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@article{nelson:2015vaginal,
	Author = {Nelson, Tiffanie M and Borgogna, Joanna-Lynn C and Brotman, Rebecca M and Ravel, Jacques and Walk, Seth T and Yeoman, Carl J},
	Date-Added = {2018-09-01 11:57:22 +0100},
	Date-Modified = {2018-09-01 11:57:22 +0100},
	Journal = {Frontiers in physiology},
	Keywords = {confirms Jean},
	Publisher = {Frontiers Media SA},
	Title = {Vaginal biogenic amines: biomarkers of bacterial vaginosis or precursors to vaginal dysbiosis?},
	Volume = {6},
	Year = {2015}}

@article{Bullard:2010,
	Abstract = {BACKGROUND: High-throughput sequencing technologies, such as the {Illumina} Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of {Illumina} transcriptome sequencing (m{RNA-seq}) data.
RESULTS: We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.
CONCLUSIONS: Our results have significant practical and methodological implications for the design and analysis of m{RNA-seq} experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for m{RNA-seq}.},
	Author = {Bullard, James H and Purdom, Elizabeth and Hansen, Kasper D and Dudoit, Sandrine},
	Date-Added = {2018-09-01 10:40:22 +0100},
	Date-Modified = {2018-09-01 10:40:22 +0100},
	Doi = {10.1186/1471-2105-11-94},
	Journal = {BMC Bioinformatics},
	Journal-Full = {BMC bioinformatics},
	Mesh = {Computational Biology; Databases, Genetic; RNA, Messenger; Sequence Analysis, RNA},
	Pages = {94},
	Pmc = {PMC2838869},
	Pmid = {20167110},
	Pst = {epublish},
	Title = {Evaluation of statistical methods for normalization and differential expression in m{RNA-seq} experiments},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/1471-2105-11-94}}

@article{Dillies:2013,
	Abstract = {During the last 3 years, a number of approaches for the normalization of {RNA} sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of {RNA-seq} data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of {RNA-seq} data.},
	Author = {Dillies, Marie-Agn{\`e}s and Rau, Andrea and Aubert, Julie and Hennequet-Antier, Christelle and Jeanmougin, Marine and Servant, Nicolas and Keime, C{\'e}line and Marot, Guillemette and Castel, David and Estelle, Jordi and Guernec, Gregory and Jagla, Bernd and Jouneau, Luc and Lalo{\"e}, Denis and Le Gall, Caroline and Scha{\"e}ffer, Brigitte and Le Crom, St{\'e}phane and Guedj, Micka{\"e}l and Jaffr{\'e}zic, Florence and {French StatOmique Consortium}},
	Date-Added = {2018-09-01 10:40:22 +0100},
	Date-Modified = {2018-09-01 10:40:22 +0100},
	Doi = {10.1093/bib/bbs046},
	Journal = {Brief Bioinform},
	Journal-Full = {Briefings in bioinformatics},
	Keywords = {{RNA-seq}; differential analysis; high-throughput sequencing; normalization},
	Month = {Nov},
	Number = {6},
	Pages = {671-83},
	Pmid = {22988256},
	Pst = {ppublish},
	Title = {A comprehensive evaluation of normalization methods for {Illumina} high-throughput {RNA} sequencing data analysis},
	Volume = {14},
	Year = {2013},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bib/bbs046}}

@article{Anders:2013aa,
	Abstract = {RNA sequencing ({RNA-seq}) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical {RNA-seq} differential expression analysis workflow largely based on the free open-source {R} language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.},
	Author = {Anders, Simon and McCarthy, Davis J and Chen, Yunshun and Okoniewski, Michal and Smyth, Gordon K and Huber, Wolfgang and Robinson, Mark D},
	Date-Added = {2018-09-01 10:37:13 +0100},
	Date-Modified = {2018-09-01 10:37:13 +0100},
	Doi = {10.1038/nprot.2013.099},
	Journal = {Nat Protoc},
	Journal-Full = {Nature protocols},
	Month = {Sep},
	Number = {9},
	Pages = {1765-86},
	Pmid = {23975260},
	Pst = {ppublish},
	Title = {Count-based differential expression analysis of {RNA} sequencing data using {R} and {Bioconductor}},
	Volume = {8},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nprot.2013.099}}

@article{Anders:2010,
	Abstract = {High-throughput sequencing assays such as {RNA-seq}, {ChIP}-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.},
	Author = {Anders, Simon and Huber, Wolfgang},
	Date-Added = {2018-09-01 10:37:13 +0100},
	Date-Modified = {2018-09-01 10:37:13 +0100},
	Doi = {10.1186/gb-2010-11-10-r106},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {Animals; Binomial Distribution; Chromatin Immunoprecipitation; Computational Biology; Drosophila; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Linear Models; Models, Genetic; Saccharomyces cerevisiae; Sequence Analysis, RNA; Stem Cells; Tissue Culture Techniques},
	Number = {10},
	Pages = {R106},
	Pmid = {20979621},
	Pst = {ppublish},
	Title = {Differential expression analysis for sequence count data},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/gb-2010-11-10-r106}}

@article{altman:1983,
	Abstract = {Methods of analysis used in the comparison of two methods of measurement are reviewed. The use of correlation, regression and the difference between means is criticized. A simple parametric approach is proposed based on analysis of variance and simple graphical methods.},
	Author = {Altman, D. G. and Bland, J. M.},
	Copyright = {Copyright {\copyright} 1983 Royal Statistical Society},
	Date-Added = {2018-09-01 10:30:16 +0100},
	Date-Modified = {2018-09-01 10:30:16 +0100},
	Issn = {00390526},
	Journal = {Journal of the Royal Statistical Society. Series D (The Statistician)},
	Jstor_Articletype = {research-article},
	Jstor_Formatteddate = {Sep., 1983},
	Language = {English},
	Number = {3},
	Pages = {pp. 307-317},
	Publisher = {Wiley for the Royal Statistical Society},
	Title = {Measurement in Medicine: The Analysis of Method Comparison Studies},
	Url = {http://www.jstor.org/stable/2987937},
	Volume = {32},
	Year = {1983},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2987937}}

@inbook{lovell:2011,
	Address = {London},
	Author = {Lovell, David and M{\"u}ller, Warren and Taylor, Jen and Zwart, Alec and Helliwell, Chris},
	Chapter = {14},
	Date-Added = {2018-09-01 10:24:18 +0100},
	Date-Modified = {2018-09-01 10:24:18 +0100},
	Editor = {Pawlowsky-Glahn, Vera and Buccianti, Antonella},
	Journal = {Compositional Data Analysis: Theory and Applications},
	Pages = {193--207},
	Publisher = {John Wiley and Sons New York, NY},
	Title = {Proportions, percentages, ppm: do the molecular biosciences treat compositional data right?},
	Year = {2011}}

@article{Friedman:2012,
	Abstract = {High-throughput sequencing based techniques, such as 16S {rRNA} gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.},
	Author = {Friedman, Jonathan and Alm, Eric J},
	Date-Added = {2018-09-01 10:20:19 +0100},
	Date-Modified = {2018-09-01 10:20:19 +0100},
	Doi = {10.1371/journal.pcbi.1002687},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Mesh = {Chromosome Mapping; Computer Simulation; Genetics, Population; Genome, Bacterial; Humans; Microbial Consortia; Models, Genetic; Recombination, Genetic; Statistics as Topic},
	Number = {9},
	Pages = {e1002687},
	Pmc = {PMC3447976},
	Pmid = {23028285},
	Pst = {ppublish},
	Title = {Inferring correlation networks from genomic survey data},
	Volume = {8},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1002687}}

@book{pawlowsky2015modeling,
	Author = {Pawlowsky-Glahn, Vera and Egozcue, Juan Jos{\'e} and Tolosana-Delgado, Raimon},
	Date-Added = {2018-09-01 10:19:45 +0100},
	Date-Modified = {2018-09-01 10:19:45 +0100},
	Publisher = {John Wiley \& Sons},
	Title = {Modeling and Analysis of Compositional Data},
	Year = {2015}}

@book{pawlowsky2011compositional,
	Author = {Pawlowsky-Glahn, Vera and Buccianti, Antonella},
	Date-Added = {2018-09-01 10:19:45 +0100},
	Date-Modified = {2018-09-01 10:19:45 +0100},
	Publisher = {John Wiley \& Sons},
	Title = {Compositional data analysis: Theory and applications},
	Year = {2011}}

@article{Pawlowsky-Glahn:2006,
	Abstract = {Compositional data are those which contain only relative information. They are parts of some whole. In most cases they are recorded as closed data, i.e. data summing to a constant, such as 100% --- whole-rock geochemical data being classic examples. Compositional data have important and particular properties that preclude the application of standard statistical techniques on such data in raw form. Standard techniques are designed to be used with data that are free to range from − ∞ to + ∞. Compositional data are always positive and range only from 0 to 100, or any other constant, when given in closed form. If one component increases, others must, perforce, decrease, whether or not there is a genetic link between these components. This means that the results of standard statistical analysis of the relationships between raw components or parts in a compositional dataset are clouded by spurious effects. Although such analyses may give apparently interpretable results, they are, at best, approximations and need to be treated with considerable circumspection. The methods outlined in this volume are based on the premise that it is the relative variation of components which is of interest, rather than absolute variation. Log-ratios of components provide the natural means of studying compositional data. In this contribution the basic terms and operations are introduced using simple numerical examples to illustrate their computation and to familiarize the reader with their use.},
	Author = {Pawlowsky-Glahn, V. and Egozcue, J. J.},
	Date-Added = {2018-09-01 10:19:45 +0100},
	Date-Modified = {2018-09-01 10:19:45 +0100},
	Doi = {10.1144/GSL.SP.2006.264.01.01},
	Eprint = {http://sp.lyellcollection.org/content/264/1/1.full.pdf+html},
	Journal = {Geological Society, London, Special Publications},
	Number = {1},
	Pages = {1-10},
	Title = {Compositional data and their analysis: an introduction},
	Url = {http://sp.lyellcollection.org/content/264/1/1.abstract},
	Volume = {264},
	Year = {2006},
	Bdsk-Url-1 = {http://sp.lyellcollection.org/content/264/1/1.abstract},
	Bdsk-Url-2 = {http://dx.doi.org/10.1144/GSL.SP.2006.264.01.01}}

@article{Thorsen:2016aa,
	Abstract = {BACKGROUND: There is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources.
RESULTS: Running more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power.
CONCLUSIONS: Our results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets.},
	Author = {Thorsen, Jonathan and Brejnrod, Asker and Mortensen, Martin and Rasmussen, Morten A and Stokholm, Jakob and Al-Soud, Waleed Abu and S{\o}rensen, S{\o}ren and Bisgaard, Hans and Waage, Johannes},
	Date-Added = {2018-08-03 15:56:30 +0000},
	Date-Modified = {2018-08-03 15:56:30 +0000},
	Doi = {10.1186/s40168-016-0208-8},
	Journal = {Microbiome},
	Journal-Full = {Microbiome},
	Keywords = {16S sequencing; Benchmark; Beta-diversity; Differential relative abundance; Microbiome},
	Month = {Nov},
	Number = {1},
	Pages = {62},
	Pmc = {PMC5123278},
	Pmid = {27884206},
	Pst = {epublish},
	Title = {Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16{S} r{RNA} gene amplicon data analysis methods used in microbiome studies},
	Volume = {4},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/s40168-016-0208-8}}

@article{Soneson:2013,
	Abstract = {BACKGROUND: Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, {DNA} microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of c{DNA} ({RNA-seq}) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of {RNA-seq} for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of {RNA-seq} data.
RESULTS: We conducted an extensive comparison of eleven methods for differential expression analysis of {RNA-seq} data. All methods are freely available within the {R} framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real {RNA-seq} data.
CONCLUSIONS: Very small sample sizes, which are still common in {RNA-seq} experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the 'limma' method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.},
	Author = {Soneson, Charlotte and Delorenzi, Mauro},
	Date-Added = {2018-08-03 15:56:21 +0000},
	Date-Modified = {2018-08-03 15:56:21 +0000},
	Doi = {10.1186/1471-2105-14-91},
	Journal = {BMC Bioinformatics},
	Journal-Full = {BMC bioinformatics},
	Pages = {91},
	Pmc = {PMC3608160},
	Pmid = {23497356},
	Pst = {epublish},
	Title = {A comparison of methods for differential expression analysis of {RNA-seq} data},
	Volume = {14},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/1471-2105-14-91}}

@article{hawinkel2017,
	Author = {Hawinkel, Stijn and Mattiello, Federico and Bijnens, Luc and Thas, Olivier},
	Date-Added = {2018-08-03 15:56:10 +0000},
	Date-Modified = {2018-08-03 15:56:10 +0000},
	Issn = {1467-5463},
	Journal = {BRIEFINGS IN BIOINFORMATICS},
	Language = {eng},
	Title = {A broken promise : microbiome differential abundance methods do not control the false discovery rate},
	Url = {http://dx.doi.org/10.1093/bib/bbx104},
	Year = {2018},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bib/bbx104}}

@book{Aitchison:1986,
	Author = {Aitchison, J.},
	Date-Added = {2018-08-03 15:55:39 +0000},
	Date-Modified = {2018-08-03 15:55:39 +0000},
	Location = {London, England},
	Publisher = {Chapman \& Hall},
	Title = {The Statistical Analysis of Compositional Data},
	Year = {1986}}

@article{bian:2017,
	Abstract = {The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.},
	Author = {Bian, Gaorui and Gloor, Gregory B and Gong, Aihua and Jia, Changsheng and Zhang, Wei and Hu, Jun and Zhang, Hong and Zhang, Yumei and Zhou, Zhenqing and Zhang, Jiangao and Burton, Jeremy P and Reid, Gregor and Xiao, Yongliang and Zeng, Qiang and Yang, Kaiping and Li, Jiangang},
	Date = {2017 Sep-Oct},
	Date-Added = {2018-08-03 15:55:24 +0000},
	Date-Modified = {2018-08-03 15:55:24 +0000},
	Doi = {10.1128/mSphere.00327-17},
	Journal = {mSphere},
	Journal-Full = {mSphere},
	Keywords = {16S rRNA gene sequencing; DNA sequencing; compositional data; cross-sectional study; gut microbiota; healthy aging; microbiota},
	Number = {5},
	Pages = {e00327-17},
	Pmc = {PMC5615133},
	Pmid = {28959739},
	Pst = {epublish},
	Title = {The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young},
	Volume = {2},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1128/mSphere.00327-17}}

@article{gloorFrontiers:2017,
	Abstract = {Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.},
	Author = {Gloor, Gregory B. and Macklaim, Jean M. and Pawlowsky-Glahn, Vera and Egozcue, Juan J.},
	Date-Added = {2018-08-03 15:55:17 +0000},
	Date-Modified = {2018-08-03 15:55:17 +0000},
	Doi = {10.3389/fmicb.2017.02224},
	Issn = {1664-302X},
	Journal = {Frontiers in Microbiology},
	Pages = {2224},
	Title = {Microbiome Datasets Are Compositional: And This Is Not Optional},
	Url = {https://www.frontiersin.org/article/10.3389/fmicb.2017.02224},
	Volume = {8},
	Year = {2017},
	Bdsk-Url-1 = {https://www.frontiersin.org/article/10.3389/fmicb.2017.02224},
	Bdsk-Url-2 = {http://dx.doi.org/10.3389/fmicb.2017.02224}}

@article{Wolfs:2016aa,
	Abstract = {The CRISPR/Cas9 nuclease is commonly used to make gene knockouts. The blunt DNA ends generated by cleavage can be efficiently ligated by the classical nonhomologous end-joining repair pathway (c-NHEJ), regenerating the target site. This repair creates a cycle of cleavage, ligation, and target site regeneration that persists until sufficient modification of the DNA break by alternative NHEJ prevents further Cas9 cutting, generating a heterogeneous population of insertions and deletions typical of gene knockouts. Here, we develop a strategy to escape this cycle and bias events toward defined length deletions by creating an RNA-guided dual active site nuclease that generates two noncompatible DNA breaks at a target site, effectively deleting the majority of the target site such that it cannot be regenerated. The TevCas9 nuclease, a fusion of the I-TevI nuclease domain to Cas9, functions robustly in HEK293 cells and generates 33- to 36-bp deletions at frequencies up to 40%. Deep sequencing revealed minimal processing of TevCas9 products, consistent with protection of the DNA ends from exonucleolytic degradation and repair by the c-NHEJ pathway. Directed evolution experiments identified I-TevI variants with broadened targeting range, making TevCas9 an easy-to-use reagent. Our results highlight how the sequence-tolerant cleavage properties of the I-TevI homing endonuclease can be harnessed to enhance Cas9 applications, circumventing the cleavage and ligation cycle and biasing genome-editing events toward defined length deletions.},
	Author = {Wolfs, Jason M and Hamilton, Thomas A and Lant, Jeremy T and Laforet, Marcon and Zhang, Jenny and Salemi, Louisa M and Gloor, Gregory B and Schild-Poulter, Caroline and Edgell, David R},
	Date-Added = {2018-08-03 15:55:03 +0000},
	Date-Modified = {2018-08-03 15:55:03 +0000},
	Doi = {10.1073/pnas.1616343114},
	Journal = {Proc Natl Acad Sci U S A},
	Journal-Full = {Proceedings of the National Academy of Sciences of the United States of America},
	Keywords = {CRISPR/Cas9; I-TevI homing endonuclease; NHEJ; genome editing},
	Month = {Dec},
	Pmid = {27956611},
	Pst = {aheadofprint},
	Title = {Biasing genome-editing events toward precise length deletions with an RNA-guided TevCas9 dual nuclease},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1616343114}}

@article{mcmurrough:2014,
	Abstract = {The active sites of enzymes consist of residues necessary for catalysis and structurally important noncatalytic residues that together maintain the architecture and function of the active site. Examples of evolutionary interactions between catalytic and noncatalytic residues have been difficult to define and experimentally validate due to a general intolerance of these residues to substitution. Here, using computational methods to predict coevolving residues, we identify a network of positions consisting of two catalytic metal-binding residues and two adjacent noncatalytic residues in LAGLIDADG homing endonucleases (LHEs). Distinct combinations of the four residues in the network map to distinct LHE subfamilies, with a striking distribution of the metal-binding Asp (D) and Glu (E) residues. Mutation of these four positions in three LHEs--I-LtrI, I-OnuI, and I-HjeMI--indicate that the combinations of residues tolerated are specific to each enzyme. Kinetic analyses under single-turnover conditions revealed that I-LtrI activity could be modulated over an ∼100-fold range by mutation of residues in the coevolving network. I-LtrI catalytic site variants with low activity could be rescued by compensatory mutations at adjacent noncatalytic sites that restore an optimal coevolving network and vice versa. Our results demonstrate that LHE activity is constrained by an evolutionary barrier of residues with strong context-dependent effects. Creation of optimal coevolving active-site networks is therefore an important consideration in engineering of LHEs and other enzymes.},
	Author = {McMurrough, Thomas A and Dickson, Russell J and Thibert, Stephanie M F and Gloor, Gregory B and Edgell, David R},
	Date-Added = {2018-08-03 15:54:52 +0000},
	Date-Modified = {2018-08-03 15:54:52 +0000},
	Doi = {10.1073/pnas.1322352111},
	Journal = {Proc Natl Acad Sci U S A},
	Journal-Full = {Proceedings of the National Academy of Sciences of the United States of America},
	Keywords = {amino acid coevolution; genetic selection},
	Mesh = {Aspartic Acid; Binding Sites; Biocatalysis; Catalytic Domain; Endonucleases; Evolution, Molecular; Glutamic Acid; Models, Genetic; Models, Molecular; Mutation; Phylogeny; Protein Structure, Tertiary},
	Month = {Jun},
	Number = {23},
	Pages = {E2376-83},
	Pmc = {PMC4060692},
	Pmid = {24912189},
	Pst = {ppublish},
	Title = {Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues},
	Volume = {111},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1322352111}}

@article{Quinn:2018aa,
	Abstract = {BACKGROUND: Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2.
RESULTS: To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations.
CONCLUSIONS: Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.},
	Author = {Quinn, Thomas P and Crowley, Tamsyn M and Richardson, Mark F},
	Date-Added = {2018-08-03 15:54:42 +0000},
	Date-Modified = {2018-08-03 15:54:42 +0000},
	Doi = {10.1186/s12859-018-2261-8},
	Journal = {BMC Bioinformatics},
	Journal-Full = {BMC bioinformatics},
	Keywords = {CoDA; Compositional analysis; Compositional data; High-throughput sequencing analysis; RNA-Seq},
	Month = {Jul},
	Number = {1},
	Pages = {274},
	Pmc = {PMC6052553},
	Pmid = {30021534},
	Pst = {epublish},
	Title = {Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods},
	Volume = {19},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1186/s12859-018-2261-8}}

@article{Erb134536,
	Abstract = {Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. An R package containing a computationally efficient implementation of the approach is in preparation and will be released shortly as an addendum to the propr package.},
	Author = {Erb, Ionas and Quinn, Thomas and Lovell, David and Notredame, Cedric},
	Date-Added = {2018-08-03 15:54:42 +0000},
	Date-Modified = {2018-08-03 15:54:42 +0000},
	Doi = {10.1101/134536},
	Eprint = {https://www.biorxiv.org/content/early/2017/05/05/134536.full.pdf},
	Journal = {bioRxiv},
	Publisher = {Cold Spring Harbor Laboratory},
	Title = {Differential Proportionality - A Normalization-Free Approach To Differential Gene Expression},
	Url = {https://www.biorxiv.org/content/early/2017/05/05/134536},
	Year = {2017},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/early/2017/05/05/134536},
	Bdsk-Url-2 = {https://dx.doi.org/10.1101/134536}}

@article{Quinn:2017,
	Abstract = {In the life sciences, many assays measure only the relative abundances of components for each sample. These data, called compositional data, require special handling in order to avoid misleading conclusions. For example, in the case of correlation, treating relative data like absolute data can lead to the discovery of falsely positive associations. Recently, researchers have proposed proportionality as a valid alternative to correlation for calculating pairwise association in relative data. Although the question of how to best measure proportionality remains open, we present here a computationally efficient R package that implements two proposed measures of proportionality. In an effort to advance the understanding and application of proportionality analysis, we review the mathematics behind proportionality, demonstrate its application to genomic data, and discuss some ongoing challenges in the analysis of relative abundance data.},
	Author = {Quinn, Thomas and Richardson, Mark F and Lovell, David and Crowley, Tamsyn},
	Date-Added = {2018-08-03 15:54:42 +0000},
	Date-Modified = {2018-08-03 15:54:42 +0000},
	Doi = {10.1101/104935},
	Eprint = {http://biorxiv.org/content/early/2017/02/01/104935.full.pdf},
	Journal = {bioRxiv},
	Publisher = {Cold Spring Harbor Labs Journals},
	Title = {propr: An {R}-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis},
	Url = {http://biorxiv.org/content/early/2017/02/01/104935},
	Year = {2017},
	Bdsk-Url-1 = {http://biorxiv.org/content/early/2017/02/01/104935},
	Bdsk-Url-2 = {http://dx.doi.org/10.1101/104935}}

@article{Quinn206425,
	Abstract = {Motivation: Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models. Results: The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.},
	Author = {Quinn, Thomas P. and Erb, Ionas and Richardson, Mark F. and Crowley, Tamsyn M.},
	Date-Added = {2018-08-03 15:54:42 +0000},
	Date-Modified = {2018-08-03 15:54:42 +0000},
	Doi = {10.1101/206425},
	Eprint = {https://www.biorxiv.org/content/early/2017/10/19/206425.full.pdf},
	Journal = {bioRxiv},
	Publisher = {Cold Spring Harbor Laboratory},
	Title = {Understanding sequencing data as compositions: an outlook and review},
	Url = {https://www.biorxiv.org/content/early/2017/10/19/206425},
	Year = {2017},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/early/2017/10/19/206425},
	Bdsk-Url-2 = {http://dx.doi.org/10.1101/206425}}

@article{Schurch:2016aa,
	Abstract = {RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.},
	Author = {Schurch, Nicholas J and Schofield, Piet{\'a} and Gierli{\'n}ski, Marek and Cole, Christian and Sherstnev, Alexander and Singh, Vijender and Wrobel, Nicola and Gharbi, Karim and Simpson, Gordon G and Owen-Hughes, Tom and Blaxter, Mark and Barton, Geoffrey J},
	Date-Added = {2018-08-03 15:54:27 +0000},
	Date-Modified = {2018-08-03 15:54:27 +0000},
	Doi = {10.1261/rna.053959.115},
	Journal = {RNA},
	Journal-Full = {RNA (New York, N.Y.)},
	Keywords = {RNA-seq; benchmarking; differential expression; experimental design; replication; statistical power; yeast},
	Month = {Jun},
	Number = {6},
	Pages = {839-51},
	Pmc = {PMC4878611},
	Pmid = {27022035},
	Pst = {ppublish},
	Title = {How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?},
	Volume = {22},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1261/rna.053959.115}}

@article{Gierlinski:2015aa,
	Abstract = {MOTIVATION: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.
RESULTS: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of 'bad' replicates, which can drastically affect the gene read-count distribution.
AVAILABILITY AND IMPLEMENTATION: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.
CONTACT: g.j.barton@dundee.ac.uk.},
	Author = {Gierli{\'n}ski, Marek and Cole, Christian and Schofield, Piet{\`a} and Schurch, Nicholas J and Sherstnev, Alexander and Singh, Vijender and Wrobel, Nicola and Gharbi, Karim and Simpson, Gordon and Owen-Hughes, Tom and Blaxter, Mark and Barton, Geoffrey J},
	Date-Added = {2018-08-03 15:54:27 +0000},
	Date-Modified = {2018-08-03 15:54:27 +0000},
	Doi = {10.1093/bioinformatics/btv425},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Month = {Jul},
	Number = {22},
	Pages = {3625-3630},
	Pmid = {26206307},
	Pst = {aheadofprint},
	Title = {Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment},
	Volume = {31},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btv425}}

@article{gloorAJS:2016,
	Author = {Gloor, Gregory B and Macklaim, Jean M and Vu, Michael and Fernandes, Andrew D},
	Date-Added = {2018-08-03 15:54:07 +0000},
	Date-Modified = {2018-08-03 15:54:07 +0000},
	Doi = {doi:10.17713/ajs.v45i4.122},
	Journal = {Austrian Journal of Statistics},
	Month = {September},
	Pages = {73-87},
	Title = {Compositional uncertainty should not be ignored in high-throughput sequencing data analysis},
	Volume = {45},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.17713/ajs.v45i4.122}}

@article{gloor:effect,
	Author = {Gloor, Gregory B and Macklaim, Jean M. and Fernandes, Andrew D.},
	Date-Added = {2018-08-03 15:54:07 +0000},
	Date-Modified = {2018-08-03 15:54:07 +0000},
	Doi = {10.1080/10618600.2015.1131161},
	Eprint = {http://dx.doi.org/10.1080/10618600.2015.1131161},
	Journal = {Journal of Computational and Graphical Statistics},
	Number = {3C},
	Pages = {971-979},
	Title = {Displaying Variation in Large Datasets: Plotting a Visual Summary of Effect Sizes},
	Url = {http://dx.doi.org/10.1080/10618600.2015.1131161},
	Volume = {25},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1080/10618600.2015.1131161}}

@article{fernandes:2014,
	Abstract = {BACKGROUND: Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible.
RESULTS: Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples.
CONCLUSION: Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.},
	Author = {Fernandes, Andrew D and Reid, Jennifer Ns and Macklaim, Jean M and McMurrough, Thomas A and Edgell, David R and Gloor, Gregory B},
	Date-Added = {2018-08-03 15:54:07 +0000},
	Date-Modified = {2018-08-03 15:54:07 +0000},
	Doi = {10.1186/2049-2618-2-15},
	Journal = {Microbiome},
	Journal-Full = {Microbiome},
	Keywords = {16S rRNA gene sequencing; Dirichlet distribution; Monte Carlo sampling; RNA-seq; centered log-ratio transformation; compositional data; differential abundance; high-throughput sequencing; microbiome},
	Pages = {15.1--15.13},
	Pmc = {PMC4030730},
	Pmid = {24910773},
	Pst = {epublish},
	Title = {Unifying the analysis of high-throughput sequencing datasets: characterizing {RNA}-seq, 16{S} r{RNA} gene sequencing and selective growth experiments by compositional data analysis},
	Volume = {2},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/2049-2618-2-15}}

@article{fernandes:2013,
	Abstract = {Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.},
	Author = {Fernandes, Andrew D and Macklaim, Jean M and Linn, Thomas G and Reid, Gregor and Gloor, Gregory B},
	Date-Added = {2018-08-03 15:54:07 +0000},
	Date-Modified = {2018-08-03 15:54:07 +0000},
	Doi = {10.1371/journal.pone.0067019},
	Journal = {PLoS One},
	Journal-Full = {PloS one},
	Mesh = {Analysis of Variance; Bacillus cereus; Gardnerella vaginalis; Gene Expression; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; Kidney; Lactobacillus; Liver; Megasphaera; Metagenome; Prevotella; RNA; RNA, Bacterial; Reproducibility of Results; Sample Size; Sequence Analysis, RNA},
	Number = {7},
	Pages = {e67019},
	Pmc = {PMC3699591},
	Pmid = {23843979},
	Pst = {epublish},
	Title = {ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq},
	Volume = {8},
	Year = {2013},
	Bdsk-Url-1 = {https://dx.doi.org/10.1371/journal.pone.0067019}}

@article{macklaim:2013,
	Author = {Macklaim, M Jean and Fernandes, D Andrew and Di Bella, M Julia and Hammond, Jo-Anne and Reid ,Gregor and Gloor, Gregory B},
	Date-Added = {2018-08-03 15:54:07 +0000},
	Date-Modified = {2018-08-03 15:54:07 +0000},
	Doi = {doi: 10.1186/2049-2618-1-12},
	Journal = {Microbiome},
	Pages = {15},
	Title = {Comparative meta-{RNA}-seq of the vaginal microbiota and differential expression by \textit{{L}actobacillus iners} in health and dysbiosis},
	Volume = {1},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/2049-2618-1-12}}
