jkaa005-html.html

FlyORF-TaDa allows rapid generation of new lines for

in vivo

cell-type-specific profiling of protein–DNA

interactions in

Drosophila melanogaster

Gabriel N. Aughey

1,†

Caroline Delandre

2,†

John P. D. McMullen,

Tony D. Southall

* and Owen J. Marshall

2,‡

Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK

Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool St, Hobart 7000, Australia

†

These authors contributed equally to this work.

‡

These authors contributed equally to this work.

*Corresponding author: t.southall@imperial.ac.uk (T.D.S.); owen.marshall@utas.edu.au (O.J.M.)

Abstract

Targeted DamID (TaDa) is an increasingly popular method of generating cell-type-specific DNA-binding profiles

in vivo

. Although sensi-

tive and versatile, TaDa requires the generation of new transgenic fly lines for every protein that is profiled, which is both time-consuming
and costly. Here, we describe the FlyORF-TaDa system for converting an existing FlyORF library of inducible open reading frames (ORFs)
to TaDa lines via a genetic cross, with recombinant progeny easily identifiable by eye color. Profiling the binding of the H3K36me3-
associated chromatin protein MRG15 in larval neural stem cells using both FlyORF-TaDa and conventional TaDa demonstrates that new
lines generated using this system provide accurate and highly reproducible DamID-binding profiles. Our data further show that MRG15
binds to a subset of active chromatin domains

in vivo

. Courtesy of the large coverage of the FlyORF library, the FlyORF-TaDa system ena-

bles the easy creation of TaDa lines for 74% of all transcription factors and chromatin-modifying proteins within the

Drosophila

genome.

Keywords:

Transcription factor, Chromatin, Transcription, Development, DamID, Neural stem cells

Introduction

Characterizing the specific protein–DNA interactions that un-
derlie gene expression is essential for understanding the biology
of any given tissue. The last decade has seen a change in think-
ing regarding the action of transcription factors (TFs), proteins
that bind to enhancer and promoter regions of genes and modify
the level of gene expression. In particular, it is now established
that TFs work in complex groups or communities to control
gene expression (for review, see

Reiter

et al.

2017

). A key recent

finding has been that the majority of TFs can act as either an ac-
tivator or as a repressor, with their function determined by the
surrounding TF community (

Stampfel

et al.

2015

). As such, the

ability to profile modules of TFs is critical to understanding reg-
ulatory function.

A major challenge in undertaking the systematic profiling of

TF binding within a cell is the availability of reagents. For ChIP-
seq, a lack of appropriate antibodies is a considerable impedi-
ment, combined with the difficulty of profiling TFs that are not
directly bound to DNA. These issues are solved by DamID, a tech-
nique in which the TF of interest is expressed as a fusion protein
with

Escherichia coli

DNA adenine methylase (Dam) and the result-

ing enriched adenine methylation surrounding the TF-binding
sites profiled. DamID effectively profiles all TFs coming into

proximity with DNA and requires no antibodies for profiling (for
review, see

Aughey

et al.

2019

Targeted DamID (TaDa) allows DamID to be applied in a cell-

type-specific manner to profile cellular transcriptional machin-
ery (

Southall

et al.

2013

), chromatin-modifying proteins (

Marshall

and Brand 2017

), nuclear structural proteins, and TFs (

Doupe´

et al.

2018

). The technique allows the binding profile of any pro-

tein associated with DNA to be mapped in living organisms with-
out the concern of fixation-induced artifacts, from very small
amounts of material (10,000 cells being enough to generate high-
quality profiles) and without the need for cell-sorting (

Marshall

et al.

2016

). Profiling is performed

in vivo

using the GAL4/UAS sys-

tem (

Brand and Perrimon 1993

) to provide cell-type specificity. As

the GAL4/UAS system drives strong expression of transgenes,
and high levels of Dam can be toxic in eukaryotic cells, TaDa
uses a bicistronic transcript to greatly reduce the translation of
Dam-fusion proteins (

Southall

et al.

2013

). In this system, a long

primary open reading frame (ORF) is separated from the Dam-
fusion ORF by two stop codons and a frame shift, with translation
of the latter arising through low rates of spontaneous ribosomal
re-initiation. To generate DNA-binding profiles, GAL4 driver lines
are crossed to lines carrying TaDa Dam-fusion proteins.
However, the time and costs of generating the transgenic fly lines
required for TaDa profiling are considerable.

Received:

August 07, 2020.

Accepted:

November 7, 2020

The Author(s) 2020. Published by Oxford University Press on behalf of Genetics Society of America.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

, 2021,

11(1),

jkaa005

DOI: 10.1093/g3journal/jkaa005

Advance Access Publication Date: 22 December 2020

Investigation

Downloaded from https://academic.oup.com/g3journal/article/11/1/jkaa005/6044134 by guest on 25 April 2024

The FlyORF library of fly lines (

Bischof

et al.

2013

) contains in-

ducible ORFs that cover 74% (560/757) of all known and predicted
TFs and chromatin-associated proteins in the

Drosophila

genome

). The library was designed with cassette

exchange features, presenting the possibility of converting exist-
ing FlyORF lines into TaDa lines to profile DNA binding via a sim-
ple cross. Here, we describe FlyORF-TaDa, a new system that
allows the conversion of FlyORF lines to TaDa lines via Flippase-
mediated cassette exchange. FlyORF-TaDa permits the rapid and
easy generation of new TF-binding profiles without cloning or the
creation of transgenic animals, with recombinants easily identi-
fied by eye color and fluorescence.

Materials and methods

Fly husbandry

Drosophila

were raised on media containing 5% (w/v) yeast, 5.5%

(w/v) dextrose, 3.5% (w/v) cornflour, 0.75% (w/v) agar, 0.25% (v/v)
Nipagin, and 0.4% (v/v) propionic acid. Flies were grown in incu-
bators at 70% humidity on a 12-h/12-h light/dark cycle.

Expression constructs

The

pFlyORF-TaDa

vector was created by cutting

pTaDaG2

) with BglII/NotI (NEB) and inserting a syn-

thetic gBlock (IDT) containing an

FRT5

site followed by an in-

frame stop codon via NEB HiFi Assembly (NEB) to create

pTaDaG2-FRT5

was cut with AfeI/BstBI and a syn-

thetic gBlock (IDT) containing

3xP3-DsRed2-small_t_intron-polyA

via NEB Hifi Assembly to generate

pTaDaG2-FRT5-3xP3-dsRed2

Finally, mini-white (including the 240 bp upstream and 630 bp
downstream

white

regulatory regions) was amplified via PCR

from

pTaDaG2

and inserted into

pTaDaG2-FRT5-3xP3-dsRed

cut

with XhoI and XbaI via NEB Hifi Assembly to generate

pFlyORF-

TaDa

pTaDaG2-MRG15

was generated by inserting a synthetic gBlock

(IDT) containing the sequence of MRG15-RA into the

pTaDaG2

vector cut with XbaI/XhoI. All plasmids were sequence-verified
via Sanger sequencing (ABI). Plasmid maps were generated using
SnapGene software (Insightful Science).

Fly lines

The

worniu-GAL4

;

tub-GAL80ts

line (

Albertson

et al.

2004

) was used

as a driver for neural stem cells. The

FlyORF-TaDa

line was cre-

ated through phiC31-integrase-mediated insertion into

ZH-86FB

(the site used by the FlyORF expression library) on chromosome
3, by injecting

pFlyORF-TaDa

at 200 ng/

l into embryos from a

y[1],M

RFP[3xP3.PB]

GFP[E.3xP3]

vas-int.Dm

ZH-2A,w[*];M

3xP3-

RFP.attP

ZH-86Fb

(Bloomington # 24749) in which the landing site

3xP3-RFP

marker had been previously removed via Cre-mediated

recombination. The resulting transgenic line was backcrossed
three times to

1118

before a homozygous

;FlyORF-TaDa

stock

was generated. The

w;hs-FlPD5;FlyORF-TaDa

was generated by

crossing to

1118

t7.7] w[

mC]

hs-FLPD5

attP40/CyO

stock

(Bloomington # 55814). The

TaDaG2-MRG15

line was generated by

BestGene, Inc (CA), through phiC31-integrase-mediated insertion
of

pTaDaG2-MRG15

into

attP2

on chromosome 3L.

TaDaG2-Dam

flies were used as previously published (

Delandre

et al.

2020

FlyORF cassette exchange

Homozygous

hs-FlpD5;FlyORF-TaDa

virgin females were crossed to

males from

FlyORF

lines. Progeny (third instar, 96 h after larval

hatching) were heat shocked at 37

C for 60 min. After eclosion, F1

male flies were crossed to TM3/TM6B virgin females. F2 males

and virgin females with TM6B and exhibiting the correct eye phe-
notype (

w-;3xP3-dsRed2

) were crossed to establish a balanced

stock.

Targeted DamID

Appropriate lines (for conventional TaDa:

TaDaG2-MRG15

and

TaDaG2-Dam

; for FlyORF-TaDa:

FlyORF-TaDa-MRG15

and

FlyORF-

TaDa

lines) were crossed to

worniu-GAL4;tub-GAL80ts

virgin

females in cages. Embryos were collected on apple juice agar
plates with yeast over a 4-h collection window and grown at 18

for 2 days. Newly hatched larvae were transferred to food plates
for a further 5 days at 18

C, before shifting to 29

C for 24 h.

Larval brains were dissected in PBS, and processed for DamID-

seq as previously described (

;

) with the following modifications. Briefly, DNA was

extracted using a Quick-DNA Miniprep plus kit (Zymo), digested
with DpnI (NEB) overnight, and cleaned up with a PCR purifica-
tion kit (Machery-Nagel), DamID adaptors were ligated, digested
with DpnII (NEB) for 2 h, and amplified via PCR using MyTaq DNA
polymerase (Bioline).

Following amplification, 2

g DNA was sonicated in a

Bioruptor Plus (Diagenode). DamID adaptors were removed by
AlwI digestion, and 500 ng of the resulting fragments end-
repaired with a mix of enzymes [T4 DNA ligase (NEB)

Klenow

Fragment (NEB)

T4 polynucleotide kinase (NEB)], A-tailed with

Klenow 3

exo- (NEB), ligated to Illumina Truseq LT adaptors

using Quick Ligase enzyme (NEB), and amplified via PCR with
NEBNext Hi-fidelity enzyme (NEB). The resulting next-generation
sequencing libraries were sequenced on a HiSeq 2500 (Illumina).

Bioinformatic analyses

DamID-binding profiles were generated from NGS reads using
damidseq_pipeline (

Marshall and Brand 2015

) and visualized

using pyGenomeTracks (

Ramı´rez

et al.

2018

). Peaks were called

using a three-state hidden markov model via the hmm.peak.cal-
ler R script (freely available from https://github.com/owenjm/
hmm.peak.caller

Heatmaps

were

generated

via

the

ComplexHeatmap R package (

et al.

2016

Gene ontology (GO) enrichment plots were performed using

the ClusterProfiler R package with Bonferroni–Holm-adjusted

values; enrichmap plots were generated by limiting GO terms to

1000 genes and using the simply() function to remove redun-

dancy (

et al.

2012

MRG15 enrichment by chromatin state (significance and odds

ratio) was assessed via Fisher’s exact test with an alternate hy-
pothesis of “greater”, for contingency tables of genomic coverage
of MRG15

genomic coverage of the chromatin state in

neural stem cells (NSCs). All

-values were Bonferroni–Holm

adjusted. All other plots were generated using R (

R Core Team,

2020

Data availability

The sequence of

pFlyORF-TaDa

is available under Genbank acces-

sion number MT733231, and the plasmid DNA is available upon
request. DamID-seq data and processed bedgraph and analysis
files have been deposited in NCBI GEO, accession number
GSE159632. The

;TaDaG2-MRG15

;TaDaG2-Dam

, and

;FlyORF-TaDa-MRG15

fly lines are available upon request; the

w;hsFlp-D5;FlyORF-TaDa

and

;

FlyORF-TaDa

lines have been de-

posited with the Bloomington Drosophila Stock Center.

Supplementary material is available at figshare DOI: https://

doi.org/10.25387/g3.13245053

2 |

G3,

2021, Vol. 11, No. 1

Downloaded from https://academic.oup.com/g3journal/article/11/1/jkaa005/6044134 by guest on 25 April 2024

Results and discussion

Design of the FlyORF-TaDa system

The FlyORF library of inducible ORFs incorporates an FRT5 site
immediately upstream of each ORF, allowing Flippase-mediated
exchange with an upstream donor cassette in the same genomic
insertion site (

Bischof

et al.

2013

). To convert these lines to TaDa

lines, FlyORF-TaDa provides the UAS-inducible bicistronic tran-
script of TaDa vectors upstream of an FRT5 site as the donor cas-
sette. FlyORF-TaDa uses the TaDaG2 version of the TaDa
bicistronic transcript, in which the primary ORF is myristoylated
GFP, providing the advantage of easily-detectable fluorescent la-
beling of cells being profiled within experimental samples
(

Delandre

et al.

2020

The bicistronic cassette is followed by an

FRT5

site in frame

with

Dam

(

Figure 1A

and Supplementary Figure S1A). Without re-

combination, a stop codon positioned directly after the FRT5 site
allows the FlyORF-TaDa line to be used as a Dam-only control for
DamID

signal

normalization

(Supplementary

Figure

S1B).

Following recombination with a FlyORF line, the FRT5 site to-
gether with the Gateway cloning residual attB1 site present as
part of the FlyORF library construction (

becomes a 23-amino-acid protein linker region (Supplementary
Figure S1C).

To prevent the need to PCR-screen progeny, the vector was ad-

ditionally designed with a

3xP3-dsRed2

eye marker upstream of

the TaDa cassette, and the

mini-white

eye marker downstream of

the

FRT5

site (

Figure 1A

and Supplementary Figure S1A). While

both parental strains used for a conversion are

, upon

Flippase-mediated strand exchange, successful recombinants
will be

w-,dsRed2

making screening of recombinants by eye

color fast and straightforward. [We note that the FlyORF lines
contain a

3xP3-RFP

marker as part of the landing site (removed in

the FlyORF-TaDa lines) making screening for recombinants on
eye fluorescence alone difficult, although we observe signifi-
cantly more intense eye fluorescence from the

3xP3-dsRed2

marker.]

The

pFlyORF-TaDa

vector was inserted into ZH-86FB, the same

insertion site used by the FlyORF library, and the resulting line
was crossed with a heat-shock-inducible Flippase (

hs-FlpD5

) line

to yield an

hs-FlpD5;FlyORF-TaDa

donor line. TaDa alleles are gen-

erated by crossing any FlyORF line to this donor line, in conjunc-
tion with a heat-shock of progeny during the larval phase.
Crossing the resulting adult males to virgin females with chromo-
some 3 balancers yields a typical recombination frequency of

50% (combined totals for two separate FlyORF-TaDa conversions:
7 vials with at least one recombinant/14 total vials scored with a
minimum of 50 progeny). An illustration of the crossing scheme
is shown in Supplementary Figure S2.

Profiling of MRG15 binding in neural stem cells
using the FlyORF-TaDa system

To determine whether FlyORF-TaDa lines generated through the
system faithfully profiled binding in a cell-type-specific manner,
we obtained binding profiles for the chromatin-binding protein
MRG15 in NSCs. We obtained profiles from a

FlyORF-TaDa-MRG15

and compared these to MRG15 profiles generated using conven-
tional TaDa, driving expression in both cases with the NSC-
specific driver,

worniu-GAL4

. Excellent correlation between sam-

ples was observed throughout (

Figure 1B

), both between individ-

ual replicates (Supplementary Figure S3) and when comparing
the average profiles from the two systems (

Figure 1B

). In particu-

lar, the two biological replicates generated using the FlyORF-
TaDa system showed extremely high reproducibility (Pearson’s
correlation between replicates, 0.95; Supplementary Figure S3).

MRG15 is a protein associated with the H3K36me3 histone

mark (

Zhang

et al.

2006

), which in turn is associated with tran-

scribed exons (

). The protein is also a key component of the “Yellow” active

chromatin state described in flies by

Filion

et al.

(2010)

, a study

that reduced chromatin configurations in

Drosophila

to five broad

classification types, or colors. In concordance with these observa-
tions, we found MRG15 bound at previously published Yellow
chromatin domains in NSCs (

Marshall and Brand 2017

)

(

Figure 2A

), with MRG15-bound peaks covering 80% (9.8 Mb of

12.2 Mb) of Yellow chromatin. While MRG15 occupancy was sig-
nificantly enriched to some degree over all three active chroma-
tin states in NSCs, by far the greatest enrichment was observed
over Yellow chromatin [

Figure 2B;

log

(Yellow chromatin)

2.91, Fisher’s exact test].

Genes covered by bound peaks were enriched for GO functions

associated with metabolism (

Figure 3

and Supplementary Figure

S4), a known association for genes covered by Yellow chromatin,
both in cell lines (

;

) and in

NSCs (

Marshall and Brand 2017

). Importantly, we also observed

an enrichment for genes involved in nervous system develop-
ment expressed in NSCs (

Figure 3

), consistent with occupancy

over transcribed exons and indicating that the FlyORF-TaDa sys-
tem can faithfully profile cell-type-specific protein binding

in vivo

Figure 1.

The FlyORF-TaDa system generates new lines for cell-type specific profiling. (A) Schematic representation of the FlyORF-TaDa system. When

the FlyORF-TaDa donor line is crossed to a FlyORF line and a heat-shock-inducible

flippase

(hs-Flp), recombination between compatible FRT5 sites

generates a new TaDa line for the ORF in the progeny. These lines can be screened for in the subsequent generation by eye color. Dashed lines show
GAL4/UAS-induced transcription; *stop codon. (B) Correlation of DNA-binding profiles generated for MRG15 in NSCs using the FlyORF-TaDa system,
compared to conventional TaDa (Pearson’s correlation is shown).

G. N. Aughey

et al.

| 3

Downloaded from https://academic.oup.com/g3journal/article/11/1/jkaa005/6044134 by guest on 25 April 2024

Limitations

Although versatile, some potential limitations of the FlyORF-TaDa
system should be considered when using the converted lines for
profiling. In particular, the FlyORF lines that are convertible to
TaDa lines (

i.e.

lines that include an FRT5 site) all incorporate a

long (22aa) flexible linker combined with 3xHA tags at the C-termi-
nal. There is some evidence that the presence of HA tags may dis-
rupt some aspects of protein function in unusual cases. For the
FlyORF library, the presence of these tags was shown—albeit in
overexpression experiments—to cause a gain of function in 11% of
lines, and a failure to cause a phenotype in 22% of lines, when
compared to overexpression of the untagged variant (

Bischof

et al.

2013

). A related study of GFP- and FLAG-tagged proteins from the

FlyFos library of lines suggested that 33% of tagged lines were un-
able to rescue the corresponding deficiency, although this failure
was ascribed to the lack of long-range regulatory elements of com-
plex developmental TFs rather than the presence of tags

themselves (

Sarov

et al.

2016

). It is unknown whether in any of

these cases protein–DNA binding was affected. Nevertheless, it is
possible that in a minority of cases the presence of the HA tags
may lead to unrepresentative binding profiles.

Another consideration is that the FlyORF-TaDa system only

generates fusions with Dam fused to the N-terminus of the ORF
protein (again via a long linker generated via the FRT5 site). It
remains unclear as to whether Dam can disrupt TF binding in
some circumstances, although previous studies have shown that
Dam-fusion proteins yield binding profiles broadly comparable to
ChIP-seq data obtained for the same protein (

Notably, the FlyORF libraries also include an FRT2 site imme-

diately 3

of the ORF. Both the removal of the C-terminal linker

and 3xHA tags, and the generation of C-terminal Dam-fusion pro-
teins, would in the future be possible with different donor lines
designed on a similar basis to the system presented here.

Figure 2.

Characteristics of MRG15 binding in NSCs. (A) MRG15 binds to regions of Yellow chromatin in NSCs. Profiles obtained via FlyORF-TaDa and

conventional TaDa are shown; peaks were called on the average binding profile of the combined MRG15 datasets. NSC chromatin state data and colors
are from

Marshall and Brand (2017)

. A 300-kb region of chrX is illustrated. (B) Significance and odds ratio (

) of enrichment of MRG15 peak occupancy

within NSC chromatin states as assessed via Fisher’s exact test.

Figure 3.

GO term enrichment for genes covering MRG15-bound peaks identifies terms for both metabolism and developmental neurogenesis (non-

redundant GO terms covering

1000 genes illustrated). Metabolism-related terms are colored in orange; developmental terms are colored in red. All

terms illustrated were significantly enriched (all adjusted

-values

4 |

G3,

2021, Vol. 11, No. 1

Downloaded from https://academic.oup.com/g3journal/article/11/1/jkaa005/6044134 by guest on 25 April 2024

Conclusion

The FlyORF-TaDa system places fast and straightforward cell-
type-specific profiling of TF binding within the reach of any fly
lab, allowing the profiling of over 74% of all TFs and chromatin-
associated proteins via a simple genetic cross. Establishment of
new recombinant lines from the parental FlyORF stocks is
achieved in two generations, without the need for cloning, se-
quencing or transgenesis. The donor

w;hs-FlpD5;FlyORF-TaDa

and

Dam-only control

;FlyORF-TaDa

lines have been deposited

with, and will be available from, the Bloomington Drosophila
Stock Center (BDSC).

With three quarters of all TFs covered by donor FlyORF lines,

comprehensive wide-scale cell-type-specific profiling of TF-
binding networks can be achieved. TaDa is ideally suited to profil-
ing such networks without introducing the variability of different
fixation and antibody isolation steps found in alternative meth-
ods such as ChIP-seq. The FlyORF-TaDa system now eliminates a
major obstacle to this approach by dramatically reducing the
time and costs required to generate the lines required to profile
TF networks. To further this aim, we have successfully generated
17 new FlyORF-TaDa lines using the system and are creating a li-
brary of converted TF and chromatin factor lines that will be de-
posited in stages at the BDSC. We anticipate that this resource
will prove highly useful to the

Drosophila

transcription and chro-

matin, and developmental biology communities.

Acknowledgment

We thank G. Jefferies for technical support.

Funding

This work was supported by National Health and Medical
Research Council (NHMRC) Australia grants APP1128784 and
APP1185220 to O.J.M., a Wellcome Trust Investigator grant
104567 to T.D.S., and a Biotechnology and Biological Sciences
Research Council (BBSRC) UK grant BB/P017924/1 to T.D.S. and
G.N.A.

Conflicts of interest

: None declared.

Literature cited

Albertson R, Chabu C, Sheehan A, Doe CQ. 2004. Scribble protein do-

main mapping reveals a multistep localization mechanism and
domains necessary for establishing cortical polarity. J Cell Sci.
117:6061–6070. doi:10.1242/jcs.01525.

Aughey GN, Cheetham SW, Southall TD. 2019. DamID as a versatile

tool for understanding gene regulation. Development. 146:1–8.
doi:10.1242/dev.173666.

Bischof J, Bjorklund M, Furger E, Schertel C, Taipale J,

et al.

2013. A

versatile platform for creating a comprehensive UAS-ORFeome
library

Drosophila.

Development.

140:2434–2442.

doi:

10.1242/dev.088757.

Bischof J, Sheils EM, Bjo¨rklund M, Basler K. 2014. Generation of a

transgenic ORFeome library in Drosophila. Nat Protoc. 9:
1607–1620. doi:10.1038/nprot.2014.105.

Brand AH, Perrimon N. 1993. Targeted gene expression as a means of

altering cell fates and generating dominant phenotypes.
Development. 118:401–415.

Cheetham SW, Gruhn WH, van den Ameele J, Krautz R, Southall TD,

et al.

2018. Targeted DamID reveals differential binding of mam-

malian pluripotency factors. Development. 145:dev170209. doi:
10.1242/dev.170209.

Delandre C, McMullen JPD, Marshall OJ. 2020. Membrane-bound

GFP-labelled vectors for Targeted DamID allow simultaneous
profiling of expression domains and DNA binding. bioRxiv 1–4.
doi:10.1101/2020.04.17.045948.

Doupe´ DP, Marshall OJ, Dayton H, Brand AH, Perrimon N. 2018.

Drosophila intestinal stem and progenitor cells are major sources
and regulators of homeostatic niche signals. Proc Natl Acad Sci
USA. 115:12218–12223. doi:10.1073/pnas.1719169115.

Filion GJ, van Bemmel JG, Braunschweig U, Talhout W, Kind J,

et al.

2010. Systematic protein location mapping reveals five principal
chromatin types in Drosophila cells. Cell. 143:212–224. doi:
10.1016/j.cell.2010.09.009.

Gu Z, Eils R, Schlesner M. 2016. Complex heatmaps reveal patterns

and

correlations

multidimensional

genomic

data.

Bioinformatics. 32:2847–2849. doi:10.1093/bioinformatics/btw313.

Kharchenko PV, Alekseyenko AA, Schwartz YB, Minoda A, Riddle NC,

et al.

2011. Comprehensive analysis of the chromatin landscape

Drosophila

melanogaster.

Nature.

471:480–485.

doi:

10.1038/nature09725.

Kolasinska-Zwierz P, Down T, Latorre I, Liu T, Liu XS,

et al.

2009.

Differential chromatin marking of introns and expressed exons
by H3K36me3. Nat Genet. 41:376–381. doi:10.1038/ng.322.

Marshall OJ, Brand AH. 2015. Damidseq_pipeline: an automated

pipeline

for

processing

DamID

sequencing

datasets.

Bioinformatics. 31:3371–3373.

Marshall OJ, Brand AH. 2017. Chromatin state changes during neural

development revealed by in vivo cell-type specific profiling. Nat
Commun. 8:2271.doi:10.1038/s41467-017-02385-4.

Marshall OJ, Southall TD, Cheetham SW, Brand AH. 2016.

Cell-type-specific profiling of protein–DNA interactions without
cell isolation using targeted DamID with next-generation se-
quencing. Nat Protoc. 11:1586–1598. doi:10.1038/nprot.2016.084.

R Core Team. 2020. R: A language and environment for statistical

computing. R Foundation for Statistical Computing, Vienna,
Austria. https://www.R-project.org/

Ramı´rez F, Bhardwaj V, Arrigoni L, Lam KC, Gru¨ning BA,

et al.

2018.

High-resolution TADs reveal DNA sequences underlying genome
organization in flies. Nat Commun. 9. doi:10.1038/s41467
-017-02525-w.

Reiter F, Wienerroither S, Stark A. 2017. Combinatorial function of

transcription factors and cofactors. Curr Opin Genet Dev. 43:
73–81. doi:10.1016/j.gde.2016.12.007.

Sarov M, Barz C, Jambor H, Hein MY, Schmied C,

et al.

2016. A

genome-wide resource for the analysis of protein localisation in
Drosophila. Elife. 5:1–38. doi:10.7554/eLife.12068.

Southall TD, Gold KS, Egger B, Davidson CM, Caygill EE,

et al.

2013.

Cell-type-specific profiling of gene expression and chromatin bind-
ing without cell isolation: assaying RNA Pol II occupancy in neural
stem cells. Dev. Cell. 26:101–112. doi:10.1016/j.devcel.2013.05.020.

Stampfel G, Kazmar T, Frank O, Wienerroither S, Reiter F,

et al.

2015.

Transcriptional

regulators

form

diverse

groups

with

context-dependent regulatory functions. Nature. 528:147–151.
doi:10.1038/nature15545.

Szczesnik T, Ho JWK, Sherwood R. 2019. Dam mutants provide im-

proved sensitivity and spatial resolution for profiling transcrip-
tion

factor

binding.

Epigenetics

Chromatin.

12:36.doi:

10.1186/s13072-019-0273-x.

G. N. Aughey

et al.

| 5

Downloaded from https://academic.oup.com/g3journal/article/11/1/jkaa005/6044134 by guest on 25 April 2024