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library(GEOquery)

# Download existing RA datasets

# Study 1
# Teixeira VH, Olaso R, Martin-Magniette ML, Lasbleiz S et al. Transcriptome 
# analysis describing new immunity and defense genes in peripheral blood 
# mononuclear cells of rheumatoid arthritis patients. 
# PLoS One 2009 Aug 27;4(8):e6803. PMID: 19710928
# Reference: 	GSE15573

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# Get SNP records based on rs ids from VCF file
# Kevin Blighe recipe from https://www.biostars.org/p/373852/

# Create a file with snp ids of interest, one id per line
# We can give the newly created file any name, for example snp.txt

bcftools view --include ID==@snp.txt target.bcf

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# Taken from https://www.biostars.org/p/111040/
# Examine and save metadata 
esearch -db sra -query PRJNA484081  | efetch -format runinfo > bioproj.csv

# The first column of comma separated runinfo file are run ids
cat bioproj.csv | cut -d ',' -f 1 | head

# Download first 4 files as an example
cat bioproj.csv | cut -d ',' -f 1 | grep 'SRR' # Check of we are selecting right files

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#! /bin/bash

# Taken from https://github.com/stephenturner/mergelanes/issues/1
# Exercise caution, does not work accurately in every case:
# Not working accurately for sample IDs like "A11_Barcodexxx_S11_L001_R1_001". 
# It cat together all L001 pertaining to sample ID A11 with different barcodes also

ls *R1* | cut -d _ -f 1 | sort | uniq \
    | while read id; do \
        cat $id*R1*.fastq.gz > $id.R1.fastq.gz;

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library(KEGGREST)
library(org.Rn.eg.db)

# Download entrez ids and corresponding KEGG pathways followed
# by creation of a table where one column is entrez id and another 
# column is a comma separated list of KEGG pathways

# Download pathway to entrez id relationship
rno_path_eg  <- keggLink("pathway", "rno")
names(rno_path_eg) <- gsub("rno:", "", names(rno_path_eg))

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library(RColorBrewer)
library(ggplot2)
library(ggrepel)

setwd("<path/to/dir>")

# Load DESeq2 object
load("expression_data/DESeqOBJ.RData")
dds

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library(AnnotationForge)
library(biomaRt)
setwd("path/to/dir")

## Get annotation data frame for Ovis aries from BiomaRt
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", host="https://www.ensembl.org")

ensembl <- useDataset("oaries_gene_ensembl", mart=ensembl)
#126 oaries_gene_ensembl Sheep (texel) genes (Oar_v3.1) Oar_v3.1

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library(ggplot2)
library(stringr)

theme_set(
  theme_bw() + 
    theme(legend.position = "right")
)

ggplot(all_go, aes(x = sample_id, y = reorder(GO.label, Enrichment))) + 
  geom_point(aes(size = Enrichment, fill = P.value), alpha = 0.75, shape = 21) + 

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In one of the conda environments I encountered the a bowtie2 installation problem breaking RSEM run.
The solution to the problem is described in the following Biostars post:
https://www.biostars.org/p/494922/

See the post content below:

```

$ conda create -n bttest -c bioconda bowtie2
$ conda activate bttest

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# Get paralogs for the list of genes

library(biomaRt)
human <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
gene_id <- c("TPM1", "BOD1", "ADAP1")
results <- getBM(attributes = c("ensembl_gene_id", 
                                "external_gene_name",
                                "hsapiens_paralog_ensembl_gene", 
                                "hsapiens_paralog_associated_gene_name"),
                 filters = "external_gene_name",