10_material_and_methods.Rmd

---
title: "Material and Methods"
author: "Florent Chuffart"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    toc_depth: 3
    number_sections: true
---


```{r, echo=FALSE, eval=TRUE}
knitr::opts_chunk$set(collapse=TRUE, comment = "#>", fig.width=9, fig.height=6, eval=TRUE, echo=FALSE, results="hide")
source("config")
```



# Material and Methods



## Sequencing

**FOLLOWING CONCERNS TGML PLATFORM** Total RNA was purified by trizol extraction followed by Qiagen RNeasy mini kit protocol. 
Removal of most rRNA was proceeded using NEBNext rRNA Depletion Kit Ribodepletion. 
Quality was verified by Agilent Bioanalyzer 2100. 
Libraries were prepared using NEBNext Ultra Directional RNA Library Prep kit for Illumina.
Base calling was performed using RTA version 2.*

Total RNA was purified by XXX.
Removal of most rRNA was proceeded using XXX.
Quality was verified by XXX. 
Libraries were prepared using XXX.
Base calling was performed XXX.

Stranded paired-end 2x75bp reads were produced by Illumina NextSeq500. 

**Data**

Transcriptomic data  were obtained from EGA study EGAS00001000925, EGA dataset EGAD00001001244: 118 paired .fastq files corresponding to 59 patients.
Clinical data were obtained from [PMID: 26168399] supplementals (`41586_2015_BFnature14664_MOESM72_ESM.xlsx` file)

**Alignment**

The sequenced reads from the raw sequence (.fastq files) were aligned on the `r annotation` `r genome_version` `r species` genome using the STAR software (2.7.1a) (Dobin et al. 2013) [PMID:23104886] to produce bam files.

**Counts**

The aligned reads (.bam files) were counted using HTSeq framework (0.11.2) (Anders et al. 2015) [PMID:25260700], with options: -t exon -f bam -r pos --stranded=`r strand` -m intersection-strict --nonunique none

**Normalization**

The normalization, were performed using the `r version$version.string` [R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2017], DESeq2 (`r packageVersion("DESeq2")`) (Anders and Huber 2010; Love et al. 2014) [PMID:20979621, PMID:25516281] and SARTools (`r packageVersion("SARTools")`) (Varet et al. 2016) [PMID:27280887] packages.


**Differential analysis**

XXX

**Grouping genes/features according to their quartile of expression**

The features corresponding to non-coding transcripts and/or with zero counts were filtered out, in order to retain all non-zero counts protein-coding genes from NCBI Reference Sequence Database (RefSeq). The corresponding genes and features were ranked according to their DESeq2 normalized expression mean value in the samples corresponding to the 4 replicates of liver from wild-type mice under normal diet, and allocated into four subsets corresponding to the quartiles of this expression and exported as .bed files.

Identification of genes up and down regulated and selection for heatmap representation
Supervised transcriptomic analyses were performed to identify genes significantly up-and down regulated between two conditions using thresholds of Student t-test p-value <0.01 and fold change absolute value of 2.

The normalized, pseudo-log transformed and standardized read counts of the up and down-regulated genes in wild type mice between those submitted to a high fat diet (HFD) and those with a normal diet (ND) were used to generate the heatmap presented in Figure 6A.

[abs_expr_q1_0.bed](./abs_expr_q1_0.bed) 
[abs_expr_q2_0.bed](./abs_expr_q2_0.bed)
[abs_expr_q3_0.bed](./abs_expr_q3_0.bed)
[abs_expr_q4_0.bed](./abs_expr_q4_0.bed)




# Grant and Acknowledgements

All (or most of) the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities.


# Session Information

```{r results="verbatim"}
sessionInfo()
date()
```