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https://github.com/mahshaaban/adiporeg_rna
Collect, process and quality control RNA-sequencing (RNA-Seq) data from differentiating 3T3-L1
https://github.com/mahshaaban/adiporeg_rna
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Collect, process and quality control RNA-sequencing (RNA-Seq) data from differentiating 3T3-L1
- Host: GitHub
- URL: https://github.com/mahshaaban/adiporeg_rna
- Owner: MahShaaban
- Created: 2018-10-13T10:30:25.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-02T06:05:57.000Z (about 6 years ago)
- Last Synced: 2023-08-03T15:25:10.516Z (over 1 year ago)
- Language: TeX
- Homepage:
- Size: 29.3 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# adiporeg_rna
Collect, process and quality control RNA-sequencing (RNA-Seq) data from differentiating 3T3-L1. Related repositories to collect and process other kinds of data are [adiporeg_dhs](https://github.com/MahShaaban/adiporeg_dhs) and [adiporeg_chip](https://github.com/MahShaaban/adiporeg_chip). The processed data are further analyzed in repositories such as [adiporeg](https://github.com/MahShaaban/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
## 1. The search strategy
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 "rna-seq" which resulted in 323 runs. Only 98 samples from 16 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
## 2. The collected data
### 2.1 Sample table
| Stage | Time (hours) | Samples |
|-------|--------------|---------|
| Early | -96 | 1 |
| | -48 | 6 |
| | 0 | 22 |
| | 1 | 1 |
| | 2 | 3 |
| | 4 | 7 |
| | 6 | 1 |
| | 10 | 2 |
| | 24 | 8 |
| | 28 | 1 |
| | 48 | 18 |
| Late | 4 | 2 |
| | 96 | 2 |
| | 120 | 1 |
| | 144 | 4 |
| | 168 | 7 |
| | 192 | 8 |
| | 240 | 4 |
### 2.2 Run table
All samples has single runs.
| Sequencer Model | Runs |
|-----------------------------|------|
| Illumina Genome Analyzer II | 3 |
| Illumina HiSeq 1500 | 20 |
| Illumina HiSeq 2000 | 47 |
| Illumina HiSeq 2500 | 39 |
| Ion Torrent Proton | 4 |
| NextSeq 500 | 6 |
| Library Type | Runs |
|--------------|------|
| PAIRED | 30 |
| SINGLE | 68 |
### 2.3 Studies
[1] H. Al Adhami, B. Evano, A. Le Digarcher, et al. “A systems-level approach to parental genomic
imprinting: The imprinted gene network includes extracellular matrix genes and regulates cell cycle
exit and differentiation”. In: _Genome Research_ 25.3 (Mar. 2015), pp. 353-367. ISSN: 15495469. DOI:
10.1101/gr.175919.114. .
[2] 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.
[3] R. Brunmeir, J. Wu, X. Peng, et al. “Comparative Transcriptomic and Epigenomic Analyses Reveal
New Regulators of Murine Brown Adipogenesis”. In: _PLoS Genetics_ 12.12 (Dec. 2016), p. e1006474.
ISSN: 15537404. DOI: 10.1371/journal.pgen.1006474.
[4] D. A. Buchner, A. Charrier, E. Srinivasan, et al. “Zinc finger protein 407 (ZFP407) regulates
insulin-stimulated glucose uptake and glucose transporter 4 (Glut4) mRNA”. In: _Journal of
Biological Chemistry_ 290.10 (Mar. 2015), pp. 6376-6386. ISSN: 1083351X. DOI:
10.1074/jbc.M114.623736. .
[5] N. Chaudhary, E. Gonzalez, S. H. Chang, et al. “Adenovirus Protein E4-ORF1 Activation of PI3
Kinase Reveals Differential Regulation of Downstream Effector Pathways in Adipocytes”. In: _Cell
Reports_ 17.12 (2016), pp. 3305-3318. ISSN: 22111247. DOI: 10.1016/j.celrep.2016.11.082. eprint:
15334406.
[6] X. Chen, I. Ayala, C. Shannon, et al. “The diabetes gene and wnt pathway effector TCF7L2
regulates adipocyte development and function”. In: _Diabetes_ 67.4 (2018), pp. 554-568. ISSN:
1939327X. DOI: 10.2337/db17-0318.
[7] 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. .
[8] G. E. Lim, T. Albrecht, M. Piske, et al. “14-3-3$\zeta$ coordinates adipogenesis of visceral
fat”. In: _Nature Communications_ (2015). ISSN: 20411723. DOI: 10.1038/ncomms8671.
[9] K. A. Lo, A. Labadorf, N. J. Kennedy, et al. “Analysis of In Vitro Insulin-Resistance Models and
Their Physiological Relevance to InVivo Diet-Induced Adipose Insulin Resistance”. In: _Cell Reports_
5.1 (Oct. 2013), pp. 259-270. ISSN: 22111247. DOI: 10.1016/j.celrep.2013.08.039. .
[10] G. Maroni, V. A. Tkachuk, A. Egorov, et al. “Prep1 prevents premature adipogenesis of
mesenchymal progenitors”. In: _Scientific Reports_ 7.1 (Nov. 2017), p. 15573. ISSN: 20452322. DOI:
10.1038/s41598-017-15828-1.
[11] Y. Park, L. Wang, A. Giampietro, et al. “Distinct Roles of Transcription Factors KLF4, Krox20,
and Peroxisome Proliferator-Activated Receptor g in Adipogenesis”. In: _Molecular and Cellular
Biology_ 37.2 (2017), pp. e00554-16. ISSN: 0270-7306. DOI: 10.1128/MCB.00554-16. .
[12] K. W. Ryu, T. Nandu, J. Kim, et al. “Metabolic regulation of transcription through
compartmentalized NAD+biosynthesis”. In: _Science_ 360.6389 (2018). ISSN: 10959203. DOI:
10.1126/science.aan5780.
[13] R. Siersbæk, S. Baek, A. Rabiee, et al. “Molecular architecture of transcription factor
hotspots in early adipogenesis”. In: _Cell Reports_ 7.5 (Jun. 2014), pp. 1434-1442. ISSN: 22111247.
DOI: 10.1016/j.celrep.2014.04.043.
[14] 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.
[15] D. You, E. Nilsson, D. E. Tenen, et al. “Dnmt3a is an epigenetic mediator of adipose insulin
resistance”. In: _eLife_ 6 (2017). ISSN: 2050084X. DOI: 10.7554/eLife.30766.
[16] X. Zhao, Y. Yang, B. F. Sun, et al. “FTO-dependent demethylation of N6-methyladenosine
regulates mRNA splicing and is required for adipogenesis”. In: _Cell Research_ 24.12 (Dec. 2014),
pp. 1403-1419. ISSN: 17487838. DOI: 10.1038/cr.2014.151.
## 3. The pre-processing and the processing of the raw data
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.
### 3.1 `download_fastq`
* Program: `wget` (1.18)
* Input: `run.csv`, the URLs column
* Output: `*.fastq.gz`
* Options: `-N`
### 3.2 `get_annotation`
* Program: `wget` (1.18)
* Input: URL for mm10 gene annotation file
* Output: `annotation.gtf`
* Options: `-N`
### 3.3 `make_index`
* Program: `hisat2-build` (2.0.5)
* Input: URL for mm10 mouse genome fasta files
* Output: `*.bt2` bowtie2 index for the mouse genome
* Options: defaults
### 3.4 `align_reads`
* Program: `hisat2` (2.0.5)
* Input: `*.fastq.gz` and `mm10/` bowtie2 index for the mouse genome
* Output: `*.sam`
* Options: defaults
### 3.5 `count_features`
* Program: `featureCounts` (1.5.1)
* Input: `*.bam` and the annotation `gtf` file for the mm10 mouse genome.
* Output: `*.txt`
* Option: defaults
### 3.6 `fastqc`
* Program: `fastqc` (0.11.5)
* Input: `*.fastq.gz` and `*.sam`
* Output: `*_fastqc.zip`
* Option: defaults