Drosophila melanogaster, the pioneer factor Zelda is essential for development through this dramatic period of reprogramming, known as the maternal-to-zygotic transition (MZT). However, it was unknown whether additional pioneer factors were required for this transition. We identified an additional maternally encoded factor required for development through the MZT, GAGA Factor (GAF). GAF is necessary to activate widespread zygotic transcription and to remodel the chromatin accessibility landscape. We demonstrated that Zelda preferentially controls expression of the earliest transcribed genes, while genes expressed during widespread activation are predominantly dependent on GAF. Thus, progression through the MZT requires coordination of multiple pioneer-like factors, and we propose that as development proceeds control is gradually transferred from Zelda to GAF.Following fertilization, the genomes of the germ cells are reprogrammed to form the totipotent embryo. Pioneer transcription factors are essential for remodeling the chromatin and driving the initial wave of zygotic gene expression. In  Most cells in an organism share the exact same genetic information, yet they still adopt distinct identities. This diversity emerges because only a selection of genes is switched on at any given time in a cell. Proteins that latch onto DNA control this specificity by activating certain genes at the right time. However, to perform this role they first need to physically access DNA: this can be difficult as the genetic information is tightly compacted so it can fit in a cell. A group of proteins can help to unpack the genome to uncover the genes that can then be accessed and activated. While these ‘pioneer factors’ can therefore shape the identity of a cell, much remains unknown about how they can work together to do so. For instance, the pioneer factor Zelda is essential in early fruit fly development, as it enables the genetic information of the egg and sperm to undergo dramatic reprogramming and generate a new organism. Yet, it was unclear whether additional helpers were required for this transition.Using this animal system, Gaskill, Gibson et al. identified GAGA Factor as a protein which works with Zelda to open up and reprogram hundreds of different sections along the genome of fruit fly embryos. This tag-team effort started with Zelda being important initially to activate genes; regulation was then handed over for GAGA Factor to continue the process. Without either protein, the embryo died.Getting a glimpse into early genetic events during fly development provides insights that are often applicable to other animals such as fish and mammals. Ultimately, this research may help scientists to understand how things can go wrong in human embryos. Pronounced changes in cellular identity are driven by pioneer transcription factors that act at the top of gene regulatory networks. While nucleosomes present a barrier to the DNA binding of many transcription factors, pioneer factors can bind DNA in the context of nucleosomes. Pioneer-factor binding establishes accessible chromatin domains, which serve to recruit additional transcription factors that drive gene expression . These uPioneer factors are also essential for the reprogramming that occurs in the early embryo. Following fertilization, specified germ cells must be rapidly and efficiently reprogrammed to generate a totipotent embryo capable of differentiating into all the cell types of the adult organism. This reprogramming is initially driven by mRNAs and proteins that are maternally deposited into the oocyte. During this time, the zygotic genome remains transcriptionally quiescent. Only after cells have been reprogrammed is the zygotic genome gradually activated. This maternal-to-zygotic transition (MZT) is broadly conserved among metazoans and essential for future development . ActivatDrosophila development is characterized by 13, rapid, synchronous nuclear divisions. Zygotic transcription gradually becomes activated starting about the eighth nuclear division, and widespread transcription occurs at the 14th nuclear division when the division cycle slows and the nuclei are cellularized  gene, has broad roles in transcriptional regulation, including functioning as a transcriptional activator (cis-regulatory regions (Ultrabiothorax (Ubx), Abdominal B (Abd-B), engrailed (en), and fushi tarazu (ftz) and in maintaining normal embryonic development , the gene encoding GAF, are lethal  at either the N- or C-termini, sfGFP-GAF(N) and GAF-sfGFP(C), respectively . There ae lethal , both sfe lethal . TogetheGAF-sfGFP(C) stage 3 (nuclear cycle (NC) 9) and stage 5 (NC14) embryos and performed chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) using an anti-GFP antibody. While alternative splicing generates two GAF protein isoforms that differ in their C-terminal polyQ domain, in the early embryo only the short isoform is detectable (sfGFP-GAF(N) embryos harvested 13–16 hr AEL. Thus, we conclude that during the MZT the C-terminally sfGFP-labeled short isoform comprises the overwhelming majority of GAF present in the GAF-sfGFP(C) embryos. We identified 3391 GAF peaks at stage 3 and 4175 GAF peaks at stage 5  embryos (heat shock promoter (hsp70), even-skipped (eve), Krüppel (Kr), Ubx, and en  promoter for strong expression in the embryo 0–2 hr after fertilization  females all maternally encoded GAF protein is tagged with GFP and thus subject to degradation by the deGradFP nanobody fusion. These embryos are hereafter referred to as GAFdeGradFP.Early embryonic GAF is maternally deposited in the oocyte, and this maternally deposited mRNA can sustain development until the third instar larval stage . To invelization . In embrdeGradFP embryos lack the punctate, nuclear GFP signal identified in control embryos that do not carry the deGradFP nanobody fusion, indicating efficient depletion of sfGFP-GAF(N) (sfGFP-GAF(N) had only a 30% hatching rate (GAF-sfGFP(C) hatched at a rate of 66%. To determine whether the differences in hatching rates were due to differences in protein expression levels, we performed in vivo fluorescent quantification of the GFP signal in nuclei of sfGFP-GAF(N) and GAF-sfGFP(C) homozygous stage 5 embryos (2–2.5 hr AEL). There was no statistically significant difference in GFP signal between the genotypes (sfGFP-GAF(N) embryos is unlikely to be caused by reduced protein levels. Nonetheless, all future experiments controlled for the effect of the N-terminal tag by using sfGFP-GAF(N) homozygous embryos as paired controls with GAFdeGradFP embryos.We verified the efficiency of the deGradFP system by imaging living embryos in which nuclei were marked by His2Av-RFP. GAFP-GAF(N) . This knP-GAF(N) . Based oing rate . By contenotypes , suggestdeGradFP embryos. GAFdeGradFP embryos in which nuclei were marked by a fluorescently labeled histone (His2Av-RFP) were imaged through several rounds of mitosis. We observed defects such as asynchronous mitosis, anaphase bridges, disordered nuclei, and nuclear dropout  homozygous embryos through several rounds of mitosis. These embryos proceeded normally through NC10-14, demonstrating that the mitotic defects in the GAFdeGradFP embryos were caused by the absence of GAF and not the sfGFP tag ) embryos harvested 2–2.5 hr AEL, during the beginning of NC14 when the widespread genome activation has initiated. Our replicates were reproducible  homozygous embryos as a control we have excluded from our analysis any genes misexpressed as a result of the sfGFP tag on GAF. Of the misexpressed genes 884 were down-regulated and 568 were up-regulated in the absence of GAF  down-regulated in GAFdeGrad embryos. Additionally, many of the gap genes, essential regulators of anterior-posterior patterning, are down-regulated: giant (gt), knirps (kni), huckebein (hkb), Krüppel (Kr), and tailless (tll)  . Gene onss (tll) . GO-termss (tll) .−16, log2(odds ratio)=1.95, two-tailed Fisher’s exact test). Genes activated late in NC14, during widespread genome activation, with a proximal GAF-binding site are significantly more down-regulated than genes activated at the same time point but lacking a GAF-binding site =4.21, two-tailed Fisher’s exact test; To determine whether GAF is functioning predominantly in transcriptional activation or repression, we used our stage 5 ChIP-seq data to determine the likely direct targets of GAF by assigning GAF-ChIP peaks to the nearest gene. We found that 45% (397) of the down-regulated genes were proximal to a GAF peak. By contrast, only 17% (99) of up-regulated genes are near GAF peaks, similar to the 15% of genes with unchanged expression levels that were proximal to GAF sites . The siging site . This aning site . Indeed,deGradFP embryos to genes down-regulated in clamp-RNAi embryos 2–4 hr AEL ) 2–2.5 hr AEL (stage 5) embryos confirmed that Zld levels were consistent between extracts   as well as genes involved in cellular blastoderm formation such as slow as molasses (slam) and bottleneck (bnk). While Zld and GAF share some targets, they each are required for expression of hundreds of individual genes.Having identified that GAF was required for ZGA, we wanted to ensure that the effects were not due to changes in the levels of Zld, the previously identified activator of the zygotic genome. Immunoblots for Zld on extract from GAFextracts . TherefoAL4-VP16 . We founcontrols , demonstNC10-14) . We idenNC10-14) . An evenNC10-14) . Genes tdeGradFP and ZldCRY2 embryos (Both), those down-regulated only in ZldCRY2 embryos (ZldCRY2), and those down-regulated only in GAFdeGradFP embryos (GAFdeGradFP) (CRY2) and those regulated by both Zld and GAF (Both) were enriched for genes expressed early (NC10-11) and Mid (NC12-13). By contrast, 73% of genes activated by GAF alone (GAFdeGradFP) were expressed either late (early NC14) or later (late NC14) =3.13, two-tailed Fisher’s exact test). This analysis supports a model in which GAF and Zld are essential activators for the initial wave of ZGA and that GAF has an additional role, independent of Zld, in activating widespread transcription during NC14.Activation of the zygotic genome is a gradual process that initiates with transcription of a small number of genes around NC8. Transcripts can therefore be divided into categories based on the timing of their initial transcription . PrevioueGradFP) . Genes azld using RNAi driven in the maternal germline by matα-GAL4-VP16 in a background containing sfGFP-GAF(N) (sfGFP-GAF(N); zld-RNAi and control (sfGFP-GAF(N)) embryos using identical acquisition settings. We observed no difference in puncta formation of GAF at the beginning of ZGA (NC10) or late ZGA (NC14)  . ImmunobP-GAF(N) , and we A (NC14) . We conczld-RNAi and sfGFP-GAF(N) along with paired sfGFP-GAF(N) controls at 2–2.5 hr AEL (stage 5). Mouse H3.3-GFP chromatin was used as a spike-in to normalize for immunoprecipitation efficiency between samples. In control sfGFP-GAF(N) embryos, we identified 6373 peaks, and these largely overlapped with the peaks identified in the GAF-sfGFP(C) embryos; 91% of the 4175 peaks identified in the GAF-sfGFP(C) embryos overlap with the peaks identified for the N-terminally tagged GAF (zld knockdown at this set of 3800 high-confidence peaks. The majority (3674) of these high-confidence GAF peaks were maintained in the zld-RNAi background and were bound at roughly equivalent levels when normalized to the spike-in control (sfGFP-GAF(N) controls and the zld-RNAi embryos ) embryos at 2–2.5 hr AEL (stage 5). We identified a set of high-confidence Zld peaks, by overlapping the Zld peaks from our control (sfGFP-GAF(N)) embryos with previously published data for Zld at NC14  and sfGFP-GAF(N); zld-RNAi embryos and r = 0.88 for Zld ChIP in sfGFP-GAF(N) and GAFdeGradFP embryos; Because of the limited number of significantly different peaks identified upon removal of either Zld (3.3% of GAF peaks changed) or GAF (1.7% of Zld peaks changed), we sought to determine whether there were global changes in the distribution of these factors. For this purpose, we ranked peaks in each dataset based on the number of reads per kilobase per million mapped reads (RPKM) and determined if the peak ranks were correlated between the mutant and control. We identified a high degree of correlation in peak rank for both comparisons =1.19, two-tailed Fisher’s exact test). Based on the enrichment for Zld binding at these GAF-bound decreased sites, we hypothesized that Zld might be pioneering chromatin accessibility at these regions to promote GAF binding. Indeed, these regions were enriched for loci that depend on Zld for chromatin accessibility =4.07, two-tailed Fisher’s exact test): 38 of the 51 GAF and Zld co-bound sites at which GAF requires Zld for occupancy also require Zld for accessibility ) embryos harvested 2–2.5 hr AEL. His2AV-RFP signal for each embryo was visually inspected prior to performing ATAC-seq to ensure that embryos did not have grossly distorted nuclear morphology. In contrast to our control, replicates from the GAFdeGradFP embryos showed higher variability  ChIP-seq, when compared to open regions with no change in accessibility =2.4, two-tailed Fisher’s exact test, Ubx, a previously identified embryonic GAF-target gene, is an example of a gene that requires GAF for chromatin accessibility at the promoter zld  . Only sed (zld-) . GAF is d (zld-) . The maj−7, log2(odds ratio)=3.2, two-tailed Fisher’s exact test). Indeed, the Ubx promoter dynamically gained accessibility at NC13 (sfGFP-GAF(N)) and GAFdeGradFP embryos, the vast majority of sites that gained accessibility at NC13 (2452) were unchanged in accessibility in the GAFdeGradFP embryos =2.7, two-tailed Fisher’s exact test)  . TogetheThe preferential requirement for GAF-mediated accessibility during NC12 and NC13 allowed us to test whether GAF binding preceded the establishment of chromatin accessibility at these regions or whether this accessibility was co-incident with GAF binding. We determined whether the 42 regions that depended on GAF for accessibility and that gained accessibility at NC13 were already bound by GAF at stage 3 (NC9) or whether these regions were among those that were only occupied by GAF at stage 5 (NC14). All 42 of these regions were bound by GAF at stage 3 and stage 5, demonstrating that GAF occupancy at these regions precedes accessibility and supports a role for GAF as a pioneer factor at these regions.Drosophila, as in mice, zebrafish, and humans, transcriptional activation during the MZT is driven by multiple factors with pioneering characteristics  and the deGradFP nanobody fusion. Together these phenotypic differences between null mutants and our deGrad-based knockdown along with our genomics data identifying misregulation of previously identified embryonic GAF-targets in the GAFdeGradFP embryos demonstrate an essential function for GAF in the early embryo.Using the deGradFP system, we demonstrated that maternally encoded GAF is required for embryogenesis. In contrast to zygotic GAF null embryos, which survive until the third instar larval stage, GAFot hatch . Our imaot hatch  and 2. S embryos  and likering ZGA . It is puce eggs , which sdeGradFP embryos support the model that GAF is broadly required for nuclear division and chromosome stability, in addition to its role in transcriptional activation during ZGA  and Dux   . Our datIn addition to a direct role in mediating chromatin accessibility through the recruitment of chromatin remodelers , GAF mayDrosophila embryo. By analyzing the individual contributions of these two factors, we have begun to elucidate the different mechanisms by which multiple pioneer factors can drive dramatic changes in cell identity.Reprogramming in culture requires a cocktail of transcription factors that possess pioneer factor activity . SimilarWhile some pioneer factors work together to stabilize their interaction on chromatin  our datadeGradFP embryos as compared to controls. We would predict that if GAF is directly functioning to stabilize Zld binding then this effect would be more evident at regions where both GAF and Zld bind, but this is not what we observed. Nonetheless, because of the proposed role of GAF in regulating three-dimensional chromatin architecture, GAF may be globally required for robust Zld occupancy. Furthermore, at a small subset of loci our data support a pioneering role for Zld in promoting GAF binding. In this experiment, the endogenously tagged GAF allowed us to use antibodies directed against the GFP epitope for ChIP. The use of the epitope tag enabled us to robustly control for immunoprecipitation efficiency by using another GFP-tagged protein as a spike-in control for the immunoprecipitation of GAF. In this manner, we identified 101 GAF-bound regions that decreased upon Zld knockdown. Of these, 51 were Zld-bound regions and 38 of these depend on Zld for chromatin accessibility. Thus, at a small subset of sites the pioneering function of Zld may be required to stabilize GAF binding.At most loci, our data support independent binding for GAF and Zld. Nonetheless, we cannot eliminate a possible global role for GAF on Zld occupancy as the Zld ChIP-seq signal is considerably lower in the GAFWe propose that there is a handoff between Zld and GAF pioneer-like activity as the embryo progresses through the MZT: Zld functions primarily at the initiation of zygotic genome activation and GAF functions primarily later during the major wave of zygotic transcription. We identified that genes regulated by Zld are enriched for those activated during NC10-13, while genes that depend on GAF are enriched for those that initiate expression during NC14. Similarly, we determined that the majority of regions that require Zld for accessibility are already accessible at NC11 while those that require GAF are enriched for regions that gain accessibility at NC12 and NC13. This is supported by prior analysis that showed that the Zld-binding motif is enriched at regions that are already accessible at NC11 and the GAF-binding motif is enriched at regions that dynamically gain accessibility later during the MZT . Based ocis-regulatory regions and drive genome activation. Future studies will enable more detailed mechanistic insights into how multiple pioneering factors work together to reshape the transcriptional landscape and transform cell fate in the early embryo.Together our data support the requirement for at least two pioneer-like transcription factors, Zld and GAF, to sequentially reprogram the zygotic genome following fertilization and allow for future embryonic development. It is likely that there are additional factors that function along with Zld and GAF to define accessible 1118w, His2Av-RFP (II) (Bloomington Drosophila Stock Center (BDSC) #23651), mat-α-GAL4-VP16 (BDSC #7062), UAS-shRNA-zld (sfGFP-GAF(N) and GAF-sfGFP(C) mutant alleles were generated using Cas9-mediated genome engineering (see below).All stocks were grown on molasses food at 25°C. Fly strains used in this study: hRNA-zld . sfGFP-Gnos-deGradFP (II) transgenic flies were made by PhiC31 integrase-mediated transgenesis into the PBac{yellow[+]-attP-3B}VK00037 docking site (BDSC #9752) by BestGene Inc. The sequence for NSlmb-vhhGFP4 was obtained from UASp-Nslmb.vhhGFP4 (BDSC #58740). The NSlmb-vhhGFP4 sequence was cloned using Gibson assembly into pattB (DGRC #1420) with the nanos promoter and 5’UTR.zld knockdown background, we crossed mat-α-GAL4-VP16 (II)/Cyo; sfGFP-GAF N(III) flies to UAS-shRNA-zld (III) flies and took mat-α-GAL4-VP16/+ (II); sfGFP-GAF(N)/ UAS-shRNA-zld (III) females. These females were crossed to their siblings, and their embryos were collected. For controls, mat-α-GAL4-VP16 (II)/Cyo; sfGFP-GAF N(III) flies were crossed to 1118w flies and embryos from mat-α-GAL4-VP16/+(II): sfGFP-GAF(N)/+(III) females crossed to their siblings were collected.To obtain the embryos for ChIP-seq and live embryo imaging in a nos-degradFP (II); sfGFP-GAF(N)/TM6c (III) flies to His2Av-RFP (II); sfGFP-GAF(N) (III) flies and selected females that were nos-degradFP/His2Av-RFP (II); sfGFP-GAF(N) (III). These females were crossed to their siblings of the same genotype, and their embryos were collected. Embryos from His2Av-RFP (II); sfGFP-GAF(N) (III) females were used as paired controls.To obtain embryos for live imaging, hatching rate assays, RNA-seq, ATAC-seq, and ChIP-seq in a GAF knockdown background we crossed TAAACATTAAATCGTCGTGT),  were cloned into pBSK under the U63 promoter using inverse PCR. Purified plasmid was injected into embryos of yw; attP40{nos-Cas9}/CyO for the N-terminal line and 1 M{vas-Cas9.RFP-}ZH-2A w1118y (BDSC#55821) for the C-terminal line by BestGene Inc Lines were screened for DsRed expression to verify integration. The entire 3xP3-DsRed cassette was cleanly removed using piggyBac transposase, followed by sequence confirmation of precise tag integration.Cas9-mediated genome engineering as described in degradFP embryos, embryos used for videos were mounted in halocarbon 700 oil without dechorionation. The living embryos were imaged on a Nikon A1R+ confocal at the University of Wisconsin-Madison Biochemistry Department Optical Core. Nuclear density, based on the number of nuclei/2500 μm2, was used to determine the cycle of pre-gastrulation embryos. Nuclei were marked with His2AV-RFP. Image J  in transfer buffer  for 60 min (75 min for Zld) at 500mA at 4°C. The membranes were blocked with blotto  for 30 min at room temperature and then incubated with anti-GFP   anti-Zld (1:750) , or antiGAF-sfGFP(C) homozygous embryos and 26 sfGFP-GAF(N) homozygous embryos.2–2.5 hr AEL embryos were dechorionated in bleach and subsequently mounted in halocarbon 700 oil. Embryos were imaged on a Nikon Ti-2e Epifluorescent microscope using ×60 magnification. Images were acquired of a single z-plane. Analysis was performed using the Nikon analysis software. Ten circular regions of interest (ROIs) were drawn around individual nuclei and the mean fluorescent intensity of each nucleus was calculated. Ten circular ROIs were drawn in the regions outside of the nuclei to measure the background fluorescent level of the embryo, and the mean fluorescent intensity of the background was calculated. To normalize values to the background fluorescent intensity, the final mean intensity of the nuclei was determined to be the mean fluorescent intensity of the nuclei after subtracting the mean fluorescent intensity of the background. This analysis was performed on images from 27 GAF-sfGFP(C) homozygous embryos, stage 3 and stage five hand selected 1118w embryos, 2–2.5 hr AEL hand selected embryos from mat-α-GAL4-VP16/+ (II); sfGFP-GAF(N)/ UAS-shRNA-zld (III) females and mat-α-GAL4-VP16/+ (II); sfGFP-GAF(N)/+ (III) females, 2–2.5 hr AEL embryos from nos-degradFP/His2Av-RFP (II); sfGFP-GAF(N) (III) females, and 2–2.5 hr AEL embryos from His2Av-RFP (II); sfGFP-GAF(N) (III) females. For each genotype, two biological replicates were collected. Briefly, 1000 stage 3 embryos or 400–500 stage five embryos were collected, dechorionated in 50% bleach for 3 min, fixed for 15 min in 0.45% formaldehyde and then lysed in 1 mL of RIPA buffer . The fixed chromatin was then sonicated for 20 s 11 times at 20% output and full duty cycle (Branson Sonifier 250). Chromatin was incubated with 6 μg of anti-GFP antibody (Abcam #ab290) or 8 μl of anti-Zld antibody (ChIP was performed as described previously  on: stagantibody  overnighDrosophila melanogaster reference genome (version dm6) using bowtie 2 v2.3.5  and the mouse genome (version mm39). Only reads that could be unambiguously aligned to one of the two reference genomes were retained. To control for any variability in the proportion of mouse chromatin in the input samples, the ratio of percentage of spike in reads in the IP relative to the input were used. A scaling factor was calculated by dividing one by this ratio. Z-score normalized read depth was adjusted by this scaling factor, and the resulting spike-in normalized values were used for heatmaps.Chromatin for GAF ChIP following depletion of Zld was prepared as described above. Prior to addition of the anti-GFP antibody, mouse chromatin prepared from cells expression an H3.3-GFP fusion protein was added to deGradFP background because GAFdeGradFP embryos die at variable times around the MZT. To attempt account for this variability, we collected embryos from a tight 2–2.5 hr AEL timepoint rather than sorting. Therefore, a portion of these embryos collected for ChIP-seq may have been dead. Additionally, the immunoprecipitation efficiency can vary between experiments and thus might have been lower in the GAFdeGradFP ChIP-seq as compared to the control. Alternatively, as discussed in the Results section, GAF may be broadly required for robust Zld occupancy.The global decrease in Zld ChIP signal in GAF-depleted embryos  caused uTo control for these technical factors, we performed an analysis based on peak rank, in addition to peak intensity. The number of reads aligning within each peak was quantified using featureCounts from the Subread package (v1.6.4) . Peaks wGAF-sfGFP(C) and heterozygous sfGFP-GAF(N) embryos (3800 peaks) were analyzed. For Zld ChIP-seq, analysis was restricted to 6003 high-confidence peaks shared between our control dataset and previously published Zld ChIP (DESeq2  was usedZld ChIP . A tableHis2Av-RFP/nos-degradFP (II); sfGFP-GAF(N) (III) and His2Av-RFP (II); sfGFP-GAF(N) (III) females were collected from a half hour lay and aged for 2 hr. For each genotype, three biological replicates were collected. Embryos were then picked into Trizol  with 200 μg/ml glycogen (Invitrogen). RNA was extracted and RNA-seq libraries were prepared using the Universal RNA-Seq with NuQuant, Drosophila AnyDeplete Universal kit . Samples were sequenced on the Illumina NextSeq500 using 75 bp single-end reads at the Northwestern Sequencing Core (NUCore).A total of 150–200 embryos from Drosophila melanogaster genome (dm6) using HISAT v2.1.0 ; sfGFP-GAF(N) (III) and His2Av-RFP (II); sfGFP-GAF(N) (III) females were collected from a half hour lay and aged for 2 hr. Embryos were dechorionated in bleach, mounted in halocarbon 700 oil, and imaged on a Nikon Ti-2e Epifluorescent microscope using 60x magnification. Embryos with nuclei that were not grossly disordered as determined by the His2Av-RFP marker were selected for single embryo ATAC-seq. Six replicates were analyzed for each genotype. Single-embryo ATAC-seq was performed as described previously (Embryos from eviously . BrieflyDrosophila melanogaster (dm6) genome using bowtie2 with the following parameters: --very-sensitive, --no-mixed, --no-discordant, -X 5000, -k 2. Reads with a mapping quality score < 30 were discarded, as were reads aligning to scaffolds or the mitochondrial genome. Analysis was restricted to fragments < 100 bp, which, as described previously, are most likely to originate from nucleosome-free regions . This resulted in 36,571 peaks that were considered for downstream analysis. Because greater sequencing depth can result in the detection of a large number of peaks with a lower level of enrichment over background, this filtering step ensured that peak sets were comparable across all datasets. 201 bp peak regions (100 bp on either side of the peak summit) were used for downstream analysis. Reads aligning within accessible regions were quantified using featureCounts, and differential accessibility analysis was performed using DESeq2 with an adjusted p-value<0.05 and a fold change > 2 as thresholds for differential accessibility. Previously published ATAC-seq datasets .eLife's policy to invite revisions only when revision experiments are unlikely to change major conclusion of the paper, we decided that this manuscript must be rejected at this point. However, we would like to note that all the reviewers are quite enthusiastic about this manuscript, and if you can address major concerns, we would be ready to review the revised manuscript as a new submission, which will be handled by the same set of editors/reviewers.In the light of Reviewer #1:Drosophila embryos. Using an elegant system for tagging and acute degradation of GAF in embryos, the authors show that: (i) the tagged version of the protein is functional and recapitulates the expected binding properties ; (ii) acute proteasomal degradation of the protein prior to ZGA leads to severe defects in embryo viability ; (iii) degradation of GAF leads to a significant downregulation of zygotic gene expression at ZGA  independently of Zelda ; and, (iv) loss of GAF leads to a significant reduction of chromatin accessibility.In this manuscript, Gaskill et al. perform a thorough investigation of the role of GAF as a pioneer transcription factor during zygotic genome activation (ZGA) in The manuscript is very well written, the data are presented in a logical manner and the findings are supported by the data. The formal identification of GAF as a critical factor for ZGA is important and the results will open up new avenues for investigation of how these molecular mechanisms orchestrate the maternal-to-zygotic transition.My main criticism of the work is the lack of a biochemical characterisation of GAF binding to nucleosomes to demonstrate a pioneer activity, and further biochemical evidence regarding the level of interaction with Zelda at the protein level. Combined, these experiments might bring a significant level of clarity to some of the results presented in Figure 4 and 5 by elucidating whether GAF is able to bind nucleosomes independently of Zelda.In addition, I have another three major points:1) I find the logic regarding the conclusions drawn in Figure 4 difficult to follow. In particular, the authors conclude that "GAF does not broadly depend on Zld for chromatin occupancy in the early embryo". However, Figure 4—figure supplement 1B shows significant differences in the binding intensities upon Zelda RNAi. Therefore, I find it difficult to reconcile these two aspects. I appreciate that the effect might be global, since both Zelda targets and non-Zelda GAF target peaks seem to change, but this needs to be clarified in the manuscript.2) The same point applies where the authors state: "Zld binding to target loci is largely unperturbed in the absence of GAF". However, Figure 4—figure supplement 1D again shows a significant effect in the binding of Zelda upon GAF removal.3) I find Figure 5F very difficult to follow and I cannot match the numbers reported in the main text that describe this analysis. This is also the case for Figure 5—figure supplement 1C. The authors should make this part more accessible. In addition, it might be more informative to perform a direct comparison of the ATAC-seq maps generated here and those for Zelda mutants from Hannon et al., 2017. Further analysis on the specific changes in nucleosome occupancy changes after Zelda or GAF depletion measured by these datasets might already help in demonstrating the pioneer activity of GAF.Reviewer #2:In the manuscript under review, Gaskill et al., address the longstanding question of the role of GAF in the process of MZT including zygotic genome activation. The question of GAF's role in early development was addressed in the past  through conventional genetic means where it was found that elimination of maternal GAF results in a highly pleiotropic, messy, lethal phenotype. Gaskill et al. bring a welcome, timely, and modern approach to revisiting this problem by combining CRISPR, new approaches for generating conditional loss of function (DegradFP), and genomics. In doing so, they recover essentially the same messy pleiotropic loss of function phenotype.While it would not be impossible to work with such a messy phenotype, in my opinion the authors have not taken sufficient care to ensure that their conclusions are free of possible confounding effects of the GAF loss of function phenotype. As highlighted below, there are several examples where conclusions are over-stated, technical or biological issues remain unaddressed, and -significantly- at least one of the presented experiments is designed so poorly that the rigor of the entire study has to be called into serious doubt (See major issue 1 as well as 3 below).If done well, the experiments in this study are important to do and the results would be valuable for not only the developmental biology community, but to the broader field of transcriptional regulation and epigenetics. I make the comments below mindful of the difficulties of life in the pandemic, and I have tried to see a way to fix the study without suggesting that significant parts of it be re-done more carefully. Unfortunately, I am not confident that the existing data are suitable for publication as detailed below.sfGFP-GAF (30% hatch rate). While this phenotype is concerning, what is more of an issue is that my interpretation of the westerns in Figure 1—figure supplement 1 suggest that substantially less protein is produced from the N-terminally tagged line as well. This expression problem does not appear to be mentioned in the text, but it definitely should be. What is the quantitative difference between protein levels in these westerns? Does this raise concerns that the “wild-type” samples are not really very wild-type? When does the lethality occur? Specifically, does this effect of the N-terminal GFP-GAF allele affect any of the conclusions?1) Significant lethality of More concerning is that the authors mixed these two alleles (in different copy numbers as well) for the GAF vs Zld ChIP-seq experiments. As stated in the Materials and methods: these experiments done comparing one versus two copies of the GFP tagged GAF allele, but *also* that the zld RNAi sample used the N-terminal tagged GAF allele (in one copy) and the control used the C-terminal tagged allele (in two copies). As mentioned above, it appears that there is a difference in the expression levels of these two alleles, which is presumably magnified significantly by choosing to compare hets and homozygotes. This experiment could easily have been done on N-terminal hets as controls. This falls well below the bar for acceptable experimental design.2) Re: conclusion: "maternal GAF is essential for progression through the MZT". The method of knockdown via DegradFP driven by mat-α-GAL4-VP16 begins eliminating GAF in mid-oogenesis. To underscore, this is not an “embryo-specific” knockdown. At the very least, it is a knockdown slightly later than what would be achieved in a germline clone. To the extent that the lethal phenotype has been examined in this manuscript, it remains unclear if it reflects a requirement of GAF specifically for the MZT, or if it reflects a requirement of GAF in some other biological process. There are high degrees of similarity between the DegradFP phenotype and the Trl[13C] phenotype reported in (Bhat '96) where ambiguity about the timing of GAF function also limited insight to when and how GAF is truly required. While the conclusion is not technically incorrect, the implication that this phenotype unambiguously implicates GAF as a regulator of the biological process of MZT  is not true.Also, the GAF loss-of-function phenotype bears absolutely no resemblance to the Zld loss of function phenotype, as the authors further conclude. As the authors are aware, the vast majority of zld germline clones are virtually indistinguishable from wild-type embryos through the cleavage stages, and nearly perfectly phenocopy embryos injected with the Pol 2 inhibitor α amanitin at least at the gross morphological scale. Some minor changes in cell cycle timing occurs in zld mutants, and occasionally an extra mitotic division . But these cleavages are mostly completed with the fidelity typical of a wild type embryo. In dramatic contrast, GAF loss-of-function results in likely pleiotropic phenotypes that either result in or are caused by errors in mitosis or extensive DNA damage. Importantly, since effects like this are not achieved by inhibiting transcription, this phenotype is likely not due to effects on the ZGA component of MZT. This is consistent with the acute effect of GAF loss of function resulting in disruption of additional unknown maternal processes. But the extent that these disrupted maternal processes specifically impact the MZT, as opposed to other maternally-supplied biological functions, has not been demonstrated in this study.3) Given the GAF loss of function phenotype: how can the authors be sure that the GAF[degradFP] embryos are at a comparable stage to "wild-type" in their genomic experiments? When gene expression is observed to be GAF-dependent, what steps have the authors taken to ensure that quantitative differences in gene expression stem from loss of GAF and are not confounded by the fact that in a 2-2.5 hr bulk collection that nearly all of the DegradFP embryos are in the process of dying, developing asynchronously, with likely extensive DNA Damage, et cetera? From the Materials and methods section, it seems that for some of the experiments, nothing at all was done. I have a hard time imagining how there is a way around this, since embryos with extensive damaged nuclei cannot be expected to continue transcribing RNA normally, and in fact there is evidence that they very much do not . Timing is also important here, since many of the genes that are listed as being sensitive to GAF loss of function are just beginning to be expressed, any delay in development would only exacerbate differences between the two experimental samples. For instance, the enrichment for up-regulated maternal genes could either reflect a role for GAF in the process of MZT, in general, or just that the embryos are either significantly delayed or outright die before they have a chance to undergo ZGA. As described above, this does not prove that GAF is involved in the process of MZT. Again, on the basis of these results, it cannot reliably be concluded, that GAF is directly playing a role in the process of ZGA.4) Looking at the GAF[degradFP] coverage in the bigWig files supplied to GEO, I am strongly concerned that there is a major global difference in the normalized read counts , which should not really be there if the different samples are truly comparable to one another. Is this difference biological or technical? Such huge differences (if they are not just an error in generating the bigWig files) I think would certainly overburden algorithmic normalization in programs like DESeq2. Not to mention that it makes evaluation of the magnitude of differences seen in the ATAC experiments impossible to do through a genome browser, and has significantly colored my interpretation of the results with the DegradFP approach towards the negative (see above). The authors need to address the reason for this difference in magnitude, as well as provide information such as read counts per sample, mapping rates, fraction of mitochondrial reads (for ATAC data). If the issue is due to significant differences in recovered reads between the two genotypes, the question should be asked whether it is a good idea to publish these particular datasets.Reviewer #3:Drosophila embryo. The authors use state of the art genome engineering technology and protein degradation techniques to demonstrate that GAF is essential for ZGA and embryonic viability and is needed for the expression of a set of genes that are known to be bound by GAF. This data provides confirmation that previous results in cell lines translate well in vivo and that the previously established mechanistic role for GAF in opening chromatin and establishing paused Pol II holds true in animals. While this manuscript contains compelling findings and will be of high general interest, the authors' reciprocal ChIP-seq data is not convincing and needs to be repeated.This manuscript by Gaskill and Gibson et al. presents compelling evidence that GAGA-factor acts as an additional pioneer factor in the early 1) The reciprocal ChIP-seq experiments are simply not convincing in their current form. The authors want to use this data to claim that GAF or Zelda depletion does not impact the binding of the other factor, so that they can say that there is not cooperativity on the "vast majority of target sites". However, they note that there is a global decrease in GAF binding after Zelda depletion and a global decrease in Zelda binding after GAF depletion. They attempt to get around this issue by citing vague technical issues and using rank order comparisons instead, but this analysis is not appropriate in this case. If GAF and Zelda were binding cooperatively, wouldn't one expect there to be a global decrease in the binding of one after depletion of the other, and not necessarily a change in peak rank order? The authors speculate in the Materials and methods section as to why technical reasons could have caused this difference, however, what they should do is perform properly controlled experiments that control for these technical variables so that they can nail this finding with certainty. This is not a trivial issue, as a major conclusion of the paper is that GAF and Zelda act as independent pioneer factors with slightly different activation times in the embryosfGFP-GAF(N) background and then compared to the sfGFP-GAF(C) dataset? This experiment should be repeated with the proper control, GAF ChIP-seq in the sfGFP-GAF(C) background. Furthermore, it is concerning that it seems the authors simply performed the zld-RNAi GAF ChIP-seq experiment and then compared back to a previously generated GAF-ChIP-seq dataset; ChIP-seq is an assay subject to a large amount of technical variance and if experiments are to be quantitatively compared as they are in the manuscript, they must be performed at the same time and processed in parallel to minimize this variance. Were the ZLD ChIP-seq experiments and controls performed at the same time and processed in parallel?a) Why was the zld-RNAi GAF ChIP-seq experiment performed in the b) Why are the authors not utilizing input controls in these ChIP-seq comparisons? Instead of using CPM, why not display fold change over input? This might be a more robust way of dealing with technical differences that lead to global changes as a result of IP efficiency, etc. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:Drosophila embryos. Using an elegant system for tagging and acute degradation of GAF in embryos, the authors show that: (i) the tagged version of the protein is functional and recapitulates the expected binding properties ; (ii) acute proteasomal degradation of the protein prior to ZGA leads to severe defects in embryo viability ; (iii) degradation of GAF leads to a significant downregulation of zygotic gene expression at ZGA  independently of Zelda ; and, (iv) loss of GAF leads to a significant reduction of chromatin accessibility.In this manuscript, Gaskill et al. perform a thorough investigation of the role of GAF as a pioneer transcription factor during zygotic genome activation (ZGA) in The manuscript is very well written, the data are presented in a logical manner and the findings are supported by the data. The formal identification of GAF as a critical factor for ZGA is important and the results will open up new avenues for investigation of how these molecular mechanisms orchestrate the maternal-to-zygotic transition.My main criticism of the work is the lack of a biochemical characterisation of GAF binding to nucleosomes to demonstrate a pioneer activity, and further biochemical evidence regarding the level of interaction with Zelda at the protein level. Combined, these experiments might bring a significant level of clarity to some of the results presented in Figure 4 and 5 by elucidating whether GAF is able to bind nucleosomes independently of Zelda.We are pleased that reviewer 1 finds the manuscript clear and that the findings are supported by the data.Our lab has done several experiments to determine which factors, if any, Zelda is interacting with on the protein level. A graduate student performed IP mass spec, yeast 2-hybrid, and BioID experiments to identify proteins that directly interact with Zelda. Although multiple proteins were identified as potential Zld-interacting factors, we were unable to verify any of these through co-immunoprecipitation followed by immunoblot. We speculate that Zld may interact transiently with a large number of factors, potentially through it’s low-complexity transcription activation domain . GAF-interacting partners have been identified using affinity purification coupled with high-throughput mass spectrometry , and Zelda was not identified as a GAF-interacting factor in this study. Based on these previously generated data, we anticipate that if we were to perform co-immunoprecipitation studies with GAF and Zld we would not identify an interaction. We recognize that the biochemical identification of interactions depend on many factors, including purification conditions, making it impossible to rule out protein:protein interactions with a negative result. Therefore, although we cannot definitively state that GAF and Zelda are not interacting on a protein level, previous data suggests it is unlikely. Further supporting independent roles for GAF and Zld in shaping the chromatin landscape, GAF is required for chromatin accessibility in S2 tissue-culture cells, in which Zld is not expressed at detectable levels.We also agree that nucleosome binding assays with GAF would be interesting and informative, however we believe they are beyond the scope of the current manuscript. A major challenge to this line of experimentation is that we have thus far been unable to purify soluble full length GAF protein in bacterial culture, and therefore anticipate purifying sufficient amounts of full-length GAF will require extensive troubleshooting. Given the difficulty of these experiments we believe it is outside the scope of this study. Although GAF has not yet been shown to directly bind nucleosomes, it has been extensively shown in vitro and in cell culture to promote chromatin accessibility at promoters through the recruitment of chromatin remodelers . Our new analysis includes data demonstrating that GAF binds to regions before accessibility is established. However, we acknowledge that GAF has not been shown to directly bind nucleosomes and therefore it is not proven to have all of the features of a canonical pioneer factor. Therefore, we have removed “pioneer factor” from the title of our manuscript and refer to GAF within the manuscript as a “pioneer-like factor”.In addition, I have another three major points:1) I find the logic regarding the conclusions drawn in Figure 4 difficult to follow. In particular, the authors conclude that "GAF does not broadly depend on Zld for chromatin occupancy in the early embryo". However, Figure 4—figure supplement 1B shows significant differences in the binding intensities upon Zelda RNAi. Therefore, I find it difficult to reconcile these two aspects. I appreciate that the effect might be global, since both Zelda targets and non-Zelda GAF target peaks seem to change, but this needs to be clarified in the manuscript.sfGFP-GAF (N) in a zld-RNAi background with paired heterozygous sfGFP-GAF (N) embryos as controls. We further included a spike-in control to allow for normalization (chromatin from mouse cells expression GFP-tagged histone H3). In the new GAF ChIP data after z-score and spike-in normalization, we no longer observe a global decrease in GAF binding in the zld-RNAi background. When we perform differential binding analysis on these data we again identify relatively few sites  that are significantly changed. We have extensively rewritten this section of the manuscript and changed Figure 4E and Figure 4—figure supplement 2A, C to incorporate the new data and clarify our conclusions.We apologize for the lack of clarity in this section. In response to the concerns of other reviewers, we have repeated these GAF ChIP-seq experiments to better control for any global effects the loss of Zld might have on GAF occupancy. We performed ChIP-seq for 2) The same point applies where the authors state: "Zld binding to target loci is largely unperturbed in the absence of GAF". However, Figure 4—figure supplement 1D again shows a significant effect in the binding of Zelda upon GAF removal.We appreciate the reviewer’s feedback and have rewritten this section to clearly convey our conclusions. We emphasize that although we observe a global decrease in Zld binding upon GAF depletion, a very small subset of Zld binding sites were statistically significantly changed in the absence of GAF (1.7%). Therefore, we conclude that at the majority of Zld binding sites the ability of Zld to bind target loci is not ablated when GAF is depleted. See rewritten section in the comment above.3) I find Figure 5F very difficult to follow and I cannot match the numbers reported in the main text that describe this analysis. This is also the case for Figure 5—figure supplement 1C. The authors should make this part more accessible. In addition, it might be more informative to perform a direct comparison of the ATAC-seq maps generated here and those for Zelda mutants from Hannon et al., 2017. Further analysis on the specific changes in nucleosome occupancy changes after Zelda or GAF depletion measured by these datasets might already help in demonstrating the pioneer activity of GAF.zld mutants from Hannon et al., 2017 in Figure 6. We also investigated when during the MZT regions that require either Zld or GAF for gain accessibility based on data from Blythe and Wieschaus, 2016. We have therefore re-written the Results and Discussion and worked to make these sections more accessible. We have also updated Figure 5, Figure 5—figure supplement1, Figure 6, Figure 6—figure supplement1, and Figure 6—figure supplement 2 to incorporate our new data and analysis.We apologize for the lack of clarity in this section. In response to another reviewer’s comments, we performed ATAC-seq on single-embryo replicates. We repeated and expanded our analysis with these new data . We now incorporate the ATAC-seq in Reviewer #2:In the manuscript under review, Gaskill et al., address the longstanding question of the role of GAF in the process of MZT including zygotic genome activation. The question of GAF's role in early development was addressed in the past  through conventional genetic means where it was found that elimination of maternal GAF results in a highly pleiotropic, messy, lethal phenotype. Gaskill et al. bring a welcome, timely, and modern approach to revisiting this problem by combining CRISPR, new approaches for generating conditional loss of function (DegradFP), and genomics. In doing so, they recover essentially the same messy pleiotropic loss of function phenotype.While it would not be impossible to work with such a messy phenotype, in my opinion the authors have not taken sufficient care to ensure that their conclusions are free of possible confounding effects of the GAF loss of function phenotype. As highlighted below, there are several examples where conclusions are over-stated, technical or biological issues remain unaddressed, and -significantly- at least one of the presented experiments is designed so poorly that the rigor of the entire study has to be called into serious doubt (See major issue 1 as well as 3 below).If done well, the experiments in this study are important to do and the results would be valuable for not only the developmental biology community, but to the broader field of transcriptional regulation and epigenetics. I make the comments below mindful of the difficulties of life in the pandemic, and I have tried to see a way to fix the study without suggesting that significant parts of it be re-done more carefully. Unfortunately, I am not confident that the existing data are suitable for publication as detailed below.We appreciate that the reviewer notes that the “experiments are important to do and that the results would be valuable” to both the developmental biology and transcription fields and regret that the reviewer did not feel we have “taken sufficient care to ensure our free of confounding effects”. To address these concerns, we have included new ChIP-seq and ATAC-seq data as well as additional data and analysis to further support our conclusions.1) Significant lethality of sfGFP-GAF (30% hatch rate). While this phenotype is concerning, what is more of an issue is that my interpretation of the westerns in Figure 1—figure supplement 1 suggest that substantially less protein is produced from the N-terminally tagged line as well. This expression problem does not appear to be mentioned in the text, but it definitely should be. What is the quantitative difference between protein levels in these westerns? Does this raise concerns that the “wild-type” samples are not really very wild-type? When does the lethality occur? Specifically, does this effect of the N-terminal GFP-GAF allele affect any of the conclusions?sfGFP-GAF (N) line. We were also concerned about any confounding effects, which was the reason we used sfGFP-GAF(N) homozygous embryos as our controls for every genomics experiment. By comparing to this control, we will have eliminated from our analysis effects caused by the sfGFP-GAF(N) allele alone. We are confident the sfGFP-GAF(N) is rescuing essential GAF function, as we have verified the line is homozygous viable and fertile and recapitulates GAF nuclear localization characteristics. Additionally, in this revised manuscript we discuss additional ChIP-seq experiments performed on sfGFP-GAF(N) heterozygous embryos. Through comparison of these newly generated data for sfGFP-GAF(N) binding to binding sites identified for the C-terminally tagged version (GAF-sfGFP(C)), which has a 66% hatching rate, we identify a high-degree of overlap. 91% of loci identified in GAF-sfGFP(C) embryos are also identified in sfGFP-GAF(N) embryos . This demonstrates that the N-terminal sfGFP tag does not alter the ability of GAF to localize to subnuclear domains or to bind specific target loci in the genome. Together, we feel this is strong evidence that, despite the reduced hatching rate, sfGFP-GAF(N) recapitulates the majority of GAF function. This analysis is described in the text.We appreciate the reviewer’s concerns about the sfGFP-GAF(N) and GAF-sfGFP(C) homozygous embryos. This strategy allowed us to directly compare single embryos, while our western blots  were performed on bulk collections. This quantification did not identify any significant difference in the fluorescent GFP signal between the two genotypes, suggesting GAF protein levels are not reduced in sfGFP-GAF(N) embryos compared to GAF-sfGFP(C) embryos. We have included these data in the manuscript in Figure 2—figure supplement 1 and described this experiment in the text.To address the question of protein levels in the two tagged versions of GAF, we performed fluorescent quantification of GFP signal in nuclei of sfGFP-GAF(N) embryos as it appears to be variable. We observe that approximately half of the sfGFP-GAF(N) homozygous embryos successfully progress to gastrulation. Therefore, a subset of sfGFP-GAF(N) embryos die prior to gastrulation, while the remainder die at some point following gastrulation.It is challenging to determine the precise timing of the lethality of the More concerning is that the authors mixed these two alleles (in different copy numbers as well) for the GAF vs Zld ChIP-seq experiments. As stated in the Materials and methods: these experiments done comparing one versus two copies of the GFP tagged GAF allele, but *also* that the zld RNAi sample used the N-terminal tagged GAF allele (in one copy) and the control used the C-terminal tagged allele (in two copies). As mentioned above, it appears that there is a difference in the expression levels of these two alleles, which is presumably magnified significantly by choosing to compare hets and homozygotes. This experiment could easily have been done on N-terminal hets as controls. This falls well below the bar for acceptable experimental design.zld-RNAi background with paired heterozygous sfGFP-GAF(N) embryos as controls. Our data for sfGFP-GAF(N) binding from heterozygous embryos identified 6373 GAF peaks. When we compared these to the 4175 GAF peaks identified in the GAF-sfGFP(C) homozygous ChIP-seq dataset, we identified that 91% of GAF-sfGFP(C) peaks identified were maintained in the sfGFP-GAF(N) dataset, and peaks unique to either dataset are among the weakest sites identified . Thus, the location of the sfGFP tag on GAF does not significantly alter the ability of GAF to bind specific genomic loci. To further enable comparison of GAF binding between control and zld-RNAi embryos, we included a spike-in control to allow for normalization (chromatin from mouse cells expression GFP-tagged histone H3.3). With the new GAF ChIP data after z-score and spike-in normalization, we no longer observe a global decrease in GAF binding in the zld-RNAi background. These new ChIP-seq data support the previous conclusions that GAF remains bound to nearly all the same loci in the presence and absence of Zld. We have updated Figure 4E and Figure 4—figure supplement 2A,C as well as the text to reflect these new data. We were also able to identify 101 GAF-binding sites that were altered upon the depletion of zld, and these have been discussed in the text.To address this concern, we performed ChIP-seq for sfGFP-GAF(N) in a 2) Re: conclusion: "maternal GAF is essential for progression through the MZT". The method of knockdown via DegradFP driven by mat-α-GAL4-VP16 begins eliminating GAF in mid-oogenesis. To underscore, this is not an “embryo-specific” knockdown. At the very least, it is a knockdown slightly later than what would be achieved in a germline clone. To the extent that the lethal phenotype has been examined in this manuscript, it remains unclear if it reflects a requirement of GAF specifically for the MZT, or if it reflects a requirement of GAF in some other biological process. There are high degrees of similarity between the DegradFP phenotype and the Trl[13C] phenotype reported in (Bhat '96) where ambiguity about the timing of GAF function also limited insight to when and how GAF is truly required. While the conclusion is not technically incorrect, the implication that this phenotype unambiguously implicates GAF as a regulator of the biological process of MZT  is not true.nanos promoter and not mat-α-GAL4-VP16, we acknowledge the point of the reviewer has raised, and we have changed the language in the manuscript to better reflect this caveat.While we highlight that the expression of the degradFP enzyme is driven by the We agree that is important to acknowledge potential downstream effects of GAF knockdown at oogenesis. Nonetheless, a number of reasons support our hypothesis that the majority of the effects we identify are the result of GAF knockdown in the embryo:1) Females in which GAF null germline clones have been generated are rarely able to produce eggs. As reported in Bejarano and Busturia, 2004, of 807 female GAF null germline clones only 23 could produce eggs. Furthermore, most of the eggs produced by these germline clones were unfertilized. These observations suggest that GAF is required during oogenesis for egg production. By contrast, our knockdown system does not show this egg-laying defect, suggesting that the essential functions of GAF in the germline are not inhibited.Trl[13C] hypomorphic GAF mutants. Based on their studies, they conclude that the reduction in Trl mRNA deposited in the Trl[13C] embryos, and the resulting reduction of GAF protein levels in the embryo, likely produce the nuclear division defects observed. Although they cannot conclusively rule out that the described phenotypes are an indirect effect of GAF knockdown in the germline, they conclude that is unlikely given their observations that GAF protein levels are similar in the ovaries of wild-type controls and Trl[13C] mutants. Therefore, the shared phenotypes between our GAFdeGradFP embryos and the hypomorphic embryos from Trl[13C] mothers in Bhat et al., 1996 are suggestive that GAF knockdown is primarily occurring in the embryo. Nonetheless, our system has advantages over the Trl[13C] allele. While Bhat et al. observed variation in phenotype and GAF-expression levels both between and within embryos, our system robustly eliminates GAF protein in every embryo examined and uniformly in all nuclei.2) In Bhat et al., 1996 the authors investigate GAF protein and mRNA levels in the ovary and embryos of wild-type control and deGradFP embryos and these were significantly enriched for genes with a proximal GAF-binding site . Further supporting the RNA-seq data and that it reflects the loss of GAF function in the embryo, we identify multiple, zygotically expressed, previously identified GAF-target genes as down-regulated in our RNA-seq analysis, such as engrailed and Ultrabithorax.3) Analysis of our genomics data indicates that the effects we observe are the direct consequence of GAF knockdown in the embryo. Our RNA-seq analysis identified only a subset of genes expressed at NC14 that were downregulated in the GAFdeGradFP embryos are due to loss of GAF protein in the female germline. Nonetheless, we feel that the evidence strongly supports that many of the effects we identify are due to protein loss in the embryo.In summary, we agree that we cannot rule out that some of the phenotypic and gene expression differences between the control and GAFAlso, the GAF loss-of-function phenotype bears absolutely no resemblance to the Zld loss of function phenotype, as the authors further conclude. As the authors are aware, the vast majority of zld germline clones are virtually indistinguishable from wild-type embryos through the cleavage stages, and nearly perfectly phenocopy embryos injected with the Pol 2 inhibitor α amanitin at least at the gross morphological scale. Some minor changes in cell cycle timing occurs in zld mutants, and occasionally an extra mitotic division . But these cleavages are mostly completed with the fidelity typical of a wild type embryo. In dramatic contrast, GAF loss-of-function results in likely pleiotropic phenotypes that either result in or are caused by errors in mitosis or extensive DNA damage. Importantly, since effects like this are not achieved by inhibiting transcription, this phenotype is likely not due to effects on the ZGA component of MZT. This is consistent with the acute effect of GAF loss of function resulting in disruption of additional unknown maternal processes. But the extent that these disrupted maternal processes specifically impact the MZT, as opposed to other maternally-supplied biological functions, has not been demonstrated in this study.zld null embryos during NC10-13. Nonetheless, we would like to point out that there are multiple reports of defects in zld null embryos prior to NC14. For example, in Liang et al., 2008, nuclear fallout is reported in maternal zld null embryos prior to NC14, which we have also observed when imaging maternal zld-RNAi embryos. In that same paper, it is described that in early NC14 maternal zld null embryos the actin network becomes disorganized and begins to degenerate, leading to highly disorganized nuclei. It is reported in Staudt et al., 2006 that embryos injected with vielfaltig (renamed zld) dsRNA begin to display anaphase bridges and abnormally condensed chromatin as early as NC10, with these defects becoming more frequent through subsequent nuclear cycles.  Therefore, we have edited the text for clarity to reflect that a subset of the GAF knockdown embryos have similar phenotypes to what has been previously published for maternal zld knockdown embryos, while a subset of GAF knockdown embryos display more dramatic phenotypes likely due to additional roles of GAF for genome stability.We agree with reviewer 2 that the phenotypic defects of a subset of GAF knockdown embryos are more dramatic than the effects of maternal 3) Given the GAF loss of function phenotype: how can the authors be sure that the GAF[degradFP] embryos are at a comparable stage to "wild-type" in their genomic experiments? When gene expression is observed to be GAF-dependent, what steps have the authors taken to ensure that quantitative differences in gene expression stem from loss of GAF and are not confounded by the fact that in a 2-2.5 hr bulk collection that nearly all of the DegradFP embryos are in the process of dying, developing asynchronously, with likely extensive DNA Damage, et cetera? From the Materials and methods section, it seems that for some of the experiments, nothing at all was done. I have a hard time imagining how there is a way around this, since embryos with extensive damaged nuclei cannot be expected to continue transcribing RNA normally, and in fact there is evidence that they very much do not . Timing is also important here, since many of the genes that are listed as being sensitive to GAF loss of function are just beginning to be expressed, any delay in development would only exacerbate differences between the two experimental samples. For instance, the enrichment for up-regulated maternal genes could either reflect a role for GAF in the process of MZT, in general, or just that the embryos are either significantly delayed or outright die before they have a chance to undergo ZGA. As described above, this does not prove that GAF is involved in the process of MZT. Again, on the basis of these results, it cannot reliably be concluded, that GAF is directly playing a role in the process of ZGA.deGradFP embryos makes exactly staging the embryos challenging and because of the issues the reviewer raises given the initiation of gene expression. Given the dramatic changes in gene expression that can occur within minutes during zygotic genome activation, we used tightly timed bulk collection to alleviate potential timing differences that would be exacerbated by single-embryo RNA-seq. To address the concerns above, we analyzed our RNA-seq data by selecting for those genes that initiate expression late in ZGA . If expression defects are caused by a failure of the embryos to reach ZGA, we would expect a global decrease in all genes expressed at this timepoint in the GAFdeGradFP embryos as compared to control. Instead, our analysis demonstrated that many non-GAF target transcripts activated during late NC14 are expressed at similar levels in GAFdeGradFP embryos as compared to control embryos. By contrast, a substantial proportion of genes activated at NC14 with a proximal GAF-binding site (as identified by our ChIP-seq) are downregulated in the GAFdeGradFP embryos as compared to controls . Thus, during NC14 direct GAF target transcript expression is affected by GAF knockdown while numerous non-GAF target transcripts are activated normally. This additional analysis suggests our tightly timed bulk collection succeeded in enriching for NC14 embryos and that expression differences identified in our RNA-seq data are not caused by a timing delay in GAFdeGradFP embryos compared to controls.We chose to perform a bulk collection of embryos for our RNA-seq experiment precisely because the nuclear defects in the GAF4) Looking at the GAF[degradFP] coverage in the bigWig files supplied to GEO, I am strongly concerned that there is a major global difference in the normalized read counts , which should not really be there if the different samples are truly comparable to one another. Is this difference biological or technical? Such huge differences (if they are not just an error in generating the bigWig files) I think would certainly overburden algorithmic normalization in programs like DESeq2. Not to mention that it makes evaluation of the magnitude of differences seen in the ATAC experiments impossible to do through a genome browser, and has significantly colored my interpretation of the results with the DegradFP approach towards the negative (see above). The authors need to address the reason for this difference in magnitude, as well as provide information such as read counts per sample, mapping rates, fraction of mitochondrial reads (for ATAC data). If the issue is due to significant differences in recovered reads between the two genotypes, the question should be asked whether it is a good idea to publish these particular datasets.deGradFP embryos from the 2-2.5 hr time point. Single-embryo ATAC-seq assays are not subject to the same timing issues as RNA-seq as the changes in accessibility identified by Blythe and Wieschaus, 2016 over the MZT are less dramatic than the changes in transcript levels during the same time point. Nonetheless, we performed these experiments in six biological replicates to minimize effects of differences in developmental timing. We imaged individual embryos to confirm that they had nuclei that were not grossly disordered using His2av-RFP prior to performing the ATAC-seq. The data from this experiment were much improved, and we have updated and expanded new Figures 5 and 6 using the data from our single embryo ATAC-seq experiment.We agree with reviewer 2 that our ATAC-seq data were noisy. Since our lab has had success with single embryo ATAC-seq, we decided to collect single embryos from control and GAFWe have updated Figure 5, Figure 5—figure supplement1, Figure 6, Figure 6—figure supplement 1, Figure 6—figure supplement 2 with our new data and expanded analysis, as well as the text.Reviewer #3:Drosophila embryo. The authors use state of the art genome engineering technology and protein degradation techniques to demonstrate that GAF is essential for ZGA and embryonic viability and is needed for the expression of a set of genes that are known to be bound by GAF. This data provides confirmation that previous results in cell lines translate well in vivo and that the previously established mechanistic role for GAF in opening chromatin and establishing paused Pol II holds true in animals. While this manuscript contains compelling findings and will be of high general interest, the authors' reciprocal ChIP-seq data is not convincing and needs to be repeated.This manuscript by Gaskill and Gibson et al. presents compelling evidence that GAGA-factor acts as an additional pioneer factor in the early We appreciate that the reviewer finds the that the manuscript contains “compelling findings and will be of high general interest.” We have repeated the reciprocal ChIP-seq data and re-analyzed these data to address the concerns raised.1) The reciprocal ChIP-seq experiments are simply not convincing in their current form. The authors want to use this data to claim that GAF or Zelda depletion does not impact the binding of the other factor, so that they can say that there is not cooperativity on the "vast majority of target sites". However, they note that there is a global decrease in GAF binding after Zelda depletion and a global decrease in Zelda binding after GAF depletion. They attempt to get around this issue by citing vague technical issues and using rank order comparisons instead, but this analysis is not appropriate in this case. If GAF and Zelda were binding cooperatively, wouldn't one expect there to be a global decrease in the binding of one after depletion of the other, and not necessarily a change in peak rank order? The authors speculate in the Materials and methods section as to why technical reasons could have caused this difference, however, what they should do is perform properly controlled experiments that control for these technical variables so that they can nail this finding with certainty. This is not a trivial issue, as a major conclusion of the paper is that GAF and Zelda act as independent pioneer factors with slightly different activation times in the embryo.sfGFP-GAF(N) in a zld-RNAi background with paired heterozygous sfGFP-GAF(N) embryos as controls. We further included a spike-in control to allow for normalization (chromatin from mouse cells expression GFP-tagged histone H3.3). In our new GAF ChIP-seq data after z-score and spike-in normalization we no longer observe a global decrease in GAF binding in the zld-RNAi background. These new data support the previous conclusion that GAF remains bound to the majority of target loci in both the presence and absence of Zld, with only 3.3% of GAF peaks statistically significantly changed in the zld-RNAi background. However, we performed additional analysis on the 101 GAF-binding sites that decrease when Zld is lost. We find these sites are enriched for Zld-bound regions that depend on Zld for accessibility. Therefore, at a small subset of GAF-binding sites, pioneering activity of Zld may be required for GAF occupancy. However at the majority of GAF-binding sites GAF does not depend on Zld for occupancy. We used this new GAF ChIP data to generate Figure 4E, Figure 4—figure supplement 2A,C, and we have incorporated textual changes.We agree with the point raised by reviewer 3 and the other reviewers that better controlled experiments eliminating technical variables would bring clarity to the global decrease in GAF and Zld binding observed in the knockdown backgrounds of the other factor. We therefore performed ChIP-seq for We agree with reviewer 3’s assessment that we cannot rule out that the global decrease we observed in Zld binding upon the loss of GAF is biological. However, we expect that if Zld required GAF for occupancy we would see a change in Zld binding in the absence of GAF specifically at regions where the two factors are co-bound. We based this assumption on previously published data on pioneer-factor cooperativity. For example, in Donaghey et al., 2018 the authors found that GATA4 can stabilize FOXA2 binding at a subset of sites where the two factors are bound when they are ectopically expressed in cell culture. Additionally, when OSKM occupancy was investigated during MEF reprogramming it was found that the cooperative binding of O, S, and K was required for targeting of these factors specifically to sites that were co-occupied by the three factors . In contrast, the Zld-binding sites that decrease in the absence of GAF are depleted for GAF and Zld co-bound regions when compared to unchanged Zld binding sites. Therefore, at these sites GAF is not directly stabilizing Zld binding or creating local chromatin accessibility. We have clarified the text to more clearly convey this.Drosophila. Like us, they used DESeq to call differential binding sites for Dorsal peaks between control and zld-RNAi embryos and for Zld peaks in Dorsal mutant and control embryos. They determined that Zld is required for Dorsal binding to a subset of sites, as 19.4% of Dorsal peaks are significantly decreased and 37.8% are significantly changed in the zld RNAi background. By contrast, only 2.5% of Zld peaks are significantly changed in the absence of Dorsal. Therefore, the authors conclude that Zld is required for Dorsal binding, but that Dorsal is not required for Zld binding. Our data shows only 3.3% of GAF peaks significantly change upon the loss of Zld and 1.7% of Zld peaks significantly change upon the loss of GAF when we call differential binding with DESeq2 . Therefore, both of our ChIP-seq experiments more closely resemble the impact of Dorsal loss on Zld than the impact of Zld loss on Dorsal. Nevertheless, we have modified the language in our manuscript to make it clear that we cannot rule out an influence of GAF on global Zld binding  and to emphasize that there is a small subset of GAF-binding sites that depend on the pioneering activity of Zld for occupancy .Comparisons to previously published reciprocal ChIP-seq data also reinforces our conclusion that neither GAF nor Zld is broadly required for the binding of the other factor to target loci. In Sun et al., 2015 the authors tested the dependency of the transcription factors Dorsal and Zld on the binding of the other factor during the MZT in a) Why was the zld-RNAi GAF ChIP-seq experiment performed in the sfGFP-GAF(N) background and then compared to the sfGFP-GAF(C) dataset? This experiment should be repeated with the proper control, GAF ChIP-seq in the sfGFP-GAF(C) background. Furthermore, it is concerning that it seems the authors simply performed the zld-RNAi GAF ChIP-seq experiment and then compared back to a previously generated GAF-ChIP-seq dataset; ChIP-seq is an assay subject to a large amount of technical variance and if experiments are to be quantitatively compared as they are in the manuscript, they must be performed at the same time and processed in parallel to minimize this variance. Were the ZLD ChIP-seq experiments and controls performed at the same time and processed in parallel?zld RNAi background with paired heterozygous sfGFP-GAF(N) controls. In our Zld ChIP-seq experiment, we performed the ChIP-seq for Zld in the GAFdeGradFP and paired homozygous sfGFP-GAF(N) controls in parallel.As mentioned above, we have repeated the ChIP-seq for sfGFP-GAF(N) in the b) Why are the authors not utilizing input controls in these ChIP-seq comparisons? Instead of using CPM, why not display fold change over input? This might be a more robust way of dealing with technical differences that lead to global changes as a result of IP efficiency, etc.zld RNAi background with paired controls using spike-in and z-score normalization we no longer observe a global decrease in GAF signal in the zld-RNAi background. However, we continue to observe a global decrease in Zld ChIP signal in GAF knockdown embryos compared to controls using both z-score normalized and input normalization heatmap generation. For consistency, we used z-score normalization throughout.Previously published ChIP-seq data for Zld at NC14  and GAF in S2 cells  that we used for comparisons to our ChIP-seq in the paper did not have publicly available input controls. Therefore, we used CPM normalization for all ChIP-seq data so that we would have a consistent normalization method for ChIP-seq data throughout the paper. Upon revision we have decided to use z-score normalization for all ChIP-seq heatmaps. With our new ChIP-seq data for GAF in a


Enhancers are non-coding regions of the genome that control the activity of target genes. Recent efforts to identify active enhancers experimentally and in silico have proven effective. While these tools can predict the locations of enhancers with a high degree of accuracy, the mechanisms underpinning the activity of enhancers are often unclear.Using machine learning (ML) and a rule-based explainable artificial intelligence (XAI) model, we demonstrate that we can predict the location of known enhancers in Drosophila with a high degree of accuracy. Most importantly, we use the rules of the XAI model to provide insight into the underlying combinatorial histone modifications code of enhancers. In addition, we identified a large set of putative enhancers that display the same epigenetic signature as enhancers identified experimentally. These putative enhancers are enriched in nascent transcription, divergent transcription and have 3D contacts with promoters of transcribed genes. However, they display only intermediary enrichment of mediator and cohesin complexes compared to previously characterised active enhancers. We also found that 10–15% of the predicted enhancers display similar characteristics to super enhancers observed in other species.Here, we applied an explainable AI model to predict enhancers with high accuracy. Most importantly, we identified that different combinations of epigenetic marks characterise different groups of enhancers. Finally, we discovered a large set of putative enhancers which display similar characteristics with previously characterised active enhancers.The online version contains supplementary material available at 10.1186/s13059-021-02532-7. Regulation of gene expression in eukaryotic cells is a complex process governed by interactions between DNA binding proteins (transcription factors), and the regulatory elements in DNA to which they bind. Mutations in these non-coding regulatory elements can cause disease states by affecting the spatial and temporal control of gene expression –4. IdentCis-acting DNA sequences that increase the transcription of one or more genes are called enhancers [nhancers , 6. Unlinhancers , the posnhancers . To achinhancers . In addinhancers . All of Enhancers act as platforms for transcription factor binding and display high DNA accessibility. However, these regions also exhibit specialised histone modifications, both overlapping and flanking transcription factor binding sites –13. SeveRecent methodological advances have made the genome-wide detection of enhancers possible. Self-transcribing active regulatory region sequencing (STARR-seq) is a massively parallel reporter assay that is able to identify enhancers based on their genome-wide activity and provides a quantitative measure of the enhancer activity , 24. ThiComputational approaches and, in particular, machine learning (ML) methods have been applied successfully to the identification of enhancers , 25, 26.Here, we apply ML driven XAI models to predict and disentangle the effect of different epigenetic modifications on enhancer activity. We train our model in Drosophila BG3 cells, using histone modifications ChIP and STARR-seq datasets, and use it to predict enhancers in a different Drosophila cell line, S2. To evaluate the performance, the XAI model is compared with a traditional Neural Network ML approach and annotation of enhancers by STARR-seq. Using this approach, we successfully trained an explainable model, that accurately predicts enhancer locations and generalises to other cell lines without adjustment. Our model also predicted a population of putative enhancers not previously annotated by STARR-seq which we further characterise and explore. Some of the predicted enhancers are longer than 1 Kb and resemble mammalian super enhancers . Our maiChIP-seq data for histone modifications , 32 and For both BG3 and S2 cells, the precision was lower Fig. B, indicaFor the XAI model, we selected bins with a probability threshold of 0.8 or higher . Our results confirm that the majority of putative enhancers and common enhancers are intronic Fig. B. In addMost putative enhancers fit the expected size based on previously identified enhancers (50 bp–1 Kb) Fig. . NeverthTo further investigate whether these putative enhancers control gene expression, we integrated the putative, common and STARR-seq only enhancer annotation together with in situ sub-kilobase-pair resolution Hi-C datasets in BG3  and S2  cell linThere is a negligible difference in size between the distal only and proximal putative enhancers Fig. B. Most iOne possibility that could explain why the putative enhancers are not detected by STARR-seq is that these putative enhancers act mainly distally, while enhancers detected by STARR-seq mostly act proximally. We found that this is not the case and both common enhancers between XAI and STARR-seq Fig. : Fig. S4There also exists the possibility that these putative enhancers are redundant enhancers. To investigate this, we plotted the enriched Hi-C contacts of putative and common enhancers to all other predicted enhancers (including both putative and common)  , WDS (involved in maintenance of H3K4me3), and MOF (enzyme responsible for acetylating H4K16) together with high levels of HP1c  that are preferentially enriched at common and putative enhancers. Additional file Our explainable AI model uncovers a new set of putative enhancers, previously not identified in two Drosophila cell lines. Using experimental data, we show that these putative enhancers display similar epigenetic characteristics to enhancers detected by STARR-seq and make 3D contacts with transcribed genes. Altogether, we could not identify any significant difference in chromatin and epigenetic modifications between the putative and common enhancers suggesting that these putative enhancers are a novel group of previously uncharacterised enhancers.High-throughput enhancer assays  have revolutionised the identification of enhancers, but they also suffer from false positives and false negatives , 63–65. In this manuscript, we use Opaque Box ML based Neural Networks and XAI (based on Type-2 Fuzzy Logic and Multi Objective Multi Constraint Evolutionary Computation) models to identify enhancers using STARR-seq enhancer annotation and a set of epigenetic features (e.g. histones and histone modifications). We train the model on a subset of regions in one cell line (BG3) and predict the enhancers genome wide in the same cell line and a different cell line (S2), which the model has not been exposed to during the training. Our results confirm that both the Opaque Box ML and XAI are able to predict enhancers with high accuracy and, most importantly, that they are generalizable see Fig. , demonstInterestingly, we observed a decrease in the precision of these computational models, meaning that they predict more enhancers than those annotated by STARR-seq. The critical question is whether these putative enhancers are true enhancers or not. We know that STARR-seq recovered enhancers are dependent on the plasmid used in the experiment, suggesting that the plasmid used could lead to an increase in false negatives (true enhancers that are missed) . In partEnhancer hubs have been previously observed in many organisms and there is evidence that these redundant enhancers have a role in providing robustness in gene activation , 69. HerThe main difference between the putative enhancers and the ones identified by both STARR-seq and the XAI models is that the putative enhancers tend to be slightly smaller (many of them are between 150 bp and 1Kb). Nevertheless, their size is within the expected size of enhancers , 71. EnhFurthermore, putative enhancers display slightly less enrichment in enhancer chromatin state and slightly less depletion in heterochromatin chromatin state compared to common enhancers. This indicates that while the putative enhancers share the same epigenetic code as common one, they might have less enrichment of the active chromatin marks. One possibility is that these putative enhancers are not as strong as the common ones and they might be missed by experimental methods. Alternatively, they might be primed enhancers , enhanceOne of the advantages of XAI is that we can see and evaluate the rules used to predict enhancer activity. Some rules are well described in the literature, for example H3K4me1 or H3K27ac being highly enriched , 16. OthInterestingly, we found that there is no rule in our XAI model where both H3K4me1 and H3K27ac are high  and architectural proteins  , 76 at pOur aim was to investigate if histone modifications are sufficient to predict enhancers and we did not include TFs in our predictive models. We found that indeed histone modifications are not only sufficient to recover most of the previously annotated enhancers by STARR-seq, but we also predicted many new putative enhancers. To investigate whether TFs could differentiate between common and putative enhancers, we tried to identify if there are any enriched motifs at the two classes of enhancers, and we found that majority of motifs are shared between the two groups of enhancers. Nevertheless, there were five TFs that were enriched preferentially at common in both BG3 and S2 cells  and five TFs that were enriched preferentially at putative in both BG3 and S2 cells . Only eg, hkb, Mes2, and maf-S are expressed in larval central nervous system (from where BG3 cells are derived), and only dl is expressed in S2 cells . This suHere, we use opaque box machine learning  and explainable AI (based on Type-2 Fuzzy Logic and Multi Objective Multi Constraint Evolutionary Computation) models to successfully identify enhancers based only on epigenetic features and using STARR-seq enhancer annotation for training. Our results confirm that both the opaque box machine learning and explainable AI models can predict enhancers with a high degree of accuracy and, most importantly, that both models were able to generalise to a previously unseen cell line. We identify a novel set of putative enhancers that display a similar epigenetic landscape as enhancers identified by STARR-seq, but only intermediary levels of mediator and cohesin complexes and nascent transcription that are all significantly above background levels. Most importantly, we were able to analyse the rules employed by the explainable AI to identify enhancers and dissect the combinations of different histone modifications that characterise different classes of enhancers.D. melanogaster cell lines (BG3 and S2) were downloaded from modEncode [The Drosophila melanogaster genome (dm6) , 81 was odEncode . The fulhttps://logicalglue-support.helpscoutdocs.com/), neural networks, and type-2 fuzzy logic based XAI models were trained using the binarised STARR-seq peaks as classification labels for active enhancers. The best fitting models for each classification method were selected based on their accuracy, recall, and gini (gini =2 *area under the curve (AUC)-1) scores across the testing and the overall one million initially uploaded bins. These models were then used to predict enhancer activity across the previously tiled dm6 genome in both cell types, and their performance was compared.One million bins were sampled, maintaining the same ratio of enhancer to non-enhancer labelled bins found across the entire dataset. The data set was then split into training and testing datasets where all maintained the ratio of enhancers to non-enhancers. Using the Temenos XAI platform  from  was thenM values smoothed over 500 bp) by The modENCODE Consortium for BG3 cell lines [We used the ChIP-chip datasets generated and pre-processed  to shortest (bottom) and extracted the ChIP-chip signal within 10 Kb window build around the enhancer centres. Initial profiles were winsorised  and rescaled to lie between 0 and 1. For 3′NT-seq profile, enhancers were sorted analogically, i.e., we did not apply the winterisation, and we rescaled positive and negative signals separately—positive signals were rescaled to the interval from 0 to 1; negative signals were rescaled to the interval from − 1 to 0. The signals of resulting profile belong to the interval from − 1 to 1.10 of the ratio between nascent RNA levels in 500 bp on the positive strand downstream of the border and on the negative strand upstream of the border. 500 bp bins that were 500 bp away were considered in both directions from the enhancer [The directionality score computed as logenhancer . The direnhancer .p value of < 0.05 in PWMenrich when compared to the standard dm6 background.PWM enrichment analysis was performed on putative enhancer regions using the PWMenrich package in R  using MoAdditional file 1:. Supplementary Figures and TablesAdditional file 2:. Review history