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@ARTICLE{Liu2021,
    title = "{DNA} methylation atlas of the mouse brain at single-cell
              resolution",
    author = "Liu, Hanqing and Zhou, Jingtian and Tian, Wei and Luo, Chongyuan
              and Bartlett, Anna and Aldridge, Andrew and Lucero, Jacinta and
              Osteen, Julia K and Nery, Joseph R and Chen, Huaming and Rivkin,
              Angeline and Castanon, Rosa G and Clock, Ben and Li, Yang Eric
              and Hou, Xiaomeng and Poirion, Olivier B and Preissl, Sebastian
              and Pinto-Duarte, Antonio and O'Connor, Carolyn and Boggeman,
              Lara and Fitzpatrick, Conor and Nunn, Michael and Mukamel, Eran A
              and Zhang, Zhuzhu and Callaway, Edward M and Ren, Bing and Dixon,
              Jesse R and Behrens, M Margarita and Ecker, Joseph R",
    abstract = "Mammalian brain cells show remarkable diversity in gene
              expression, anatomy and function, yet the regulatory DNA
              landscape underlying this extensive heterogeneity is poorly
              understood. Here we carry out a comprehensive assessment of the
              epigenomes of mouse brain cell types by applying single-nucleus
              DNA methylation sequencing1,2 to profile 103,982 nuclei
              (including 95,815 neurons and 8,167 non-neuronal cells) from 45
              regions of the mouse cortex, hippocampus, striatum, pallidum and
              olfactory areas. We identified 161 cell clusters with distinct
              spatial locations and projection targets. We constructed
              taxonomies of these epigenetic types, annotated with signature
              genes, regulatory elements and transcription factors. These
              features indicate the potential regulatory landscape supporting
              the assignment of putative cell types and reveal repetitive usage
              of regulators in excitatory and inhibitory cells for determining
              subtypes. The DNA methylation landscape of excitatory neurons in
              the cortex and hippocampus varied continuously along spatial
              gradients. Using this deep dataset, we constructed an artificial
              neural network model that precisely predicts single neuron
              cell-type identity and brain area spatial location. Integration
              of high-resolution DNA methylomes with single-nucleus chromatin
              accessibility data3 enabled prediction of high-confidence
              enhancer-gene interactions for all identified cell types, which
              were subsequently validated by cell-type-specific chromatin
              conformation capture experiments4. By combining multi-omic
              datasets (DNA methylation, chromatin contacts, and open
              chromatin) from single nuclei and annotating the regulatory
              genome of hundreds of cell types in the mouse brain, our DNA
              methylation atlas establishes the epigenetic basis for neuronal
              diversity and spatial organization throughout the mouse cerebrum.",
    journal = "Nature",
    volume = 598,
    number = 7879,
    pages = "120--128",
    month = oct,
    year = 2021,
    language = "en",
}

@ARTICLE{He2020,
    title = "Spatiotemporal {DNA} methylome dynamics of the developing mouse
              fetus",
    author = "He, Yupeng and Hariharan, Manoj and Gorkin, David U and Dickel,
              Diane E and Luo, Chongyuan and Castanon, Rosa G and Nery, Joseph
              R and Lee, Ah Young and Zhao, Yuan and Huang, Hui and Williams,
              Brian A and Trout, Diane and Amrhein, Henry and Fang, Rongxin and
              Chen, Huaming and Li, Bin and Visel, Axel and Pennacchio, Len A
              and Ren, Bing and Ecker, Joseph R",
    abstract = "Cytosine DNA methylation is essential for mammalian development
              but understanding of its spatiotemporal distribution in the
              developing embryo remains limited1,2. Here, as part of the mouse
              Encyclopedia of DNA Elements (ENCODE) project, we profiled 168
              methylomes from 12 mouse tissues or organs at 9 developmental
              stages from embryogenesis to adulthood. We identified 1,808,810
              genomic regions that showed variations in CG methylation by
              comparing the methylomes of different tissues or organs from
              different developmental stages. These DNA elements predominantly
              lose CG methylation during fetal development, whereas the trend
              is reversed after birth. During late stages of fetal development,
              non-CG methylation accumulated within the bodies of key
              developmental transcription factor genes, coinciding with their
              transcriptional repression. Integration of genome-wide DNA
              methylation, histone modification and chromatin accessibility
              data enabled us to predict 461,141 putative developmental
              tissue-specific enhancers, the human orthologues of which were
              enriched for disease-associated genetic variants. These
              spatiotemporal epigenome maps provide a resource for studies of
              gene regulation during tissue or organ progression, and a
              starting point for investigating regulatory elements that are
              involved in human developmental disorders.",
    journal = "Nature",
    volume = 583,
    number = 7818,
    pages = "752--759",
    month = jul,
    year = 2020,
    language = "en"
}

@ARTICLE{Schultz2015,
    title = "Human body epigenome maps reveal noncanonical {DNA} methylation
              variation",
    author = "Schultz, Matthew D and He, Yupeng and Whitaker, John W and
              Hariharan, Manoj and Mukamel, Eran A and Leung, Danny and
              Rajagopal, Nisha and Nery, Joseph R and Urich, Mark A and Chen,
              Huaming and Lin, Shin and Lin, Yiing and Jung, Inkyung and
              Schmitt, Anthony D and Selvaraj, Siddarth and Ren, Bing and
              Sejnowski, Terrence J and Wang, Wei and Ecker, Joseph R",
    abstract = "Understanding the diversity of human tissues is fundamental to
              disease and requires linking genetic information, which is
              identical in most of an individual's cells, with epigenetic
              mechanisms that could have tissue-specific roles. Surveys of DNA
              methylation in human tissues have established a complex landscape
              including both tissue-specific and invariant methylation
              patterns. Here we report high coverage methylomes that catalogue
              cytosine methylation in all contexts for the major human organ
              systems, integrated with matched transcriptomes and genomic
              sequence. By combining these diverse data types with each
              individuals' phased genome, we identified widespread
              tissue-specific differential CG methylation (mCG), partially
              methylated domains, allele-specific methylation and
              transcription, and the unexpected presence of non-CG methylation
              (mCH) in almost all human tissues. mCH correlated with
              tissue-specific functions, and using this mark, we made novel
              predictions of genes that escape X-chromosome inactivation in
              specific tissues. Overall, DNA methylation in several genomic
              contexts varies substantially among human tissues.",
    journal = "Nature",
    volume = 523,
    number = 7559,
    pages = "212--216",
    month = jul,
    year = 2015,
    language = "en"
}


@ARTICLE{Vierstra2020,
    title = "Global reference mapping of human transcription factor footprints",
    author = "Vierstra, Jeff and Lazar, John and Sandstrom, Richard and Halow,
              Jessica and Lee, Kristen and Bates, Daniel and Diegel, Morgan and
              Dunn, Douglas and Neri, Fidencio and Haugen, Eric and Rynes, Eric
              and Reynolds, Alex and Nelson, Jemma and Johnson, Audra and
              Frerker, Mark and Buckley, Michael and Kaul, Rajinder and
              Meuleman, Wouter and Stamatoyannopoulos, John A",
    abstract = "Combinatorial binding of transcription factors to regulatory DNA
              underpins gene regulation in all organisms. Genetic variation in
              regulatory regions has been connected with diseases and diverse
              phenotypic traits1, but it remains challenging to distinguish
              variants that affect regulatory function2. Genomic DNase I
              footprinting enables the quantitative, nucleotide-resolution
              delineation of sites of transcription factor occupancy within
              native chromatin3-6. However, only a small fraction of such sites
              have been precisely resolved on the human genome sequence6. Here,
              to enable comprehensive mapping of transcription factor
              footprints, we produced high-density DNase I cleavage maps from
              243 human cell and tissue types and states and integrated these
              data to delineate about 4.5 million compact genomic elements that
              encode transcription factor occupancy at nucleotide resolution.
              We map the fine-scale structure within about 1.6 million DNase
              I-hypersensitive sites and show that the overwhelming majority
              are populated by well-spaced sites of single transcription
              factor-DNA interaction. Cell-context-dependent cis-regulation is
              chiefly executed by wholesale modulation of accessibility at
              regulatory DNA rather than by differential transcription factor
              occupancy within accessible elements. We also show that the
              enrichment of genetic variants associated with diseases or
              phenotypic traits in regulatory regions1,7 is almost entirely
              attributable to variants within footprints, and that functional
              variants that affect transcription factor occupancy are nearly
              evenly partitioned between loss- and gain-of-function alleles.
              Unexpectedly, we find increased density of human genetic
              variation within transcription factor footprints, revealing an
              unappreciated driver of cis-regulatory evolution. Our results
              provide a framework for both global and nucleotide-precision
              analyses of gene regulatory mechanisms and functional genetic
              variation.",
    journal = "Nature",
    volume = 583,
    number = 7818,
    pages = "729--736",
    month = jul,
    year = 2020,
    language = "en"
}



@ARTICLE{Khan2018,
    title = "{JASPAR} 2018: update of the open-access database of
              transcription factor binding profiles and its web framework",
    author = "Khan, Aziz and Fornes, Oriol and Stigliani, Arnaud and Gheorghe,
              Marius and Castro-Mondragon, Jaime A and van der Lee, Robin and
              Bessy, Adrien and Ch{\`e}neby, Jeanne and Kulkarni, Shubhada R
              and Tan, Ge and Baranasic, Damir and Arenillas, David J and
              Sandelin, Albin and Vandepoele, Klaas and Lenhard, Boris and
              Ballester, Beno{\^\i}t and Wasserman, Wyeth W and Parcy, Fran{\c
              c}ois and Mathelier, Anthony",
    abstract = "JASPAR (http://jaspar.genereg.net) is an open-access database of
              curated, non-redundant transcription factor (TF)-binding profiles
              stored as position frequency matrices (PFMs) and TF flexible
              models (TFFMs) for TFs across multiple species in six taxonomic
              groups. In the 2018 release of JASPAR, the CORE collection has
              been expanded with 322 new PFMs (60 for vertebrates and 262 for
              plants) and 33 PFMs were updated (24 for vertebrates, 8 for
              plants and 1 for insects). These new profiles represent a 30\%
              expansion compared to the 2016 release. In addition, we have
              introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3
              for insects). This release incorporates clusters of similar PFMs
              in each taxon and each TF class per taxon. The JASPAR 2018 CORE
              vertebrate collection of PFMs was used to predict TF-binding
              sites in the human genome. The predictions are made available to
              the scientific community through a UCSC Genome Browser track data
              hub. Finally, this update comes with a new web framework with an
              interactive and responsive user-interface, along with new
              features. All the underlying data can be retrieved
              programmatically using a RESTful API and through the JASPAR 2018
              R/Bioconductor package.",
    journal = "Nucleic Acids Res.",
    volume = 46,
    number = "D1",
    pages = "D260--D266",
    month = jan,
    year = 2018,
    language = "en"
}



@ARTICLE{Kulakovskiy2018,
    title = "{HOCOMOCO}: towards a complete collection of transcription factor
              binding models for human and mouse via large-scale {ChIP-Seq}
              analysis",
    author = "Kulakovskiy, Ivan V and Vorontsov, Ilya E and Yevshin, Ivan S and
              Sharipov, Ruslan N and Fedorova, Alla D and Rumynskiy, Eugene I
              and Medvedeva, Yulia A and Magana-Mora, Arturo and Bajic,
              Vladimir B and Papatsenko, Dmitry A and Kolpakov, Fedor A and
              Makeev, Vsevolod J",
    abstract = "We present a major update of the HOCOMOCO collection that
              consists of patterns describing DNA binding specificities for
              human and mouse transcription factors. In this release, we
              profited from a nearly doubled volume of published in vivo
              experiments on transcription factor (TF) binding to expand the
              repertoire of binding models, replace low-quality models
              previously based on in vitro data only and cover more than a
              hundred TFs with previously unknown binding specificities. This
              was achieved by systematic motif discovery from more than five
              thousand ChIP-Seq experiments uniformly processed within the
              BioUML framework with several ChIP-Seq peak calling tools and
              aggregated in the GTRD database. HOCOMOCO v11 contains binding
              models for 453 mouse and 680 human transcription factors and
              includes 1302 mononucleotide and 576 dinucleotide position weight
              matrices, which describe primary binding preferences of each
              transcription factor and reliable alternative binding
              specificities. An interactive interface and bulk downloads are
              available on the web: http://hocomoco.autosome.ru and
              http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we
              complement HOCOMOCO by MoLoTool (Motif Location Toolbox,
              http://molotool.autosome.ru) that applies HOCOMOCO models for
              visualization of binding sites in short DNA sequences.",
    journal = "Nucleic Acids Res.",
    volume = 46,
    number = "D1",
    pages = "D252--D259",
    month = jan,
    year = 2018,
    language = "en"
}

@ARTICLE{Jolma2013,
    title = "{DNA-binding} specificities of human transcription factors",
    author = "Jolma, Arttu and Yan, Jian and Whitington, Thomas and Toivonen,
              Jarkko and Nitta, Kazuhiro R and Rastas, Pasi and Morgunova,
              Ekaterina and Enge, Martin and Taipale, Mikko and Wei, Gonghong
              and Palin, Kimmo and Vaquerizas, Juan M and Vincentelli, Renaud
              and Luscombe, Nicholas M and Hughes, Timothy R and Lemaire,
              Patrick and Ukkonen, Esko and Kivioja, Teemu and Taipale, Jussi",
    abstract = "Although the proteins that read the gene regulatory code,
              transcription factors (TFs), have been largely identified, it is
              not well known which sequences TFs can recognize. We have
              analyzed the sequence-specific binding of human TFs using
              high-throughput SELEX and ChIP sequencing. A total of 830 binding
              profiles were obtained, describing 239 distinctly different
              binding specificities. The models represent the majority of human
              TFs, approximately doubling the coverage compared to existing
              systematic studies. Our results reveal additional specificity
              determinants for a large number of factors for which a partial
              specificity was known, including a commonly observed A- or T-rich
              stretch that flanks the core motifs. Global analysis of the data
              revealed that homodimer orientation and spacing preferences, and
              base-stacking interactions, have a larger role in TF-DNA binding
              than previously appreciated. We further describe a binding model
              incorporating these features that is required to understand
              binding of TFs to DNA.",
    journal = "Cell",
    volume = 152,
    number = "1-2",
    pages = "327--339",
    month = jan,
    year = 2013,
    language = "en"
}



@ARTICLE{La_Manno2021,
    title = "Molecular architecture of the developing mouse brain",
    author = "La Manno, Gioele and Siletti, Kimberly and Furlan, Alessandro and
              Gyllborg, Daniel and Vinsland, Elin and Mossi Albiach, Alejandro
              and Mattsson Langseth, Christoffer and Khven, Irina and Lederer,
              Alex R and Dratva, Lisa M and Johnsson, Anna and Nilsson, Mats
              and L{\"o}nnerberg, Peter and Linnarsson, Sten",
    abstract = "The mammalian brain develops through a complex interplay of
              spatial cues generated by diffusible morphogens, cell-cell
              interactions and intrinsic genetic programs that result in
              probably more than a thousand distinct cell types. A complete
              understanding of this process requires a systematic
              characterization of cell states over the entire spatiotemporal
              range of brain development. The ability of single-cell RNA
              sequencing and spatial transcriptomics to reveal the molecular
              heterogeneity of complex tissues has therefore been particularly
              powerful in the nervous system. Previous studies have explored
              development in specific brain regions1-8, the whole adult brain9
              and even entire embryos10. Here we report a comprehensive
              single-cell transcriptomic atlas of the embryonic mouse brain
              between gastrulation and birth. We identified almost eight
              hundred cellular states that describe a developmental program for
              the functional elements of the brain and its enclosing membranes,
              including the early neuroepithelium, region-specific secondary
              organizers, and both neurogenic and gliogenic progenitors. We
              also used in situ mRNA sequencing to map the spatial expression
              patterns of key developmental genes. Integrating the in situ data
              with our single-cell clusters revealed the precise spatial
              organization of neural progenitors during the patterning of the
              nervous system.",
    journal = "Nature",
    month = jul,
    year = 2021,
    language = "en"
}


@ARTICLE{Zeisel2018,
    title = "Molecular Architecture of the Mouse Nervous System",
    author = "Zeisel, Amit and Hochgerner, Hannah and L{\"o}nnerberg, Peter and
              Johnsson, Anna and Memic, Fatima and van der Zwan, Job and
              H{\"a}ring, Martin and Braun, Emelie and Borm, Lars E and La
              Manno, Gioele and Codeluppi, Simone and Furlan, Alessandro and
              Lee, Kawai and Skene, Nathan and Harris, Kenneth D and
              Hjerling-Leffler, Jens and Arenas, Ernest and Ernfors, Patrik and
              Marklund, Ulrika and Linnarsson, Sten",
    abstract = "The mammalian nervous system executes complex behaviors
              controlled by specialized, precisely positioned, and interacting
              cell types. Here, we used RNA sequencing of half a million single
              cells to create a detailed census of cell types in the mouse
              nervous system. We mapped cell types spatially and derived a
              hierarchical, data-driven taxonomy. Neurons were the most diverse
              and were grouped by developmental anatomical units and by the
              expression of neurotransmitters and neuropeptides. Neuronal
              diversity was driven by genes encoding cell identity, synaptic
              connectivity, neurotransmission, and membrane conductance. We
              discovered seven distinct, regionally restricted astrocyte types
              that obeyed developmental boundaries and correlated with the
              spatial distribution of key glutamate and glycine
              neurotransmitters. In contrast, oligodendrocytes showed a loss of
              regional identity followed by a secondary diversification. The
              resource presented here lays a solid foundation for understanding
              the molecular architecture of the mammalian nervous system and
              enables genetic manipulation of specific cell types.",
    journal = "Cell",
    volume = 174,
    number = 4,
    pages = "999--1014.e22",
    month = aug,
    year = 2018,
    keywords = "RNA sequencing; cell type; classification; single-cell
              transcriptomics; transcriptomics",
    language = "en"
}


@ARTICLE{Kobak2019,
    title = "The art of using {t-SNE} for single-cell transcriptomics",
    author = "Kobak, Dmitry and Berens, Philipp",
    abstract = "Single-cell transcriptomics yields ever growing data sets
              containing RNA expression levels for thousands of genes from up
              to millions of cells. Common data analysis pipelines include a
              dimensionality reduction step for visualising the data in two
              dimensions, most frequently performed using t-distributed
              stochastic neighbour embedding (t-SNE). It excels at revealing
              local structure in high-dimensional data, but naive applications
              often suffer from severe shortcomings, e.g. the global structure
              of the data is not represented accurately. Here we describe how
              to circumvent such pitfalls, and develop a protocol for creating
              more faithful t-SNE visualisations. It includes PCA
              initialisation, a high learning rate, and multi-scale similarity
              kernels; for very large data sets, we additionally use
              exaggeration and downsampling-based initialisation. We use
              published single-cell RNA-seq data sets to demonstrate that this
              protocol yields superior results compared to the naive
              application of t-SNE.",
    journal = "Nat. Commun.",
    volume = 10,
    number = 1,
    pages = "5416",
    month = nov,
    year = 2019,
    language = "en"
}


@ARTICLE{Policar2019,
    title = "{openTSNE}: a modular Python library for {t-SNE} dimensionality
              reduction and embedding",
    author = "Poli{\v c}ar, Pavlin G and Stra{\v z}ar, Martin and Zupan, Bla{\v
              z}",
    abstract = "Abstract Summary Point-based visualisations of large,
              multi-dimensional data from molecular biology can reveal
              meaningful clusters. One of the most popular techniques to
              construct such visualisations is t-distributed stochastic
              neighbor embedding (t-SNE), for which a number of extensions have
              recently been proposed to address issues of scalability and the
              quality of the resulting visualisations. We introduce openTSNE, a
              modular Python library that implements the core t-SNE algorithm
              and its extensions. The library is orders of magnitude faster
              than existing popular implementations, including those from
              scikit-learn. Unique to openTSNE is also the mapping of new data
              to existing embeddings, which can surprisingly assist in solving
              batch effects.Availability openTSNE is available at
              https://github.com/pavlin-policar/openTSNE.Contact
              pavlin.policar@fri.uni-lj.si, blaz.zupan@fri.uni-lj.si",
    journal = "bioRxiv",
    pages = "731877",
    month = aug,
    year = 2019,
    language = "en"
}


@ARTICLE{Tabix,
    title = "Tabix: fast retrieval of sequence features from generic
              {TAB-delimited} files",
    author = "Li, Heng",
    abstract = "UNLABELLED: Tabix is the first generic tool that indexes position
              sorted files in TAB-delimited formats such as GFF, BED, PSL, SAM
              and SQL export, and quickly retrieves features overlapping
              specified regions. Tabix features include few seek function calls
              per query, data compression with gzip compatibility and direct
              FTP/HTTP access. Tabix is implemented as a free command-line tool
              as well as a library in C, Java, Perl and Python. It is
              particularly useful for manually examining local genomic features
              on the command line and enables genome viewers to support huge
              data files and remote custom tracks over networks. AVAILABILITY
              AND IMPLEMENTATION: http://samtools.sourceforge.net.",
    journal = "Bioinformatics",
    volume = 27,
    number = 5,
    pages = "718--719",
    month = mar,
    year = 2011,
    language = "en"
}


@ARTICLE{RufZamojski2021,
    title = "Single nucleus multi-omics regulatory landscape of the murine
              pituitary",
    author = "Ruf-Zamojski, Frederique and Zhang, Zidong and Zamojski, Michel
              and Smith, Gregory R and Mendelev, Natalia and Liu, Hanqing and
              Nudelman, German and Moriwaki, Mika and Pincas, Hanna and
              Castanon, Rosa Gomez and Nair, Venugopalan D and Seenarine,
              Nitish and Amper, Mary Anne S and Zhou, Xiang and Ongaro, Luisina
              and Toufaily, Chirine and Schang, Gauthier and Nery, Joseph R and
              Bartlett, Anna and Aldridge, Andrew and Jain, Nimisha and Childs,
              Gwen V and Troyanskaya, Olga G and Ecker, Joseph R and Turgeon,
              Judith L and Welt, Corrine K and Bernard, Daniel J and Sealfon,
              Stuart C",
    abstract = "To provide a multi-omics resource and investigate transcriptional
              regulatory mechanisms, we profile the transcriptome, chromatin
              accessibility, and methylation status of over 70,000 single
              nuclei (sn) from adult mouse pituitaries. Paired snRNAseq and
              snATACseq datasets from individual animals highlight a continuum
              between developmental epigenetically-encoded cell types and
              transcriptionally-determined transient cell states.
              Co-accessibility analysis-based identification of a putative Fshb
              cis-regulatory domain that overlaps the fertility-linked
              rs11031006 human polymorphism, followed by experimental
              validation illustrate the use of this resource for hypothesis
              generation. We also identify transcriptional and chromatin
              accessibility programs distinguishing each major cell type.
              Regulons, which are co-regulated gene sets sharing binding sites
              for a common transcription factor driver, recapitulate cell type
              clustering. We identify both cell type-specific and sex-specific
              regulons that are highly correlated with promoter accessibility,
              but not with methylation state, supporting the centrality of
              chromatin accessibility in shaping cell-defining transcriptional
              programs. The sn multi-omics atlas is accessible at
              snpituitaryatlas.princeton.edu.",
    journal = "Nat. Commun.",
    volume = 12,
    number = 1,
    pages = "2677",
    month = may,
    year = 2021,
    language = "en"
}


@ARTICLE{scrublet,
  title    = "Scrublet: Computational Identification of Cell Doublets in
              {Single-Cell} Transcriptomic Data",
  author   = "Wolock, Samuel L and Lopez, Romain and Klein, Allon M",
  abstract = "Single-cell RNA-sequencing has become a widely used, powerful
              approach for studying cell populations. However, these methods
              often generate multiplet artifacts, where two or more cells
              receive the same barcode, resulting in a hybrid transcriptome. In
              most experiments, multiplets account for several percent of
              transcriptomes and can confound downstream data analysis. Here,
              we present Single-Cell Remover of Doublets (Scrublet), a
              framework for predicting the impact of multiplets in a given
              analysis and identifying problematic multiplets. Scrublet avoids
              the need for expert knowledge or cell clustering by simulating
              multiplets from the data and building a nearest neighbor
              classifier. To demonstrate the utility of this approach, we test
              Scrublet on several datasets that include independent knowledge
              of cell multiplets. Scrublet is freely available for download at
              github.com/AllonKleinLab/scrublet.",
  journal  = "Cell Syst",
  volume   =  8,
  number   =  4,
  pages    = "281--291.e9",
  month    =  apr,
  year     =  2019,
  keywords = "RNA-seq; artifact detection; bioinformatics; cell doublets; decoy
              classifier; high dimensional data analysis; single-cell",
  language = "en"
}
