Mechanistic dissection of dominant AIRE mutations in mouse models reveals AIRE autoregulation

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Abstract

Aire is a master of central tolerance. Goldfarb et al. compare mice with recessive or dominant-negative Aire patient mutations to dissect the mechanisms that underlie dominance and identify an autoregulatory mechanism by which Aire negatively modulates its own expression.

Abstract

The autoimmune regulator (AIRE) is essential for the establishment of central tolerance and prevention of autoimmunity. Interestingly, different AIRE mutations cause autoimmunity in either recessive or dominant-negative manners. Using engineered mouse models, we establish that some monoallelic mutants, including C311Y and C446G, cause breakdown of central tolerance. By using RNAseq, ATACseq, ChIPseq, and protein analyses, we dissect the underlying mechanisms for their dominancy. Specifically, we show that recessive mutations result in a lack of AIRE protein expression, while the dominant mutations in both PHD domains augment the expression of dysfunctional AIRE with altered capacity to bind chromatin and induce gene expression. Finally, we demonstrate that enhanced AIRE expression is partially due to increased chromatin accessibility of the AIRE proximal enhancer, which serves as a docking site for AIRE binding. Therefore, our data not only elucidate why some AIRE mutations are recessive while others dominant, but also identify an autoregulatory mechanism by which AIRE negatively modulates its own expression.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2023)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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Cutadapt removes adapter sequences from high-throughput sequencing reads

Marcel Martin (2011)

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Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang, Tao Liu, Clifford Meyer … (2008)

Background The determination of the 'cistrome', the genome-wide set of in vivo cis-elements bound by trans-factors [1], is necessary to determine the genes that are directly regulated by those trans-factors. Chromatin immunoprecipitation (ChIP) [2] coupled with genome tiling microarrays (ChIP-chip) [3,4] and sequencing (ChIP-Seq) [5-8] have become popular techniques to identify cistromes. Although early ChIP-Seq efforts were limited by sequencing throughput and cost [2,9], tremendous progress has been achieved in the past year in the development of next generation massively parallel sequencing. Tens of millions of short tags (25-50 bases) can now be simultaneously sequenced at less than 1% the cost of traditional Sanger sequencing methods. Technologies such as Illumina's Solexa or Applied Biosystems' SOLiD™ have made ChIP-Seq a practical and potentially superior alternative to ChIP-chip [5,8]. While providing several advantages over ChIP-chip, such as less starting material, lower cost, and higher peak resolution, ChIP-Seq also poses challenges (or opportunities) in the analysis of data. First, ChIP-Seq tags represent only the ends of the ChIP fragments, instead of precise protein-DNA binding sites. Although tag strand information and the approximate distance to the precise binding site could help improve peak resolution, a good tag to site distance estimate is often unknown to the user. Second, ChIP-Seq data exhibit regional biases along the genome due to sequencing and mapping biases, chromatin structure and genome copy number variations [10]. These biases could be modeled if matching control samples are sequenced deeply enough. However, among the four recently published ChIP-Seq studies [5-8], one did not have a control sample [5] and only one of the three with control samples systematically used them to guide peak finding [8]. That method requires peaks to contain significantly enriched tags in the ChIP sample relative to the control, although a small ChIP peak region often contains too few control tags to robustly estimate the background biases. Here, we present Model-based Analysis of ChIP-Seq data, MACS, which addresses these issues and gives robust and high resolution ChIP-Seq peak predictions. We conducted ChIP-Seq of FoxA1 (hepatocyte nuclear factor 3α) in MCF7 cells for comparison with FoxA1 ChIP-chip [1] and identification of features unique to each platform. When applied to three human ChIP-Seq datasets to identify binding sites of FoxA1 in MCF7 cells, NRSF (neuron-restrictive silencer factor) in Jurkat T cells [8], and CTCF (CCCTC-binding factor) in CD4+ T cells [5] (summarized in Table S1 in Additional data file 1), MACS gives results superior to those produced by other published ChIP-Seq peak finding algorithms [8,11,12]. Results Modeling the shift size of ChIP-Seq tags ChIP-Seq tags represent the ends of fragments in a ChIP-DNA library and are often shifted towards the 3' direction to better represent the precise protein-DNA interaction site. The size of the shift is, however, often unknown to the experimenter. Since ChIP-DNA fragments are equally likely to be sequenced from both ends, the tag density around a true binding site should show a bimodal enrichment pattern, with Watson strand tags enriched upstream of binding and Crick strand tags enriched downstream. MACS takes advantage of this bimodal pattern to empirically model the shifting size to better locate the precise binding sites. Given a sonication size (bandwidth) and a high-confidence fold-enrichment (mfold), MACS slides 2bandwidth windows across the genome to find regions with tags more than mfold enriched relative to a random tag genome distribution. MACS randomly samples 1,000 of these high-quality peaks, separates their Watson and Crick tags, and aligns them by the midpoint between their Watson and Crick tag centers (Figure 1a) if the Watson tag center is to the left of the Crick tag center. The distance between the modes of the Watson and Crick peaks in the alignment is defined as 'd', and MACS shifts all the tags by d/2 toward the 3' ends to the most likely protein-DNA interaction sites. Figure 1 MACS model for FoxA1 ChIP-Seq. (a,b) The 5' ends of strand-separated tags from a random sample of 1,000 model peaks, aligned by the center of their Watson and Crick peaks (a) and by the FKHR motif (b). (c) The tag count in ChIP versus control in 10 kb windows across the genome. Each dot represents a 10 kb window; red dots are windows containing ChIP peaks and black dots are windows containing control peaks used for FDR calculation. (d) Tag density profile in control samples around FoxA1 ChIP-Seq peaks. (e,f) MACS improves the motif occurrence in the identified peak centers (e) and the spatial resolution (f) for FoxA1 ChIP-Seq through tag shifting and λlocal. Peaks are ranked by p-value. The motif occurrence is calculated as the percentage of peaks with the FKHR motif within 50 bp of the peak summit. The spatial resolution is calculated as the average distance from the summit to the nearest FKHR motif. Peaks with no FKHR motif within 150 bp of the peak summit are removed from the spatial resolution calculation. When applied to FoxA1 ChIP-Seq, which was sequenced with 3.9 million uniquely mapped tags, MACS estimates the d to be only 126 bp (Figure 1a; suggesting a tag shift size of 63 bp), despite a sonication size (bandwidth) of around 500 bp and Solexa size-selection of around 200 bp. Since the FKHR motif sequence dictates the precise FoxA1 binding location, the true distribution of d could be estimated by aligning the tags by the FKHR motif (122 bp; Figure 1b), which gives a similar result to the MACS model. When applied to NRSF and CTCF ChIP-Seq, MACS also estimates a reasonable d solely from the tag distribution: for NRSF ChIP-Seq the MACS model estimated d as 96 bp compared to the motif estimate of 70 bp; applied to CTCF ChIP-Seq data the MACS model estimated a d of 76 bp compared to the motif estimate of 62 bp. Peak detection For experiments with a control, MACS linearly scales the total control tag count to be the same as the total ChIP tag count. Sometimes the same tag can be sequenced repeatedly, more times than expected from a random genome-wide tag distribution. Such tags might arise from biases during ChIP-DNA amplification and sequencing library preparation, and are likely to add noise to the final peak calls. Therefore, MACS removes duplicate tags in excess of what is warranted by the sequencing depth (binomial distribution p-value 2). Among the remaining 34.6% ChIP-Seq unique peaks, 1,045 (13.3%) were not tiled or only partially tiled on the arrays due to the array design. Therefore, only 21.4% of ChIP-Seq peaks are indeed specific to the sequencing platform. Furthermore, ChIP-chip targets with higher fold-enrichments are more likely to be reproducibly detected by ChIP-Seq with a higher tag count (Figure 3b). Meanwhile, although the signals of array probes at the ChIP-Seq specific peak regions are below the peak-calling cutoff, they show moderate signal enrichments that are significantly higher than the genomic background (Wilcoxon p-value 2) and ChIP-Seq (MACS; FDR <1%). Shown are the numbers of regions detected by both platforms (that is, having at least 1 bp in common) or unique to each platform. (b) The distributions of ChIP-Seq tag number and ChIP-chip MATscore [13] for FoxA1 binding sites identified by both platforms. (c) MATscore distributions of FoxA1 ChIP-chip at ChIP-Seq/chip overlapping peaks, ChIP-Seq unique peaks, and genome background. For each peak, the mean MATscore for all probes within the 300 bp region centered at the ChIP-Seq peak summit is used. Genome background is based on MATscores of all array probes in the FoxA1 ChIP-chip data. (d) Width distributions of FoxA1 ChIP-Seq/chip overlapping peaks and ChIP-Seq unique peaks at different fold-enrichments (less than 25, 25 to 50, and larger than 50). (e) Spatial resolution for FoxA1 ChIP-chip and ChIP-Seq peaks. The Wilcoxon test was used to calculate the p-values for (d) and (e). (f) Motif occurrence within the central 200 bp regions for FoxA1 ChIP-Seq/chip overlapping peaks and platform unique peaks. Error bars showing standard deviation were calculated from random sampling of 500 peaks ten times for each category. Background motif occurrences are based on 100,000 randomly selected 200 bp regions in the human genome, excluding regions in genome assembly gaps (containing 'N'). Comparing the difference between ChIP-chip and ChIP-Seq peaks, we find that the average peak width from ChIP-chip is twice as large as that from ChIP-Seq. The average distance from peak summit to motif is significantly smaller in ChIP-Seq than ChIP-chip (Figure 3e), demonstrating the superior resolution of ChIP-Seq. Under the same 1% FDR cutoff, the FKHR motif occurrence within the central 200 bp from ChIP-chip or ChIP-Seq specific peaks is comparable with that from the overlapping peaks (Figure 3f). This suggests that most of the platform-specific peaks are genuine binding sites. A comparison between NRSF ChIP-Seq and ChIP-chip (Figure S3 in Additional data file 1) yields similar results, although the overlapping peaks for NRSF are of much better quality than the platform-specific peaks. Discussion ChIP-Seq users are often curious as to whether they have sequenced deep enough to saturate all the binding sites. In principle, sequencing saturation should be dependent on the fold-enrichment, since higher-fold peaks are saturated earlier than lower-fold ones. In addition, due to different cost and throughput considerations, different users might be interested in recovering sites at different fold-enrichment cutoffs. Therefore, MACS produces a saturation table to report, at different fold-enrichments, the proportion of sites that could still be detected when using 90% to 20% of the tags. Such tables produced for FoxA1 (3.9 million tags) and NRSF (2.2 million tags) ChIP-Seq data sets (Figure S4 in Additional data file 1; CTCF does not have a control to robustly estimate fold-enrichment) show that while peaks with over 60-fold enrichment have been saturated, deeper sequencing could still recover more sites less than 40-fold enriched relative to the chromatin input DNA. As sequencing technologies improve their throughput, researchers are gradually increasing their sequencing depth, so this question could be revisited in the future. For now, we leave it up to individual users to make an informed decision on whether to sequence more based on the saturation at different fold-enrichment levels. The d modeled by MACS suggests that some short read sequencers such as Solexa may preferentially sequence shorter fragments in a ChIP-DNA pool. This may contribute to the superior resolution observed in ChIP-Seq data, especially for activating transcription and epigenetic factors in open chromatin. However, for repressive factors targeting relatively compact chromatin, the target regions might be harder to sonicate into the soluble extract. Furthermore, in the resulting ChIP-DNA, the true targets may tend to be longer than the background DNA in open chromatin, making them unfavorable for size-selection and sequencing. This implies that epigenetic markers of closed chromatin may be harder to ChIP, and even harder to ChIP-Seq. To assess this potential bias, examining the histone mark ChIP-Seq results from Mikkelsen et al. [7], we find that while the ChIP-Seq efficiency of the active mark H3K4me3 remains high as pluripotent cells differentiate, that of repressive marks H3K27me3 and H3K9me3 becomes lower with differentiation (Table S2 in Additional data file 1), even though it is likely that there are more targets for these repressive marks as cells differentiate. We caution ChIP-Seq users to adopt measures to compensate for this bias when ChIPing repressive marks, such as more vigorous sonication, size-selecting slightly bigger fragments for library preparation, or sonicating the ChIP-DNA further between decrosslinking and library preparation. MACS calculates the FDR based on the number of peaks from control over ChIP that are called at the same p-value cutoff. This FDR estimate is more robust than calculating the FDR from randomizing tags along the genome. However, we notice that when tag counts from ChIP and controls are not balanced, the sample with more tags often gives more peaks even though MACS normalizes the total tag counts between the two samples (Figure S5 in Additional data file 1). While we await more available ChIP-Seq data with deeper coverage to understand and overcome this bias, we suggest to ChIP-Seq users that if they sequence more ChIP tags than controls, the FDR estimate of their ChIP peaks might be overly optimistic. Conclusion As developments in sequencing technology popularize ChIP-Seq, we propose a novel algorithm, MACS, for its data analysis. MACS offers four important utilities for predicting protein-DNA interaction sites from ChIP-Seq. First, MACS improves the spatial resolution of the predicted sites by empirically modeling the distance d and shifting tags by d/2. Second, MACS uses a dynamic λlocal parameter to capture local biases in the genome and improves the robustness and specificity of the prediction. It is worth noting that in addition to ChIP-Seq, λlocal can potentially be applied to other high throughput sequencing applications, such as copy number variation and digital gene expression, to capture regional biases and estimate robust fold-enrichment. Third, MACS can be applied to ChIP-Seq experiments without controls, and to those with controls with improved performance. Last but not least, MACS is easy to use and provides detailed information for each peak, such as genome coordinates, p-value, FDR, fold_enrichment, and summit (peak center). Materials and methods Dataset ChIP-Seq data for three factors, NRSF, CTCF, and FoxA1, were used in this study. ChIP-chip and ChIP-Seq (2.2 million ChIP and 2.8 million control uniquely mapped reads, simplified as 'tags') data for NRSF in Jurkat T cells were obtained from Gene Expression Omnibus (GSM210637) and Johnson et al. [8], respectively. ChIP-Seq (2.9 million ChIP tags) data for CTCF in CD4+ T cells were derived from Barski et al. [5]. ChIP-chip data for FoxA1 and controls in MCF7 cells were previously published [1], and their corresponding ChIP-Seq data were generated specifically for this study. Around 3 ng FoxA1 ChIP DNA and 3 ng control DNA were used for library preparation, each consisting of an equimolar mixture of DNA from three independent experiments. Libraries were prepared as described in [8] using a PCR preamplification step and size selection for DNA fragments between 150 and 400 bp. FoxA1 ChIP and control DNA were each sequenced with two lanes by the Illumina/Solexa 1G Genome Analyzer, and yielded 3.9 million and 5.2 million uniquely mapped tags, respectively. Software implementation MACS is implemented in Python and freely available with an open source Artistic License at [16]. It runs from the command line and takes the following parameters: -t for treatment file (ChIP tags, this is the ONLY required parameter for MACS) and -c for control file containing mapped tags; --format for input file format in BED or ELAND (output) format (default BED); --name for name of the run (for example, FoxA1, default NA); --gsize for mappable genome size to calculate λBG from tag count (default 2.7G bp, approximately the mappable human genome size); --tsize for tag size (default 25); --bw for bandwidth, which is half of the estimated sonication size (default 300); --pvalue for p-value cutoff to call peaks (default 1e-5); --mfold for high-confidence fold-enrichment to find model peaks for MACS modeling (default 32); --diag for generating the table to evaluate sequence saturation (default off). In addition, the user has the option to shift tags by an arbitrary number (--shiftsize) without the MACS model (--nomodel), to use a global lambda (--nolambda) to call peaks, and to show debugging and warning messages (--verbose). If a user has replicate files for ChIP or control, it is recommended to concatenate all replicates into one input file. The output includes one BED file containing the peak chromosome coordinates, and one xls file containing the genome coordinates, summit, p-value, fold_enrichment and FDR (if control is available) of each peak. For FoxA1 ChIP-Seq in MCF7 cells with 3.9 million and 5.2 million ChIP and control tags, respectively, it takes MACS 15 seconds to model the ChIP-DNA size distribution and less than 3 minutes to detect peaks on a 2 GHz CPU Linux computer with 2 GB of RAM. Figure S6 in Additional data file 1 illustrates the whole process with a flow chart. Abbreviations ChIP, chromatin immunoprecipitation; CTCF, CCCTC-binding factor; FDR, false discovery rate; FoxA1, hepatocyte nuclear factor 3α; MACS, Model-based Analysis of ChIP-Seq data; NRSF, neuron-restrictive silencer factor. Authors' contributions XSL, WL and YZ conceived the project and wrote the paper. YZ, TL and CAM designed the algorithm, performed the research and implemented the software. JE, DSJ, BEB, CN, RMM and MB performed FoxA1 ChIP-Seq experiments and contributed to ideas. All authors read and approved the final manuscript. Additional data files The following additional data are available. Additional data file 1 contains supporting Figures S1-S6, and supporting Tables S1 and S2. Supplementary Material Additional data file 1 Figures S1-S6, and Tables S1 and S2. Click here for file

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Author and article information

Contributors

Yael Goldfarb:

ORCID: https://orcid.org/0000-0002-9472-9577

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Tal Givony:

ORCID: https://orcid.org/0000-0002-0568-8049

Role: Investigation

Noam Kadouri:

ORCID: https://orcid.org/0000-0002-2978-4318

Role: InvestigationRole: Writing - review & editing

Jan Dobeš:

ORCID: https://orcid.org/0000-0003-1853-1603

Role: InvestigationRole: Writing - review & editing

Cristina Peligero-Cruz:

ORCID: https://orcid.org/0000-0001-7465-5008

Role: Investigation

Itay Zalayat:

ORCID: https://orcid.org/0000-0002-5988-0044

Role: InvestigationRole: Visualization

Golda Damari:

ORCID: https://orcid.org/0000-0002-5451-0378

Role: Methodology

Bareket Dassa:

ORCID: https://orcid.org/0000-0002-5226-7554

Role: Formal analysisRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing - review & editing

Shifra Ben-Dor:

ORCID: https://orcid.org/0000-0001-9604-1939

Role: Formal analysisRole: Writing - review & editing

Yael Gruper:

ORCID: https://orcid.org/0000-0001-7912-2243

Role: Investigation

Bergithe E. Oftedal:

ORCID: https://orcid.org/0000-0003-4248-7737

Role: InvestigationRole: Writing - review & editing

Eirik Bratland:

ORCID: https://orcid.org/0000-0002-8359-9818

Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing - original draft

Martina M. Erichsen:

ORCID: https://orcid.org/0000-0001-9815-972X

Role: InvestigationRole: Resources

Amund Berger:

ORCID: https://orcid.org/0000-0001-6973-2048

Role: Formal analysisRole: Visualization

Ayelet Avin:

ORCID: https://orcid.org/0000-0003-3321-5190

Role: Conceptualization

Shir Nevo:

ORCID: https://orcid.org/0000-0002-1345-6301

Role: Investigation

Uku Haljasorg:

ORCID: https://orcid.org/0000-0002-1541-6020

Role: Formal analysisRole: Investigation

Yael Kuperman:

ORCID: https://orcid.org/0000-0003-3830-1856

Role: InvestigationRole: Resources

Adi Ulman:

ORCID: https://orcid.org/0000-0002-3175-5006

Role: Validation

Rebecca Haffner-Krausz:

ORCID: https://orcid.org/0000-0001-6503-7646

Role: MethodologyRole: ResourcesRole: Writing - review & editing

Ziv Porat:

ORCID: https://orcid.org/0000-0003-3059-181X

Role: Formal analysisRole: Methodology

Ulus Atasoy:

ORCID: https://orcid.org/0000-0002-4095-1359

Role: ConceptualizationRole: Formal analysisRole: Validation

Dena Leshkowitz:

ORCID: https://orcid.org/0000-0002-4703-2830

Role: Formal analysisRole: Writing - review & editing

Eystein S. Husebye:

ORCID: https://orcid.org/0000-0002-7886-2976

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing - review & editing

Jakub Abramson:

ORCID: https://orcid.org/0000-0002-1745-8996

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): J Exp Med

Journal ID (iso-abbrev): J Exp Med

Journal ID (publisher-id): jem

Title: The Journal of Experimental Medicine

Publisher: Rockefeller University Press

ISSN (Print): 0022-1007

ISSN (Electronic): 1540-9538

Publication date Collection: 01 November 2021

Publication date (Electronic): 03 September 2021

Volume: 218

Issue: 11

Electronic Location Identifier: e20201076

Affiliations

[1 ] Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

[2 ] Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel

[3 ] Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

[4 ] Department of Clinical Science and K.G. Jebsen Center for Autoimmune Disorders, University of Bergen, Bergen, Norway

[5 ] Department of Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway

[6 ] Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia

[7 ] Flow Cytometry Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

[8 ] Division of Allergy and Immunology, University of Michigan, Ann Arbor, MI

[9 ] Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden

[10 ] Department of Medicine, Haukeland University and Hospital, Bergen, Norway

Author notes

Correspondence to Jakub Abramson: jakub.abramson@ 123456weizmann.ac.il

Eystein S. Husebye: eystein.sverre.husebye@ 123456helse-bergen.no

Disclosures: The authors declare no competing interests exist.

[*]

E.S. Husebye and J. Abramson contributed equally to this paper.

Author information

Yael Goldfarb https://orcid.org/0000-0002-9472-9577

Tal Givony https://orcid.org/0000-0002-0568-8049

Noam Kadouri https://orcid.org/0000-0002-2978-4318

Jan Dobeš https://orcid.org/0000-0003-1853-1603

Cristina Peligero-Cruz https://orcid.org/0000-0001-7465-5008

Itay Zalayat https://orcid.org/0000-0002-5988-0044

Golda Damari https://orcid.org/0000-0002-5451-0378

Bareket Dassa https://orcid.org/0000-0002-5226-7554

Shifra Ben-Dor https://orcid.org/0000-0001-9604-1939

Yael Gruper https://orcid.org/0000-0001-7912-2243

Bergithe E. Oftedal https://orcid.org/0000-0003-4248-7737

Eirik Bratland https://orcid.org/0000-0002-8359-9818

Martina M. Erichsen https://orcid.org/0000-0001-9815-972X

Amund Berger https://orcid.org/0000-0001-6973-2048

Ayelet Avin https://orcid.org/0000-0003-3321-5190

Shir Nevo https://orcid.org/0000-0002-1345-6301

Uku Haljasorg https://orcid.org/0000-0002-1541-6020

Yael Kuperman https://orcid.org/0000-0003-3830-1856

Adi Ulman https://orcid.org/0000-0002-3175-5006

Rebecca Haffner-Krausz https://orcid.org/0000-0001-6503-7646

Ziv Porat https://orcid.org/0000-0003-3059-181X

Ulus Atasoy https://orcid.org/0000-0002-4095-1359

Dena Leshkowitz https://orcid.org/0000-0002-4703-2830

Eystein S. Husebye https://orcid.org/0000-0002-7886-2976

Jakub Abramson https://orcid.org/0000-0002-1745-8996

Article

Publisher ID: jem.20201076

DOI: 10.1084/jem.20201076

PMC ID: 8421262

PubMed ID: 34477806

SO-VID: cc43d0c7-b19f-4801-b095-f2c4e14af625

License:

This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).

History

Date received : 08 June 2020

Date revision received : 07 February 2021

Date accepted : 09 August 2021

Page count

Pages: 24

Funding

Funded by: European Research Council, DOI http://dx.doi.org/10.13039/501100000781;

Award ID: ERC-2016-CoG-724821

Funded by: Israel Science Foundation, DOI http://dx.doi.org/10.13039/501100003977;

Award ID: 1796/16

Funded by: Bill and Marika Glied and Family Fund;

Funded by: Wohl Biology Endowment Fund;

Funded by: Erica Drake Fund;

Funded by: Enoch Foundation;

Funded by: Ruth and Samuel David Gameroff Family Foundation;

Funded by: Lilly Fulop Fund for Multiple Sclerosis Research;

Funded by: Estonian Research Council;

Award ID: PUTJD707

Funded by: Ariane de Rothschild Women Doctoral Program;

Funded by: Weizmann Institute of Science, DOI http://dx.doi.org/10.13039/501100001735;

Funded by: Czech Academy of Sciences;

Funded by: Weizmann–La Caixa Fellowship;

Funded by: Weizmann–Dean of Faculty Fellowship;

Funded by: Kristian Gerhard Jebsen Foundation, DOI http://dx.doi.org/10.13039/100007793;

Funded by: Norwegian Research Council;

Award ID: 262677

Funded by: Novonordisk Foundation;

Funded by: Weston Visiting Scholar Program;

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Mechanistic dissection of dominant AIRE mutations in mouse models reveals AIRE autoregulation

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Most cited references 85

Fast gapped-read alignment with Bowtie 2.

Cutadapt removes adapter sequences from high-throughput sequencing reads

Model-based Analysis of ChIP-Seq (MACS)

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