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robustness-RNP

A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation

######################################################################

Experiment 1

Figure 1: mutagenesis of a fluorescent RNA and protein

  • This is to map
  • Dependencies include:
    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################

Experiment 2

Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families

  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:
    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1

-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate. -Alignments are unaffected though.

cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:

    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv

  • Plot graphs:

cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3

Figure 3A&B:

See data/delta-delta/README.md

  • Dependencies include:
    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out
  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).

  • Dependencies include:
    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)

SgrS/SgrT structure

RNA & protein point mutations

#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh
  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment

Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.

  • Plot graphs:
../bin/plotFluoro.R

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A series of comparisons of the mutational robustness of RNAs and proteins.

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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

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A series of comparisons of the mutational robustness of RNAs and proteins. 
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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

About
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injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

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A series of comparisons of the mutational robustness of RNAs and proteins. 
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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

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A series of comparisons of the mutational robustness of RNAs and proteins. 
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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

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robustness-RNP
A collection of scripts, datasets, results and documents from an investigation of the robustness of ncRNAs and proteins to mutation
######################################################################
Experiment 1
Figure 1: mutagenesis of a fluorescent RNA and protein
  • This is to map
  • Dependencies include:

    • bowtie2 (version 2.4.4)
cd data/fluoro-II
# create bowtie2 index databases
bowtie2-build broccoli.fasta broccoli
bowtie2-build egfp.fasta egfp
# bowtie2 mapping of sequencing reads to broccoli/eGFP fasta files bowtie2 -x broccoli -1 fastq/broccoli_R1.fastq -2 fastq/broccoli_R2.fastq -S broccoli.sam
bowtie2 -x egfp -1 fastq/egfp_R1.fastq -2 fastq/egfp_R2.fastq -S egfp.sam
#NOTE: fastq and sam files are managed with git-lfs (large file management)
# Call variants (low PHRED threshold in the first round "-p 25", and give the region where variants are expected "-f 118 -t 318")
# Conservative mode (-c) used to identify variants found in both forward and reverse reads (labelled "T" for true, otherwise "F")
../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 25 -f 118 -t 318 -c | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 25 -f 2410 -t 2610 -c | sort -n > sam2var.egfp-2410-2610.txt
# Calculate accuracy statistics to identify ../../bin/trainPhredThresh.pl -i broccoli.sam.variation --from 1 --to 200
../../bin/trainPhredThresh.pl -i egfp.sam.variation --from 1 --to 75
#BROCCOLI
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 40	0.00	1.00	0.25	0.00	0.00	0.00	6	2	229730	108726
# 39	0.26	0.77	0.30	0.23	0.38	0.03	60809	25496	168927	83232
# 38	0.48	0.54	0.32	0.46	0.56	0.01	109162	50490	120574	58238
# 37	0.68	0.44	0.28	0.56	0.70	0.12	156254	60792	73482	47936
# 36	0.73	0.39	0.28	0.61	0.73	0.13	168469	65817	61267	42911
# 35	0.76	0.37	0.28	0.63	0.74	0.14	175129	68378	54607	40350
# 34	0.80	0.33	0.28	0.67	0.76	0.15	184296	72536	45440	36192
# 33	0.85	0.30	0.28	0.70	0.78	0.18	196406	76034	33330	32694
# 32	0.89	0.28	0.28	0.72	0.80	0.21	203569	78249	26167	30479
# 31	0.90	0.26	0.28	0.74	0.80	0.21	206561	80479	23175	28249
# 30	0.93	0.21	0.29	0.79	0.81	0.21	213236	85558	16500	23170 <-- # 29	0.94	0.19	0.29	0.81	0.81	0.20	215030	87763	14706	20965 <-- # 28	0.94	0.17	0.29	0.83	0.81	0.18	216504	90184	13232	18544 <-- # 27	0.97	0.06	0.32	0.94	0.80	0.06	222332	102279	7404	6449
# 26	0.98	0.04	0.32	0.96	0.81	0.06	225998	104709	3738	4019
# 25	1.00	0.00	0.32	1.00	0.81	0.00	229736	108728	0	0
#eGFP
# phred	sens	spec	fdr	fpr	f1	mcc	tp	fp	fn	tn
# 39	0.39	0.55	0.04	0.45	0.56	-0.02	36602	1426	57220	1770
# 38	0.65	0.41	0.03	0.59	0.78	0.02	60902	1870	32920	1326
# 37	0.96	0.21	0.03	0.79	0.97	0.15	90308	2535	3514	661
# 36	0.97	0.19	0.03	0.81	0.97	0.16	91090	2579	2732	617
# 35	0.98	0.17	0.03	0.83	0.97	0.16	91614	2646	2208	550
# 34	0.98	0.16	0.03	0.84	0.98	0.16	92018	2693	1804	503
# 33	0.99	0.10	0.03	0.90	0.98	0.16	93022	2871	800	325
# 32	0.99	0.09	0.03	0.91	0.98	0.16	93235	2907	587	289
# 31	0.99	0.09	0.03	0.91	0.98	0.16	93332	2918	490	278
# 30	1.00	0.08	0.03	0.92	0.98	0.17	93470	2949	352	247 <-- # 29	1.00	0.07	0.03	0.93	0.98	0.16	93488	2962	334	234 <-- # 28	1.00	0.07	0.03	0.93	0.98	0.16	93520	2979	302	217 <-- # 27	1.00	0.04	0.03	0.96	0.98	0.12	93617	3059	205	137
# 26	1.00	0.01	0.03	0.99	0.98	0.05	93724	3158	98	38
# 25	1.00	0.00	0.03	1.00	0.98	0.00	93822	3196	0	0
# Both report PHRED score of 29 as a good balance between true and false positives based upon MCC: ../../bin/sam2var.pl -b broccoli-barcodes.txt -r ./broccoli.fasta -s ./broccoli.sam -p 29 -f 118 -t 318 | sort -n > sam2var.broccoli-118-318.txt
../../bin/sam2var.pl -b egfp-barcodes.txt -r ./egfp.fasta -s ./egfp.sam -p 29 -f 2410 -t 2610 | sort -n > sam2var.egfp-2410-2610.txt
# Plot summaries of variant calls:
R CMD BATCH ../../bin/plotMutagenesis.R ; tail -n 20 plotMutagenesis.Rout
rm -f *appended.Rdata
#Simulate data, train a RF, predict variant classes based on bin freqs. Repeat 100x
for i in {1..100}; do echo "ROUND $i:"; ../../bin/runTrainClassifier2.sh; done
../../bin/summariseRFFire.sh

######################################################################
Experiment 2
Figure 2: variation of recent (shallow) vs old (deep) divergence of RNA and protein families
  • this is to pull out linked RNA & protein families from E. coli (ENA ID: U00096.3), S. enterica (ENA ID:AE014613.1) and N. meningitidis (ENA ID:AL157959.1):
  • Dependencies include:

    • hmmer-3.1b2
    • pal2nal.pl
    • infernal-1.1.2
    • tRNAscan-SE-1.3.1
-Some error messages from pal2nal.pl due to phenylalanine/leucine bug in esl-translate.
-Alignments are unaffected though.
cd data/genomes/
# Generate alignments for the deep and shallow ncRNAs and protein domains (bacteria first, fungi second):
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AL157959-pfam31.gff AL157959.fasta"}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" U00096-pfam31.gff U00096.fasta AE014613-pfam31.gff AE014613.fasta"}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AL157959-ncRNAs.gff AL157959.fasta"}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" U00096-ncRNAs.gff U00096.fasta AE014613-ncRNAs.gff AE014613.fasta"}' | sh
#cd -
cd ../genomes-fungi
# Proteins, deep
grep deep rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa pombe-pfam32.gff pombe.fa "}' | sh
# Proteins, shallow
grep shallow rna-protein-pairs.tsv | cut -f 2 | sort -d | uniq | awk '{print "../../bin/fetchPfam.sh "$1" cerevisiae-pfam32.gff cerevisiae.fa kudriavzevii-pfam32.gff kudriavzevii.fa "}' | sh
# RNAs, deep
grep deep rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa pombe-ncRNAs.gff pombe.fa "}' | sh
# RNAs, shallow
grep shallow rna-protein-pairs.tsv | cut -f 1 | sort -d | uniq | awk '{print "../../bin/fetchRfam.sh "$1" cerevisiae-ncRNAs.gff cerevisiae.fa kudriavzevii-ncRNAs.gff kudriavzevii.fa "}' | sh
# The Rfam Group I Intron misses this sequence, so here's a work around:
~/inst/infernal-1.1.2/src/cmalign introns/IC2.cm ncrna-seqs/Intron_gpI-IC2.fasta > ncrna-seqs/blah && ~/inst/infernal-1.1.2/easel/miniapps/esl-reformat pfam ncrna-seqs/blah > ncrna-seqs/Intron_gpI-IC2.stk
######################################################################
# Run "computeSynonNonsynon.pl" over the alignments:
ls -1 ncrna-seqs/*.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 1 -i "$1}' | sh > synonNonsynon-ncRNA-results.txt
ls -1 mrna-seqs/*.nuc.stk | awk '{print "echo "$1"&& ../../bin/computeSynonNonsynon.pl -r 0 -i "$1}' | sh > synonNonsynon-mRNA-results.txt
# Some hoop-jumping to get into tabular format:
echo -e "ID\tdistID\ttotVar\tlen\taaSynTot\taaSynProp\taaNonSynTot\taaNonSynProp\tbanpSynTot\tbanpSynProp\tbanpNonSynTot\tbanpNonSynProp\tblossSynTot\tblossSynProp\tblossNonSynTot\tblossNonSynProp" > synonNonsynon-mRNA-results.tsv
cat synonNonsynon-mRNA-results.txt | perl -lane 'if( /^mrna-seqs\/(\S+)\.nuc/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/aaSynon:\s+(\d+) \((\S+)\)\s+aaNonsynon:\s+(\d+) \((\S+)\)/){($aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP)=($1,$2,$3,$4);}elsif(/banpSynon:\s+(\d+) \((\S+)\)\s+banpNonsynon:\s+(\d+) \((\S+)\)/){($banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP)=($1,$2,$3,$4);}elsif(/blossumSynon:\s+(\d+) \((\S+)\)\s+blossumNonsynon:\s+(\d+) \((\S+)\)/){($blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$aaSynT,$aaSynP,$aaNonSynT,$aaNonSynP,$banpSynT,$banpSynP,$banpNonSynT,$banpNonSynP,$blossSynT,$blossSynP,$blossNonSynT,$blossNonSynP); $a=join("\t",@a); print $a}' >> synonNonsynon-mRNA-results.tsv
echo -e "ID\tdistID\ttotVar\tlen\trySynTot\trySynProp\tryNonSynTot\tryNonSynProp\tbpSynTot\tbpSynProp\tbpNonSynTot\tbpNonSynProp" > synonNonsynon-ncRNA-results.tsv
cat synonNonsynon-ncRNA-results.txt | perl -lane 'if(/^ncrna-seqs\/(\S+)\.stk/){$id=$1}elsif(/Total variation:\s+(\d+)\s+len=(\d+)/){($tV,$len)=($1,$2)}elsif(/rySynon:\s+(\d+) \((\S+)\)\s+ryNonsynon:\s+(\d+) \((\S+)\)/){($rySynT,$rySynP,$ryNonSynT,$ryNonSynP)=($1,$2,$3,$4);}elsif(/bpSynon:\s+(\d+) \((\S+)\)\s+bpNonsynon:\s+(\d+) \((\S+)\)/){($bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP)=($1,$2,$3,$4); $gg = `grep $id rna-protein-pairs.tsv `; @gg = split(/\t/, $gg); $dist=$gg[2]; @a=($id,$dist,$tV,$len,$rySynT,$rySynP,$ryNonSynT,$ryNonSynP,$bpSynT,$bpSynP,$bpNonSynT,$bpNonSynP); $a=join("\t",@a); print $a }' >> synonNonsynon-ncRNA-results.tsv
# Repeat for bacteria and fungi
# Clean up files:
# rm *cm *hmm blah ncrna-seqs/*fasta mrna-seqs/*pep mrna-seqs/*fasta mrna-seqs/*afa mrna-seqs/*aln

  • FINAL RESULTS:
    • synonNonsynon-ncRNA-results.tsv
    • synonNonsynon-mRNA-results.tsv
  • Plot graphs:
cd ../../
./bin/plotConservation.R
convert -flatten -density 300 -trim manuscript/figures/figure1.pdf -quality 100 manuscript/figures/figure1.png
convert -flatten -density 300 -trim manuscript/figures/figure1c.pdf -quality 100 manuscript/figures/figure1c.png
convert -flatten -density 200 -trim manuscript/figures/suppfigure1.pdf -quality 100 manuscript/figures/suppfigure1.png
######################################################################

Experiment 3
Figure 3A&B:
See data/delta-delta/README.md
  • Dependencies include:

    • RNAfold (v2.4.9)
    • MAESTRO (v1.1)
    • HMMER (v3.1b2)
    • INFERNAL (v1.1.2)
cd data/delta-delta/
../../bin/computeDeltaDelta.pl -v -numMutations 1 > computeDeltaDelta-1mut.out &&
../../bin/computeDeltaDelta.pl -v -numMutations 4 > computeDeltaDelta-4mut.out

  • Plot graphs:
cd ../..
./bin/plotDelta.R

Figure 3C&D: Robustness of structure predictions to random in silico mutagenesis for a protein (SgrT) and non-coding RNA (SgrS).
  • Dependencies include:

    • RNAfold (v2.4.9)
    • I-TASSER (v5.1)
SgrS/SgrT structure
RNA & protein point mutations
#WARNING: this simulation uses a lot of CPU...
for name in 0.001 0.01 0.05 0.1 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -mrate $name -indelrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-mrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -mrate $name -indelrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-mrate$name.txt"
done | sh
### RNA & protein INDEL mutations
for name in 0.0001 0.001 0.01 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
do
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrS-sRNA.fasta -m 100 -indelrate $name -mrate 0.0 > data/sgr-structural/tmp/sgrS-sRNA-indelrate$name.txt"
echo "./bin/structureMutagenerator.pl -dir sgr-structural -i data/sgr-structural/sgrT-mRNA.fasta -m 100 -indelrate $name -mrate 0.0 -p > data/sgr-structural/tmp/sgrT-mRNA-indelrate$name.txt"
done | sh
cd data/sgr-structural/tmp
ls -1 | awk '{print "egrep -v \42^ID|^ECOL\42 "$1" > ../"$1}' | sh

  • Plot graphs:
cd data/sgr-structural/
../../bin/plotStructureMutagenerator.R

Supplemental Experiment
Supplemental Figure 3: Relative fluorescence intensities of mutated RNA Broccoli and mutated protein mCherry.
  • Plot graphs:
../bin/plotFluoro.R

About
A series of comparisons of the mutational robustness of RNAs and proteins. 
Resources
Readme
Activity
Custom properties
Stars
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2 watching
Forks
1 fork
Report repository
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