@Article{Aaron2016,
author = {Aaron T. L. Lun and Davis J. McCarthy and John C. Marioni},
title = {A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor},
journal = {F1000Research},
year = {2016},
volume = {5},
pages = {2122},
doi = {10.12688/f1000research.9501.2},
}
@Manual{Melville2020,
    title = {uwot: The Uniform Manifold Approximation and Projection (UMAP) Method
for Dimensionality Reduction},
    author = {James Melville},
    year = {2020},
    note = {R package version 0.1.8},
    url = {https://CRAN.R-project.org/package=uwot},
}
@article{Maaten2014,
title = {Accelerating t-SNE using Tree-Based Algorithms},
volume = {15},
pages = {3221--3245},
year = {2014},
author = {L.J.P. {van der Maaten}},
journal = {Journal of Machine Learning Research},
}
@article{Maaten2008,
title = {Visualizing High-Dimensional Data Using t-SNE},
volume = {9},
pages = {2579--2605},
year = {2008},
author = {L.J.P. {van der Maaten} and G.E. Hinton},
journal = {Journal of Machine Learning Research},
}
@article{Kuleshov2016,
author = {Kuleshov, Maxim V. and Jones, Matthew R. and Rouillard, Andrew D. and Fernandez, Nicolas F. and Duan, Qiaonan and Wang, Zichen and Koplev, Simon and Jenkins, Sherry L. and Jagodnik, Kathleen M. and Lachmann, Alexander and McDermott, Michael G. and Monteiro, Caroline D. and Gundersen, Gregory W. and Ma'ayan, Avi},
doi = {10.1093/nar/gkw377},
issn = {13624962},
journal = {Nucleic acids research},
month = {jul},
number = {W1},
pages = {W90--W97},
pmid = {27141961},
publisher = {Oxford Academic},
title = {{Enrichr: a comprehensive gene set enrichment analysis web server 2016 update}},
url = {http://genome.ucsc.edu/ENCODE},
volume = {44},
year = {2016}
}
@article{Chen2013,
abstract = {Background: System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.Results: Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.Conclusions: Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr. {\textcopyright} 2013 Chen et al.; licensee BioMed Central Ltd.},
author = {Chen, Edward Y. and Tan, Christopher M. and Kou, Yan and Duan, Qiaonan and Wang, Zichen and Meirelles, Gabriela V. and Clark, Neil R. and Ma'ayan, Avi},
doi = {10.1186/1471-2105-14-128},
file = {:C$\backslash$:/Users/HP/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2013 - Enrichr Interactive and collaborative HTML5 gene list enrichment analysis tool.pdf:pdf},
issn = {14712105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Bioinformatics,Computational Biology/Bioinformatics,Computer Appl. in Life Sciences,Microarrays},
mendeley-groups = {SCTK},
month = {apr},
number = {1},
pages = {1--14},
pmid = {23586463},
publisher = {BioMed Central},
title = {{Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool}},
url = {http://amp.pharm.mssm.edu/Enrichr.},
volume = {14},
year = {2013}
}
@article{Hanzelmann2013,
author = {H{\"{a}}nzelmann, Sonja and Castelo, Robert and Guinney, Justin},
doi = {10.1186/1471-2105-14-7},
issn = {14712105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Bioinformatics,Computational Biology/Bioinformatics,Computer Appl. in Life Sciences,Microarrays},
mendeley-groups = {SCTK},
month = {jan},
number = {1},
pages = {7},
pmid = {23323831},
publisher = {BioMed Central},
title = {{GSVA: Gene set variation analysis for microarray and RNA-Seq data}},
url = {http://www.biomedcentral.com/1471-2105/14/7http://www.bioconductor.org.Background},
volume = {14},
year = {2013}
}
@article{Amezquita2020,
abstract = {Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.},
author = {Amezquita, Robert A. and Lun, Aaron T.L. and Becht, Etienne and Carey, Vince J. and Carpp, Lindsay N. and Geistlinger, Ludwig and Marini, Federico and Rue-Albrecht, Kevin and Risso, Davide and Soneson, Charlotte and Waldron, Levi and Pag{\`{e}}s, Herv{\'{e}} and Smith, Mike L. and Huber, Wolfgang and Morgan, Martin and Gottardo, Raphael and Hicks, Stephanie C.},
doi = {10.1038/s41592-019-0654-x},
issn = {15487105},
journal = {Nature Methods},
keywords = {Genomic analysis,Software},
mendeley-groups = {SCTK},
month = {feb},
number = {2},
pages = {137--145},
pmid = {31792435},
publisher = {Nature Research},
title = {{Orchestrating single-cell analysis with Bioconductor}},
url = {https://www.nature.com/articles/s41592-019-0654-x},
volume = {17},
year = {2020}
}
@Article{Hao2021,
  author = {Yuhan Hao and Stephanie Hao and Erica Andersen-Nissen and William M. Mauck III and Shiwei Zheng and Andrew Butler and Maddie J. Lee and Aaron J. Wilk and Charlotte Darby and Michael Zagar and Paul Hoffman and Marlon Stoeckius and Efthymia Papalexi and Eleni P. Mimitou and Jaison Jain and Avi Srivastava and Tim Stuart and Lamar B. Fleming and Bertrand Yeung and Angela J. Rogers and Juliana M. McElrath and Catherine A. Blish and Raphael Gottardo and Peter Smibert and Rahul Satija},
  title = {Integrated analysis of multimodal single-cell data},
  journal = {Cell},
  year = {2021},
  doi = {10.1016/j.cell.2021.04.048},
  url = {https://doi.org/10.1016/j.cell.2021.04.048},
}
@Article{Satija2017,
  author = {Rahul Satija and Jeffrey A Farrell and David Gennert and Alexander F Schier and Aviv Regev},
  title = {Spatial reconstruction of single-cell gene expression data},
  journal = {Nature Biotechnology},
  year = {2015},
  volume = {33},
  pages = {495-502},
  doi = {10.1038/nbt.3192},
  url = {https://doi.org/10.1038/nbt.3192},
}
@article{McCarthy2017,
abstract = {Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.},
author = {McCarthy, Davis J. and Campbell, Kieran R. and Lun, Aaron T.L. and Wills, Quin F.},
doi = {10.1093/bioinformatics/btw777},
issn = {14602059},
journal = {Bioinformatics},
month = {apr},
number = {8},
pages = {1179--1186},
pmid = {28088763},
publisher = {Oxford University Press},
title = {{Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R}},
url = {http://dx.doi.org/10.5281/zenodo.59897.},
volume = {33},
year = {2017}
}
@article{Butler2018,
abstract = {Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.},
author = {Butler, Andrew and Hoffman, Paul and Smibert, Peter and Papalexi, Efthymia and Satija, Rahul},
doi = {10.1038/nbt.4096},
issn = {15461696},
journal = {Nature Biotechnology},
keywords = {Computational biology and bioinformatics,Data integration,Statistical methods,Systems biology},
month = {jun},
number = {5},
pages = {411--420},
pmid = {29608179},
publisher = {Nature Publishing Group},
title = {{Integrating single-cell transcriptomic data across different conditions, technologies, and species}},
url = {https://www.nature.com/articles/nbt.4096},
volume = {36},
year = {2018}
}
@article{Stuart2019,
abstract = {Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to “anchor” diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.},
author = {Stuart, Tim and Butler, Andrew and Hoffman, Paul and Hafemeister, Christoph and Papalexi, Efthymia and Mauck, William M. and Hao, Yuhan and Stoeckius, Marlon and Smibert, Peter and Satija, Rahul},
doi = {10.1016/j.cell.2019.05.031},
file = {::},
issn = {10974172},
journal = {Cell},
keywords = {integration,multi-modal,scATAC-seq,scRNA-seq,single cell,single-cell ATAC sequencing,single-cell RNA sequencing},
month = {jun},
number = {7},
pages = {1888--1902.e21},
pmid = {31178118},
publisher = {Cell Press},
title = {{Comprehensive Integration of Single-Cell Data}},
url = {http://www.cell.com/article/S0092867419305598/fulltext http://www.cell.com/article/S0092867419305598/abstract https://www.cell.com/cell/abstract/S0092-8674(19)30559-8},
volume = {177},
year = {2019}
}
@article{Hafemeister2019,
abstract = {Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from "regularized negative binomial regression," where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.},
author = {Hafemeister, Christoph and Satija, Rahul},
doi = {10.1186/s13059-019-1874-1},
issn = {1474760X},
journal = {Genome Biology},
keywords = {Normalization,Single-cell RNA-seq},
month = {dec},
number = {1},
pages = {1--15},
pmid = {31870423},
publisher = {BioMed Central Ltd.},
title = {{Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression}},
url = {https://doi.org/10.1186/s13059-019-1874-1},
volume = {20},
year = {2019}
}
@article{Butler2018a,
abstract = {Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.},
author = {Butler, Andrew and Hoffman, Paul and Smibert, Peter and Papalexi, Efthymia and Satija, Rahul},
doi = {10.1038/nbt.4096},
issn = {15461696},
journal = {Nature Biotechnology},
keywords = {Computational biology and bioinformatics,Data integration,Statistical methods,Systems biology},
month = {jun},
number = {5},
pages = {411--420},
pmid = {29608179},
publisher = {Nature Publishing Group},
title = {{Integrating single-cell transcriptomic data across different conditions, technologies, and species}},
url = {https://www.nature.com/articles/nbt.4096},
volume = {36},
year = {2018}
}
@article{Polanski2020,
author = {PolaƄski, Krzysztof and Young, M.D. and Miao, Zhichao and Meyer, K.B. and Teichmann, S.A. and Park, J.E.},
doi = {10.1093/bioinformatics/btz625},
issn = {14602059},
journal = {Bioinformatics},
month = {aug},
pages = {964–-965},
pmid = {31400197},
publisher = {Oxford University Press},
title = {{BBKNN: fast batch alignment of single cell transcriptomes}},
url = {https://academic.oup.com/bioinformatics/article/36/3/964/5545955},
volume = {36},
year = {2020}
}
@article{ZhangY2020,
author = {Zhang, Yuqing and Parmigiani, Giovanni and Johnson, W.E.},
doi = {10.1093/nargab/lqaa078},
issn = {26319268},
journal = {NAR Genomics and Bioinformatics},
month = {sep},
pages = {lqaa078},
pmid = {33015620},
publisher = {Oxford University Press},
title = {{ComBat-seq: batch effect adjustment for RNA-seq count data}},
url = {https://academic.oup.com/nargab/article/2/3/lqaa078/5909519},
volume = {2},
year = {2020}
}
@article{Haghverdi2018,
doi = {10.1038/nbt.4091},
author = {Haghverdi, Laleh and Lun, A.T.L. and Morgan, M.D. and Marioni, J.C.},
journal = {Nature Biotechnology},
month = {apr},
pages = {421–-427},
pmid = {29608177},
publisher = {Nature Publishing Group},
title = {{Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors}},
url = {https://www.nature.com/articles/nbt.4091},
volume = {36},
year = {2018}
}
@article{Korsunsky2019,
author = {Korsunsky, Ilya and Millard, Nghia and Fan, Jean and Slowikowski, Kamil and Zhang, Fan and Wei, Kevin and Baglaenko, Yuriy and Brenner, Michael and Loh, Po-ru and Raychaudhuri, Soumya},
doi = {10.1038/s41592-019-0619-0},
issn = {15487105},
journal = {Nature Methods},
month = {nov},
pages = {1289–-1296},
pmid = {31740819},
publisher = {Nature Publishing Group},
title = {{ Fast, sensitive and accurate integration of single-cell data with Harmony}},
url = {https://www.nature.com/articles/s41592-019-0619-0},
volume = {16},
year = {2019}
}
@article{LiuL2020,
author = {Liu, Jialin and Gao, Chao and Sodicoff, Joshua and Kozareva, Velina and Macosko, E.Z. and Welch, J.D.},
doi = {10.1038/s41596-020-0391-8},
issn = {17502799},
journal = {Nature Protocols},
month = {oct},
pages = {3632-–3662},
publisher = {Nature Publishing Group},
title = {{Jointly defining cell types from multiple single-cell datasets using LIGER}},
url = {https://www.nature.com/articles/s41596-020-0391-8},
volume = {15},
year = {2020}
}
@article{Ritchie2015,
author = {Ritchie, Matthew E. and Phipson, Belinda and Wu, Di and Hu, Yifang and Law, Charity W. and Shi, Wei and Smyth, Gordon K.},
doi = {10.1093/nar/gkv007},
journal = {Nucleic Acids Research},
month = {apr},
pages = {e47},
title = {{limma powers differential expression analyses for RNA-sequencing and microarray studies}},
url = {https://academic.oup.com/nar/article/43/7/e47/2414268},
volume = {43},
issue = {7},
year = {2015}
}
@article{Hie2019,
author = {Hie, Brian and Bryson, Bryan and Berger, Bonnie},
doi = {10.1038/s41587-019-0113-3},
journal = {Nature Biotechnology},
month = {may},
pages = {685–-691},
title = {{Efficient integration of heterogeneous single-cell transcriptomes using Scanorama}},
url = {https://www.nature.com/articles/s41587-019-0113-3},
volume = {37},
year = {2019}
}
@article{LinY2019,
author = {Lin, Yingxin and Ghazanfar, Shila and Wang, Kevin Y. X. and Gagnon-Bartsch, Johann A. and Lo, Kitty K. and Su, Xianbin and Han, Ze-Guang and Ormerod, John T. and Speed, Terence P. and Yang, Pengyi and Yang, Jean Yee Hwa},
doi = {10.1073/pnas.1820006116},
journal = {PNAS},
month = {may},
pages = {9775--9784},
title = {{scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets}},
url = {https://www.pnas.org/content/116/20/9775},
volume = {116},
year = {2019}
}
@article{Risso2018,
author = {Risso, Davide and Perraudeau, Fanny and Gribkova, Svetlana and Dudoit, Sandrine and Vert, Jean-Philippe},
doi = {10.1038/s41467-017-02554-5},
journal = {Nature Communications},
month = {jan},
number = {284},
title = {{A general and flexible method for signal extraction from single-cell RNA-seq data}},
url = {https://www.nature.com/articles/s41467-017-02554-5},
volume = {9},
year = {2018}
}
@article{Finak2015,
author = {Finak, Greg and McDavid, Andrew and Yajima, Masanao and Deng, Jingyuan and Gersuk, Vivian and Shalek, Alex K. and Slichter, Chloe K. and Miller, Hannah W. and McElrath, M. Juliana and Prlic, Martin and Linsley, Peter S. and Gottardo, Raphael},
doi = {10.1186/s13059-015-0844-5},
journal = {Genome Biology},
month = {dec},
number = {278},
title = {{MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data}},
url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0844-5},
volume = {16},
year = {2015}
}
@article{Love2014,
author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
doi = {10.1186/s13059-014-0550-8},
journal = {Genome Biology},
month = {dec},
number = {550},
title = {{Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2}},
url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8},
volume = {15},
year = {2014}
}
@article{Forgy65,
author = {E. Forgy},
journal = {Biometrics},
pages = {768--780},
title = {{Cluster analysis of multivariate data: efficiency versus interpretability of classifications}},
volume = {21},
year = {1965}
}
@article{Gu2016,
author = {Gu, Zuguang and Eils, Roland and Schlesner, Matthias},
doi = {10.1093/bioinformatics/btw313},
journal = {Bioinformatics},
month = {sep},
pages = {2847-–2849},
title = {{Complex heatmaps reveal patterns and correlations in multidimensional genomic data}},
url = {https://academic.oup.com/bioinformatics/article/32/18/2847/1743594},
volume = {32},
issue = {18},
year = {2016}
}
@article{Zheng2017,
author = {Zheng, G.X.Y. and Terry, J.M. and Belgrader, Phillip and Ryvkin, Paul and Bent, Z.W. and Wilson, Ryan and Ziraldo, S.B. and Wheeler, T.D. and McDermott, G.P. and Zhu, Junjie and Gregory, M.T. and Shuga, Joe and Luz Montesclaros and Jason G. Underwood and Donald A. Masquelier and Stefanie Y. Nishimura and Michael Schnall-Levin and Paul W. Wyatt and Christopher M. Hindson and Rajiv Bharadwaj and Alexander Wong and Kevin D. Ness and Lan W. Beppu and H. Joachim Deeg and Christopher McFarland and Keith R. Loeb and William J. Valente and Nolan G. Ericson and Emily A. Stevens and Jerald P. Radich and Tarjei S. Mikkelsen and Benjamin J. Hindson and Jason H. Bielas},
doi = {10.1038/ncomms14049},
journal = {Nature Communications},
number = {14049},
title = {{Massively parallel digital transcriptional profiling of single cells}},
url = {https://www.nature.com/articles/ncomms14049},
volume = {8},
year = {2017}
}
@article{Petukhov2018,
author = {Petukhovm, Viktor and Guo, Jimin and Baryawno, Ninib and Severe, Nicolas and Scadden, D.T. and Samsonova, M.G. and Kharchenko, P.V.},
doi = {10.1186/s13059-018-1449-6},
journal = {Genome Biology},
number = {78},
title = {{dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments}},
url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1449-6},
volume = {19},
year = {2018}
}
@article{Melsted2019,
author = {Melsted, Páll and Ntranos, Vasilis and Pachter, Lior},
title = {{The barcode, UMI, set format and BUStools}},
journal = {Bioinformatics},
volume = {35},
issue = {21},
pages = {4472-–4473},
month = {nov},
year = {2019},
url = {https://academic.oup.com/bioinformatics/article/35/21/4472/5487510},
doi = {10.1093/bioinformatics/btz279},
}
@article{Dobin2013,
author = {Dobin, Alexander and Davis, C.A. and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, T.R.},
title = {{STAR: ultrafast universal RNA-seq aligner}},
journal = {Bioinformatics},
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