Cited by 659Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsGene expression and cancer classification

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Synthetic spike-in standards for RNA-seq experiments

Open access

Jiang, Lichun, Schlesinger, Felix, Davis, Carrie A., Zhang, Yu, Li, Renhua, Salit, Marc, Gingeras, Thomas R., Oliver, Brian·2011·Genome Research

DOI 10.1101/gr.121095.111PMC PMC3166838

Open this paper in CoChat — free account

Abstract

High-throughput sequencing of cDNA (RNA-seq) is a widely deployed transcriptome profiling and annotation technique, but questions about the performance of different protocols and platforms remain. We used a newly developed pool of 96 synthetic RNAs with various lengths, and GC content covering a 2 20 concentration range as spike-in controls to measure sensitivity, accuracy, and biases in RNA-seq experiments as well as to derive standard curves for quantifying the abundance of transcripts. We observed linearity between read density and RNA input over the entire detection range and excellent agreement between replicates, but we observed significantly larger imprecision than expected under pure Poisson sampling errors. We use the control RNAs to directly measure reproducible protocol-dependent biases due to GC content and transcript length as well as stereotypic heterogeneity in coverage across transcripts correlated with position relative to RNA termini and priming sequence bias. These effects lead to biased quantification for short transcripts and individual exons, which is a serious problem for measurements of isoform abundances, but that can partially be corrected using appropriate models of bias. By using the control RNAs, we derive limits for the discovery and detection of rare transcripts in RNA-seq experiments. By using data collected as part of the model organism and human Encyclopedia of DNA Elements projects (ENCODE and modENCODE), we demonstrate that external RNA controls are a useful resource for evaluating sensitivity and accuracy of RNA-seq experiments for transcriptome discovery and quantification. These quality metrics facilitate comparable analysis across different samples, protocols, and platforms.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1101/gr.121095.111

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
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
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
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
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Global analysis of protein expression in yeast
Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O'Shea, E. K., & Weissman, J. S. (2003) · Nature · doi:10.1038/nature02046 · Cited by 3,750
Nonparametrics: Statistical Methods Based on Ranks
Mergenthaler, M. J., & Lehmann, E. L. (1979) · Technometrics · doi:10.2307/1268531 · Cited by 3,353
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

Flagged first, then by citations

Show all 44 references

Sorted by citations

Items 301–400 of 659

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers
Li, Q.-G., He, Y., Wu, H., Yang, C., Pu, S.-Y., Fan, S., Jiang, L., Shen, Q., Wang, X., Chen, X., et al. (2017) · Theranostics · doi:10.7150/thno.19425 · Cited by 22
Self-citation
Transcriptomic profiling of rose flower under treatment of various phytohormones and plant growth regulators
Liu, X., Wu, J., Ji, F., Cao, X., Zhao, Q., Cheng, C., Ma, N., Zhou, X., & Zhang, Z. (2022) · Scientific Data · doi:10.1038/s41597-022-01800-w · Cited by 22
Essential guidelines for computational method benchmarking
Weber, L. M., Saelens, W., Cannoodt, R., Soneson, C., Hapfelmeier, A., Gardner, P. P., Boulesteix, A.-L., Saeys, Y., & Robinson, M. D. (2019) · Genome Biology · doi:10.1186/s13059-019-1738-8 · Cited by 21
A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory
Athanasiadou, R., Neymotin, B., Brandt, N., Wang, W., Christiaen, L., Gresham, D., & Tranchina, D. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006794 · Cited by 23
In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
Singh, S., Wang, L., Schaff, D. L., Sutcliffe, M. D., Koeppel, A. F., Kim, J., Önengüt-Gümüşcü, S., Park, K.-S., Zong, H., & Janes, K. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-41235-9 · Cited by 20
Histone deacetylase knockouts modify transcription, CAG instability and nuclear pathology in Huntington disease mice
Kovalenko, M. V., Erdin, S., Andrew, M. A., Claire, J. S., Shaughnessey, M., Hubert, L., Neto, J. L., Stortchevoi, A., Fass, D. M., Pinto, R. M., et al. (2020) · eLife · doi:10.7554/elife.55911 · Cited by 22
Choice of Next-Generation Sequencing Pipelines
CHIERICO, F. D., Ancora, M., Marcacci, M., Cammà, C., Putignani, L., & Conti, S. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1720-4_3 · Cited by 21
ANAQUIN: a software toolkit for the analysis of spike-in controls for next generation sequencing
Wong, T., Deveson, I. W., Hardwick, S. A., & Mercer, T. R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx038 · Cited by 21
Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network
Martins, A. J., Narayanan, M., Prüstel, T., Fixsen, B. R., Park, K., Gottschalk, R. A., Lu, Y. C., Andrews-Pfannkoch, C. M., Lau, W. W., Wendelsdorf, K. V., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.002 · Cited by 20
Delineating biological and technical variance in single cell expression data
Arzalluz-Luque, Á., Devailly, G., Mantsoki, A., & Joshi, A. (2017) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2017.07.006 · Cited by 21
Drosophila Heterochromatin Stabilization Requires the Zinc-Finger Protein Small Ovary
Benner, L., Castro, E. A., Whitworth, C., Venken, K. J. T., Yang, H., Fang, J., Oliver, B., Cook, K. R., & Lerit, D. A. (2019) · Genetics · doi:10.1534/genetics.119.302590 · Cited by 17
Self-citation
Sequencing enabling design and learning in synthetic biology
Gilliot, P.-A., & Gorochowski, T. E. (2020) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2020.06.002 · Cited by 21
RNA Polymerase II CTD phosphatase Rtr1 fine-tunes transcription termination
Fox, M. J., Victorino, J. F., Smith-Kinnaman, W. R., Justice, S. A. P., Gao, H., Boyd, A. K., Zimmerly, M. A., Chan, R. R., Hunter, G. O., Liu, Y., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008317 · Cited by 19
The Wild West of spike-in normalization
Patel, L. A., Cao, Y., Mendenhall, E. M., Benner, C., & Goren, A. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02377-y · Cited by 20
Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements
Peters, T. J., French, H. J., Bradford, S. T., Pidsley, R., Stirzaker, C., Varinli, H., Nair, S. S., Qu, W., Song, J. Z., Giles, K. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty675 · Cited by 20
Integrated transcriptome and metabolome provide insights into flavonoid biosynthesis in 'P113', a new purple tea of Camellia tachangensis
Li, F., Deng, X., Huang, Z., Zhao, Z., Li, C., Song, Q., He, Y., & Niu, S. (2023) · Beverage Plant Research · doi:10.48130/bpr-2023-0003 · Cited by 20
Bioinformatics approaches to single-cell analysis in developmental biology
Yalcin, D., Hakguder, Z., & Otu, H. H. (2015) · Molecular Human Reproduction · doi:10.1093/molehr/gav050 · Cited by 20
Chiral DNA sequences as commutable controls for clinical genomics
Deveson, I. W., Madala, B. S., Blackburn, J., Barker, C., Wong, T., Barton, K., Smith, M. A., Watkins, D. N., & Mercer, T. R. (2019) · Nature Communications · doi:10.1038/s41467-019-09272-0 · Cited by 20
Transcriptomic-proteomics-anticoagulant bioactivity integrated study of Pheretima guillemi
Wu, Y., Ma, Y., Hu, S., Zhao, B., Yang, W., Sun, Z., Zhu, B., Lu, Y., Li, P., & Du, S. (2019) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2019.112101 · Cited by 19
An international comparability study on quantification of mRNA gene expression ratios: CCQM-P103.1
Devonshire, A. S., Sanders, R., Whale, A. S., Nixon, G. J., Cowen, S., Ellison, S. L. R., Parkes, H., Pine, P. S., Salit, M., McDaniel, J., et al. (2016) · Biomolecular Detection and Quantification · doi:10.1016/j.bdq.2016.05.003 · Cited by 19
Self-citation
Quality control on the frontier
Paszkiewicz, K. H., Farbos, A., O'Neill, P., & Moore, K. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00157 · Cited by 21
Recent Developments in Circulating Biomarkers in Parkinson’s Disease: The Potential Use of Mirnas in A Clinical Setting
dos Santos, M. C. T., Bell, R., & da Costa, A. N. (2016) · Bioanalysis · doi:10.4155/bio-2016-0166 · Cited by 19
Advances in the application of high-throughput sequencing in invertebrate virology
van Aerle, R., & Santos, E. M. (2017) · Journal of Invertebrate Pathology · doi:10.1016/j.jip.2017.02.006 · Cited by 21
Transcriptomic Characterization of the Human Cell Cycle in Individual Unsynchronized Cells
Karlsson, J. W., Kroneis, T., Jonasson, E., Larsson, E., & Ståhlberg, A. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.10.011 · Cited by 18
Review of GPU implementation to process of RNA sequence on cancer
Rahman, M. A., & Muniyandi, R. C. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.10.008 · Cited by 20
LSD1 Cooperates with Noncanonical NF-κB Signaling to Regulate Marginal Zone B Cell Development
Haines, R. R., Scharer, C. D., Lobby, J. L., & Boss, J. M. (2019) · The Journal of Immunology · doi:10.4049/jimmunol.1900654 · Cited by 19
Quality assessment and control of tissue specific RNA-seq libraries of Drosophila transgenic RNAi models
Amaral, A. J., Brito, F., Chobanyan, T., Yoshikawa, S., Yokokura, T., Vactor, D. V., & Gama‐Carvalho, M. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00043 · Cited by 17
Processing, visualising and reconstructing network models from single‐cell data
Woodhouse, S., Moignard, V., Göttgens, B., & Fisher, J. (2015) · Immunology and Cell Biology · doi:10.1038/icb.2015.102 · Cited by 18
Coordinated analysis of exon and intron data reveals novel differential gene expression changes
Eghbalnia, H. R., Wilfinger, W. W., Mackey, K., & Chomczyński, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-72482-w · Cited by 19
Assessing and removing the effect of unwanted technical variations in microbiome data
Fachrul, M., Méric, G., Inouye, M., Pamp, S. J., & Salim, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-26141-x · Cited by 17
Benchmarking full-length transcript single cell mRNA sequencing protocols
Probst, V., Simonyan, A., Pacheco, F., Guo, Y., Nielsen, F. C., & Bagger, F. O. (2022) · BMC Genomics · doi:10.1186/s12864-022-09014-5 · Cited by 19
The Role of Spike-In Standards in the Normalization of RNA-seq
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_9 · Cited by 18
Network reconstruction of the mouse secretory pathway applied on CHO cell transcriptome data
Lund, A. M., Kaas, C. S., Brandl, J., Pedersen, L. E., Kildegaard, H. F., Kristensen, C., & Andersen, M. R. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0414-4 · Cited by 20
Ensuring validation in diagnostic testing is fit for purpose: a view from the plant virology laboratory
Roenhorst, J. W., de Krom, C., Fox, A., Mehle, N., Ravnikar, M., & Werkman, A. W. (2018) · EPPO Bulletin · doi:10.1111/epp.12445 · Cited by 18
Plant single-cell biology and abiotic stress tolerance
Tanveer, M., & Yousaf, U. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818204-8.00026-6 · Cited by 18
High-throughput full-length single-cell RNA-seq automation
Mamanova, L., Miao, Z., Jinat, A., Ellis, P., Shirley, L., & Teichmann, S. A. (2021) · Nature Protocols · doi:10.1038/s41596-021-00523-3 · Cited by 18
Distinguishing Relapse From Reinfection With Whole-Genome Sequencing in Recurrent Pulmonary Tuberculosis: A Retrospective Cohort Study in Beijing, China
Du, J., Li, Q., Liu, M., Wang, Y., Xue, Z., Huo, F., Zhang, X., Shang, Y., Li, S., Huang, H., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.754352 · Cited by 17
Sbk2, a Newly Discovered Atrium-Enriched Regulator of Sarcomere Integrity
van Gorp, P. R. R., Zhang, J., Liu, J., Tsonaka, R., Mei, H., Dekker, S. O., Bart, C. I., Coster, T. D., Post, H., Heck, A. J. R., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.319300 · Cited by 17
Navigating and mining modENCODE data
Boley, N. P., Wan, K. H., Bickel, P. J., & Celniker, S. (2014) · Methods · doi:10.1016/j.ymeth.2014.03.007 · Cited by 17
X Chromosome and Autosome Dosage Responses in Drosophila melanogaster Heads
Chen, Z., & Oliver, B. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.017632 · Cited by 17
Self-citation
A primer for generating and using transcriptome data and gene sets
Cockrum, C., Kaneshiro, K. R., Rechtsteiner, A., Tabuchi, T. M., & Strome, S. (2020) · Development · doi:10.1242/dev.193854 · Cited by 17
Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing
Manes, N. P., Angermann, B., Koppenol-Raab, M., An, E., Sjoelund, V. H., Sun, J., Ishii, M., Germain, R. N., Meier‐Schellersheim, M., & Nita‐Lazar, A. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.048918 · Cited by 17
Quantitative single cell gene expression profiling in the avian embryo
Morrison, J. A., Box, A., McKinney, M. C., McLennan, R., & Kulesa, P. M. (2015) · Developmental Dynamics · doi:10.1002/dvdy.24274 · Cited by 17
A graph-based algorithm for RNA-seq data normalization
Tran, D.-T. T., Bhaskara, A., Might, M., & Kuberan, B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227760 · Cited by 16
Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows
Lagerborg, K. A., Normandin, E., Bauer, M. R., Adams, G. C., Figueroa, K., Loreth, C., Gladden, A. D., Shaw, B. M., Pearlman, L. R., Berenzy, D., et al. (2021) · Nature Microbiology · doi:10.1038/s41564-021-01019-2 · Cited by 23
Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing
Gunter, H. M., Youlten, S. E., Madala, B. S., Reis, A. L. M., Stevanovski, I., Wong, T., Kummerfield, S., Deveson, I. W., Santini, N. S., Marcellin, E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34028-8 · Cited by 15
Harnessing human microbiomes for disease prediction
Liu, Y., Fachrul, M., Inouye, M., & Méric, G. (2024) · Trends in Microbiology · doi:10.1016/j.tim.2023.12.004 · Cited by 18
A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning‐expressed responses
Laosuntisuk, K., Vennapusa, A. R., Somayanda, I. M., Leman, A. R., Jagadish, S. V. K., & Doherty, C. J. (2024) · The Plant Journal · doi:10.1111/tpj.16654 · Cited by 17
RNA-seq in the tetraploid Xenopus laevis enables genome-wide insight in a classic developmental biology model organism
Amin, N. M., Tandon, P., Nishimura, E. O., & Conlon, F. L. (2013) · Methods · doi:10.1016/j.ymeth.2013.06.009 · Cited by 16
Use of the nuclear walk-on methodology to determine sites of RNA polymerase II initiation and pausing and quantify nascent RNAs in cells
Ball, C. B., Nilson, K. A., & Price, D. H. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.003 · Cited by 15
Transcriptome Profiles of Alternaria oxytropis Provides Insights into Swainsonine Biosynthesis
Li, X., & Lu, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-42173-2 · Cited by 16
BEARscc determines robustness of single-cell clusters using simulated technical replicates
Severson, D. T., Owen, R. P., White, M. J., Lü, X., & Schuster‐Böckler, B. (2018) · Nature Communications · doi:10.1038/s41467-018-03608-y · Cited by 16
GAPDH spike RNA as an alternative for housekeeping genes in relative gene expression assay using real-time PCR
Atwan, Z. (2020) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-020-00284-1 · Cited by 16
Comparative Transcriptomic Analysis of Staphylococcus aureus Associated with Periprosthetic Joint Infection under in Vivo and in Vitro Conditions
Masters, T. L., Johnson, S. P., Jeraldo, P., Greenwood‐Quaintance, K. E., Cunningham, S. A., Abdel, M. P., Chia, N., & Patel, R. (2021) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2021.05.011 · Cited by 16
Exploring the pathogenesis of type 2 diabetes mellitus intestinal damp-heat syndrome and the therapeutic effect of Gegen Qinlian Decoction from the perspective of exosomal miRNA
He, L., Bao, T., Yang, Y., Wang, H., Gu, C., Chen, J., Zhai, T., He, X., Wu, M., Zhao, L., et al. (2021) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2021.114786 · Cited by 16
Abnormal Osmotic Avoidance Behavior in C. elegans Is Associated with Increased Hypertonic Stress Resistance and Improved Proteostasis
Lee, E. C., Kim, H., Ditano, J. P., Manion, D., King, B. L., & Strange, K. B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154156 · Cited by 16
Nanopore direct RNA sequencing maps an Arabidopsis N6 methyladenosine epitranscriptomePreprint
Parker, M. T., Knop, K., Sherwood, A. V., Schurch, N., Mackinnon, K., Gould, P. D., Hall, A. J. W., Barton, G. J., & Simpson, G. G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/706002 · Cited by 16
Efficient profiling of total RNA in single cells with STORM-seqPreprint
Johnson, B. K., Majewski, M. F., Jang, H. J., Siwicki, R. A., Wegener, M., Semwal, A., Morrison, J., Gallik, K., Hagemann-Jensen, M., Foy, K. K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484332 · Cited by 18
The nuclear factor of activated T cells 5 (NFAT5) contributes to the renal corticomedullary differences in gene expression
Chernyakov, D., Fischer, A., Brandau, M., Petrillo, F., Fenton, R. A., & Edemir, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-24237-y · Cited by 15
The Association Between Thyroid Differentiation Score and Survival Outcomes in Papillary Thyroid Carcinoma
Wang, J. R., Zafereo, M., Cabanillas, M. E., Wu, C.-C., Xu, L., Dai, Y., Wang, W., Lai, S. Y., Henderson, Y. C., Erasmus, L. T., et al. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae532 · Cited by 19
Microbial signatures of oral dysbiosis, periodontitis and edentulism revealed by Gene Meter methodology
Hunter, M. C., Pozhitkov, A. E., & Noble, P. A. (2016) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2016.09.019 · Cited by 15
SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data
Das, S., & Rai, S. N. (2021) · Genomics · doi:10.1016/j.ygeno.2021.02.014 · Cited by 15
Molecular signatures and cellular diversity during mouse habenula development
van de Haar, L. L., Riga, D., Boer, J. E., Garritsen, O., Adolfs, Y., Sieburgh, T. E., van Dijk, R. E., Watanabe, K., van Kronenburg, N. C. H., Broekhoven, M. H., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111029 · Cited by 15
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
Effect of Fibroblast Growth Factor 10 and an Interacting Non-Coding RNA on Secondary Hair Follicle Dermal Papilla Cells in Cashmere Goats’ Follicle Development Assessed by Whole-Transcriptome Sequencing Technology
Gao, Y. Z., Song, W., Hao, F., Duo, L., Zhe, X., Gao, C., Guo, X., & Liu, D. (2023) · Animals · doi:10.3390/ani13132234 · Cited by 14
Non-AUG HIV-1 uORF translation elicits specific T cell immune response and regulates viral transcript expression
Labaronne, E., Décimo, D., Bertrand, L., Guiguettaz, L., Sohier, T. J. M., Cluet, D., Vivet‐Boudou, V., Valadão, A. L. C., Dahoui, C., François, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56772-3 · Cited by 15
Productive mRNA chromatin escape is promoted by PRMT5 activity
DeAngelo, J. D., Maron, M. I., Roth, J. S., Hegde, S. S., Silverstein, A. M., Gupta, V., Stransky, S., Basken, J., Azofeifa, J., Query, C. C., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.09.021 · Cited by 14
Microparticle Delivery of Protein Markers for Single‐Cell Western Blotting from Microwells
Kim, J. J., Chan, P. P. Y., Vlassakis, J., Geldert, A., & Herr, A. E. (2018) · Small · doi:10.1002/smll.201802865 · Cited by 14
Normalizing single-cell RNA sequencing data with internal spike-in-like genes
Lin, L., Song, M., Jiang, Y., Zhao, X., Wang, H., & Zhang, L. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa059 · Cited by 16
Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision
Davies, P., Jones, M., Liu, J., & Hebenstreit, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab148 · Cited by 14
Mitochondrial genome and phylogenetic analysis of Chaohu duck
Jia, Y., Qiu, G., Cao, C., Wang, X., Jiang, L., Zhang, T., Geng, Z., & Jin, S. (2022) · Gene · doi:10.1016/j.gene.2022.147018 · Cited by 14
Self-citation
Omics Technologies in Personalized Combination Therapy for Cardiovascular Diseases: Challenges and Opportunities
Banerjee, S., Basrur, N. P., & Padmalatha, S. (2021) · Personalized Medicine · doi:10.2217/pme-2021-0087 · Cited by 13
Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering
Fang, J., Chan, C. C. W., Owzar, K., Wang, L., Qin, D., Li, Q.-J., & Xie, J. (2022) · Genome biology · doi:10.1186/s13059-022-02825-5 · Cited by 16
Sex-Biased Expression Is Associated With Chromatin State in DrosophilamelanogasterandDrosophila simulans
Nanni, A. V., Martinez, N., Graze, R. M., Morse, A. M., Newman, J. R. B., Jain, V., Vlaho, S., Signor, S. A., Nuzhdin, S. V., Renne, R., et al. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad078 · Cited by 13
Transcriptome assembly for a colour-polymorphic grasshopper (Gomphocerus sibiricus) with a very large genome size
Shah, A., Hoffman, J. I., & Schielzeth, H. (2019) · BMC Genomics · doi:10.1186/s12864-019-5756-4 · Cited by 13
Molecular characteristics of a novel ssRNA virus isolated from Auricularia heimuer in China
Li, X., Xiè, J., Hai, D., Sui, K., Yin, W., Sossah, F. L., Jiāng, D., Song, B., & Li, Y. (2020) · Archives of Virology · doi:10.1007/s00705-020-04615-5 · Cited by 13
Notch3 Knockout Suppresses Mouse Mammary Gland Development and Inhibits the Proliferation of 4T1 Murine Mammary Carcinoma Cells via CCL2/CCR4 Axis
Xiong, W., Tan, J., Guo, Y., Chen, S., Fan, L., & Li, Y. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.594372 · Cited by 13
Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls
Wilson, S., Shen, S. Y., Harmon, L., Burgener, J., Triche, T., Bratman, S., Carvalho, D. D. D., & Hoffman, M. M. (2021) · bioRxiv · doi:10.1016/j.crmeth.2022.100294 · Cited by 16
Three steps towards comparability and standardization among molecular methods for characterizing insect communities
Iwaszkiewicz-Eggebrecht, E., Zizka, V. M. A., & Lynggaard, C. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0118 · Cited by 14
Analysis of aggregated cell–cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells
Schissler, A. G., Li, Q., Chen, J. L., Kenost, C., Achour, I., Billheimer, D. D., Li, H., Piegorsch, W. W., & Lussier, Y. A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw248 · Cited by 14
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
Chi, W., & Deng, M. (2020) · Genes · doi:10.3390/genes11050532 · Cited by 13
Circular RNAs in Cardiovascular Disease: Mechanisms, Biomarkers, and Therapeutic Frontiers
Alali, R. A., Almansori, M., Vatte, C., Akhtar, M. S., Abduljabbar, S. S., Al-Matroud, H., Alnuwaysir, M. J., Radhi, H. A., Keating, B. J., Habara, A. H., et al. (2025) · Biomolecules · doi:10.3390/biom15101455 · Cited by 12
Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes
Zhang, T., Li, J., Tang, C., Wu, Y., Wu, H., Xi-Tong, Z., Luo, Z., Qin, H., Ding, L., Zeng, Y., et al. (2026) · iMeta · doi:10.1002/imt2.70136 · Cited by 17
Self-citation
Using mixtures of biological samples as process controls for RNA-sequencing experiments
Parsons, J., Munro, S. A., Pine, P. S., McDaniel, J., Mehaffey, M. G., & Salit, M. (2015) · BMC Genomics · doi:10.1186/s12864-015-1912-7 · Cited by 10
Self-citation
Detection and mitigation of spurious antisense expression with RoSA
Mourão, K., Schurch, N., Lucoszek, R., Froussios, K., Mackinnon, K., Duc, C., Simpson, G. G., & Barton, G. J. (2019) · F1000Research · doi:10.12688/f1000research.18952.1 · Cited by 12
Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients
Lim, L. J., Yu, J. H., Yang, H. H., Chung, A. Y. F., Goh, B. K. P., Chow, P. K. H., Chan, C. Y., Blanks, W. K., Cheow, P. C., Lee, S. Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67742-8 · Cited by 12
Protocol for Patch-Seq of Small Interneurons
Lipovsek, M., Browne, L., & Grubb, M. S. (2020) · STAR Protocols · doi:10.1016/j.xpro.2020.100146 · Cited by 12
Systems analysis of dilated cardiomyopathy in the next generation sequencing era
Harakalova, M., & Asselbergs, F. (2018) · Wiley Interdisciplinary Reviews: Systems Biology and Medicine · doi:10.1002/wsbm.1419 · Cited by 11
Long Noncoding RNA 3632454L22Rik Contributes to Corneal Epithelial Wound Healing by Sponging miR-181a-5p in Diabetic Mice
Chen, X., & Hu, J. (2021) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.62.14.16 · Cited by 12
scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methodsPreprint
Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/433102 · Cited by 11
Reference materials for cellular therapeutics
Bravery, C. A., & French, A. (2014) · Cytotherapy · doi:10.1016/j.jcyt.2014.05.024 · Cited by 11
The impact of single-cell genomics on the field of mycobacterial infection
Geraldes, I., Fernandes, M., Fraga, A. G., & Osório, N. S. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.989464 · Cited by 11
Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome
Lee, H., Cho, D.-Y., Wójtowicz, D., Harbison, S. T., Russell, S. R. H., Oliver, B., & Przytycka, T. M. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300400 · Cited by 11
Self-citation
Contribution of Single-Cell Transcriptomics to the Characterization of Human Spermatogonial Stem Cells: Toward an Application in Male Fertility Regenerative Medicine?
Gille, A., Lapoujade, C., Wolf, J., Fouchet, P., & Barraud‐Lange, V. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20225773 · Cited by 13
Single Cell Sequencing in Cancer Diagnostics
Bagger, F. O., & Probst, V. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-4494-1_15 · Cited by 10
Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data
Rojas, F., Chakraborty, S., Vissers, C., Reiman, D. J., Patel, A. M., Lee, K.-H., Hong, A., Park, C.-W., Reska, T., Gagneur, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01136-7 · Cited by 12
Unifying and generalizing methods for removing unwanted variation based on negative controls
Gerard, D., & Stephens, M. (2023) · Figshare · doi:10.57912/23894760 · Cited by 11
Development of Reference Genes for Horticultural Plants
Ahmed, U., Xie, Q., Shi, X., & Zheng, B. (2022) · Critical Reviews in Plant Sciences · doi:10.1080/07352689.2022.2084227 · Cited by 10
A universal molecular control for DNA, mRNA and protein expression
Gunter, H. M., Youlten, S. E., Reis, A. L. M., McCubbin, T., Madala, B. S., Wong, T., Stevanovski, I., Cipponi, A., Deveson, I. W., Santini, N. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46456-9 · Cited by 10
Singletrome enhances detection of long noncoding RNAs in single cell transcriptomes
Rahman, R., Ahmad, I., Li, Z., Sparks, R. P., Saad, A. B., & Mullen, A. C. (2025) · Scientific Reports · doi:10.1038/s41598-025-13528-9 · Cited by 10

Authors: Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B.
Affiliations: National Institutes of Health (US)
Cold Spring Harbor Laboratory (US)
National Institute of Standards and Technology (US)
ORCID: Jiang, Lichun
Zhang, Yu
Salit, Marc
Gingeras, Thomas R.
Oliver, Brian
Venue: Genome Research
Publisher: Cold Spring Harbor Laboratory
Published: 2011-08-04
Volume: 21
Issue: 9
Pages: 1543-1551
ISSN: 1088-9051 (ISSN-L)
DOI: 10.1101/gr.121095.111
PMC: PMC3166838
OpenAlex: W2009458183
Cited by: 653
References: 44
Citations by year: 2026: 18 · 2025: 35 · 2024: 50 · 2023: 36 · 2022: 53 · 2021: 45 · 2020: 58 · 2019: 54 · 2018: 51 · 2017: 67 · 2016: 64 · 2015: 38 · 2014: 40 · 2013: 25 · 2012: 15
Type: posted content
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Genome Research (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
HighWire Press Open Archive (submittedVersion)
Funders: National Institutes of Health
National Institute of Standards and Technology
National Human Genome Research Institute (U54HG004557)
National Institute of Diabetes and Digestive and Kidney Diseases (ZIADK015600)
Topics: 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
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: RNA-seq, transcriptome quantification, GC content bias, calibration curve, isoform abundance, ENCODE, modENCODE
MeSH terms: Animals, Humans, Quality Control, RNA, Sensitivity and Specificity, Reproducibility of Results, Gene Library, Bias, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing

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 ›

Are you an author, or do you see a problem with this page? Request a review

Synthetic spike-in standards for RNA-seq experiments

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks