., Jia, R., & Jia, J. (2019) · PeerJ · doi:10.7717/peerj.6978 · Cited by 14
Early events leading to water deficit responses in the liverwort Marchantia polymorpha
Godinez-Vidal, D., López-Leal, G., Covarrubias, A. A., & Reyes, J. L. (2020) · Environmental and Experimental Botany · doi:10.1016/j.envexpbot.2020.104172 · Cited by 16
Botero, D., Monk, J. M., Rodríguez-Cubillos, M. J., Cubillos, A. E. R., Restrepo-Benavides, M., Bernal‐Galeano, V., Reyes, A., Barrios, A. F. G., Palsson, B. Ø., Restrepo, S., et al. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00837 · Cited by 14
Du, Q., Liu, Y., Song, Y., & Qin, Y. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.597828 · Cited by 14
Bartley, K., Chen, W., Mills, R. I. L., Nunn, F., Price, D. R. G., Rombauts, S., de Peer, Y. V., Roy, L., Nisbet, A. J., & Burgess, S. T. G. (2021) · BMC Genomics · doi:10.1186/s12864-021-07547-9 · Cited by 14
Yue, Z., Tian, E., Chen, Y., Luo, L., Yang, L., He, L., Li, L., & Wang, J. (2021) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2020.111875 · Cited by 14
NF-κB pathway play a role in SCD1 deficiency-induced ceramide de novo synthesis
Zheng, R., Zhang, Y., Qiu, F., Liu, Z., Han, Y., Huang, R., Zhao, Y., Yao, P., Qiu, Y., & Ren, J. (2021) · Cancer Biology & Therapy · doi:10.1080/15384047.2021.1883414 · Cited by 14
Molecular Diagnosis of Koala Retrovirus (KoRV) in South Australian Koalas (Phascolarctos cinereus)
Stephenson, T., Speight, N., Low, W. Y., Woolford, L., Tearle, R. G., & Hemmatzadeh, F. (2021) · Animals · doi:10.3390/ani11051477 · Cited by 14
Enhanced locomotor behaviour is mediated by activation of tyrosine hydroxylase in the silkworm brain
Li, Y., Wang, X., Xie, X., Liu, Q., Dong, H., Hou, Y., Xia, Q., & Zhao, P. (2023) · Insect Molecular Biology · doi:10.1111/imb.12828 · Cited by 14
Brunsch, M., Schubert, D., Gube, M., Ring, C., Hanisch, L., Linde, J., Krause, K., & Kothe, E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135616 · Cited by 14
Shao, J., Yu, M., Jiang, L., Wu, F., & Liu, X. (2016) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2016.2821 · Cited by 14
Bayesian nonparametric discovery of isoforms and individual specific quantification
Aguiar, D., Cheng, L., Dumitrascu, B., Mordelet, F., Pai, A. A., & Engelhardt, B. E. (2018) · Nature Communications · doi:10.1038/s41467-018-03402-w · Cited by 14
TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
Lim, J. H., Lee, S., & Kim, J. H. (2017) · Genomics & Informatics · doi:10.5808/gi.2017.15.1.51 · Cited by 14
Ambühl, L. M. M., Villadsen, A. B., Baandrup, U. T., Dybkær, K., & Sørensen, S. (2017) · Pathogens · doi:10.3390/pathogens6030040 · Cited by 14
A novel feature selection for RNA-seq analysis
Han, H. (2017) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2017.10.010 · Cited by 14
Tokuoka, M., Kobayashi, K., & Satou, Y. (2018) · Development · doi:10.1242/dev.163915 · Cited by 14
Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands
Moulos, P., Αλεξανδράτος, Α., Nellas, I., & Dedos, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4896-2 · Cited by 14
Wu, H., Chu, Z., Zhang, W., Zhang, C., Ni, J., Fang, H., Chen, Y., Wang, Y., Zhang, L., & Zhang, B. (2019) · Journal of Biological Engineering · doi:10.1186/s13036-018-0135-2 · Cited by 14
Targeting Smyd3 by next-generation antisense oligonucleotides suppresses liver tumor growth
Kontaki, H., Koukaki, M., Vasilarou, M., Giakountis, A., Deligianni, E., Luo, X., Kim, Y., & Talianidis, I. (2021) · iScience · doi:10.1016/j.isci.2021.102473 · Cited by 14
Chaiboonchoe, A., Ghamsari, L., Dohai, B., Ng, P., Khraiwesh, B., Jaiswal, A. K., Jijakli, K., Koussa, J., Nelson, D. R., Cai, H., et al. (2016) · Molecular BioSystems · doi:10.1039/c6mb00237d · Cited by 12
Genome-Wide Analysis of Gene Expression
Chung, D.-W. D., & Roch, K. G. L. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-378630-2.00634-4 · Cited by 15
How thoughtful experimental design can empower biologists in the omics era
Wagner, M. R., & Kleiner, M. (2025) · Nature Communications · doi:10.1038/s41467-025-62616-x · Cited by 15
Depth normalization of small RNA sequencing: using data and biology to select a suitable method
Düren, Y., Lederer, J., & Qin, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac064 · Cited by 13
Historical perspective and future directions: computational science in immuno-oncology
Ricker, C. A., Meli, K., & Allen, E. M. V. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-008306 · Cited by 14
Havird, J. C., & Santos, S. R. (2016) · Integrative and Comparative Biology · doi:10.1093/icb/icw003 · Cited by 13
Ansari, A. R., Li, N.-Y., Sun, Z.-J., Huang, H., Zhao, X., Cui, L., Hu, Y., Zhong, J., Karrow, N. A., & Liu, H. (2017) · Oncotarget · doi:10.18632/oncotarget.19964 · Cited by 13
Au, E. D., Fernández‐Godino, R., Kaczynksi, T. J., Sousa, M. E., & Farkas, M. H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183939 · Cited by 11
Díaz‐Mendoza, M., Velasco‐Arroyo, B., Santamaria, M. E., Dı́az, I., & Martínez, M. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01585 · Cited by 13
Lian, B., & Lwigale, P. Y. (2019) · Developmental Dynamics · doi:10.1002/dvdy.43 · Cited by 14
Luo, Z., Xu, L., Lu, J., Shen, Y., Tang, Y., Wang, X., Wu, Y., Sun, H., & Guo, T. (2020) · Clinical Science · doi:10.1042/cs20190255 · Cited by 13
Yamazaki, H., Nishimura, M., Uehara, M., Souta-Kuribara, A., Yamamoto, M., Yoshikawa, N., Morohashi, K.-I., & Tanaka, H. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00184.2020 · Cited by 16
Chen, E., Duan, J.-Y., Song, W., Wang, D., & Tang, P. (2020) · Journal of Economic Entomology · doi:10.1093/jee/toaa273 · Cited by 13
Wang, Y., Tan, Y., Wang, Y., Ge, Y., Liu, Y., Liu, H., Shao, L., Liu, Y., Ren, X., & Liu, Z. (2021) · Fungal Biology · doi:10.1016/j.funbio.2021.04.009 · Cited by 13
Chen, S., Liu, W., Chen, Y., Li, X., Shen, X., Jiang, D., Huang, Y., & Tian, Y. (2021) · Animal Reproduction · doi:10.1590/1984-3143-ar2021-0036 · Cited by 13
Yuan, W., Zhang, Q., Li, Y., Wang, Q., Xu, F., Dang, X., Xu, W., Zhang, J., & Miao, R. (2021) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2021.11.002 · Cited by 13
Fan, X., Zhong-hua, B. I. A. N., Bo, S. O. N. G., & Xu, H. (2022) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(21)63784-x · Cited by 12
Yamazaki, H., Uehara, M., Yoshikawa, N., Souta-Kuribara, A., Yamamoto, M., Hirakawa, Y., Kabe, Y., Suematsu, M., & Tanaka, H. (2023) · JCI Insight · doi:10.1172/jci.insight.162382 · Cited by 14
Wang, J., Li, T., Zhou, M., Hu, Z., Zhou, G., Zhou, S., Wang, N., Huang, L., Zhao, L., Cao, Y., et al. (2015) · Scientific Reports · doi:10.1038/srep18454 · Cited by 14
Hook, S. E., Kroon, F. J., Greenfield, P. A., Warne, M. S. J., Smith, R. A., & Turner, R. D. R. (2017) · Marine Environmental Research · doi:10.1016/j.marenvres.2017.05.006 · Cited by 13
Xing, L., Sun, L., Liu, S., Li, X., Zhang, L., & Yang, H. (2017) · Aquaculture · doi:10.1016/j.aquaculture.2017.12.016 · Cited by 13
Genome-wide analysis of alternative splicing divergences between Brassica hexaploid and its parents
Wang, R., Liu, H., Liu, Z., Zou, J., Meng, J., & Wang, J. (2019) · Planta · doi:10.1007/s00425-019-03198-z · Cited by 13
Nash, C. E., Boufaied, N., Badescu, D., Wang, Y. C., Paliouras, M., Trifiro, M. A., Ragoussis, J., & Thomson, A. A. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.039297 · Cited by 13
Villemur, R., Payette, G., Geoffroy, V., Mauffrey, F., & Martineau, C. (2019) · PeerJ · doi:10.7717/peerj.7497 · Cited by 13
Fecal IgA Levels and Gut Microbiota Composition Are Regulated by Invariant Natural Killer T Cells
de Aguiar, C. F., Castoldi, Â., Amano, M. T., Ignácio, A., Terra, F. F., Cruz, M. C., Felizardo, R. J. F., Braga, T. T., Davanzo, G. G., Gambarini, V. M. D. O., et al. (2019) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izz300 · Cited by 13
Nair, R. R., Piktel, D., Hathaway, Q. A., Rellick, S. L., Thomas, P., Saralkar, P. A., Martin, K. H., Geldenhuys, W. J., Hollander, J. M., & Gibson, L. F. (2020) · Pharmaceutical Research · doi:10.1007/s11095-020-2767-4 · Cited by 13
Jue, D., Liu, L., Sang, X., Shu, B., Wang, J., Wang, Y., Zhang, C., & Shi, S. (2021) · Scientia Horticulturae · doi:10.1016/j.scienta.2021.110086 · Cited by 13
Impact of the next-generation sequencing data depth on various biological result inferences
Hou, R., Yang, Z., Li, M., & Xiao, H. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4441-0 · Cited by 15
Using normalization to resolve RNA-Seq biases caused by amplification from minimal input
Ager-Wick, E., Henkel, C. V., Haug, T. M., & Weltzien, F. (2014) · Physiological Genomics · doi:10.1152/physiolgenomics.00196.2013 · Cited by 13
A novel nitrogen-dependent gene associates with the lesion mimic trait in wheat
Li, L., Shi, X., Zheng, F., Li, C., Wu, D., Bai, G., Gao, D., Wu, J.-C., & Li, T. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-016-2758-3 · Cited by 13
Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq DataPreprint
Rocke, D. M., Ruan, L., Zhang, Y., Gossett, J. J., Durbin-Johnson, B. P., & Aviran, S. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/020784 · Cited by 16
Li, S., Wang, M., Du, X., Wang, Y., Pan, Y., Ji, D., You, J., Shan, M., Bao, G., Liu, X., et al. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.70359 · Cited by 12
Transcriptome Analysis of the Adipose Tissue of Luchuan and Duroc Pigs
Pan, H., Huang, T., Yu, L., Wang, P., Su, S., Wu, T., Bai, Y., Teng, Y., Wei, Y., Zhou, L., et al. (2022) · Animals · doi:10.3390/ani12172258 · Cited by 12
Kong, D., Wei, M., Liu, D., Zhang, Z., Ma, Y., & Zhang, Z. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411537 · Cited by 12
Nitrate improves aluminium resistance through SLAH‐mediated citrate exudation from roots
Wang, P., Cao, H., Quan, S., Wang, Y., Li, M., Wei, P., Zhang, M., Wang, H., Ma, H., Li, X., et al. (2023) · Plant Cell & Environment · doi:10.1111/pce.14688 · Cited by 12
Cao, S., Liu, Y., Sun, W., Zhao, L., Zhang, L., Liu, X., & Yu, T. (2015) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2015/756576 · Cited by 12
Dozmorov, M. G., Domínguez, N., Bean, K. M., Macwana, S., Roberts, V., Glass, E., James, J. A., & Guthridge, J. M. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s29470 · Cited by 11
Little, S. A., Boyes, I., Donaleshen, K., von Aderkas, P., & Ehlting, J. (2016) · Plant Reproduction · doi:10.1007/s00497-016-0291-9 · Cited by 12
Identification and evolutionary analysis of long non-coding RNAs in zebra finch
Chen, C.-K., Yu, C.-P., Li, S., Wu, S.-M., Lu, M. J., Chen, Y.-H., Chen, D., Ng, C. S., Ting, C., & Li, W. (2017) · BMC Genomics · doi:10.1186/s12864-017-3506-z · Cited by 13
Zhang, F., Yuan, W., Li, Z., Zhang, Y., Ye, Y., Li, K., Ding, Z., Chen, Y., Cheng, T., Wu, Q., et al. (2020) · Frontiers in Veterinary Science · doi:10.3389/fvets.2020.00492 · Cited by 12
Identifying cortical specific long noncoding RNAs modified by m6A RNA methylation in mouse brains
Nie, Y., Tian, G. G., Zhang, L., Lee, T., Zhang, Z., Li, J., & Sun, T. (2020) · Epigenetics · doi:10.1080/15592294.2020.1861170 · Cited by 12
Evaluation of Oxford Nanopore MinION RNA-Seq Performance for Human Primary Cells
Massaiu, I., Songia, P., Chiesa, M., Valerio, V., Moschetta, D., Alfieri, V., Myasoedova, V. A., Schmid, M., Cassetta, L., Colombo, G. I., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126317 · Cited by 12
Zhou, J., Huang, P., Li, X., Vaistij, F. E., & Dai, C. (2022) · Plant Molecular Biology · doi:10.1007/s11103-022-01268-7 · Cited by 12
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Metadata
- Authors
- Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A.
- Affiliations
- Centro de Investigacion Principe Felipe (ES)
- Universitat Politècnica de València (ES)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-09-08
- Volume
- 21
- Issue
- 12
- Pages
- 2213-2223
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3227109
- OpenAlex
- W2161106528
- Cited by
- 1,632
- References
- 42
- Citations by year
- 2026: 45 · 2025: 61 · 2024: 90 · 2023: 70 · 2022: 104 · 2021: 171 · 2020: 156 · 2019: 162 · 2018: 151 · 2017: 129 · 2016: 131 · 2015: 128 · 2014: 123 · 2013: 71 · 2012: 37
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- Generalitat Valenciana
- Ministerio de Ciencia e Innovación (BIO2008-04212)
- Instituto de Salud Carlos III (RD06/0020)
- 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, sequencing depth, false discovery rate, nonparametric statistics, transcript detection, sequencing noise, replication, gene expression profiling
- MeSH terms
- Algorithms, Gene Expression Regulation, Humans, Models, Genetic, Expressed Sequence Tags, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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