Many complex functions carried out by mammalian brains arise through the concerted efforts of different cell types in the neocortex. The cortex is organized during development into a laminar structure, with each layer composed of a distinct set of cell types with different morphological, electrophysiological, and connectional properties. These diverse cellular phenotypes are established and maintained by complex interactions of sequence-specific transcription factors (TFs), and they are reinforced by chromatin modifiers. Defining cell-type specific chromatin signatures will provide an understanding of the regulatory landscapes that influence transcription, as well as identification of putative cell type-specific cis-regulatory elements. Most variation associated with phenotypic differences in humans identified through genome-wide association studies is located in non-coding regions of the genome (Albert and Kruglyak, 2015; Tak and Farnham, 2015). However, the exact function of these polymorphisms and the cell types that they affect is established only in a minority of cases (Soldner et al., 2016). Assigning regulatory elements to cell types provides insight into their potential function (Corces et al., 2016). In addition, specific regulatory elements could be used to build cell-type specific genetic tools similar to those created by careful selection of well-studied regulatory regions near genes (Bou-Gharios et al., 1996; Pinkert et al., 1987) or by large genomic screens (Shima et al., 2016).

Until recently, two major hurdles have restricted access to epigenetic landscapes of specific primary cell types: selective access to those types, and the large numbers of cells required as an input for epigenomic characterization using ChIP-seq, DNAse-seq, or FAIRE-seq. Recent development of a number of Cre-recombinase transgenic lines allowed access to specific cortical cell types (Harris et al., 2014), and has enabled transcriptomic characterization and classification of cells from the visual cortex using single-cell RNA-seq (scRNA-seq) (Tasic et al., 2016). Though these transgenic lines have differing degrees of cell type heterogeneity, they provide a platform for accessing populations of related cell types from different layers of the cortex. New techniques, including ATAC-seq (Buenrostro et al., 2013) and THS-seq (Sos et al., 2016), enable measurement of chromatin accessibility from a few hundred or even single cells (Buenrostro et al., 2015; Corces et al., 2016; Cusanovich et al., 2015; Lara-Astiaso et al., 2014).

Recent studies have started to probe the correspondence between transcription and epigenetics in cortical cell types labelled by the expression of somatostatin (Sst), parvalbumin (Pvalb), and calcium/calmodulin-dependent protein kinase II alpha (Camk2a) from whole mouse cortex (Mo et al., 2015), rod and cone photoreceptor cells in retina (Mo et al., 2016), and in the developing and adult cerebellum (Frank et al., 2015). These studies have profiled the chromatin accessibility landscapes of broad cell classes, but did not examine layer-specific differences in the neocortex. Here, we take advantage of pan-GABAergic and layer-specific glutamatergic Cre-driver lines, low-input ATAC-seq, and fluorescence-activated cell sorting (FACS) to investigate chromatin accessibility landscapes in a specific cortical region, the adult mouse visual cortex (VISp).

Each layer of VISp contains distinct populations of glutamatergic cells with different transcriptional, functional, and connectional properties: layer 4 cells are the primary recipients of the visual signals from the dorsal portion of the lateral geniculate nucleus (LGd); layer 2/3 cells receive signals from L4, and communicate with L5 cells within the same cortical region and other cortical regions; layer 5 cells are highly diverse, and include cells that project to many other cortical and subcortical regions; and the layer 6 cells we examine in this study project to the thalamus (Bortone et al., 2014; Sorensen et al., 2015). Thus, within even a small region of the cortex, there is great diversity of cell types, each of which carries out a distinct transcriptional program (Tasic et al., 2016). However, the regulatory programs that produce these different transcriptional and cellular phenotypes are not known. In order to define potential regulatory elements and corresponding transcriptional regulators, we examined chromatin landscapes of these cell classes.

We found broad differences between GABAergic and glutamatergic cell types, as well as layer-specific differential chromatin accessibility in glutamatergic cell types that correlated with differential gene expression. Putative regulatory elements were identified through TF motif searches and comparisons to existing ChIP-seq datasets for each cell class. With these components, we built a putative regulatory network of TF binding sites near layer-specific TF genes that may govern layer-specific transcriptomic states. This network suggests that Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb are key regulators for the maintenance of molecular identity of deep layer and upper-layer cortical cells.

In the mouse visual cortex, at least 49 transcriptomic cell types have been defined by single cell RNA-seq (Tasic et al., 2016). As a first step in uncovering layer-specific regulatory networks, including regulatory transcription factors and their targets, we performed ATAC-seq on small populations of cells derived from layer-specific transgenic lines (Figure 1). Comparisons of our data to previously published ChIP-seq (Figure 2) and scRNA-seq (Figures 4 and 5) enabled identification of potentially significant regulatory sites. By restricting our analysis to a single cortical region, the mouse visual cortex, and through the use of layer-specific Cre lines, our data provide examination of the chromatin accessibility state at the resolution of layer-specific cortical cell classes (Figures 9 and 10). Previous studies have examined the state of cells across whole brain (Yue et al., 2014), in the whole cerebellum (Frank et al., 2015), or in pooled GABAergic or glutamatergic cells across all layers in the entire mouse cortex (Mo et al., 2015). The cell class resolution of our study allowed us to identify open chromatin sites that are unique to each layer-specific cell class, and assign accessible sites from previous studies on more heterogeneous populations to more specific cell classes (Figure 3—figure supplements 3 and 4). In doing so, we were also able to build a network of transcription factor interactions that may be responsible for maintaining the identity of layer-specific cell classes (Figure 7).

Analysis of TF motif enrichment and accessible motif targets were used to identify putative regulatory TFs for cell classes from L2/3, L4, and L6 (Figure 6). However, distinct transcription factor families for L5 were not clearly identified by our analysis. This result may be due to the heterogeneity of cell types that are present in the L5 cell class defined by the Rbp4-Cre line. Rbp4-Cre labels at least nine transcriptomic cell types, including Layer 5a cells, which show some similarity to L4 cell types, and L5b types, which share some transcriptomic patterns with L6 cell types (Tasic et al., 2016). This heterogeneity may mask detection of distinct networks that regulate the cell types within L5, which could be uncovered in future studies that will require new Cre-driver lines or other labeling strategies to access L5 subtypes. We also note that L6 includes several transcriptomic cell types that were not surveyed in the current study: only corticothalamic L6a cells are labeled by the Ntsr1-Cre driver line (Tasic et al., 2016).

Development of laminar cell type identities occurs through several waves of differentiation driven by sequential changes in chromatin and transcriptional landscapes (Telley et al., 2016). In our analysis, we see largely distinct networks of interactions that define upper and lower layers of the cortex. Previous studies have shown that layer-specific populations arise at distinct times in development. Several of the TFs we predict to be key regulators of layer-specific transcription have been examined for their function in cortical development. Loss of Tbr1, which is a key lower-layer regulator expressed by lower-layer neurons, results in gross abnormalities in cortex. Curiously, in rostral cortical regions, Tbr1 knockouts appear to lack L6, while other layers appear normal, whereas in caudal regions, Tbr1 knockouts present a generally disorganized cortical structure (Hevner et al., 2001). These findings suggest that independent networks may be involved in upper versus lower laminar development in these regions. Double knockout of Pou3f2 and Pou3f1, which we predict are key regulators in upper cortical layers, results in incorrect migration of upper cortical neurons (layers 2/3 and 4), but not lower layer neurons (McEvilly et al., 2002; Sugitani et al., 2002). Loss of key TFs can also affect layer-specific cortical projection patterns: knockdown of Cux1 ablates ipsilateral cortico-cortical projections of upper-layer neurons (Rodríguez-Tornos et al., 2016), and loss of Tbr1 results in truncated corticothalamic projections by L6 neurons (Hevner et al., 2001).

While these broad phenotypes show the importance of these factors in the development of cortical structure and function, specific interactions previously studied in development are frequently not found in our accessibility data from adult cortex. For example, a previously described regulatory interaction between RORB and its biding site near Pou3f2, which results in down-regulation of Pou3f2 during laminar development of L2/3 and L4 (Oishi et al., 2016), is not part of our putative regulatory network (Figure 7). In single-cell RNA-seq of adult visual cortex, we often see simultaneous expression of Rorb and Pou3f2 in Layer 2/3 and Layer 4 cells (Tasic et al., 2016, Figure 6—figure supplement 2), suggesting that this interaction may not be present in the adult visual cortex. We investigated this discrepancy between adult and developmental states by examining the RORB target sites that were previously described near the Pou3f2 gene (Figure 11). We found very little chromatin accessibility at or around these RORB-binding sites, which suggests that the chromatin state in adult cells prevents this interaction, thus allowing coexpression of Rorb and Pou3f2 after differentiation (Figure 6—figure supplement 2). Our network also lacks a previously reported interaction between POU3F2 and Foxp2 through a highly conserved POU3F2-binding site in the Foxp2 gene that is mutated in human compared to most other species (Maricic et al., 2013). In reporter assays, the conserved POU3F2 motif, which is present in mouse, acts as a cis-regulatory enhancer element (Maricic et al., 2013). In adult visual cortex, the chromatin is inaccessible near this POU3F2 motif (Figure 11), suggesting that POU3F2 does not bind to this site to regulate Foxp2 expression. This is consistent with gene expression data from adult cortex, where we see no expression of Foxp2 in cell classes that express Pou3f2 (layers 2–5, Figure 6—figure supplement 2). Further chromatin accessibility experiments would be needed to determine if this regulatory element is accessible during development or other cell types in visual cortex, or in other brain regions.

Figure 11

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A recent publication has shown that FOXP2 directly suppresses Mef2c expression in the striatum, where knockout of Foxp2 allows higher Mef2c expression, which, in turn, suppresses synaptogenesis (Chen et al., 2016). Our network analysis suggests that FOXP2 binding may also repress Mef2c in lower layers of the adult visual cortex (Figure 7). We examined the FOXP motifs near Mef2c exon three that were identified in this recent study (Figure 12B). In adult cortex, we observe only a modest and statistically insignificant decrease in their accessibility in lower layers. Thus, we expanded our search to all FOXP motif-containing peaks near Mef2c in the cortex (Figure 12A). We found two peaks upstream of the Mef2c TSS that are less accessible in L6 than in upper layers. Therefore, FOXP2 may regulate Mef2c by binding different sites in these different tissues: sites upstream of TSS in the cortex, and sites near exon three in the striatum. This hypothesis could be tested by future experiments that measure the chromatin accessibility landscape of the striatum, and genome-wide interaction sites of FOXP2 by ChIP-seq in striatum and cortex, as well as the effect of decreasing FOXP2 levels through cell-type specific knockout or knockdown on these chromatin landscapes.

Figure 12

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These comparisons emphasize the importance of developmental, anatomical, and cell type context in studies of chromatin accessibility and transcriptomic regulation. We note that several transcription factors identified in our network are known to be expressed in development. Some of their target sites in development may be retained in adulthood, while others may be different due to changes in accessibility. Future studies performed on layer-specific cell classes in development will be needed to determine how changes to the epigenetic landscape affect transcription during development.

Our sampling strategy utilized only Cre lines that yielded at least 500 tdTomato-positive FACS-isolated cells per animal. This limited our genetic access to relatively abundant cell classes in the visual cortex, all of which label multiple cell types that can be distinguished by single-cell RNA-seq (Figure 1C). Because GABAergic cell types are much less abundant in the neocortex than glutamatergic cells, we sampled only a single, pan-GABAergic cell line, Gad2-IRES-Cre. A previous study was able to examine Pvalb+ and Vip+ (but not Sst+ or Ndnf+) GABAergic cell type classes using the whole mouse cortex (Mo et al., 2015) by utilizing the INTACT nuclear labelling technique. The higher spatial resolution of our glutamatergic data, but lower cell-type resolution among GABAergic types makes our study complementary to this work. Further advances in low-input chromatin accessibility assays, low input ChIP-seq methods, and improvements in Cre-driver specificity will increase our ability to analyze the epigenomic state of specific neural cell types.

Our integrated analysis of chromatin accessibility, gene expression, and TF motif enrichment provides an unprecedented look at the key interactions that underlie layer-specific transcriptomes. We were able to identify layer-wide patterns of TF accessibility from analysis of the entire set of peaks from each layer, and found interactions that may drive expression of key transcriptomic regulators Nfia and Cux1 by looking at coordinated TF expression and motif accessibility. Further research into the roles that these and other TFs play in development and maintenance of layer-specific cell classes will enhance our understanding of the functional consequences of these transcriptomic programs. Conditional loss-of-function experiments in specific cell classes at specific times in development or adulthood would help to disentangle the specific roles from more global roles of these TFs. For instance, constitutive, homozygous loss-of-function of Foxp2 is lethal by postnatal day 21, and causes developmental delays and motor dysfunction that may interfere with studies of adult brain function (French et al., 2007), whereas heterozygous Foxp2 knockout mice display few differences in cortical structure (Fujita et al., 2008). Our network suggests that Foxp2 could be a key regulator of L6-specific transcriptional program in VISp. Investigation of this role of Foxp2 would require generation of a conditional knockout in specific cortical cell populations, and perhaps at specific times in development or adulthood (for example, through conditional alleles combined with Cre/CreER lines). Another area that would greatly benefit from further study is assessing the correspondence between chromatin modifications and chromatin accessibility. New techniques for ChIP-seq enable enrichment of modified histones from as few as 500 cells (Lara-Astiaso et al., 2014), which may allow the correspondence between accessibility and chromatin modifications in specific cell classes to be studied directly. The synthesis of these data modalities will further our understading of the mechanisms of cell type-specific transcriptional regulation. This new knowledge will be useful for building new cell-type specific genetic tools, and for testing the reprogramming potential of layer-specific cell classes (De la Rossa et al., 2013).

1. 1. Gray LT
   2. Yao Z
   3. Nguyen TN
   4. Kim SK
   5. Zeng H
   6. Tasic B

   (2017) Layer-specific ATAC-seq of the neurons of adult mouse visual cortex defined by Cre-driver lines

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE87548).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87548

1. 1. Mo A
   2. Mukamel EA
   3. Davis FP
   4. Luo C
   5. Eddy SR
   6. Ecker JR
   7. Nathans J

   (2015) Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE63137).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63137
2. 1. Tasic B
   2. Menon V
   3. Nguyen TN
   4. Kim TK
   5. Yao Z
   6. Gray LT
   7. Hawrylycz M
   8. Koch C
   9. Zeng H

   (2016) Adult mouse cortical cell taxonomy by single cell transcriptomics

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE71585).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585

1. 1. Albert FW
   2. Kruglyak L

   (2015) The role of regulatory variation in complex traits and disease

   Nature Reviews Genetics 16:197–212.

   https://doi.org/10.1038/nrg3891

   • PubMed
   • Google Scholar
2. 1. Baglietto MG
   2. Caridi G
   3. Gimelli G
   4. Mancardi M
   5. Prato G
   6. Ronchetto P
   7. Cuoco C
   8. Tassano E

   (2014) RORB gene and 9q21.13 microdeletion: report on a patient with epilepsy and mild intellectual disability

   European Journal of Medical Genetics 57:44–46.

   https://doi.org/10.1016/j.ejmg.2013.12.001

   • PubMed
   • Google Scholar
3. 1. Becker M
   2. Devanna P
   3. Fisher SE
   4. Vernes SC

   (2015) A chromosomal rearrangement in a child with severe speech and language disorder separates FOXP2 from a functional enhancer

   Molecular Cytogenetics 8:69.

   https://doi.org/10.1186/s13039-015-0173-0

   • PubMed
   • Google Scholar
4. 1. Benadiba C
   2. Magnani D
   3. Niquille M
   4. Morlé L
   5. Valloton D
   6. Nawabi H
   7. Ait-Lounis A
   8. Otsmane B
   9. Reith W
   10. Theil T
   11. Hornung JP
   12. Lebrand C
   13. Durand B

   (2012) The ciliogenic transcription factor RFX3 regulates early midline distribution of guidepost neurons required for corpus callosum development

   PLoS Genetics 8:e1002606.

   https://doi.org/10.1371/journal.pgen.1002606

   • PubMed
   • Google Scholar
5. 1. Bengtsson H

   (2016) matrixStats: functions that apply to rows and columns of matrices (and to vectors)

   matrixStats: functions that apply to rows and columns of matrices (and to vectors), R package version 0.50.2, https://CRAN.R-project.org/package=matrixStats.

   https://CRAN.R-project.org/package=matrixStats

   • Google Scholar
6. 1. Bortone DS
   2. Olsen SR
   3. Scanziani M

   (2014) Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex

   Neuron 82:474–485.

   https://doi.org/10.1016/j.neuron.2014.02.021

   • PubMed
   • Google Scholar
7. 1. Bou-Gharios G
   2. Garrett LA
   3. Rossert J
   4. Niederreither K
   5. Eberspaecher H
   6. Smith C
   7. Black C
   8. Crombrugghe B

   (1996) A potent far-upstream enhancer in the mouse pro alpha 2(I) collagen gene regulates expression of reporter genes in transgenic mice

   The Journal of Cell Biology 134:1333–1344.

   https://doi.org/10.1083/jcb.134.5.1333

   • PubMed
   • Google Scholar
8. Software

   1. BroadInstitute

   (2015) Picard Tools

   Picard Tools.

   http://broadinstitute.github.io/picard/
9. 1. Buenrostro JD
   2. Giresi PG
   3. Zaba LC
   4. Chang HY
   5. Greenleaf WJ

   (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position

   Nature Methods 10:1213–1218.

   https://doi.org/10.1038/nmeth.2688

   • Google Scholar
10. 1. Buenrostro JD
    2. Wu B
    3. Litzenburger UM
    4. Ruff D
    5. Gonzales ML
    6. Snyder MP
    7. Chang HY
    8. Greenleaf WJ

    (2015) Single-cell chromatin accessibility reveals principles of regulatory variation

    Nature 523:486–490.

    https://doi.org/10.1038/nature14590

    • PubMed
    • Google Scholar
11. 1. Castellucci GA
    2. McGinley MJ
    3. McCormick DA

    (2016) Knockout of Foxp2 disrupts vocal development in mice

    Scientific Reports 6:23305.

    https://doi.org/10.1038/srep23305

    • PubMed
    • Google Scholar
12. 1. Chen Q
    2. Heston JB
    3. Burkett ZD
    4. White SA

    (2013) Expression analysis of the speech-related genes FoxP1 and FoxP2 and their relation to singing behavior in two songbird species

    Journal of Experimental Biology 216:3682–3692.

    https://doi.org/10.1242/jeb.085886

    • PubMed
    • Google Scholar
13. 1. Chen YC
    2. Kuo HY
    3. Bornschein U
    4. Takahashi H
    5. Chen SY
    6. Lu KM
    7. Yang HY
    8. Chen GM
    9. Lin JR
    10. Lee YH
    11. Chou YC
    12. Cheng SJ
    13. Chien CT
    14. Enard W
    15. Hevers W
    16. Pääbo S
    17. Graybiel AM
    18. Liu FC

    (2016) Foxp2 controls synaptic wiring of corticostriatal circuits and vocal communication by opposing Mef2c

    Nature Neuroscience 19:1513–1522.

    https://doi.org/10.1038/nn.4380

    • PubMed
    • Google Scholar
14. 1. Chiu YC
    2. Li MY
    3. Liu YH
    4. Ding JY
    5. Yu JY
    6. Wang TW
    7. My L
    8. Jy Y
    9. Jy Y

    (2014) Foxp2 regulates neuronal differentiation and neuronal subtype specification

    Developmental Neurobiology 74:723–738.

    https://doi.org/10.1002/dneu.22166

    • PubMed
    • Google Scholar
15. 1. Chokas AL
    2. Trivedi CM
    3. Lu MM
    4. Tucker PW
    5. Li S
    6. Epstein JA
    7. Morrisey EE
    8. Mm L
    9. Li S

    (2010) Foxp1/2/4-NuRD interactions regulate gene expression and epithelial injury response in the lung via regulation of interleukin-6

    Journal of Biological Chemistry 285:13304–13313.

    https://doi.org/10.1074/jbc.M109.088468

    • PubMed
    • Google Scholar
16. 1. Corces MR
    2. Buenrostro JD
    3. Wu B
    4. Greenside PG
    5. Chan SM
    6. Koenig JL
    7. Snyder MP
    8. Pritchard JK
    9. Kundaje A
    10. Greenleaf WJ
    11. Majeti R
    12. Chang HY

    (2016) Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution

    Nature Genetics 48:1193–1203.

    https://doi.org/10.1038/ng.3646

    • PubMed
    • Google Scholar
17. 1. Crooks GE
    2. Hon G
    3. Chandonia JM
    4. Brenner SE

    (2004) WebLogo: a sequence logo generator

    Genome Research 14:1188–1190.

    https://doi.org/10.1101/gr.849004

    • PubMed
    • Google Scholar
18. 1. Cubelos B
    2. Sebastián-Serrano A
    3. Beccari L
    4. Calcagnotto ME
    5. Cisneros E
    6. Kim S
    7. Dopazo A
    8. Alvarez-Dolado M
    9. Redondo JM
    10. Bovolenta P
    11. Walsh CA
    12. Nieto M

    (2010) Cux1 and Cux2 regulate dendritic branching, spine morphology, and synapses of the upper layer neurons of the cortex

    Neuron 66:523–535.

    https://doi.org/10.1016/j.neuron.2010.04.038

    • PubMed
    • Google Scholar
19. 1. Cusanovich DA
    2. Daza R
    3. Adey A
    4. Pliner HA
    5. Christiansen L
    6. Gunderson KL
    7. Steemers FJ
    8. Trapnell C
    9. Shendure J

    (2015) Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

    Science 348:910–914.

    https://doi.org/10.1126/science.aab1601

    • PubMed
    • Google Scholar
20. 1. Daley T
    2. Smith AD

    (2013) Predicting the molecular complexity of sequencing libraries

    Nature Methods 10:325–327.

    https://doi.org/10.1038/nmeth.2375

    • PubMed
    • Google Scholar
21. 1. das Neves L
    2. Duchala CS
    3. Tolentino-Silva F
    4. Haxhiu MA
    5. Colmenares C
    6. Macklin WB
    7. Campbell CE
    8. Butz KG
    9. Gronostajski RM
    10. Godinho F

    (1999) Disruption of the murine nuclear factor I-A gene (Nfia) results in perinatal lethality, hydrocephalus, and agenesis of the corpus callosum

    PNAS 96:11946–11951.

    https://doi.org/10.1073/pnas.96.21.11946

    • PubMed
    • Google Scholar
22. 1. De la Rossa A
    2. Bellone C
    3. Golding B
    4. Vitali I
    5. Moss J
    6. Toni N
    7. Lüscher C
    8. Jabaudon D

    (2013) In vivo reprogramming of circuit connectivity in postmitotic neocortical neurons

    Nature Neuroscience 16:193–200.

    https://doi.org/10.1038/nn.3299

    • PubMed
    • Google Scholar
23. 1. Dekker J
    2. Marti-Renom MA
    3. Mirny LA

    (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data

    Nature Reviews Genetics 14:390–403.

    https://doi.org/10.1038/nrg3454

    • PubMed
    • Google Scholar
24. 1. Deneen B
    2. Ho R
    3. Lukaszewicz A
    4. Hochstim CJ
    5. Gronostajski RM
    6. Anderson DJ

    (2006) The transcription factor NFIA controls the onset of gliogenesis in the developing spinal cord

    Neuron 52:953–968.

    https://doi.org/10.1016/j.neuron.2006.11.019

    • PubMed
    • Google Scholar
25. 1. Fong AP
    2. Yao Z
    3. Zhong JW
    4. Johnson NM
    5. Farr GH
    6. Maves L
    7. Tapscott SJ

    (2015) Conversion of MyoD to a neurogenic factor: binding site specificity determines lineage

    Cell Reports 10:1937–1946.

    https://doi.org/10.1016/j.celrep.2015.02.055

    • PubMed
    • Google Scholar
26. 1. Frank CL
    2. Liu F
    3. Wijayatunge R
    4. Song L
    5. Biegler MT
    6. Yang MG
    7. Vockley CM
    8. Safi A
    9. Gersbach CA
    10. Crawford GE
    11. West AE

    (2015) Regulation of chromatin accessibility and Zic binding at enhancers in the developing cerebellum

    Nature Neuroscience 18:647–656.

    https://doi.org/10.1038/nn.3995

    • PubMed
    • Google Scholar
27. 1. French CA
    2. Groszer M
    3. Preece C
    4. Coupe AM
    5. Rajewsky K
    6. Fisher SE

    (2007) Generation of mice with a conditional Foxp2 null allele

    Genesis 45:440–446.

    https://doi.org/10.1002/dvg.20305

    • PubMed
    • Google Scholar
28. 1. Fujita E
    2. Tanabe Y
    3. Shiota A
    4. Ueda M
    5. Suwa K
    6. Momoi MY
    7. Momoi T

    (2008) Ultrasonic vocalization impairment of Foxp2 (R552H) knockin mice related to speech-language disorder and abnormality of Purkinje cells

    PNAS 105:3117–3122.

    https://doi.org/10.1073/pnas.0712298105

    • PubMed
    • Google Scholar
29. 1. Galili T

    (2015) Dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering

    Bioinformatics 31:3718–3720.

    https://doi.org/10.1093/bioinformatics/btv428

    • PubMed
    • Google Scholar
30. 1. Garnier S

    (2016) viridis: default color maps from 'matplotlib'

    viridis: default color maps from 'matplotlib', R package version 0.3.4, https://CRAN.R-project.org/package=viridis.

    https://CRAN.R-project.org/package=viridis

    • Google Scholar
31. 1. Geoffroy PA
    2. Lajnef M
    3. Bellivier F
    4. Jamain S
    5. Gard S
    6. Kahn JP
    7. Henry C
    8. Leboyer M
    9. Etain B

    (2015) Genetic association study of circadian genes with seasonal pattern in bipolar disorders

    Scientific Reports 5:10232.

    https://doi.org/10.1038/srep10232

    • PubMed
    • Google Scholar
32. 1. Glasgow SM
    2. Zhu W
    3. Stolt CC
    4. Huang TW
    5. Chen F
    6. LoTurco JJ
    7. Neul JL
    8. Wegner M
    9. Mohila C
    10. Deneen B

    (2014) Mutual antagonism between Sox10 and NFIA regulates diversification of glial lineages and glioma subtypes

    Nature Neuroscience 17:1322–1329.

    https://doi.org/10.1038/nn.3790

    • PubMed
    • Google Scholar
33. 1. Grant CE
    2. Bailey TL
    3. Noble WS

    (2011) FIMO: scanning for occurrences of a given motif

    Bioinformatics 27:1017–1018.

    https://doi.org/10.1093/bioinformatics/btr064

    • PubMed
    • Google Scholar
34. 1. Hahne F
    2. Ivanek R

    (2016) Visualizing genomic data using gviz and bioconductor

    Methods in Molecular Biology 1418:335–351.

    https://doi.org/10.1007/978-1-4939-3578-9_16

    • PubMed
    • Google Scholar
35. 1. Han W
    2. Kwan KY
    3. Shim S
    4. Lam MM
    5. Shin Y
    6. Xu X
    7. Zhu Y
    8. Li M
    9. Sestan N

    (2011) TBR1 directly represses Fezf2 to control the laminar origin and development of the corticospinal tract

    PNAS 108:3041–3046.

    https://doi.org/10.1073/pnas.1016723108

    • PubMed
    • Google Scholar
36. 1. Harris JA
    2. Hirokawa KE
    3. Sorensen SA
    4. Gu H
    5. Mills M
    6. Ng LL
    7. Bohn P
    8. Mortrud M
    9. Ouellette B
    10. Kidney J
    11. Smith KA
    12. Dang C
    13. Sunkin S
    14. Bernard A
    15. Oh SW
    16. Madisen L
    17. Zeng H

    (2014) Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation

    Frontiers in Neural Circuits 8:76.

    https://doi.org/10.3389/fncir.2014.00076

    • PubMed
    • Google Scholar
37. 1. Hevner RF
    2. Shi L
    3. Justice N
    4. Hsueh Y
    5. Sheng M
    6. Smiga S
    7. Bulfone A
    8. Goffinet AM
    9. Campagnoni AT
    10. Rubenstein JL

    (2001) Tbr1 regulates differentiation of the preplate and layer 6

    Neuron 29:353–366.

    https://doi.org/10.1016/S0896-6273(01)00211-2

    • PubMed
    • Google Scholar
38. 1. Huang TN
    2. Hsueh YP

    (2015) Brain-specific transcriptional regulator T-brain-1 controls brain wiring and neuronal activity in autism spectrum disorders

    Frontiers in Neuroscience 9:406.

    https://doi.org/10.3389/fnins.2015.00406

    • PubMed
    • Google Scholar
39. 1. Jabaudon D
    2. Shnider SJ
    3. Tischfield DJ
    4. Galazo MJ
    5. Macklis JD

    (2012) Rorβ induces barrel-like neuronal clusters in the developing neocortex

    Cerebral Cortex 22:996–1006.

    https://doi.org/10.1093/cercor/bhr182

    • PubMed
    • Google Scholar
40. 1. John S
    2. Sabo PJ
    3. Thurman RE
    4. Sung MH
    5. Biddie SC
    6. Johnson TA
    7. Hager GL
    8. Stamatoyannopoulos JA

    (2011) Chromatin accessibility pre-determines glucocorticoid receptor binding patterns

    Nature Genetics 43:264–268.

    https://doi.org/10.1038/ng.759

    • PubMed
    • Google Scholar
41. 1. Lai YC
    2. Kao CF
    3. Lu ML
    4. Chen HC
    5. Chen PY
    6. Chen CH
    7. Shen WW
    8. Wu JY
    9. Lu RB
    10. Kuo PH
    11. Ml L
    12. Chen PY

    (2015) Investigation of associations between NR1D1, RORA and RORB genes and bipolar disorder

    PLoS One 10:e0121245.

    https://doi.org/10.1371/journal.pone.0121245

    • PubMed
    • Google Scholar
42. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • PubMed
    • Google Scholar
43. 1. Lara-Astiaso D
    2. Weiner A
    3. Lorenzo-Vivas E
    4. Zaretsky I
    5. Jaitin DA
    6. David E
    7. Keren-Shaul H
    8. Mildner A
    9. Winter D
    10. Jung S
    11. Friedman N
    12. Amit I

    (2014) Chromatin state dynamics during blood formation

    Science 345:943–949.

    https://doi.org/10.1126/science.1256271

    • Google Scholar
44. 1. Lawrence M
    2. Gentleman R
    3. Carey V

    (2009) Rtracklayer: an R package for interfacing with genome browsers

    Bioinformatics 25:1841–1842.

    https://doi.org/10.1093/bioinformatics/btp328

    • PubMed
    • Google Scholar
45. 1. Lawrence M
    2. Huber W
    3. Pagès H
    4. Aboyoun P
    5. Carlson M
    6. Gentleman R
    7. Morgan MT
    8. Carey VJ

    (2013) Software for computing and annotating genomic ranges

    PLoS Computational Biology 9:e1003118.

    https://doi.org/10.1371/journal.pcbi.1003118

    • PubMed
    • Google Scholar
46. 1. Lex A
    2. Gehlenborg N
    3. Strobelt H
    4. Vuillemot R
    5. Pfister H

    (2014) UpSet: Visualization of intersecting sets

    IEEE Transactions on Visualization and Computer Graphics 20:1983–1992.

    https://doi.org/10.1109/TVCG.2014.2346248

    • Google Scholar
47. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
48. 1. Li N
    2. Zhao CT
    3. Wang Y
    4. Yuan XB

    (2010) The transcription factor Cux1 regulates dendritic morphology of cortical pyramidal neurons

    PLoS One 5:e10596.

    https://doi.org/10.1371/journal.pone.0010596

    • PubMed
    • Google Scholar
49. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
50. 1. Lu W
    2. Quintero-Rivera F
    3. Fan Y
    4. Alkuraya FS
    5. Donovan DJ
    6. Xi Q
    7. Turbe-Doan A
    8. Li QG
    9. Campbell CG
    10. Shanske AL
    11. Sherr EH
    12. Ahmad A
    13. Peters R
    14. Rilliet B
    15. Parvex P
    16. Bassuk AG
    17. Harris DJ
    18. Ferguson H
    19. Kelly C
    20. Walsh CA
    21. Gronostajski RM
    22. Devriendt K
    23. Higgins A
    24. Ligon AH
    25. Quade BJ
    26. Morton CC
    27. Gusella JF
    28. Maas RL

    (2007) NFIA Haploinsufficiency is associated with a CNS malformation syndrome and urinary tract defects

    PLoS Genetics 3:e80.

    https://doi.org/10.1371/journal.pgen.0030080

    • PubMed
    • Google Scholar
51. 1. Madisen L
    2. Zwingman TA
    3. Sunkin SM
    4. Oh SW
    5. Zariwala HA
    6. Gu H
    7. Ng LL
    8. Palmiter RD
    9. Hawrylycz MJ
    10. Jones AR
    11. Lein ES
    12. Zeng H

    (2010) A robust and high-throughput Cre reporting and characterization system for the whole mouse brain

    Nature Neuroscience 13:133–140.

    https://doi.org/10.1038/nn.2467

    • PubMed
    • Google Scholar
52. 1. Magnani D
    2. Morlé L
    3. Hasenpusch-Theil K
    4. Paschaki M
    5. Jacoby M
    6. Schurmans S
    7. Durand B
    8. Theil T

    (2015) The ciliogenic transcription factor Rfx3 is required for the formation of the thalamocortical tract by regulating the patterning of prethalamus and ventral telencephalon

    Human Molecular Genetics 24:2578–2593.

    https://doi.org/10.1093/hmg/ddv021

    • PubMed
    • Google Scholar
53. 1. Maricic T
    2. Günther V
    3. Georgiev O
    4. Gehre S
    5. Curlin M
    6. Schreiweis C
    7. Naumann R
    8. Burbano HA
    9. Meyer M
    10. Lalueza-Fox C
    11. de la Rasilla M
    12. Rosas A
    13. Gajovic S
    14. Kelso J
    15. Enard W
    16. Schaffner W
    17. Pääbo S

    (2013) A recent evolutionary change affects a regulatory element in the human FOXP2 gene

    Molecular Biology and Evolution 30:844–852.

    https://doi.org/10.1093/molbev/mss271

    • PubMed
    • Google Scholar
54. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.Journal 17:10–12.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
55. 1. Mathelier A
    2. Fornes O
    3. Arenillas DJ
    4. Chen CY
    5. Denay G
    6. Lee J
    7. Shi W
    8. Shyr C
    9. Tan G
    10. Worsley-Hunt R
    11. Zhang AW
    12. Parcy F
    13. Lenhard B
    14. Sandelin A
    15. Wasserman WW

    (2016) JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles

    Nucleic Acids Research 44:D110–D115.

    https://doi.org/10.1093/nar/gkv1176

    • PubMed
    • Google Scholar
56. 1. McEvilly RJ
    2. de Diaz MO
    3. Schonemann MD
    4. Hooshmand F
    5. Rosenfeld MG

    (2002) Transcriptional regulation of cortical neuron migration by POU domain factors

    Science 295:1528–1532.

    https://doi.org/10.1126/science.1067132

    • PubMed
    • Google Scholar
57. 1. McKenna WL
    2. Betancourt J
    3. Larkin KA
    4. Abrams B
    5. Guo C
    6. Rubenstein JL
    7. Chen B

    (2011) Tbr1 and Fezf2 regulate alternate corticofugal neuronal identities during neocortical development

    Journal of Neuroscience 31:549–564.

    https://doi.org/10.1523/JNEUROSCI.4131-10.2011

    • PubMed
    • Google Scholar
58. 1. McLeay RC
    2. Bailey TL

    (2010) Motif enrichment analysis: a unified framework and an evaluation on ChIP data

    BMC Bioinformatics 11:165.

    https://doi.org/10.1186/1471-2105-11-165

    • PubMed
    • Google Scholar
59. 1. Mo A
    2. Luo C
    3. Davis FP
    4. Mukamel EA
    5. Henry GL
    6. Nery JR
    7. Urich MA
    8. Picard S
    9. Lister R
    10. Eddy SR
    11. Beer MA
    12. Ecker JR
    13. Nathans J

    (2016) Epigenomic landscapes of retinal rods and cones

    eLife 5:e11613.

    https://doi.org/10.7554/eLife.11613

    • PubMed
    • Google Scholar
60. 1. Mo A
    2. Mukamel EA
    3. Davis FP
    4. Luo C
    5. Henry GL
    6. Picard S
    7. Urich MA
    8. Nery JR
    9. Sejnowski TJ
    10. Lister R
    11. Eddy SR
    12. Ecker JR
    13. Nathans J

    (2015) Epigenomic signatures of neuronal diversity in the mammalian brain

    Neuron 86:1369–1384.

    https://doi.org/10.1016/j.neuron.2015.05.018

    • PubMed
    • Google Scholar
61. 1. Murugan M
    2. Harward S
    3. Scharff C
    4. Mooney R

    (2013) Diminished FoxP2 levels affect dopaminergic modulation of corticostriatal signaling important to song variability

    Neuron 80:1464–1476.

    https://doi.org/10.1016/j.neuron.2013.09.021

    • PubMed
    • Google Scholar
62. 1. Neuwirth E

    (2014) RColorBrewer: ColorBrewer Palettes

    RColorBrewer: ColorBrewer Palettes, R package version 1.1-2, https://CRAN.R-project.org/package=RColorBrewer.

    https://CRAN.R-project.org/package=RColorBrewer

    • Google Scholar
63. 1. Oishi K
    2. Aramaki M
    3. Nakajima K

    (2016) Mutually repressive interaction between Brn1/2 and Rorb contributes to the establishment of neocortical layer 2/3 and layer 4

    PNAS 113:3371–3376.

    https://doi.org/10.1073/pnas.1515949113

    • PubMed
    • Google Scholar
64. 1. Pachkov M
    2. Balwierz PJ
    3. Arnold P
    4. Ozonov E
    5. van Nimwegen E

    (2013) SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates

    Nucleic Acids Research 41:D214–D220.

    https://doi.org/10.1093/nar/gks1145

    • PubMed
    • Google Scholar
65. 1. Pachkov M
    2. Erb I
    3. Molina N
    4. van Nimwegen E

    (2007) SwissRegulon: a database of genome-wide annotations of regulatory sites

    Nucleic Acids Research 35:D127–D131.

    https://doi.org/10.1093/nar/gkl857

    • PubMed
    • Google Scholar
66. 1. Pagès H

    (2016) BSgenome: Infrastructure for Biostrings-based genome data packages and support for efficient SNP representation

    BSgenome: Infrastructure for Biostrings-based genome data packages and support for efficient SNP representation, R package version 1.40.1, https://bioconductor.org/packages/release/bioc/html/BSgenome.html.

    https://bioconductor.org/packages/release/bioc/html/BSgenome.html

    • Google Scholar
67. 1. Pinkert CA
    2. Ornitz DM
    3. Brinster RL
    4. Palmiter RD

    (1987) An albumin enhancer located 10 kb upstream functions along with its promoter to direct efficient, liver-specific expression in transgenic mice

    Genes & Development 1:268–276.

    https://doi.org/10.1101/gad.1.3.268

    • PubMed
    • Google Scholar
68. 1. Pique-Regi R
    2. Degner JF
    3. Pai AA
    4. Gaffney DJ
    5. Gilad Y
    6. Pritchard JK

    (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data

    Genome Research 21:447–455.

    https://doi.org/10.1101/gr.112623.110

    • PubMed
    • Google Scholar
69. 1. Quaggin SE
    2. Heuvel GB
    3. Golden K
    4. Bodmer R
    5. Igarashi P

    (1996) Primary structure, neural-specific expression, and chromosomal localization of Cux-2, a second murine homeobox gene related to Drosophila cut

    The Journal of Biological Chemistry 271:22624–22634.

    https://doi.org/10.1074/jbc.271.37.22624

    • PubMed
    • Google Scholar
70. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
71. 1. Robinson MD
    2. Oshlack A

    (2010) A scaling normalization method for differential expression analysis of RNA-seq data

    Genome Biology 11:R25.

    https://doi.org/10.1186/gb-2010-11-3-r25

    • PubMed
    • Google Scholar
72. 1. Rodríguez-Tornos FM
    2. Briz CG
    3. Weiss LA
    4. Sebastián-Serrano A
    5. Ares S
    6. Navarrete M
    7. Frangeul L
    8. Galazo M
    9. Jabaudon D
    10. Esteban JA
    11. Nieto M

    (2016) Cux1 enables interhemispheric connections of layer II/III Neurons by Regulating Kv1-Dependent Firing

    Neuron 89:494–506.

    https://doi.org/10.1016/j.neuron.2015.12.020

    • PubMed
    • Google Scholar
73. 1. Ross-Innes CS
    2. Stark R
    3. Teschendorff AE
    4. Holmes KA
    5. Ali HR
    6. Dunning MJ
    7. Brown GD
    8. Gojis O
    9. Ellis IO
    10. Green AR
    11. Ali S
    12. Chin SF
    13. Palmieri C
    14. Caldas C
    15. Carroll JS

    (2012) Differential oestrogen receptor binding is associated with clinical outcome in breast cancer

    Nature 481:389–393.

    https://doi.org/10.1038/nature10730

    • PubMed
    • Google Scholar
74. 1. Ruan J
    2. Li H
    3. Chen Z
    4. Coghlan A
    5. Coin LJ
    6. Guo Y
    7. Hériché JK
    8. Hu Y
    9. Kristiansen K
    10. Li R
    11. Liu T
    12. Moses A
    13. Qin J
    14. Vang S
    15. Vilella AJ
    16. Ureta-Vidal A
    17. Bolund L
    18. Wang J
    19. Durbin R

    (2008) TreeFam: 2008 update

    Nucleic Acids Research 36:D735–D740.

    https://doi.org/10.1093/nar/gkm1005

    • PubMed
    • Google Scholar
75. 1. Rudolf G
    2. Lesca G
    3. Mehrjouy MM
    4. Labalme A
    5. Salmi M
    6. Bache I
    7. Bruneau N
    8. Pendziwiat M
    9. Fluss J
    10. de Bellescize J
    11. Scholly J
    12. Møller RS
    13. Craiu D
    14. Tommerup N
    15. Valenti-Hirsch MP
    16. Schluth-Bolard C
    17. Sloan-Béna F
    18. Helbig KL
    19. Weckhuysen S
    20. Edery P
    21. Coulbaut S
    22. Abbas M
    23. Scheffer IE
    24. Tang S
    25. Myers CT
    26. Stamberger H
    27. Carvill GL
    28. Shinde DN
    29. Mefford HC
    30. Neagu E
    31. Huether R
    32. Lu HM
    33. Dica A
    34. Cohen JS
    35. Iliescu C
    36. Pomeran C
    37. Rubenstein J
    38. Helbig I
    39. Sanlaville D
    40. Hirsch E
    41. Szepetowski P

    (2016) Loss of function of the retinoid-related nuclear receptor (RORB) gene and epilepsy

    European Journal of Human Genetics 24:1761–1770.

    https://doi.org/10.1038/ejhg.2016.80

    • PubMed
    • Google Scholar
76. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
77. 1. Shima Y
    2. Sugino K
    3. Hempel CM
    4. Shima M
    5. Taneja P
    6. Bullis JB
    7. Mehta S
    8. Lois C
    9. Nelson SB

    (2016) A Mammalian enhancer trap resource for discovering and manipulating neuronal cell types

    eLife 5:e13503.

    https://doi.org/10.7554/eLife.13503

    • PubMed
    • Google Scholar
78. 1. Soldner F
    2. Stelzer Y
    3. Shivalila CS
    4. Abraham BJ
    5. Latourelle JC
    6. Barrasa MI
    7. Goldmann J
    8. Myers RH
    9. Young RA
    10. Jaenisch R

    (2016) Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression

    Nature 533:95–99.

    https://doi.org/10.1038/nature17939

    • PubMed
    • Google Scholar
79. 1. Sorensen SA
    2. Bernard A
    3. Menon V
    4. Royall JJ
    5. Glattfelder KJ
    6. Desta T
    7. Hirokawa K
    8. Mortrud M
    9. Miller JA
    10. Zeng H
    11. Hohmann JG
    12. Jones AR
    13. Lein ES

    (2015) Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity

    Cerebral Cortex 25:433–449.

    https://doi.org/10.1093/cercor/bht243

    • PubMed
    • Google Scholar
80. 1. Sos BC
    2. Fung HL
    3. Gao DR
    4. Osothprarop TF
    5. Kia A
    6. He MM
    7. Zhang K

    (2016) Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay

    Genome Biology 17:20.

    https://doi.org/10.1186/s13059-016-0882-7

    • PubMed
    • Google Scholar
81. 1. Stark R
    2. Brown GD

    (2011)

    DiffBind: differential binding analysis of ChIP-Seq peak data

    DiffBind: differential binding analysis of ChIP-Seq peak data, R package version 100.

    • Google Scholar
82. 1. Sugitani Y
    2. Nakai S
    3. Minowa O
    4. Nishi M
    5. Jishage K
    6. Kawano H
    7. Mori K
    8. Ogawa M
    9. Noda T

    (2002) Brn-1 and Brn-2 share crucial roles in the production and positioning of mouse neocortical neurons

    Genes & Development 16:1760–1765.

    https://doi.org/10.1101/gad.978002

    • PubMed
    • Google Scholar
83. 1. Tak YG
    2. Farnham PJ

    (2015) Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

    Epigenetics & Chromatin 8:57.

    https://doi.org/10.1186/s13072-015-0050-4

    • PubMed
    • Google Scholar
84. 1. Tasic B
    2. Menon V
    3. Nguyen TN
    4. Kim TK
    5. Jarsky T
    6. Yao Z
    7. Levi B
    8. Gray LT
    9. Sorensen SA
    10. Dolbeare T
    11. Bertagnolli D
    12. Goldy J
    13. Shapovalova N
    14. Parry S
    15. Lee C
    16. Smith K
    17. Bernard A
    18. Madisen L
    19. Sunkin SM
    20. Hawrylycz M
    21. Koch C
    22. Zeng H

    (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

    Nature Neuroscience 19:335–346.

    https://doi.org/10.1038/nn.4216

    • PubMed
    • Google Scholar
85. 1. Telley L
    2. Govindan S
    3. Prados J
    4. Stevant I
    5. Nef S
    6. Dermitzakis E
    7. Dayer A
    8. Jabaudon D

    (2016) Sequential transcriptional waves direct the differentiation of newborn neurons in the mouse neocortex

    Science 351:1443–1446.

    https://doi.org/10.1126/science.aad8361

    • PubMed
    • Google Scholar
86. 1. Thurman RE
    2. Rynes E
    3. Humbert R
    4. Vierstra J
    5. Maurano MT
    6. Haugen E
    7. Sheffield NC
    8. Stergachis AB
    9. Wang H
    10. Vernot B
    11. Garg K
    12. John S
    13. Sandstrom R
    14. Bates D
    15. Boatman L
    16. Canfield TK
    17. Diegel M
    18. Dunn D
    19. Ebersol AK
    20. Frum T
    21. Giste E
    22. Johnson AK
    23. Johnson EM
    24. Kutyavin T
    25. Lajoie B
    26. Lee BK
    27. Lee K
    28. London D
    29. Lotakis D
    30. Neph S
    31. Neri F
    32. Nguyen ED
    33. Qu H
    34. Reynolds AP
    35. Roach V
    36. Safi A
    37. Sanchez ME
    38. Sanyal A
    39. Shafer A
    40. Simon JM
    41. Song L
    42. Vong S
    43. Weaver M
    44. Yan Y
    45. Zhang Z
    46. Zhang Z
    47. Lenhard B
    48. Tewari M
    49. Dorschner MO
    50. Hansen RS
    51. Navas PA
    52. Stamatoyannopoulos G
    53. Iyer VR
    54. Lieb JD
    55. Sunyaev SR
    56. Akey JM
    57. Sabo PJ
    58. Kaul R
    59. Furey TS
    60. Dekker J
    61. Crawford GE
    62. Stamatoyannopoulos JA

    (2012) The accessible chromatin landscape of the human genome

    Nature 489:75–82.

    https://doi.org/10.1038/nature11232

    • PubMed
    • Google Scholar
87. 1. Tsien JZ
    2. Chen DF
    3. Gerber D
    4. Tom C
    5. Mercer EH
    6. Anderson DJ
    7. Mayford M
    8. Kandel ER
    9. Tonegawa S

    (1996) Subregion- and cell type-restricted gene knockout in mouse brain

    Cell 87:1317–1326.

    https://doi.org/10.1016/S0092-8674(00)81826-7

    • PubMed
    • Google Scholar
88. 1. Tsui D
    2. Vessey JP
    3. Tomita H
    4. Kaplan DR
    5. Miller FD

    (2013) FoxP2 regulates neurogenesis during embryonic cortical development

    Journal of Neuroscience 33:244–258.

    https://doi.org/10.1523/JNEUROSCI.1665-12.2013

    • PubMed
    • Google Scholar
89. 1. Vernes SC
    2. Oliver PL
    3. Spiteri E
    4. Lockstone HE
    5. Puliyadi R
    6. Taylor JM
    7. Ho J
    8. Mombereau C
    9. Brewer A
    10. Lowy E
    11. Nicod J
    12. Groszer M
    13. Baban D
    14. Sahgal N
    15. Cazier JB
    16. Ragoussis J
    17. Davies KE
    18. Geschwind DH
    19. Fisher SE

    (2011) Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain

    PLoS Genetics 7:e1002145.

    https://doi.org/10.1371/journal.pgen.1002145

    • PubMed
    • Google Scholar
90. 1. Vierstra J
    2. Stamatoyannopoulos JA

    (2016) Genomic footprinting

    Nature Methods 13:213–221.

    https://doi.org/10.1038/nmeth.3768

    • PubMed
    • Google Scholar
91. 1. Warnes GR
    2. Bolker B
    3. Bonebakker L
    4. Gentleman R
    5. Huber W
    6. Lumley T
    7. Maechler M
    8. Magnusson A
    9. Moeller S
    10. Schwartz M
    11. Venables B

    (2016) gplots: Various R Programming Tools for Plotting Data

    gplots: Various R Programming Tools for Plotting Data, R package version 3.0.1, https://CRAN.R-project.org/package=gplots.

    https://CRAN.R-project.org/package=gplots

    • Google Scholar
92. 1. Warnes GR
    2. Bolker B
    3. Gorjanc G
    4. Grothendieck G
    5. Korosec A
    6. Lumley T
    7. MacQueen D
    8. Magnusson A
    9. Rogers J

    (2015) gdata: Various R Programming Tools for Data Manipulation

    gdata: Various R Programming Tools for Data Manipulation, R package version 2.17.0, https://CRAN.R-project.org/package=gdata.

    https://CRAN.R-project.org/package=gdata

    • Google Scholar
93. 1. Wickham H
    2. Francois R

    (2016) dplyr: A Grammar of Data Manipulation

    dplyr: A Grammar of Data Manipulation, R package version 0.5.0, https://CRAN.R-project.org/package=dplyr.

    https://CRAN.R-project.org/package=dplyr

    • Google Scholar
94. 1. Wickham H

    (2007) Reshaping Data with the reshape Package

    Journal of Statistical Software 21:20.

    https://doi.org/10.18637/jss.v021.i12

    • Google Scholar
95. Book

    1. Wickham H

    (2009)

    ggplot2: Elegant Graphics for Data Analysis

    New York: Springer-Verlag.

    • Google Scholar
96. 1. Wickham H

    (2016) purrr: Functional Programming Tools

    purrr: Functional Programming Tools, R package version 0.2.2, https://CRAN.R-project.org/package=purrr.

    https://CRAN.R-project.org/package=purrr

    • Google Scholar
97. 1. Yue F
    2. Cheng Y
    3. Breschi A
    4. Vierstra J
    5. Wu W
    6. Ryba T
    7. Sandstrom R
    8. Ma Z
    9. Davis C
    10. Pope BD
    11. Shen Y
    12. Pervouchine DD
    13. Djebali S
    14. Thurman RE
    15. Kaul R
    16. Rynes E
    17. Kirilusha A
    18. Marinov GK
    19. Williams BA
    20. Trout D
    21. Amrhein H
    22. Fisher-Aylor K
    23. Antoshechkin I
    24. DeSalvo G
    25. See LH
    26. Fastuca M
    27. Drenkow J
    28. Zaleski C
    29. Dobin A
    30. Prieto P
    31. Lagarde J
    32. Bussotti G
    33. Tanzer A
    34. Denas O
    35. Li K
    36. Bender MA
    37. Zhang M
    38. Byron R
    39. Groudine MT
    40. McCleary D
    41. Pham L
    42. Ye Z
    43. Kuan S
    44. Edsall L
    45. Wu YC
    46. Rasmussen MD
    47. Bansal MS
    48. Kellis M
    49. Keller CA
    50. Morrissey CS
    51. Mishra T
    52. Jain D
    53. Dogan N
    54. Harris RS
    55. Cayting P
    56. Kawli T
    57. Boyle AP
    58. Euskirchen G
    59. Kundaje A
    60. Lin S
    61. Lin Y
    62. Jansen C
    63. Malladi VS
    64. Cline MS
    65. Erickson DT
    66. Kirkup VM
    67. Learned K
    68. Sloan CA
    69. Rosenbloom KR
    70. Lacerda de Sousa B
    71. Beal K
    72. Pignatelli M
    73. Flicek P
    74. Lian J
    75. Kahveci T
    76. Lee D
    77. Kent WJ
    78. Ramalho Santos M
    79. Herrero J
    80. Notredame C
    81. Johnson A
    82. Vong S
    83. Lee K
    84. Bates D
    85. Neri F
    86. Diegel M
    87. Canfield T
    88. Sabo PJ
    89. Wilken MS
    90. Reh TA
    91. Giste E
    92. Shafer A
    93. Kutyavin T
    94. Haugen E
    95. Dunn D
    96. Reynolds AP
    97. Neph S
    98. Humbert R
    99. Hansen RS
    100. De Bruijn M
    101. Selleri L
    102. Rudensky A
    103. Josefowicz S
    104. Samstein R
    105. Eichler EE
    106. Orkin SH
    107. Levasseur D
    108. Papayannopoulou T
    109. Chang KH
    110. Skoultchi A
    111. Gosh S
    112. Disteche C
    113. Treuting P
    114. Wang Y
    115. Weiss MJ
    116. Blobel GA
    117. Cao X
    118. Zhong S
    119. Wang T
    120. Good PJ
    121. Lowdon RF
    122. Adams LB
    123. Zhou XQ
    124. Pazin MJ
    125. Feingold EA
    126. Wold B
    127. Taylor J
    128. Mortazavi A
    129. Weissman SM
    130. Stamatoyannopoulos JA
    131. Snyder MP
    132. Guigo R
    133. Gingeras TR
    134. Gilbert DM
    135. Hardison RC
    136. Beer MA
    137. Ren B
    138. Mouse ENCODE Consortium

    (2014) A comparative encyclopedia of DNA elements in the mouse genome

    Nature 515:355–364.

    https://doi.org/10.1038/nature13992

    • PubMed
    • Google Scholar
98. 1. Zhang HM
    2. Chen H
    3. Liu W
    4. Liu H
    5. Gong J
    6. Wang H
    7. Guo AY

    (2012) AnimalTFDB: a comprehensive animal transcription factor database

    Nucleic Acids Research 40:D144–D149.

    https://doi.org/10.1093/nar/gkr965

    • PubMed
    • Google Scholar

Author details

1. Lucas T Gray

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   LTG, Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-8814-6818

2. Zizhen Yao

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   ZY, Software, Formal analysis, Visualization, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1210-4195

3. Thuc Nghi Nguyen

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   TNN, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6466-5883

4. Tae Kyung Kim

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   TKK, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9646-5969

5. Hongkui Zeng

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   HZ, Supervision, Funding acquisition, Project administration, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0326-5878

6. Bosiljka Tasic

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   BT, Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   bosiljkat@alleninstitute.org

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506

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