Last updated: 2024-05-14

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library("knitr")
library("rmdformats")
library("tidyverse")
library("DT")  # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")

## Global options
options(max.print="10000")
knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE,
    cache = FALSE,
    comment = FALSE,
    prompt = FALSE,
    tidy = TRUE
)
opts_knit$set(width=75)

The following workflow results from a standardization and several independent computational tests between Maeva Techer (Texas A&M University) and David Bellini (BCM). We will analyse transcriptomes from six sister species, but they may not have 1:1 orthologs for each gene. Thus, we propose two analysis strategies to find shared evolutionary patterns regarding gene expression changes associated with conspecific density.

The first method consist in mapping all transcript reads to a common RefSeq genome which is also the closest to the LCA: the swarming desert locust S. gregaria.
Pros: All genes LOCID are shared and this ease downstream overlap/venn diagram analysis for cross-species comparison.
Cons: Rare transcripts species specific might be disregarded and we loose resolution.

The second method consist in mapping all transcript reads to their respective RefSeq genome and then matching species genes using orthologs predictions made with OrthoFinder.
Pros: Rare transcript species specific are well represented and a fine resolution is conserved. Highly conserved gene families are well represented.
Cons: More time spend before achieving cross-species comparison without the guarantee that orthologous relationships are the most exacts without manual curation.

1. Indexing RefSeq genomes

We used STAR for mapping reads to either their own species reference genome (thereafter name strategy 1) or to their respective reference genome (strategy 2). For both methods, we will first index the genomes with parameters that relies on observations during S. americana genome annotation curation.

We index each genome using the following bash script (example for piceifrons):

#!/bin/bash

##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=STARindex        
#SBATCH --time=12:00:00         
#SBATCH --ntasks=2              
#SBATCH --cpus-per-task=12       
#SBATCH --mem=100G              

module load GCC/11.2.0 STAR/2.7.9a

REFDIR="/scratch/group/songlab/maeva/refgenomes/locusts_complete/index_GCF_021461385.2_iqSchPice1.1_genomic/STAR_INTRONS"
GENOME="/scratch/group/songlab/maeva/refgenomes/locusts_complete/GCF_021461385.2_iqSchPice1.1_genomic.fna"
ANNOTATION="/scratch/group/songlab/maeva/refgenomes/locusts_complete/GCF_021461385.2_iqSchPice1.1_genomic.gtf"

STAR --runMode genomeGenerate --runThreadN 20 --genomeDir $REFDIR --genomeFastaFiles $GENOME --sjdbGTFfile $ANNOTATION --sjdbOverhang 149 --alignIntronMax 2000000 --alignIntronMin 75

The parameters were chosen for the following reasons:
--runMode genomeGenerate indicates we are in the mode to build genome index --alignIntronMin 75 This is the minimum intron region size observed in americana/gregaria genomes so we will keep this value. --alignIntronMax 2000000 David found evidence that introns can be as long as 2mil bp long. --sjdbGTFfile $ANNOTATION we use these parameters to indicates that we want the annotation file to be already accounted for.
--sjdbOverhang 149 this should be ideally (mate_length -1), and our reads are PE150.

We kept the rest to be default values.

2. `STAR` reads mapping

Here we made some test to check whether the selection of certain parameters in the mapping will influence the discovery of differentially expressed genes (DEG) in the downstream analysis (see next section). We were interested, in particular to see how the specified intron size and splicing modes. Our choices here relies on preliminary runs on all species head tissues.

########################################
# Snakefile rule
########################################

#Ahead of the alignment I will build independently the index for STAR

rule STAR_align:
        input:
                index = REFdir + "/locusts_complete/index_{genome}/STAR_INTRONS",
                annotation = REFdir + "/locusts_complete/{genome}.gtf",
                read1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
                read2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz"
        params:
                prefix = OUTdir + "/alignment/STAR_newparams/{genome}/{locust}_"
        output:
                OUTdir + "/alignment/STAR_newparams/{genome}/{locust}_Aligned.sortedByCoord.out.bam"
        shell:
                """
                module load GCC/11.2.0 STAR/2.7.9a
                STAR --runThreadN 8 --genomeDir {input.index} --genomeLoad NoSharedMemory --limitBAMsortRAM 32000000000 --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM GeneCounts --twopassMode Basic --sjdbGTFfile {input.annotation} --sjdbOverhang 149 --outSAMattributes NH HI AS NM MD --alignIntronMin 75 --alignIntronMax 2000000  --outSAMunmapped Within --readFilesCommand zcat --readFilesIn {input.read1} {input.read2} --outFileNamePrefix {params.prefix}
                """


        
########################################
# Parameters in the cluster.json file
########################################

    "STAR_align":
    {
        "cpus-per-task" : 10,
        "partition" : "medium",
        "ntasks": 1,
        "mem" : "100G",
        "time": "0-08:00:00"
    }

The parameters were chosen as follow:
--runThreadN 8 indicates that we run the mapping process using 8 threads.
--genomeDir {input.index} indicates where the genome index is located.
--genomeLoad NoSharedMemory
--limitBAMsortRAM 32000000000 --outSAMtype BAM SortedByCoordinate indicates that the output should be in a .bam format and sorted by coordinates.
--quantMode TranscriptomeSAM GeneCounts indicates that we wish to have two outputs, one with the Read Count for each gene and one with the gene aligned to the transcriptome only.
--twopassMode Basic indicates that we wish to use a two-passes mapping mode that will first extract the junctions and insert them into the genome index and re-map everything during a 2nd pass. The option basic allows us to perform this on multiple files in parallel. Recommended for de-novo junction discovery. --sjdbGTFfile {input.annotation} indicates the path of our annotation file, however not necessary if already done in genomegenerate step.
--sjdbOverhang 149 is the same parameter used for building our index, however not necessary if already done in genomegenerate step.
--outSAMattributes NH HI AS NM MD indicates that the temporary .sam alignment file should contain headers.
--alignIntronMin 75 same as index
--alignIntronMax 2000000 same as index
--outSAMunmapped Within Sends unmapped reads to the main SAM file. --readFilesCommand zcat signifies that we our reads are compressed and need to be read as fastq.gz files.
--readFilesIn {input.read1} {input.read2} path to the paired-end reads.
--outFileNamePrefix {params.prefix} is the prefix of our output file.

After mapping, we obtained alignment statistics from the *_Log.final.out file and filled out the metadata table with it.

grep 'Number of input reads' *_Log.final.out
grep 'Average input read length' *_Log.final.out
grep 'Uniquely mapped reads number' *_Log.final.out
grep 'Number of reads mapped to multiple loci' *_Log.final.out
grep 'Number of reads mapped to too many loci' *_Log.final.out
grep 'Number of reads unmapped: too many mismatches' *Log.final.out
grep 'Number of reads unmapped: too short' *Log.final.out
grep 'Number of reads unmapped: other' *Log.final.out

3. Quantification using `GeneCount`

We can note that the option --quantMode GeneCounts from STAR produces the same output as the htseq-count tool if we used the –-sjdbGTFfile option.

In the output file {locust}_ReadsPerGene.out.tab we can obtain the read counts per gene depending if our data is unstranded (column 2) or stranded (columns 3 and 4).

column 1: gene ID
column 2: counts for unstranded RNA-seq.
column 3: counts for the 1st read strand aligned with RNA
column 4: counts for the 2nd read strand aligned with RNA (the most common protocol nowadays)

For our pilot S. gregaria project, we know we used Illumina Total Stranded kit but to check we can with the following code:

grep -v "N_" {locust}_ReadsPerGene.out.tab | awk '{unst+=$2;forw+=$3;rev+=$4}END{print unst,forw,rev}'

#or as a loop
for i in *_ReadsPerGene.out.tab; do echo $i; grep -v "N_" $i | awk '{unst+=$2;forw+=$3;rev+=$4}END{print unst,forw,rev}'; done

In a stranded library preparation protocol, there should be a strong imbalance between number of reads mapped to known genes in forward versus reverse strands.

########################################
# Snakefile rule
########################################

#either ran the following rule
rule reads_count:
        input:
                readtable = OUTdir + "/alignment/STAR2/{locust}_ReadsPerGene.out.tab",
        output:
                OUTdir + "/DESeq2/counts_4thcol/{locust}_counts.txt"
        shell:
                """
                cut -f1,4 {input.readtable} | grep -v "_" > {output}  
                """

#or simply this loop for less core usage
# for i in $SCRATCH/locust_phase/data/alignment/STAR/*ReadsPerGene.out.tab; do echo $i; cut -f1,4 $i | grep -v "_" > $SCRATCH/locust_phase/data/DESeq2/counts_4thcol/`basename $i ReadsPerGene.out.tab`counts.txt; done

ALTERNATIVE OPTION: We can also build a single matrix of expression with all individuals targeted. Below is the example for S. piceifrons:

paste SPICE_*_ReadsPerGene.out.tab | grep -v "_" | awk '{printf "%s\t", $1}{for (i=4;i<=NF;i+=4) printf "%s\t", $i; printf "\n" }' > tmp
sed -e "1igene_name\t$(ls SPICE_*ReadsPerGene.out.tab | tr '\n' '\t' | sed 's/_ReadsPerGene.out.tab//g')" tmp > raw_counts_piceifrons_matrix.txt

paste *ReadsPerGene.out.tab | grep -v “” | awk ‘{printf “%s, $1}{for (i=4;i<=NF;i+=4) printf”%s, $i; printf “” }’ > tmp

ls *.tab | awk ‘BEGIN{ORS=““;print”gene name}{print $0”}END{print “”}’| sed ’s/_ReadsPerGene.out.tab//g’ > raw_count_ALB.txt ; cat tmp >> raw_count_ALB.txt

4. Quantification using `featureCounts`

While GeneCounts is very easy to use, another options is to refine the read count using featureCounts from the Subread module, to be able to summarise and parse the reads counts on genomic features such as genes, exons, promoter, rRNA…

To run featureCounts onto Grace cluster, we have adapted the script to be as follow (later will be implemented in Snakemake).

#!/bin/bash

##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=feature          
#SBATCH --time=22:00:00         
#SBATCH --ntasks=2              
#SBATCH --cpus-per-task=12       
#SBATCH --mem=40G              

ml GCC/11.2.0 Subread/2.0.3

# Specify the input folder containing .bam files
input_folder="/scratch/group/songlab/maeva/headthor-locusts-rna/gregaria-rna/data/alignment/STAR/GCF_023897955.1_iqSchGreg1.2_genomic"

# Specify the output folder
output_folder="/scratch/group/songlab/maeva/headthor-locusts-rna/gregaria-rna/data/deg_counts/STAR/gregaria/featurecounts"

# Ensure the input folder exists
if [ ! -d "$input_folder" ]; then
    echo "Error: Input folder not found."
    exit 1
fi

# Ensure the output folder exists
if [ ! -d "$output_folder" ]; then
    mkdir -p "$output_folder" || exit 1
fi

# Create finalcounts folder if it doesn't exist
finalcounts_folder="$output_folder/finalcounts"
mkdir -p "$finalcounts_folder" || exit 1

# Specify the path of gtf 
annot="/scratch/group/songlab/maeva/refgenomes/locusts_complete/GCF_023897955.1_iqSchGreg1.2_genomic.gtf"

for bamfile in "$input_folder"/*Aligned.sortedByCoord.out*.bam; do
    featureCounts -p --countReadPairs -t transcript --extraAttributes gene_name -a "$annot" -R BAM "$bamfile" -T 20 -o "$output_folder/$(basename "${bamfile%.bam}.txt")"
    cut -f 1,8 "$output_folder/$(basename "${bamfile%.bam}.txt")" | tail -n +2 > "$output_folder/finalcounts/$(basename "${bamfile%.bam}.txt")"
    echo "Processing completed for $bamfile."
done

Then to make the output compatible with our R scripts in the future, we want to also run the following renaming code in the finalcounts folder:

for file in *_Aligned.sortedByCoord.out.txt; do   
    # Extract the filename without the extension
    filename="${file%_Aligned.sortedByCoord.out.txt}"
    # Remove the first line and save to new file
    tail -n +2 "$file" > "${filename}_counts.txt"
    
    echo "Processing completed for $file."
done

STRATEGY 1: Mapping onto S. gregaria

Mapping success

gregaria

#### Load our SRA metadata table
metaseq <- read_table("data/metadata/RNAseq_modified_METADATA2022.txt", col_names  = TRUE,  guess_max = 5000)

#### Create an interactive search table
metaseq %>%
    datatable(extensions = "Buttons", options = list(dom = "Blfrtip", buttons = c("copy",
        "csv", "excel"), lengthMenu = list(c(10, 20, 50, 100, 200, -1), c(10, 20,
        50, 100, 200, "All"))))

mycol_species <- c("green", "deeppink", "orange", "orange2", "blue2", "red2", "yellow2")

#### READS AVERAGE colored by STATUS
eren <- ggplot(metaseq, aes(x=Map_SUM, y = Inputtrim_reads, color = Species, label = SampleID))
eren <- eren + geom_point(size=2, alpha =0.7)
eren <- eren + scale_color_manual(values = mycol_species)
eren <- eren + theme_calc()
eren <- eren + geom_hline(yintercept=30000000, linetype="dotted", color = "green3")
eren <- eren + geom_hline(yintercept=50000000, linetype="dotted", color = "green3")
eren <- eren + geom_vline(xintercept=80, linetype="dotted", color = "blue2")
eren <- eren +  xlim(0,100)
options(scipen=20) #to remove the scientific annotation of the axis

#### make an interactive version of the scatter plot
attacktitan <- ggplotly(eren)
attacktitan

Figure XX: Interactive plot of the mapping rate success of each sample against their respective reference genome.

We added the green thresholds to indicate how many reads are recommended by Illumina (lower end and optimal). The blue line demonstrates where the mapping ratio could be considered not contaminated.


## READS AVERAGE colored by STATUS
mikasa <- ggplot(metaseq, aes(x=Map_SUM, y = Inputtrim_reads, color = Species, label = SampleID))
mikasa <- mikasa + geom_point(size=2, alpha =0.7)
mikasa <- mikasa + scale_color_manual(values = mycol_species)
mikasa <- mikasa + theme_calc()
mikasa <- mikasa + geom_hline(yintercept=30000000, linetype="dotted", color = "green3")
mikasa <- mikasa + geom_hline(yintercept=50000000, linetype="dotted", color = "green3")
mikasa <- mikasa + geom_vline(xintercept=80, linetype="dotted", color = "blue2")
mikasa <- mikasa +  xlim(0,100)
options(scipen=20) #to remove the scientific annotation of the axis

## make an interactive version of the scatter plot
attacktitan <- ggplotly(eren)
attacktitan

Figure XX: Interactive plot of the mapping rate success of each sample against gregaria genome.

We added the green thresholds to indicate how many reads are recommended by Illumina (lower end and optimal). The blue line demonstrates where the mapping ratio could be considered not contaminated.

piceifrons

cancellata

americana

cubense

nitens

STRATEGY 2: Mapping onto own species

`STAR` reads mapping

This follows the same code as for STRATEGY 1 except that we will change the RefSeq to the transcript species genome path.

Mapping success

gregaria

piceifrons

cancellata

americana

cubense

nitens

sessionInfo()

FALSE R version 4.3.1 (2023-06-16)
FALSE Platform: x86_64-apple-darwin20 (64-bit)
FALSE Running under: macOS Sonoma 14.4.1
FALSE 
FALSE Matrix products: default
FALSE BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
FALSE LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
FALSE 
FALSE locale:
FALSE [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
FALSE 
FALSE time zone: America/Chicago
FALSE tzcode source: internal
FALSE 
FALSE attached base packages:
FALSE [1] stats     graphics  grDevices utils     datasets  methods   base     
FALSE 
FALSE other attached packages:
FALSE  [1] reshape2_1.4.4   ggthemes_5.1.0   plotly_4.10.4    DT_0.33         
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Reads mapping and Gene Quantification using STAR

Maeva Techer

2024-05-14

1. Indexing RefSeq genomes

2. STAR reads mapping

3. Quantification using GeneCount

4. Quantification using featureCounts

STRATEGY 1: Mapping onto S. gregaria

Mapping success

gregaria

Figure XX: Interactive plot of the mapping rate success of each sample against their respective reference genome.

Figure XX: Interactive plot of the mapping rate success of each sample against gregaria genome.

piceifrons

cancellata

americana

cubense

nitens

STRATEGY 2: Mapping onto own species

STAR reads mapping

Mapping success

gregaria

piceifrons

cancellata

americana

cubense

nitens

2. `STAR` reads mapping

3. Quantification using `GeneCount`

4. Quantification using `featureCounts`

`STAR` reads mapping