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README_sequence_prediction.txt 0 → 100644
October 2017
Sequence-based prediction code used in University of Manchester protein-sol server.
Available from download tab at www.protein-sol.manchester.ac.uk.
see Hebdtich et al (2017) Bioinformatics 33:3098-3100.
Jim Warwicker and Mex Hebditch, Manchester
*****************
Copyright (C) 2017 Jim Warwicker and Max Hebdtich
These programs are free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License.
These programs are distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
The code is available 'as is', we are planning further developments to the server
and code, and here is just a snapshot that should allow interested users to make
calculations with multiple fasta sequences (as opposed to the single sequence
operation of the web server).
*****************
CODE is in various perl scripts (.pl)
fasta_seq_reformat_export.pl
seq_compositions_perc_pipeline_export.pl
server_prediction_seq_export.pl
seq_props_ALL_export.pl
profiles_gather_export.pl
RUN is initiated with
'multiple_prediction_wrapper_export.sh sequence_input_file'
sequence_input_file has something like a fasta format, it will convert to
paired records of ID and sequence for each entry:
>blah11
MVKVYAPASSANMSVGFDVLFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFDKLPSEPRENIVYQCWERFCQE
>blah22
MVKVYAPASSANMSVGFDVLFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFDKLPSEPRENIVYQCWERFCQE
but regular fasta should be OK (fasta_seq_reformat_export to convert).
OTHER INPUT (DATA) FILES:
ss_propensities.txt (sec struc propensities)
seq_reference_data_NIWA.txt (fitting to experimental solubility data)
RUNNING, as set up, will occur with all files in the local directort/
OUTPUT
seq_prediction.txt (CSV) contains the data relating to that provided on the server:
LEGEND records - brief information on features used for the predictions
HEADERS records - matched with keywords for the data output SEQUENCE records:
HEADERS PREDICTIONS with SEQUENCE PREDICTIONS
percent-sol, scaled-sol, population-sol, pI
HEADERS FEATURES ORIGINAL gives definitions of the features
HEADERS FEATURES PLOT give short-hand names for the features
both of these HEADERS lines are matched columns with SEQUENCE WEIGHTS and SEQUENCE DEVIATIONS
lines, where:
SEQUENCE WEIGHTS - weights from the fit to experimental data, only 10 features are non-zero
SEQUENCE DEVIATIONS - these are the z-score (see publication) deviation for each feature
then follows PROFILE data across successive 21 amino acid windows for:
Kyte-Doolittle
Uversky fold value - plotted on server
sequence entropy
windowed net charge - plotted on server
blah.txt 0 → 100644
This diff is collapsed. Click to expand it.
fasta_seq_reformat_export.pl 0 → 100644
#!/usr/bin/perl
use FindBin;
$file_here = "$FindBin::Bin/blah.txt";
printf $file_here;
open (BLAH, ">>$file_here") or die "cannot open blah.txt\n"; # file for comments, throughout server code
open (IN, "< $FindBin::Bin/reformat.in") or die "cannot open reformat_in\n";
@in=<IN>;
close (IN);
$file_here = "$FindBin::Bin/reformat_out";
open (OUT, ">$file_here") or die "cannot open reformat_out\n";
$seq_stored = 'no';
$skip_seq = 'no';
foreach $line (@in) {
chomp $line;
@words = split (" ",$line);
if ((substr ($line,0,1) eq '>') or ($words[0] eq '//')) { # new sequence or a // terminator
if ($seq_stored eq 'yes') { # process and write out the previous sequence, if no errors
$seqUC = uc($seq_here); # uppercase the sequence
$seqlen = length($seqUC);
$seq_new = "";
$naa = 0;
$n = 1; # ?? VERY ODD does the loop not fix starting n at 1 ??
for ($n==1; $n<=$seqlen; $n++) {
$char_here = substr($seqUC,$n-1,1);
if (($char_here ne ' ') and ($char_here ne '*')) { # ignore blanks and asterisks
if ($char_here =~ /[ACDEFGHIKLMNPQRSTVWY]/) {
$seq_new .= $char_here;
$naa++;
# } else { # not happy, but we can get <cr> at end, and easier to take only aa than skip allowed non-aa
# printf BLAH "**** stopping - fasta_seq_reformat - non 20 aa detected for protein ID = $current_id\n";
# $skip_seq = 'yes';
}
}
}
if ($skip_seq ne 'yes') {
if ($naa >= 21) {
printf OUT "$current_id\n"; # print the stored id
printf OUT "$seq_new\n";
} else { # no aas
printf BLAH "**** stopping - fasta_seq_reformat - shorter than 21 aas for protein ID = $current_id\n";
}
}
$skip_seq = 'no';
}
if (substr ($line,0,1) eq '>') {
$current_id = $line; # store the new seq ID - printer later, if seq is cleared
$seq_stored = 'yes'; # set up for new sequence
$seq_here = '';
}
} else { # add on to curr seq, if not blank
$len_here = length($line);
if ($len_here != 0) {
$seq_here .= $line;
}
}
}
if ($seq_stored eq 'yes') { # print the last seq if it conforms
$seqUC = uc($seq_here); # uppercase the sequence
$seqlen = length($seqUC);
$seq_new = "";
$naa = 0;
$n = 1; # ?? VERY ODD does the loop not fix starting n at 1 ??
for ($n==1; $n<=$seqlen; $n++) {
$char_here = substr($seqUC,$n-1,1);
if (($char_here ne ' ') and ($char_here ne '*')) { # ignore blanks and asterisks
if ($char_here =~ /[ACDEFGHIKLMNPQRSTVWY]/) {
$seq_new .= $char_here;
$naa++;
}
}
}
if ($skip_seq ne 'yes') {
if ($naa >= 21) {
printf OUT "$current_id\n"; # print the stored id
printf OUT "$seq_new\n";
} else { # no aas
printf BLAH "**** stopping - fasta_seq_reformat - shorter than 21 aas for protein ID = $current_id\n";
}
}
$skip_seq = 'no';
}
close (OUT);
close (BLAH);
exit;
multiple_prediction_wrapper_export.sh 0 → 100644
#!/bin/bash
FASTA_in=$1
SCRIPTDIR="/opt/workspace/D3/src/static/protein-sol"
cp $FASTA_in $SCRIPTDIR/reformat.in
perl $SCRIPTDIR/fasta_seq_reformat_export.pl > $SCRIPTDIR/run.log
mv $SCRIPTDIR/reformat_out $FASTA_in
cp $FASTA_in $SCRIPTDIR/composition.in
perl $SCRIPTDIR/seq_compositions_perc_pipeline_export.pl >> $SCRIPTDIR/run.log
mv $SCRIPTDIR/composition_all.out $SCRIPTDIR/seq_composition.txt
perl $SCRIPTDIR/server_prediction_seq_export.pl >> $SCRIPTDIR/run.log
cp $FASTA_in $SCRIPTDIR/seq_props.in
perl $SCRIPTDIR/seq_props_ALL_export.pl >> $SCRIPTDIR/run.log
mv $SCRIPTDIR/seq_prediction.txt $SCRIPTDIR/seq_prediction_OLD.txt
perl $SCRIPTDIR/profiles_gather_export.pl > $SCRIPTDIR/run.log
#rm bins.txt reformat.in seq_props.in seq_props.out STYprops.out composition.in seq_prediction_OLD.txt
profiles_gather_export.pl 0 → 100644
#!/usr/bin/perl -w
use FindBin;
$file_here = "$FindBin::Bin/blah.txt";
open (BLAH, ">>$file_here"); # file for comments, throughout server code
open (my $PROPS, "<", "$FindBin::Bin/STYprops.out") or die "cannot open STYprops.out profiles_gather\n";
@props = <$PROPS>;
close ($PROPS);
$nseq = 0;
foreach $line (@props) {
chomp $line;
@words = split (" ",$line);
$nwords = @words;
if (exists $words[0]) { # non-null line
if ($words[0] eq 'STARTID') { # new sequence - store the ID
$id_here = $words[1];
$nseq++;
$seqs{$id_here} = $nseq;
$nwin = 0;
} elsif ($words[0] eq 'NONP') { # a window line, assume associate with id_here
$nwin++;
$KD[$nseq][$nwin] = $words[5];
$FI[$nseq][$nwin] = $words[6];
$ent[$nseq][$nwin] = $words[7];
$meanQ[$nseq][$nwin] = $words[10];
} elsif ($words[0] eq 'ENDID') { # have the profiles
if ($words[1] ne $id_here) {
printf BLAH "**** STOPPING - IDs mismatch in profiles_gather.pl\n";
exit;
}
$numwin[$nseq] = $nwin; # store the number of windows (naa - 20) for this sequence
}
}
}
# read in the seq_prediction_OLD.txt file and write out the updated version with profiles added (interstitial)
$file_here = "$FindBin::Bin/seq_prediction_OLD.txt";
open (OLD, "<$file_here") or die "cannot open seq_prediction_OLD.txt";
@old = <OLD>;
close (OLD);
$file_here = "$FindBin::Bin/../../tmp/seq_prediction.txt";
open (NEW, ">$file_here") or die "cannot open seq_prediction.txt";
foreach $line (@old) {
chomp $line;
@words = split (",",$line);
$nwords = @words;
if (exists $words[0]) { # non-null line
if ($words[0] eq 'SEQUENCE DEVIATIONS') { # write matched seq profiles after this line
printf NEW "$line\n"; # write the SEQUENCE DEVIATIONS line
$id_here = $words[1];
if (exists $seqs{$id_here}) {
$nseq_here = $seqs{$id_here};
$nwin_here = $numwin[$nseq_here];
printf NEW "SEQUENCE PROFILE KyteDoolittle,$id_here";
for ($n=1; $n<=$nwin_here; $n++) { printf NEW ",$KD[$nseq_here][$n]"; }
printf NEW "\n";
printf NEW "SEQUENCE PROFILE Uversky??,$id_here";
for ($n=1; $n<=$nwin_here; $n++) { printf NEW ",$FI[$nseq_here][$n]"; }
printf NEW "\n";
printf NEW "SEQUENCE PROFILE entropy,$id_here";
for ($n=1; $n<=$nwin_here; $n++) { printf NEW ",$ent[$nseq_here][$n]"; }
printf NEW "\n";
printf NEW "SEQUENCE PROFILE charge,$id_here";
for ($n=1; $n<=$nwin_here; $n++) { printf NEW ",$meanQ[$nseq_here][$n]"; }
printf NEW "\n";
} else {
# ?? - sequence unmatched - what to do?
}
} else {
printf NEW "$line\n"; # all other (than SEQUENCE DEVIATIONS) lines write and carry on
}
} else { # null line write
printf NEW "$line\n";
}
}
close (NEW);
close (BLAH);
exit;
seq_composition.txt 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
seq_reference_data.txt 0 → 100644
# Niwa population 3049 ids matched, and a further 653 excluded by MEMBRANE_EXCLUDE
# Niwa - following averages and std deviations refer to the 3049-653 population
# Niwa - following data segment from seq_compositions_perc_pipeline.pl followed by seq_compositions_stats_pipeline w/ STATS_MODE unset
# Added POP, TOP, LOW to start of transferred data (seq_compositions), for ease of reading
# Also added the data source to each horizantal data line, for ease of reading
POP, NIWA, AVG, 53.347, DEV, 33.910, N, 2396, TAG, percentage-solubility
POP, NIWA, WHOLE-SEQ,headers,K-R,D-E,naa,totperc,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,K+R,D+E,K+R-D-E,K+R+D+E,F+W+Y,pI,KyteDoo,abs-charge,FoldIndex,disorder,entropy,betapropensity
POP, NIWA, WHOLE-SEQ,average, -0.922, -0.983,307.996,100.000, 9.347, 1.360, 5.654, 6.637, 3.456, 7.021, 2.543, 5.668, 4.947, 9.969, 2.754, 3.921, 4.409, 4.697, 5.868, 5.542, 5.307, 6.847, 1.297, 2.755, 10.815, 12.291, -1.476, 23.106, 7.508, 6.693, 0.475, 0.029, 0.143, -0.084, 4.067, 0.993
POP, NIWA, WHOLE-SEQ,std-dev, 3.438, 2.759,178.157, 0.000, 2.752, 1.217, 1.614, 2.132, 1.409, 2.287, 1.289, 1.835, 2.327, 2.568, 1.114, 1.557, 1.632, 1.844, 2.183, 1.876, 1.743, 2.017, 0.994, 1.383, 2.923, 2.586, 3.708, 4.088, 2.529, 1.854, 0.025, 0.027, 0.080, 0.021, 0.093, 0.026
TOP, NIWA, AVG, 113.241, DEV, 8.620, N, 145, TAG, percentage-solubility
TOP, NIWA, WHOLE-SEQ,headers,K-R,D-E,naa,totperc,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,K+R,D+E,K+R-D-E,K+R+D+E,F+W+Y,pI,KyteDoo,abs-charge,FoldIndex,disorder,entropy,betapropensity
TOP, NIWA, WHOLE-SEQ,average, 1.042, -1.529,188.055,100.000, 8.993, 1.277, 6.064, 7.593, 2.964, 6.639, 2.259, 5.472, 6.560, 9.202, 3.041, 3.774, 4.125, 4.691, 5.518, 5.770, 5.464, 7.112, 1.052, 2.431, 12.078, 13.657, -1.579, 25.735, 6.447, 6.572, 0.462, 0.047, 0.089, -0.084, 3.995, 0.986
TOP, NIWA, WHOLE-SEQ,std-dev, 4.125, 3.843,122.263, 0.000, 3.163, 1.502, 2.193, 2.973, 1.697, 2.544, 1.628, 1.989, 3.529, 2.816, 1.642, 1.941, 2.154, 2.685, 2.740, 2.226, 2.097, 2.674, 1.094, 1.514, 4.786, 3.540, 6.298, 5.586, 2.709, 2.227, 0.033, 0.045, 0.109, 0.026, 0.115, 0.032
LOW, NIWA, AVG, 5.208, DEV, 1.747, N, 125, TAG, percentage-solubility
LOW, NIWA, WHOLE-SEQ,headers,K-R,D-E,naa,totperc,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,K+R,D+E,K+R-D-E,K+R+D+E,F+W+Y,pI,KyteDoo,abs-charge,FoldIndex,disorder,entropy,betapropensity
LOW, NIWA, WHOLE-SEQ,average, -2.651, -0.520,437.096,100.000, 8.922, 1.228, 5.438, 5.957, 3.800, 6.932, 2.923, 5.307, 3.812, 10.676, 2.674, 4.060, 4.525, 4.901, 6.463, 5.892, 5.369, 6.001, 1.833, 3.286, 10.275, 11.395, -1.120, 21.670, 8.918, 6.988, 0.473, 0.016, 0.150, -0.082, 4.113, 0.992
LOW, NIWA, WHOLE-SEQ,std-dev, 2.618, 2.230,204.698, 0.000, 2.227, 0.743, 1.187, 1.547, 1.272, 1.912, 1.117, 1.682, 1.387, 2.481, 0.906, 1.623, 1.274, 1.557, 1.764, 1.517, 1.470, 1.585, 0.956, 1.565, 1.793, 1.623, 1.643, 2.998, 2.599, 1.476, 0.021, 0.011, 0.060, 0.020, 0.056, 0.021
# calc_flag, signed_diff_zscore, y/yes or n/no to include or not in prediction, taken from p-value of overall correlations
# also avoid most straight linear combinations of aas in prediction
# then, in the prediction scheme, for each feature to be included (y/n) we will have:
# feature x, let xL = x avg in LOW set, xT = x avg in TOP set, and measured prop y, yL = avg in LOW ser, yT = avg in TOP set
# for feature value x:
# if x < xL, x=xL or if x > xT, x=xT, and predicted y = yL + [(x-xL)/( xT-xL)]*(yT-yL)
# followed by linear combination with abs value of zscore_diff for each x/feature
# our current (hopefully FINAL) list of 10 features for prediction is given in as y/n
ZDF,NIWA,WHOLE-SEQ,headers,K-R,D-E,naa,totperc,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,K+R,D+E,K+R-D-E,K+R+D+E,F+W+Y,pI,KyteDoo,abs-charge,FoldIndex,disorder,entropy,betapropensity
ZDF, NIWA, WHOLE-SEQ, zscore-diff, 1.096, -0.333, -1.523, 0.000, 0.026, 0.043, 0.370, 0.724, -0.563, -0.132, -0.484, 0.090, 1.118, -0.557, 0.288, -0.160, -0.234, -0.099, -0.419, -0.065, 0.053, 0.522, -0.762, -0.555, 0.548, 0.876, -0.116, 0.947, -0.931, -0.225, -0.390, 1.105, -0.711, -0.103, -1.382, -0.258
ZDF, NIWA, WHOLE-SEQ, use_for_prob,y,n,y,n,n,n,n,n,n,n,y,n,n,y,n,n,n,n,n,n,n,y,n,n,n,y,n,n,y,n,n,y,y,n,y,n
server_prediction_seq_export.pl 0 → 100644
#!/usr/bin/perl -w
use FindBin;
$file_here = "$FindBin::Bin/blah.txt";
open (BLAH, ">>$file_here"); # file for comments used throughout server code
# read in the reference data for this dataset.
$file_here = "$FindBin::Bin/seq_reference_data.txt";
open (REF, "<$file_here") or die "cannot open seq_reference_data.txt\n";
@ref = <REF>;
close (REF);
foreach $line_spaces (@ref) {
chomp $line_spaces;
$line = $line_spaces;
$line =~ s/\s//g;
@words = split (",",$line);
$nwords = @words;
if (exists $words[0]) { # non-null line
if (substr ($line,0,1) ne "#") { # not comment line
if ($words[2] eq 'AVG') {
if ($words[0] eq 'POP') {
$population_avg = $words[3];
}
if ($nwords != 10) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in AVG line mismatch\n";
exit;
} else {
$AVG_line{$words[0]} = 'seen';
if ($words[0] eq 'TOP') { $top_prop_avg = $words[3]; }
if ($words[0] eq 'LOW') { $low_prop_avg = $words[3]; }
}
} elsif ($words[3] eq 'headers') {
if ($nwords != 40) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in headers line mismatch\n";
exit;
} else {
$headers_line{$words[0]} = 'seen';
}
if ($words[0] eq 'POP') {
$headers_store = $line; # store the headers just once, for results write
}
} elsif ($words[3] eq 'average') {
if ($nwords != 40) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in average line mismatch\n";
exit;
} else {
$average_line{$words[0]} = 'seen';
}
if ($words[0] eq 'TOP') { # store top and low averages to derive the linear fits (per feature)
$top_avg_line = $line;
} elsif ($words[0] eq 'LOW') {
$low_avg_line = $line;
} elsif ($words[0] eq 'POP') {
$pop_avg_line = $line;
}
} elsif ($words[3] eq 'std-dev') {
if ($nwords != 40) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in std-dev line mismatch\n";
exit;
} else {
$std_dev_line{$words[0]} = 'seen';
}
if ($words[0] eq 'POP') { # store population std deviations for features
$pop_dev_line = $line;
#unused } elsif ($words[0] eq 'TOP') {
# $top_dev_line = $line;
# } elsif ($words[0] eq 'LOW') {
# $low_dev_line = $line;
}
} elsif (($words[0] eq 'ZDF') and ($words[3] eq 'zscore-diff')) {
if ($nwords != 40) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in ZDF zscore-diff line mismatch\n";
exit;
} else {
$zdiff_line = $line;
$zdf_values = 'seen';
}
} elsif (($words[0] eq 'ZDF') and ($words[3] eq 'use_for_prob')) {
if ($nwords != 40) { # check the number, but not format, of expected fields
printf BLAH "stopping *** number of fields in ZDF use_for_prob line mismatch\n";
exit;
} else {
$use_for_prob_line = $line;
$zdf_use = 'seen'
}
}
} # end not comment line
} # end not null line
} # end line read
for $key_here ('POP', 'TOP', 'LOW' ) {
if (($headers_line{$key_here} ne 'seen') or ($average_line{$key_here} ne 'seen') or ($std_dev_line{$key_here} ne 'seen')) {
printf BLAH "stopping **** headers/average/std_dev line unseen for $key_here\n";
exit;
}
if ($AVG_line{$key_here} ne 'seen') {
printf BLAH "stopping **** AVG line unseen for $key_here\n";
exit;
}
}
if (($zdf_values ne 'seen') or ($zdf_use ne 'seen')) {
printf BLAH "stopping **** ZDF zscore-diff/use_for_prob line unseen\n";
}
# store the reference values needed for prediction
$low_prop = $low_prop_avg;
$top_prop = $top_prop_avg;
@lows = split (",",$low_avg_line); # values run from 4-39 (inclusive), with array index starting at 0
@tops = split (",",$top_avg_line); # as above
@pops = split (",",$pop_avg_line); # as above
@pop_devs = split (",",$pop_dev_line); # as above
@zdiffs = split (",",$zdiff_line); # as above, zdiff_scores remain signed at this point
@use = split (",",$use_for_prob_line); # as above, values are y,n
@heads = split (",",$headers_store); # as above
# read and store data values for this sequence set
$file_here = "$FindBin::Bin/seq_composition.txt";
open (COMP, "<$file_here") or die "cannot open seq_composition.txt\n";
$headers_found = "no";
$nseqs = 0;
while ($line_spaces = <COMP>) {
chomp $line_spaces;
$line = $line_spaces;
$line =~ s/\s//g;
@words = split (",",$line);
$nwords = @words;
if ($headers_found eq "no") {
if ((exists $words[0]) and (exists $words[1])) {
if (($words[0] eq "WHOLE-SEQ") and ($words[1] eq "ORF-ID")) {
$headers_found = "yes";
# @heads_comp = @words; # not used currently
}
}
}
if (exists $words[0]) {
if ($words[0] eq "WHOLE-SEQ") { # only process WHOLE-SEQ here - will need other of our code for seq-profiles
if ($words[1] ne "ORF-ID") { # check not on header line
if ($nwords != 38) { # flag, ID, 36 comp etc data fields
printf BLAH "stopping **** fields ne 38 in composition file data line\n";
exit;
} else {
$nseqs++;
$ids[$nseqs] = $words[1];
for ($n=1; $n<=36; $n++) { $data[$nseqs][$n] = $words[$n+1]; }
}
}
}
}
}
close (COMP);
$file_here = "$FindBin::Bin/seq_prediction.txt";
open (PREDA, ">$file_here") or die "cannot open seq_prediction.txt\n";
# before the sequence data writes, output things that don't change
$wt_sum = 0;
for ($d=1; $d<=36; $d++) {
if ($use[$d+3] eq 'y') {
$wt_feature[$d] = abs ($zdiffs[$d+3]); # get feature weighting for prediction (mostly zero probably)
$wt_sum += $wt_feature[$d];
} else {
$wt_feature[$d] = 0;
}
}
printf PREDA "LEGEND 35 sequence features are calculated, including 20 amino acid compositions\n";
printf PREDA "LEGEND All features are calculated over a sliding 21 amino acid window\n";
printf PREDA "LEGEND KmR=KminusR DmE=DminusE KpR=KplusR PmN=K+R-D-E PpN=K+R+D+N aro=F+W+Y\n";
printf PREDA "LEGEND fld = border of folded (pos) / unfolded (neg) Uversky et al Proteins 2000 41:415\n";
printf PREDA "LEGEND dis = disorder propensity Rune & Linding NAR 2003 31:3701\n";
printf PREDA "LEGEND bet = beta strand propensities Costantini et al 2006 BBRC 342:441\n";
printf PREDA "LEGEND mem = Kyte-Doolittle hydropathy normalised (-1 to +1) JMB 157:105\n";
printf PREDA "LEGEND Only a subset of features used for prediction, according to best fit against data\n";
printf PREDA "LEGEND underlying solubility data: cell-free expression Niwa et al PNAS 2009 106:4201\n";
printf PREDA "LEGEND Features used for prediction are scaled 0 - 1 over range in underlying dataset\n";
printf PREDA "HEADERS PREDICTIONS LINE,ID,percent-sol,scaled-sol,population-sol,pI\n";
printf PREDA "HEADERS FEATURES ORIGINAL,ID";
for ($d=1; $d<=3; $d++) { printf PREDA ",$heads[$d+3]"; }
for ($d=5; $d<=36; $d++) { printf PREDA ",$heads[$d+3]"; }
printf PREDA "\n";
printf PREDA "HEADERS FEATURES PLOT,ID,";
printf PREDA "KmR,DmE,len,";
for ($d=5; $d<=24; $d++) { printf PREDA "$heads[$d+3],"; }
printf PREDA "KpR,DpE,PmN,PpN,aro,pI,mem,chr,fld,dis,ent,bet\n";
printf PREDA "\n";
for ($n=1; $n<=$nseqs; $n++) {
$wt_sum = 0;
$pred_sum = 0;
for ($d=1; $d<=36; $d++) {
if ($d != 4) { # get the values of (X-Xavg-pop) / std-dev-pop i.e. a neg/pos scale
$norm_dev[$d] = ($data[$n][$d] - $pops[$d+3]) / $pop_devs[$d+3];
}
$wt_feature[$d] = 0; # initialise to zero, since will only overwrite if feature used for prediction
if ($use[$d+3] eq 'y') { # we are using this feature (based on correlation analysis)
$data_here = $data[$n][$d];
if ($lows[$d+3] <= $tops[$d+3]) { # range as the words say, bounds check
if ($data[$n][$d] < $lows[$d+3]) { $data_here = $lows[$d+3]; }
if ($data[$n][$d] > $tops[$d+3]) { $data_here = $tops[$d+3]; }
} else { # inverted range, bounds check the other way around
if ($data[$n][$d] > $lows[$d+3]) { $data_here = $lows[$d+3]; }
if ($data[$n][$d] < $tops[$d+3]) { $data_here = $tops[$d+3]; }
}
$pred_here = $low_prop + ($top_prop - $low_prop) * ( ($data_here - $lows[$d+3]) / ($tops[$d+3] - $lows[$d+3]) );
$wt_here = abs ($zdiffs[$d+3]);
$pred_sum += $wt_here*$pred_here;
$wt_sum += $wt_here;
$wt_feature[$d] = $wt_here;
}
}
$prediction = $pred_sum / $wt_sum;
$prediction_scaled = ($prediction - $low_prop) / ($top_prop - $low_prop);
$population_scaled = ($population_avg - $low_prop) / ($top_prop - $low_prop);
$pI_here = $data[$n][30];
printf PREDA "SEQUENCE PREDICTIONS,$ids[$n]"; # output sequence-specific data
printf PREDA ",%6.3f,%6.3f,%6.3f,%6.3f\n", $prediction,$prediction_scaled,$population_scaled,$pI_here;
printf PREDA "SEQUENCE WEIGHTS,$ids[$n]";
for ($d=1; $d<=3; $d++) {
$wt_print = $wt_feature[$d]/$wt_sum;
printf PREDA ",%6.3f", $wt_print;
}
for ($d=5; $d<=36; $d++) {
$wt_print = $wt_feature[$d]/$wt_sum;
printf PREDA ",%6.3f", $wt_print;
}
printf PREDA "\n"; # output the devs/std-dev for each feature, for this sequence
printf PREDA "SEQUENCE DEVIATIONS,$ids[$n]";
for ($d=1; $d<=3; $d++) { printf PREDA ",%6.3f", $norm_dev[$d]; }
for ($d=5; $d<=36; $d++) { printf PREDA ",%6.3f", $norm_dev[$d]; }
printf PREDA "\n\n";
}
close (PREDA);
close (BLAH);
exit;
ss_propensities.txt 0 → 100644
comment amino acid sec struc propensities from
comment Constantini et al (2006) BBRC 342:441-451
comment aa3 aa1 freq Pa Pb Pc
data ALA A 7.73 1.39 0.75 0.80
data CYS C 1.84 0.74 1.31 1.05
data ASP D 5.82 0.89 0.55 1.33
data GLU E 6.61 1.35 0.72 0.86
data PHE F 4.05 1.01 1.43 0.76
data GLY G 7.11 0.47 0.65 1.62
data HIS H 2.35 0.92 0.99 1.07
data ILE I 5.66 1.04 1.71 0.59
data LYS K 6.27 1.11 0.83 1.00
data LEU L 8.83 1.32 1.10 0.68
data MET M 2.08 1.21 0.99 0.83
data ASN N 4.50 0.77 0.62 1.39
data PRO P 4.52 0.50 0.44 1.72
data GLN Q 3.94 1.29 0.76 0.89
data ARG R 5.03 1.17 0.91 0.91
data SER S 6.13 0.82 0.85 1.24
data THR T 5.53 0.76 1.23 1.07
data VAL V 6.91 0.89 1.86 0.64
data TRP W 1.51 1.06 1.30 0.79
data TYR Y 3.54 0.95 1.50 0.78
comment suggested algorithm is simply to sum over
comment window (eg 7) and take largest value = ss
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