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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Evaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays
Daniel Bottomly, Nicole A. R. Walter, Jessica Ezzell Hunter, et al.
PLoS ONE (2011) Vol. 6, Iss. 3, pp. e17820-e17820
Open Access | Times Cited: 237

Showing 1-25 of 237 citing articles:

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I. Love, Wolfgang Huber, Simon Anders
Genome biology (2014) Vol. 15, Iss. 12
Open Access | Times Cited: 74284
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law, Yunshun Chen, Wei Shi, et al.
Genome biology (2014) Vol. 15, Iss. 2
Open Access | Times Cited: 5555
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Charlotte Soneson, Michael I. Love, Mark D. Robinson
F1000Research (2015) Vol. 4, pp. 1521-1521
Open Access | Times Cited: 3475
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
Anqi Zhu, Joseph G. Ibrahim, Michael I. Love
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2084-2092
Open Access | Times Cited: 1505
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Charlotte Soneson, Michael I. Love, Mark D. Robinson
F1000Research (2016) Vol. 4, pp. 1521-1521
Open Access | Times Cited: 1466
Differential analysis of RNA-seq incorporating quantification uncertainty
Harold Pimentel, Nicolas Bray, Suzette Puente, et al.
Nature Methods (2017) Vol. 14, Iss. 7, pp. 687-690
Open Access | Times Cited: 1416
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis
Andrew D. Fernandes, Jennifer Reid, Jean M. Macklaim, et al.
Microbiome (2014) Vol. 2, Iss. 1
Open Access | Times Cited: 1054
Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells
Shanrong Zhao, Wai‐Ping Fung‐Leung, Anton Bittner, et al.
PLoS ONE (2014) Vol. 9, Iss. 1, pp. e78644-e78644
Open Access | Times Cited: 885
A comparison of methods for differential expression analysis of RNA-seq data
Charlotte Soneson, Mauro Delorenzi
BMC Bioinformatics (2013) Vol. 14, Iss. 1
Open Access | Times Cited: 884
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I. Love, Wolfgang Huber, Simon Anders
bioRxiv (Cold Spring Harbor Laboratory) (2014)
Open Access | Times Cited: 875
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
Nick Schurch, Pietá Schofield, Marek Gierliński, et al.
RNA (2016) Vol. 22, Iss. 6, pp. 839-851
Open Access | Times Cited: 753
Removing technical variability in RNA-seq data using conditional quantile normalization
Kasper D. Hansen, Rafael A. Irizarry, Zhijin Wu
Biostatistics (2012) Vol. 13, Iss. 2, pp. 204-216
Open Access | Times Cited: 609
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
Nikolaos Ignatiadis, Bernd Klaus, Judith B. Zaugg, et al.
Nature Methods (2016) Vol. 13, Iss. 7, pp. 577-580
Open Access | Times Cited: 597
The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
Charles Wang, Binsheng Gong, Pierre R. Bushel, et al.
Nature Biotechnology (2014) Vol. 32, Iss. 9, pp. 926-932
Open Access | Times Cited: 497
Comparison of software packages for detecting differential expression in RNA-seq studies
Fatemeh Seyednasrollah, Asta Laiho, Laura L. Elo
Briefings in Bioinformatics (2013) Vol. 16, Iss. 1, pp. 59-70
Open Access | Times Cited: 396
Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories
Peter A.C. ’t Hoen, Marc R. Friedländer, Jonas Carlsson Almlöf, et al.
Nature Biotechnology (2013) Vol. 31, Iss. 11, pp. 1015-1022
Closed Access | Times Cited: 273
Power analysis and sample size estimation for RNA-Seq differential expression
Travers Ching, Sijia Huang, Lana X. Garmire
RNA (2014) Vol. 20, Iss. 11, pp. 1684-1696
Open Access | Times Cited: 242
Data-based filtering for replicated high-throughput transcriptome sequencing experiments
Andréa Rau, Mélina Gallopin, Gilles Celeux, et al.
Bioinformatics (2013) Vol. 29, Iss. 17, pp. 2146-2152
Open Access | Times Cited: 218
Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies
Mohan Rao, Terry R. Van Vleet, Rita Ciurlionis, et al.
Frontiers in Genetics (2019) Vol. 9
Open Access | Times Cited: 209
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Koen Van den Berge, Fanny Perraudeau, Charlotte Soneson, et al.
Genome biology (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 205
GeneNetwork: A Toolbox for Systems Genetics
Megan K. Mulligan, Khyobeni Mozhui, Pjotr Prins, et al.
Methods in molecular biology (2016), pp. 75-120
Open Access | Times Cited: 187
Bon-EV: an improved multiple testing procedure for controlling false discovery rates
Dongmei Li, Zidian Xie, Martin S. Zand, et al.
BMC Bioinformatics (2017) Vol. 18, Iss. 1
Open Access | Times Cited: 180
ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
Alyssa C. Frazee, Ben Langmead, Jeffrey T. Leek
BMC Bioinformatics (2011) Vol. 12, Iss. 1
Open Access | Times Cited: 169
Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size
Danni Yu, Wolfgang Huber, Olga Vitek
Bioinformatics (2013) Vol. 29, Iss. 10, pp. 1275-1282
Open Access | Times Cited: 142
Displaying Variation in Large Datasets: Plotting a Visual Summary of Effect Sizes
Gregory B. Gloor, Jean M. Macklaim, Andrew D. Fernandes
Journal of Computational and Graphical Statistics (2015) Vol. 25, Iss. 3, pp. 971-979
Closed Access | Times Cited: 140
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