Scripts used for the analysis of Alternaria genomes Note - all this work was performed in the directory: /home/groups/harrisonlab/project_files/alternaria
The following is a summary of the work presented in this Readme.
The following processes were applied to Alternaria genomes prior to analysis: Data qc Genome assembly Repeatmasking Gene prediction Functional annotation
Analyses performed on these genomes involved BLAST searching for: Known toxin genes
CDC contigs were identified using: Alignment of raw reads to assembled genomes Assembly of remaining reads
programs: fastqc fastq-mcf kmc
Data quality was visualised using fastqc:
for RawData in raw_dna/paired/*/*/*/*.fastq*?; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/dna_qc
echo $RawData;
qsub $ProgDir/run_fastqc.sh $RawData
doneTrimming was performed on data to trim adapters from sequences and remove poor quality data. This was done with fastq-mcf
for StrainPath in raw_dna/paired/*/*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/rna_qc
IlluminaAdapters=/home/armita/git_repos/emr_repos/tools/seq_tools/illumina_full_adapters.fa
ReadsF=$(ls $StrainPath/F/*.fastq*)
ReadsR=$(ls $StrainPath/R/*.fastq*)
echo $ReadsF
echo $ReadsR
qsub $ProgDir/rna_qc_fastq-mcf.sh $ReadsF $ReadsR $IlluminaAdapters DNA
doneData quality was visualised once again following trimming:
for RawData in qc_dna/paired/*/*/*/*.fastq*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/dna_qc
echo $RawData;
qsub $ProgDir/run_fastqc.sh $RawData
donekmer counting was performed using kmc This allowed estimation of sequencing depth and total genome size
for TrimPath in qc_dna/paired/*/*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/dna_qc
TrimF=$(ls $TrimPath/F/*.fastq*)
TrimR=$(ls $TrimPath/R/*.fastq*)
echo $TrimF
echo $TrimR
qsub $ProgDir/kmc_kmer_counting.sh $TrimF $TrimR
doneAssembly was performed using Velvet
A range of hash lengths were used and the best assembly selected for subsequent analysis
for TrimPath in qc_dna/paired/*/*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/assemblers/velvet
Strain=$(printf $TrimPath | rev | cut -f1 -d '/' | rev)
MinHash=41
MaxHash=81
HashStep=2
TrimF=$(ls $TrimPath/F/*.fastq*)
TrimR=$(ls $TrimPath/R/*.fastq*)
GenomeSz=36
echo $Strain
if [ "$Strain" == '675' ]; then
ExpCov=27
MinCov=9
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '97.0013' ]; then
ExpCov=30
MinCov=10
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '97.0016' ]; then
ExpCov=23
MinCov=8
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '650' ]; then
ExpCov=27
MinCov=9
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '1082' ]; then
ExpCov=16
MinCov=6
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '1164' ]; then
ExpCov=24
MinCov=8
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '1166' ]; then
ExpCov=34
MinCov=11
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '1177' ]; then
ExpCov=50
MinCov=17
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '24350' ]; then
ExpCov=30
MinCov=10
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '635' ]; then
ExpCov=21
MinCov=7
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '648' ]; then
ExpCov=43
MinCov=14
InsLgth=600
echo "$Strain set"
elif [ "$Strain" == '743' ]; then
ExpCov=32
MinCov=11
InsLgth=600
echo "$Strain set"
fi
qsub $ProgDir/submit_velvet_range.sh $MinHash $MaxHash $HashStep \
$TrimF $TrimR $GenomeSz $ExpCov $MinCov $InsLgth
done
Assemblies were summarised to allow the best assembly to be determined by eye
for StrainPath in $(ls -d assembly/velvet/A.alternata_ssp._*/*); do
printf "N50\tMax_contig_size\tNumber of bases in contigs\tNumber of contigs\tNumber of contigs >=1kb\tNumber of contigs in N50\tNumber of bases in contigs >=1kb\tGC Content of contigs\n" > $StrainPath/assembly_stats.csv
for StatsFile in $(ls $StrainPath/*/stats.txt); do
cat $StatsFile | rev | cut -f1 -d ' ' | rev | paste -d '\t' -s >> $StrainPath/assembly_stats.csv
done
done
tail -n+1 assembly/velvet/A.alternata_ssp._*/*/assembly_stats.csv > assembly/velvet/alternaria_assembly_stats.csvRepeat masking was performed and used the following programs: Repeatmasker Repeatmodeler
The best assemblies were used to perform repeatmasking
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/repeat_masking
BestAss675=assembly/velvet/A.alternata_ssp._arborescens/675/A.alternata_ssp._arborescens_675_69/sorted_contigs.fa
BestAss970013=assembly/velvet/A.alternata_ssp._arborescens/97.0013/A.alternata_ssp._arborescens_97.0013_59/sorted_contigs.fa
BestAss970016=assembly/velvet/A.alternata_ssp._arborescens/97.0016/A.alternata_ssp._arborescens_97.0016_77/sorted_contigs.fa
BestAss650=assembly/velvet/A.alternata_ssp._gaisen/650/A.alternata_ssp._gaisen_650_67/sorted_contigs.fa
BestAss1082=assembly/velvet/A.alternata_ssp._tenuissima/1082/A.alternata_ssp._tenuissima_1082_49/sorted_contigs.fa
BestAss1164=assembly/velvet/A.alternata_ssp._tenuissima/1164/A.alternata_ssp._tenuissima_1164_67/sorted_contigs.fa
BestAss1166=assembly/velvet/A.alternata_ssp._tenuissima/1166/A.alternata_ssp._tenuissima_1166_43/sorted_contigs.fa
BestAss1177=assembly/velvet/A.alternata_ssp._tenuissima/1177/A.alternata_ssp._tenuissima_1177_63/sorted_contigs.fa
BestAss24350=assembly/velvet/A.alternata_ssp._tenuissima/24350/A.alternata_ssp._tenuissima_24350_63/sorted_contigs.fa
BestAss635=assembly/velvet/A.alternata_ssp._tenuissima/635/A.alternata_ssp._tenuissima_635_59/sorted_contigs.fa
BestAss648=assembly/velvet/A.alternata_ssp._tenuissima/648/A.alternata_ssp._tenuissima_648_45/sorted_contigs.fa
BestAss743=assembly/velvet/A.alternata_ssp._tenuissima/743/A.alternata_ssp._tenuissima_743_69/sorted_contigs.fa
qsub $ProgDir/rep_modeling.sh $BestAss675
qsub $ProgDir/rep_modeling.sh $BestAss970013
qsub $ProgDir/rep_modeling.sh $BestAss970016
qsub $ProgDir/rep_modeling.sh $BestAss650
qsub $ProgDir/rep_modeling.sh $BestAss1082
qsub $ProgDir/rep_modeling.sh $BestAss1164
qsub $ProgDir/rep_modeling.sh $BestAss1166
qsub $ProgDir/rep_modeling.sh $BestAss1177
qsub $ProgDir/rep_modeling.sh $BestAss24350
qsub $ProgDir/rep_modeling.sh $BestAss635
qsub $ProgDir/rep_modeling.sh $BestAss648
qsub $ProgDir/rep_modeling.sh $BestAss743
qsub $ProgDir/transposonPSI.sh $BestAss675
qsub $ProgDir/transposonPSI.sh $BestAss970013
qsub $ProgDir/transposonPSI.sh $BestAss970016
qsub $ProgDir/transposonPSI.sh $BestAss650
qsub $ProgDir/transposonPSI.sh $BestAss1082
qsub $ProgDir/transposonPSI.sh $BestAss1164
qsub $ProgDir/transposonPSI.sh $BestAss1166
qsub $ProgDir/transposonPSI.sh $BestAss1177
qsub $ProgDir/transposonPSI.sh $BestAss24350
qsub $ProgDir/transposonPSI.sh $BestAss635
qsub $ProgDir/transposonPSI.sh $BestAss648
qsub $ProgDir/transposonPSI.sh $BestAss743Gene prediction followed three steps: Pre-gene prediction - Quality of genome assemblies were assessed using Cegma to see how many core eukaryotic genes can be identified. Gene model training - Gene models were trained for isolates 1166 and 650 using assembled RNAseq data Gene prediction - Gene models were used to predict genes in A. alternata genomes. This used RNAseq data as hints for gene models.
Quality of genome assemblies was assessed by looking for the gene space in the assemblies.
ProgDir=/home/armita/git_repos/emr_repos/tools/gene_prediction/cegma
cd /home/groups/harrisonlab/project_files/alternaria/
for Genome in $(ls repeat_masked/A.*/*/*/*_contigs_unmasked.fa); do
echo $Genome;
qsub $ProgDir/sub_cegma.sh $Genome dna;
doneOutputs were summarised using the commands:
for File in $(ls gene_pred/cegma/A.alternata_ssp._*/*/*_dna_cegma.completeness_report); do
Strain=$(echo $File | rev | cut -f2 -d '/' | rev);
Species=$(echo $File | rev | cut -f3 -d '/' | rev);
printf "$Species\t$Strain\n";
cat $File | head -n18 | tail -n+4;printf "\n";
done > gene_pred/cegma/cegma_results_dna_summary.txt
less gene_pred/cegma/cegma_results_dna_summary.txtData quality was visualised using fastqc:
for RawData in raw_rna/paired/*/*/*/*.fastq.gz; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/dna_qc
echo $RawData;
qsub $ProgDir/run_fastqc.sh $RawData
doneTrimming was performed on data to trim adapters from sequences and remove poor quality data. This was done with fastq-mcf
for StrainPath in raw_rna/paired/*/*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/rna_qc
IlluminaAdapters=/home/armita/git_repos/emr_repos/tools/seq_tools/illumina_full_adapters.fa
ReadsF=$(ls $StrainPath/F/*.fastq.gz)
ReadsR=$(ls $StrainPath/R/*.fastq.gz)
echo $ReadsF
echo $ReadsR
qsub $ProgDir/rna_qc_fastq-mcf.sh $ReadsF $ReadsR $IlluminaAdapters RNA
doneData quality was visualised once again following trimming:
for TrimData in qc_rna/paired/*/*/*/*.fastq.gz; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/dna_qc
echo $RawData;
qsub $ProgDir/run_fastqc.sh $TrimData
doneRNAseq data was assembled into transcriptomes using Trinity
for StrainPath in qc_rna/paired/*/*; do
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/transcriptome_assembly
ReadsF=$(ls $StrainPath/F/*.fastq.gz)
ReadsR=$(ls $StrainPath/R/*.fastq.gz)
echo $ReadsF
echo $ReadsR
qsub $ProgDir/transcriptome_assembly_trinity.sh $ReadsF $ReadsR
doneGene training was performed using RNAseq data. The cluster can not run this script using qlogin. As such it was run on the head node (-naughty) using screen. Training for 650 and 1166 was performed in two instances of screen and occassionally viewed to check progress over time. (screen is detached after opening using ctrl+a then ctrl+d. - if just ctrl+d is pressed the instance of screen is deleted. - be careful)
screen -a
ProgDir=/home/armita/git_repos/emr_repos/tools/gene_prediction/augustus
Assembly650=assembly/trinity/A.alternata_ssp._gaisen/650/650_rna_contigs/Trinity.fasta
Genome650=repeat_masked/A.alternata_ssp._gaisen/650/A.alternata_ssp._gaisen_650_67_repmask/650_contigs_unmasked.fa
$ProgDir/training_by_transcriptome.sh $Assembly650 $Genome650
screen -a
ProgDir=/home/armita/git_repos/emr_repos/tools/gene_prediction/augustus
Assembly1166=assembly/trinity/A.alternata_ssp._tenuissima/1166/1166_rna_contigs/Trinity.fasta
Genome1166=repeat_masked/A.alternata_ssp._tenuissima/1166/A.alternata_ssp._tenuissima_1166_43_repmask/1166_contigs_unmasked.fa
$ProgDir/training_by_transcriptome.sh $Assembly1166 $Genome1166Quality of Trinity assemblies were assessed using Cegma to assess gene-space within the transcriptome
ProgDir=/home/armita/git_repos/emr_repos/tools/gene_prediction/cegma
for Transcriptome in $(ls assembly/trinity/A.*/*/*_rna_contigs/Trinity.fasta); do
echo $Transcriptome;
qsub $ProgDir/sub_cegma.sh $Transcriptome rna;
doneOutputs were summarised using the commands:
for File in $(ls gene_pred/cegma/A.alternata_ssp._*/*/*_rna_cegma.completeness_report); do
Strain=$(echo $File | rev | cut -f2 -d '/' | rev);
Species=$(echo $File | rev | cut -f3 -d '/' | rev);
printf "$Species\t$Strain\n";
cat $File | head -n18 | tail -n+4;printf "\n";
done > gene_pred/cegma/cegma_results_rna_summary.txt
less gene_pred/cegma/cegma_results_rna_summary.txtGene prediction was performed for A. alternata isolates. RNAseq reads were used as Hints for the location of CDS. A concatenated dataset of both ssp. tenuissima and ssp. gaisen RNAseq reads were used as hints for all strains. Genes were predicted for ssp. tenuissima using the gene model trained to ssp. tenunissima. Genes were predicted for ssp. gaisen using the gene model trained to ssp. gaisen. Genes were predicted for ssp. arborescens using the gene model trained to ssp. tenuisima.
ProgDir=/home/armita/git_repos/emr_repos/tools/gene_prediction/augustus
mkdir -p qc_rna/concatenated
RnaFiles=$(ls qc_rna/paired/A.alternata_ssp._*/*/*/*.fastq.gz | paste -s -d ' ')
ConcatRna=qc_rna/concatenated/A.alternata_RNA_650_1166.fa.gz
cat $RnaFiles > $ConcatRna
for Genome in repeat_masked/A.alternata_ssp._*/*/*/*_contigs_unmasked.fa; do
Species=$(printf $Genome | rev | cut -f4 -d '/' | rev)
if [ "$Species" == 'A.alternata_ssp._tenuissima' ]; then
GeneModel=A.alternata_ssp._tenuissima_1166
elif [ "$Species" == 'A.alternata_ssp._gaisen' ]; then
GeneModel=A.alternata_ssp._gaisen_650
elif [ "$Species" == 'A.alternata_ssp._arborescens' ]; then
GeneModel=A.alternata_ssp._tenuissima_1166
fi
qsub $ProgDir/augustus_pipe.sh $Genome $ConcatRna $GeneModel
doneInterproscan was used to give gene models functional annotations.
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/feature_annotation/interproscan/
for Genes in $(ls gene_pred/augustus/A.alternata_ssp._*/*/*_augustus_preds.aa); do
echo $Genes
$ProgDir/sub_interproscan.sh $Genes
doneFollowing interproscan annotation split files were combined using the following commands:
ProgDir=/home/armita/git_repos/emr_repos/tools/seq_tools/feature_annotation/interproscan
for StrainPath in $(ls -d gene_pred/interproscan/A.*/*); do
Strain=$(basename $StrainPath)
Organism=$(echo $StrainPath | rev | cut -d "/" -f2 | rev)
echo $Strain
PredGenes=gene_pred/augustus/"$Organism"/"$Strain"/"$Strain"_augustus_preds.aa
InterProRaw=gene_pred/interproscan/"$Organism"/"$Strain"/raw
$ProgDir/append_interpro.sh $PredGenes $InterProRaw
doneOrthomcl was used to identify orthologous groups between Alternaria spp. genomes
Genomes were grouped by subspecies and orthology was determined within each subspecies group. Orthology was also determined between subspecies groups.
| A. gaisen | A. tenuissima | A. tenuissima apple pathotype | A. arborescens |
|---|---|---|---|
| 650 | 1166 | 648 | 675 |
| 635 | 1082 | 97.0013 | |
| 1177 | 1164 | 97.0016 | |
| 743 | 24350 |
The Commands used to run this analysis are shown in pathogen/orthology/A.tenuissima_orthology.md
The Commands used to run this analysis are shown in pathogen/orthology/A.tenuissima_apple_pathotype_orthology.md
The Commands used to run this analysis are shown in pathogen/orthology/A.tenuissima_orthology.md
6d) Orthology between A. alternata ssp. gaisen, A. alternata ssp. tenuissima, A. alternata ssp. tenuissima apple pathotype & A. alternata ssp. arborescens
The Commands used to run this analysis are shown in pathogen/orthology/A.alternata_ssp_orthology.md
The first analysis was based upon BLAST searches for genes known to be involved in toxin production
ProgDir=/home/armita/git_repos/emr_repos/tools/pathogen/blast/
Query=analysis/blast_homology/CDC_genes/A.alternata_CDC_genes.fa
BestAss675=assembly/velvet/A.alternata_ssp._arborescens/675/A.alternata_ssp._arborescens_675_69/sorted_contigs.fa
BestAss970013=assembly/velvet/A.alternata_ssp._arborescens/97.0013/A.alternata_ssp._arborescens_97.0013_59/sorted_contigs.fa
BestAss970016=assembly/velvet/A.alternata_ssp._arborescens/97.0016/A.alternata_ssp._arborescens_97.0016_77/sorted_contigs.fa
BestAss650=assembly/velvet/A.alternata_ssp._gaisen/650/A.alternata_ssp._gaisen_650_67/sorted_contigs.fa
BestAss1082=assembly/velvet/A.alternata_ssp._tenuissima/1082/A.alternata_ssp._tenuissima_1082_49/sorted_contigs.fa
BestAss1164=assembly/velvet/A.alternata_ssp._tenuissima/1164/A.alternata_ssp._tenuissima_1164_67/sorted_contigs.fa
BestAss1166=assembly/velvet/A.alternata_ssp._tenuissima/1166/A.alternata_ssp._tenuissima_1166_43/sorted_contigs.fa
BestAss1177=assembly/velvet/A.alternata_ssp._tenuissima/1177/A.alternata_ssp._tenuissima_1177_63/sorted_contigs.fa
BestAss24350=assembly/velvet/A.alternata_ssp._tenuissima/24350/A.alternata_ssp._tenuissima_24350_63/sorted_contigs.fa
BestAss635=assembly/velvet/A.alternata_ssp._tenuissima/635/A.alternata_ssp._tenuissima_635_59/sorted_contigs.fa
BestAss648=assembly/velvet/A.alternata_ssp._tenuissima/648/A.alternata_ssp._tenuissima_648_45/sorted_contigs.fa
BestAss743=assembly/velvet/A.alternata_ssp._tenuissima/743/A.alternata_ssp._tenuissima_743_69/sorted_contigs.fa
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss675
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss970013
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss970016
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss650
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss1082
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss1164
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss1166
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss1177
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss24350
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss635
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss648
qsub $ProgDir/blast_pipe.sh $Query dna $BestAss743BLAST search results were summarised into a presence/absence table
The presence /absence table determines presence if a hit is present and the alignment represents >50% of the query sequence. This thresholding means that some hits have not been summarised including AMT11, AMT15 and ALT1.
ProgDir=/home/armita/git_repos/emr_repos/tools/pathogen/blast/
InFiles=$(ls analysis/blast_homology/A.alternata_ssp._*/*/*_A.alternata_CDC_genes.fa_homologs.csv | paste -s -d ' ')
echo $InFiles
$ProgDir/blast_differentials.pl $InFiles
mv *.csv analysis/blast_homology/CDC_genes/.Raw reads were aligned against assembled genomes to identify contigs that were unique to a isolate or clade
for Pathz in $(ls -d qc_dna/paired/A.alternata_ssp._*/*); do
Strain=$(echo $Pathz | cut -d '/' -f4)
echo "using reads for $Strain"
ProgPath=/home/armita/git_repos/emr_repos/tools/pathogen/lineage_specific_regions
F_IN=$(ls $Pathz/F/*.fastq.gz)
R_IN=$(ls $Pathz/R/*.fastq.gz)
for Assemblyz in $(ls repeat_masked/A.alternata_ssp._*/*/*/*_contigs_unmasked.fa); do
basename $Assemblyz
qsub "$ProgPath"/bowtie2_alignment_pipe.sh $F_IN $R_IN $Assemblyz
done
doneA summary file was made from the alignment logs. The percentage of reads aligning to each set of assembled contigs was determined.
SummaryFile=analysis/ls_contigs/alignment_summaries.txt
printf "" > "$SummaryFile"
for OUTPUT in $(ls bowtie2_alignment_pipe.sh.e*); do
ID=$(echo $OUTPUT | rev | cut -d 'e' -f1 | rev | less);
cat bowtie2_alignment_pipe.sh.o"$ID" | grep -E "Trimmed .* reads .*/F/|Output files: " | sed -e 's/.*\/F\///g' | cut -f1 -d ')' | cut -f2 -d '"' >> "$SummaryFile";
cat $OUTPUT >> "$SummaryFile";
printf "\n" >> "$SummaryFile";
done
cat $SummaryFile | grep ' overall alignment rate' | cut -f1 -d ' ' | lessThese are the reads percentages reported by bowtie but do not actually reflect the percentage unaligned reads where neither pair matched.
These were identified using SAM FLAGS to extract unaligned pairs.
for Pathz in $(ls assembly/ls_contigs/A.alternata_ssp._*/*/vs_*/*_sorted.bam); do
OutFileF=$(echo $Pathz | sed 's/.bam/_unaligned_F.txt/g')
OutFileR=$(echo $Pathz | sed 's/.bam/_unaligned_R.txt/g')
OutFileSum=$(echo $Pathz | sed 's/.bam/_sum.txt/g')
samtools view -f 77 "$Pathz" | cut -f1 > $OutFileF
samtools view -f 141 "$Pathz" | cut -f1 > $OutFileR
NoReads=$(samtools view -f 1 "$Pathz" | wc -l)
NoReadsF=$(cat "$OutFileF" | wc -l)
NoReadsR=$(cat "$OutFileR" | wc -l)
printf "File\tNo. paired reads\tF reads unaligned\tR reads unaligned\n" > "$OutFileSum"
printf "$Pathz\t$NoReads\t$NoReadsF\t$NoReadsR\n" >> "$OutFileSum"
done
for File in $(ls assembly/ls_contigs/A.alternata_ssp._*/*/vs_*/*_sum.txt); do
cat $File | tail -n+2;
done > analysis/ls_contigs/assembly_summaries2.txtThe number of reads aligning per bp of assembly was determined. Typical alignment values were 0.20 reads per bp. Contigs were detemined as unique to that alignment if they contained an average of 0 reads per bp. The number of bp unique to each assembly were identified.
mkdir -p analysis/ls_contigs
Outfile=analysis/ls_contigs/ls_contig_size.csv
printf "Reads" > $Outfile
for Genome in $(ls -d assembly/ls_contigs/A.alternata_ssp._arborescens/675/vs_A.alternata_ssp._*); do
NameG=$(basename "$Genome" | sed 's/vs_//g' | sed 's/_repmask_contigs//g')
printf "\t$NameG" >> $Outfile
done
printf "\n" >> $Outfile
for Reads in $(ls -d assembly/ls_contigs/A.alternata_ssp._*/*); do
NameR=$(basename "$Reads")
printf "$NameR" >> $Outfile
for Genome in $(ls -d $Reads/*); do
printf "\t" >> $Outfile
cat $Genome/*_sorted_indexstats_coverage.csv | head -n-1 | grep -E -w "0.00$" | cut -f2 | awk '{s+=$1} END {printf s}' >> $Outfile
done
printf "\n" >> $Outfile
doneThis did not give clear results.
Contigs that had no reads align to them were identified.