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@article{VandenBerge2021,
abstract = {Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample-specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins, and evaluate it together with eleven different normalization procedures on eight public ATAC-seq datasets. Our work clearly shows that accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, leading to improved accuracy and interpretability of the results. Using two case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset. {\#}{\#}{\#} Competing Interest Statement The authors have declared no competing interest.},
author = {{Van den Berge}, Koen and Chou, Hsin-Jung and {Roux de B{\'{e}}zieux}, Hector and Street, Kelly and Risso, Davide and Ngai, John and Dudoit, Sandrine},
doi = {10.1101/2021.01.26.428252},
journal = {bioRxiv},
month = {may},
pages = {2021.01.26.428252},
publisher = {Cold Spring Harbor Laboratory},
title = {{Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects}},
url = {https://www.biorxiv.org/content/10.1101/2021.01.26.428252v2},
year = {2021}
}


@article{Yu2014,
	Abstract = {The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.},
	Author = {Yu, Ying and Fuscoe, James C and Zhao, Chen and Guo, Chao and Jia, Meiwen and Qing, Tao and Bannon, Desmond I and Lancashire, Lee and Bao, Wenjun and Du, Tingting and Luo, Heng and Su, Zhenqiang and Jones, Wendell D and Moland, Carrie L and Branham, William S and Qian, Feng and Ning, Baitang and Li, Yan and Hong, Huixiao and Guo, Lei and Mei, Nan and Shi, Tieliu and Wang, Kevin Y and Wolfinger, Russell D and Nikolsky, Yuri and Walker, Stephen J and Duerksen-Hughes, Penelope and Mason, Christopher E and Tong, Weida and Thierry-Mieg, Jean and Thierry-Mieg, Danielle and Shi, Leming and Wang, Charles},
	Date-Added = {2016-08-04 15:27:19 +0000},
	Date-Modified = {2016-08-04 15:27:25 +0000},
	Doi = {10.1038/ncomms4230},
	Journal = {Nat Commun},
	Journal-Full = {Nature communications},
	Mesh = {Alternative Splicing; Animals; Female; Gene Expression Profiling; Male; Protein Isoforms; Rats, Inbred F344; Sequence Analysis, RNA; Sex Characteristics; Transcriptome},
	Pages = {3230},
	Pmc = {PMC3926002},
	Pmid = {24510058},
	Pst = {ppublish},
	Title = {A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages},
	Volume = {5},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/ncomms4230}}

@article{Loven2012,
	Abstract = {Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms.},
	Author = {Lov{\'e}n, Jakob and Orlando, David A and Sigova, Alla A and Lin, Charles Y and Rahl, Peter B and Burge, Christopher B and Levens, David L and Lee, Tong Ihn and Young, Richard A},
	Date-Added = {2016-08-04 15:26:36 +0000},
	Date-Modified = {2016-08-04 15:26:41 +0000},
	Doi = {10.1016/j.cell.2012.10.012},
	Journal = {Cell},
	Journal-Full = {Cell},
	Mesh = {Gene Expression Profiling; Genome-Wide Association Study; Humans; Oligonucleotide Array Sequence Analysis; Proto-Oncogene Proteins c-myc; Sequence Analysis, RNA; Transcription, Genetic},
	Month = {Oct},
	Number = {3},
	Pages = {476-82},
	Pmc = {PMC3505597},
	Pmid = {23101621},
	Pst = {ppublish},
	Title = {Revisiting global gene expression analysis},
	Volume = {151},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.cell.2012.10.012}}

@article{Eisenberg2013,
	Abstract = {Housekeeping genes are involved in basic cell maintenance and, therefore, are expected to maintain constant expression levels in all cells and conditions. Identification of these genes facilitates exposure of the underlying cellular infrastructure and increases understanding of various structural genomic features. In addition, housekeeping genes are instrumental for calibration in many biotechnological applications and genomic studies. Advances in our ability to measure RNA expression have resulted in a gradual increase in the number of identified housekeeping genes. Here, we describe housekeeping gene detection in the era of massive parallel sequencing and RNA-seq. We emphasize the importance of expression at a constant level and provide a list of 3804 human genes that are expressed uniformly across a panel of tissues. Several exceptionally uniform genes are singled out for future experimental use, such as RT-PCR control genes. Finally, we discuss both ways in which current technology can meet some of past obstacles encountered, and several as yet unmet challenges.},
	Author = {Eisenberg, Eli and Levanon, Erez Y},
	Date-Added = {2016-08-04 15:25:45 +0000},
	Date-Modified = {2016-08-04 15:25:58 +0000},
	Doi = {10.1016/j.tig.2013.05.010},
	Journal = {Trends Genet},
	Journal-Full = {Trends in genetics : TIG},
	Mesh = {Databases, Genetic; Genes, Essential; High-Throughput Nucleotide Sequencing; Humans},
	Month = {Oct},
	Number = {10},
	Pages = {569-74},
	Pmid = {23810203},
	Pst = {ppublish},
	Title = {Human housekeeping genes, revisited},
	Volume = {29},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.tig.2013.05.010}}

@article{Robinson2010,
	Abstract = {The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.},
	Author = {Robinson, Mark D and Oshlack, Alicia},
	Date-Added = {2016-08-04 15:24:56 +0000},
	Date-Modified = {2016-08-04 15:25:02 +0000},
	Doi = {10.1186/gb-2010-11-3-r25},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {Base Sequence; Computer Simulation; Gene Expression Profiling; Gene Library; Models, Statistical; RNA},
	Number = {3},
	Pages = {R25},
	Pmc = {PMC2864565},
	Pmid = {20196867},
	Pst = {ppublish},
	Title = {A scaling normalization method for differential expression analysis of RNA-seq data},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/gb-2010-11-3-r25}}

@article{aanes2014normalization,
	Abstract = {Methods for normalization of RNA-sequencing gene expression data commonly assume equal total expression between compared samples. In contrast, scenarios of global gene expression shifts are many and increasing. Here we compare the performance of three normalization methods when polyA(+) RNA content fluctuates significantly during zebrafish early developmental stages. As a benchmark we have used reverse transcription-quantitative PCR. The results show that reads per kilobase per million (RPKM) and trimmed mean of M-values (TMM) normalization systematically leads to biased gene expression estimates. Biological scaling normalization (BSN), designed to handle differences in total expression, showed improved accuracy compared to the two other methods in estimating transcript level dynamics. The results have implications for past and future studies using RNA-sequencing on samples with different levels of total or polyA(+) RNA.},
	Author = {Aanes, H{\aa}vard and Winata, Cecilia and Moen, Lars F and {\O}strup, Olga and Mathavan, Sinnakaruppan and Collas, Philippe and Rognes, Torbj{\o}rn and Alestr{\"o}m, Peter},
	Date-Added = {2016-08-04 15:23:48 +0000},
	Date-Modified = {2016-08-04 15:24:05 +0000},
	Doi = {10.1371/journal.pone.0089158},
	Journal = {PLoS One},
	Journal-Full = {PloS one},
	Mesh = {Animals; Base Sequence; Gene Expression; Gene Expression Profiling; Polymerase Chain Reaction; RNA, Messenger; Sequence Analysis, RNA; Zebrafish},
	Number = {2},
	Pages = {e89158},
	Pmc = {PMC3934880},
	Pmid = {24586560},
	Pst = {epublish},
	Title = {Normalization of RNA-sequencing data from samples with varying mRNA levels},
	Volume = {9},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pone.0089158}}

@article{Irizarry2003,
	Abstract = {In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.},
	Author = {Irizarry, Rafael A and Hobbs, Bridget and Collin, Francois and Beazer-Barclay, Yasmin D and Antonellis, Kristen J and Scherf, Uwe and Speed, Terence P},
	Date-Added = {2016-08-04 15:23:13 +0000},
	Date-Modified = {2016-08-04 15:23:19 +0000},
	Doi = {10.1093/biostatistics/4.2.249},
	Journal = {Biostatistics},
	Journal-Full = {Biostatistics (Oxford, England)},
	Mesh = {Algorithms; Animals; DNA Probes; Data Interpretation, Statistical; Gene Expression Profiling; Humans; Linear Models; Mice; Normal Distribution; Oligonucleotide Array Sequence Analysis; Reproducibility of Results; Statistics, Nonparametric},
	Month = {Apr},
	Number = {2},
	Pages = {249-64},
	Pmid = {12925520},
	Pst = {ppublish},
	Title = {Exploration, normalization, and summaries of high density oligonucleotide array probe level data},
	Volume = {4},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/biostatistics/4.2.249}}

@article{Hicks2015,
	Abstract = {Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html .},
	Author = {Hicks, Stephanie C and Irizarry, Rafael A},
	Date-Added = {2016-08-04 15:22:18 +0000},
	Date-Modified = {2016-08-04 15:22:23 +0000},
	Doi = {10.1186/s13059-015-0679-0},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {CpG Islands; DNA Methylation; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; Male; Normal Distribution; Oligonucleotide Array Sequence Analysis; Software},
	Pages = {117},
	Pmc = {PMC4495646},
	Pmid = {26040460},
	Pst = {epublish},
	Title = {quantro: a data-driven approach to guide the choice of an appropriate normalization method},
	Volume = {16},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/s13059-015-0679-0}}

@article{Bolstad2003,
	Abstract = {MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations.
RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably.
AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org.
SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html},
	Author = {Bolstad, B M and Irizarry, R A and Astrand, M and Speed, T P},
	Date-Added = {2016-08-04 15:22:01 +0000},
	Date-Modified = {2016-08-04 15:22:08 +0000},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Calibration; Models, Genetic; Molecular Probes; Nonlinear Dynamics; Oligonucleotide Array Sequence Analysis; Quality Control; Sequence Analysis, DNA; Stochastic Processes},
	Month = {Jan},
	Number = {2},
	Pages = {185-93},
	Pmid = {12538238},
	Pst = {ppublish},
	Title = {A comparison of normalization methods for high density oligonucleotide array data based on variance and bias},
	Volume = {19},
	Year = {2003}}
