Collect, process and quality control Chromatin Immunoprecipitation followed by sequencing (ChIP-Seq) data from differentiating 3T3-L1. Related repositories to collect and process other kinds of data are adiporeg_rna and adiporeg_dhs. The processed data are further analyzed in repositories such as adiporeg

This repository aims to detail the following:

1. The search strategy
2. The collected data
3. The pre-processing and the processing of the raw data

The term "3T3-L1" was used to search the NCBI SRA repository. The results were sent to the run selector. 1,176 runs were viewed. The runs were faceted by Assay Type and the "chip-seq" which resulted in 739 runs. Only 235 samples from 20 different studies were included after being manually reviewed to fit the following criteria:

• The raw data is available from GEO and has a GEO identifier (GSM#)
• The raw data is linked to a published publicly available article
• The protocols for generating the data sufficiently describe the origin of the cell line, the differentiation medium and the time points when the samples were collected.
• In case the experimenal designs included treatment other than the differentiation medias, the control (non-treated) samples were included.

Note: The data quality and the platform discrepencies are not inluded in these criteria

[1] A. S. B. Brier, A. Loft, J. G. Madsen, et al. “The KDM5 family is required for activation of pro-proliferative cell cycle genes during adipocyte differentiation”. In: Nucleic Acids Research 45.4 (2017), pp. 1743-1759. ISSN: 13624962. DOI: 10.1093/nar/gkw1156. eprint: 1611.06654.

[2] M. D. Cardamone, B. Tanasa, M. Chan, et al. “GPS2/KDM4A pioneering activity regulates promoter-specific recruitment of PPARG”. In: Cell Reports 8.1 (2014), pp. 163-176. ISSN: 22111247. DOI: 10.1016/j.celrep.2014.05.041. eprint: NIHMS150003.

[3] A. Catic, C. Y. Suh, C. T. Hill, et al. “Genome-wide Map of nuclear protein degradation shows NCoR1 turnover as a key to mitochondrial gene regulation”. In: Cell 155.6 (2013), pp. 1380-1395. ISSN: 00928674. DOI: 10.1016/j.cell.2013.11.016. eprint: NIHMS150003.

[4] A. G. Cristancho, M. Schupp, M. I. Lefterova, et al. “Repressor transcription factor 7-like 1 promotes adipogenic competency in precursor cells”. In: Proceedings of the National Academy of Sciences 108.39 (2011), pp. 16271-16276. ISSN: 0027-8424. DOI: 10.1073/pnas.1109409108. <URL: http://www.pnas.org/cgi/doi/10.1073/pnas.1109409108>.

[5] D. Duteil, E. Metzger, D. Willmann, et al. “LSD1 promotes oxidative metabolism of white adipose tissue”. In: Nature Communications 5.May (Jun. 2014), p. 4093. ISSN: 20411723. DOI: 10.1038/ncomms5093. <URL: http://www.nature.com/doifinder/10.1038/ncomms5093>.

[6] A. K. Haakonsson, M. Stahl Madsen, R. Nielsen, et al. “Acute Genome-Wide Effects of Rosiglitazone on PPARG Transcriptional Networks in Adipocytes”. In: Molecular Endocrinology 27.9 (2013), pp. 1536-1549. ISSN: 0888-8809. DOI: 10.1210/me.2013-1080. <URL: https://academic.oup.com/mend/article-lookup/doi/10.1210/me.2013-1080>.

[7] S. Kang, L. T. Tsai, Y. Zhou, et al. “Identification of nuclear hormone receptor pathways causing insulin resistance by transcriptional and epigenomic analysis”. In: Nature Cell Biology 17.1 (2015), pp. 44-56. ISSN: 14764679. DOI: 10.1038/ncb3080. eprint: NIHMS150003.

[8] B. Lai, J. E. Lee, Y. Jang, et al. “MLL3/MLL4 are required for CBP/p300 binding on enhancers and super-enhancer formation in brown adipogenesis”. In: Nucleic Acids Research 45.11 (2017), pp. 6388-6403. ISSN: 13624962. DOI: 10.1093/nar/gkx234.

[9] M. I. Lefterova, D. J. Steger, D. Zhuo, et al. “Cell-Specific Determinants of Peroxisome Proliferator-Activated Receptor Function in Adipocytes and Macrophages”. In: Molecular and Cellular Biology 30.9 (2010), pp. 2078-2089. ISSN: 0270-7306. DOI: 10.1128/MCB.01651-09. <URL: http://mcb.asm.org/cgi/doi/10.1128/MCB.01651-09>.

[10] X. Luo, K. W. Ryu, D. S. Kim, et al. “PARP-1 Controls the Adipogenic Transcriptional Program by PARylating C/EBPB and Modulating Its Transcriptional Activity”. In: Molecular Cell 65.2 (Jan. 2017), pp. 260-271. ISSN: 10974164. DOI: 10.1016/j.molcel.2016.11.015. <URL: http://www.ncbi.nlm.nih.gov/pubmed/28107648 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5258183>.

[11] K. D. Macisaac, K. A. Lo, W. Gordon, et al. “A quantitative model of transcriptional regulation reveals the influence of binding location on expression”. In: PLoS Computational Biology 6.4 (Apr. 2010), p. e1000773. ISSN: 1553734X. DOI: 10.1371/journal.pcbi.1000773. <URL: http://www.ncbi.nlm.nih.gov/pubmed/20442865 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2861697>.

[12] Y. Matsumura, R. Nakaki, T. Inagaki, et al. “H3K4/H3K9me3 Bivalent Chromatin Domains Targeted by Lineage-Specific DNA Methylation Pauses Adipocyte Differentiation”. In: Molecular Cell 60.4 (2015), pp. 584-596. ISSN: 10974164. DOI: 10.1016/j.molcel.2015.10.025.

[13] T. S. Mikkelsen, Z. Xu, X. Zhang, et al. “Comparative epigenomic analysis of murine and human adipogenesis”. In: Cell 143.1 (Oct. 2010), pp. 156-169. ISSN: 00928674. DOI: 10.1016/j.cell.2010.09.006. <URL: http://www.ncbi.nlm.nih.gov/pubmed/20887899 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2950833>.

[14] R. Nielsen, T. . Pedersen, D. Hagenbeek, et al. “Genome-wide profiling of PPARG:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis”. In: Genes and Development 22.21 (2008), pp. 2953-2967. ISSN: 08909369. DOI: 10.1101/gad.501108.

[15] R. Siersbæk, J. G. S. Madsen, B. M. Javierre, et al. “Dynamic Rewiring of Promoter-Anchored Chromatin Loops during Adipocyte Differentiation”. In: Molecular Cell 66.3 (2017), pp. 420-435.e5. ISSN: 10974164. DOI: 10.1016/j.molcel.2017.04.010.

[16] R. Siersbaek, R. Nielsen, S. John, et al. “Extensive chromatin remodelling and establishment of transcription factor hotspots during early adipogenesis”. In: EMBO Journal 30.8 (Apr. 2011), pp. 1459-1472. ISSN: 02614189. DOI: 10.1038/emboj.2011.65. <URL: http://www.ncbi.nlm.nih.gov/pubmed/21427703 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3102274>.

[17] R. Siersbæk, A. Rabiee, R. Nielsen, et al. “Transcription factor cooperativity in early adipogenic hotspots and super-enhancers”. In: Cell Reports 7.5 (Jun. 2014), pp. 1443-1455. ISSN: 22111247. DOI: 10.1016/j.celrep.2014.04.042. <URL: http://www.ncbi.nlm.nih.gov/pubmed/24857652>.

[18] D. J. Steger, G. R. Grant, M. Schupp, et al. “Propagation of adipogenic signals through an epigenomic transition state”. In: Genes and Development 24.10 (May. 2010), pp. 1035-1044. ISSN: 08909369. DOI: 10.1101/gad.1907110. <URL: http://www.ncbi.nlm.nih.gov/pubmed/20478996 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2867208>.

[19] S. E. Step, H. W. Lim, J. M. Marinis, et al. “Anti-diabetic rosiglitazone remodels the adipocyte transcriptome by redistributing transcription to PPARG-driven enhancers”. In: Genes and Development 28.9 (May. 2014), pp. 1018-1028. ISSN: 15495477. DOI: 10.1101/gad.237628.114. <URL: http://www.ncbi.nlm.nih.gov/pubmed/24788520 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4018489>.

[20] L. Wang, S. Xu, J. E. Lee, et al. “Histone H3K9 methyltransferase G9a represses PPARG expression and adipogenesis”. In: EMBO Journal 32.1 (2013), pp. 45-59. ISSN: 02614189. DOI: 10.1038/emboj.2012.306.

The scripts to download and process the raw data are located in scripts/ and are glued together to run sequentially by the GNU make file Makefile. The following is basically a description of the recipies in the Makefile with emphasis on the software versions, options, inputs and outputs.

• Program: wget (1.18)
• Input: run.csv, the URLs column
• Output: *.fastq.gz
• Options: -N

• Program: wget (1.18)
• Input: URL for mm10 gene annotation file
• Output: annotation.gtf
• Options: -N

• Program: bowtie2-build (2.3.0)
• Input: URL for mm10 mouse genome fasta files
• Output: *.bt2 bowtie2 index for the mouse genome
• Options: defaults

• Program: bowtie2 (2.3.0)
• Input: *.fastq.gz and mm10/ bowtie2 index for the mouse genome
• Output: *.sam
• Options: --no-unal

• Program: samtools view (1.3.1)
• Input: *.sam
• Output: *.bam
• Options: -Sb

• Program: samtools sort and samtools index (1.3.1)
• Input: *.bam
• Output: *.bam and *.bai
• Option: defaults

• Program: featureCounts (1.5.1)
• Input: *.bam and the annotation gtf file for the mm10 mouse genome.
• Output: *.txt
• Option: defaults

• Program: fastqc (0.11.5)
• Input: *.fastq.gz, *.sam and *.bam
• Output: *_fastqc.zip
• Option: defaults