1.0_metaG_processing

#!/bin/bash

##Trimming Reads

#Takes in a list of untrimmed reads that will be trimmed. Ran on directory with all the raw read locations.
for element in $(<$1)
do
sickle pe -f "$element"_01.fastq -r "$element"_02.fastq -t sanger -o  R1_"$element"_trimmed.fastq -p R2_"$element"_trimmed.fastq -s "$element"_trimmed_out.fastq
done

##Merging reads and assembling
#Takes in a tab-delim file where column A contains read paths for trimmed reads and column B is the sample ID.
while read a b
 do
cd $a
echo "Moved into $a"
fq2fa --merge --filter R1_"$b"_trimmed.fastq R2_"$b"_trimmed.fastq R1R2_"$b"_trimmed.fa
idba_ud -r R1R2_"$b"_trimmed.fa -o /idba_assembly --num_threads 20 #For IDBA-ud assemblies
/opt/SPAdes-3.13.0-Linux/bin/metaspades.py -t 10 -1 R1_"$element"_trimmed.fastq	-2 R2_"$element"_trimmed.fastq -m 300 -o /metaspades_assembly #For metaSPAdes assemblies on reads

done
exit

##Rarefying reads
#After trimming, reads were rarefied in order to achieve equal read depth across both deeply sequenced and shallow sequenced datasets for comparisons of relative abundances of MAGs and vMAGs. Rarefied reads were used for read mapping later on. See manuscript for details.
reformat.sh -in1=forward_reads -in2=reverse_reads -out1=forward.subsampled_10148883_1234.fastq -out2=reverse.subsampled_10148883_1234.fastq -samplereadstarget=10148883 -sampleseed=1234