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Commit 0c7fe1f3 by Mustafa Tekpinar
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Removed ESGEMME_PATH dependecy for R files. 

Since esgemme calls two R scripts, namely
pred.R and computePred.R, to do the calculations,
I added them as package resource. They are copied
to the folder where ESGEMME is initiated. I'm
trying to remove this kind of path dependencies
to make the program automatically installable and
portable.
parent a17f0f90
Showing with 398 additions and 157 deletions
Dockerfile
......@@ -15,7 +15,7 @@ COPY ./ESGEMME/requirements.txt ./ESGEMME/requirements.txt
COPY ./ESGEMME/README.md ./ESGEMME/README.md
COPY ./ESGEMME/pred.R ./ESGEMME/pred.R
COPY ./ESGEMME/computePred.R ./ESGEMME/computePred.R
COPY ./ESGEMME/default.conf ./ESGEMME/default.conf
COPY ./ESGEMME/esgemme/default.conf ./ESGEMME/esgemme/default.conf
COPY ./ESGEMME/data/ ./ESGEMME/data/
COPY ./ESGEMME/examples/ ./ESGEMME/examples/
COPY ./demust/ ./demust/
......
docs/_build/html/_sources/analysis.rst.txt
Analyzing the ESGEMME output
----------------------------
By default, ESGEMME will output the following files: \*
myProt_normPred_evolEpi.txt \* myProt_normPred_evolInd.txt \*
myProt_normPred_evolCombi.txt
The most important output file is **myProt_normPred_evolCombi.txt**. The
values of interest are the normalized predictions (normPred). Each file
contains a 20 x n matrix, where n is the number of positions (columns) in the
query sequence. If the user provides her/his own list of mutations, then
only the global epistatic model will be run and the output file will
contain 2 columns, the first one with the mutations, the second one with
the normalized predicted effects.
Analyzing and Modifying the ESGEMME Output
==========================================
Raw ESGEMME Scores and Their Interpretation
-------------------------------------------
There is not a hardcoded limit for raw ESGEMME scores. However, the values
range between [-12, 2] generally. The lower values mean the mutations is impactful,
while values close to 0 means the mutation does not have any significant impact.
We should note that most of the 'impactful' mutations are deleterious but
it is not always the case.
Entire Single Point Mutation Landscape Calculations
---------------------------------------------------
By default, ESGEMME will only output the combined (independent
and epistatic) scores*:
There are three output files:
#. myProt_normPred_evolCombi.txt
myProt is the short name in the MSA file for your protein. The
'myProt_normPred_evolCombi.txt' file contains 20 rows (for 20 amino acids
in alphabetical order) and L columns, where L is the number of amino acids
in your protein of interest. Since this file is horizontal, it is easy to
read it in R or Python but difficult to find the mutations you are interested.
#. myProt_normPred_evolCombiTransposed.txt
As the name implies, this is the transposed version of the combined results.
It is easier to find the mutations you are interested in this file. Just
check the row corresponding to the mutation.
#. myProt_normPred_evolCombi.Preparing
This is the image file of the combined results. It selects 'Oranges_r'
matplotlib color map by default. You can change it by adding '--colormap turbo_r'
for a more fancy look during the esgemme call. It --colormap argument accepts
all the color maps in matplotlib.
Note*: If you want to see epistatic and independent contributions as well,
you should add '--verbose true' argument while calling esgemme.
Selected Single Point or Multiple Point Mutation Calculations
-------------------------------------------------------------
Since you used a mutation file to predict mutations, you will have your combined (epistatic+independent)
results in the same format, such as :
*. myProt_normPred_evolCombi.txt:
A2C -6.23
A2D -1.23
.
.
.
D286F -0.23
docs/_build/html/analysis.html
......@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="docker.html">Using ESGEMME via Docker</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Analyzing the ESGEMME output</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Analyzing and Modifying the ESGEMME Output</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#raw-esgemme-scores-and-their-interpretation">Raw ESGEMME Scores and Their Interpretation</a></li>
<li class="toctree-l2"><a class="reference internal" href="#entire-single-point-mutation-landscape-calculations">Entire Single Point Mutation Landscape Calculations</a></li>
<li class="toctree-l2"><a class="reference internal" href="#selected-single-point-or-multiple-point-mutation-calculations">Selected Single Point or Multiple Point Mutation Calculations</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="input-preparation.html">Preparing Your Own Input</a></li>
<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
</ul>
......@@ -64,7 +69,7 @@
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<ul class="wy-breadcrumbs">
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
<li class="breadcrumb-item active">Analyzing the ESGEMME output</li>
<li class="breadcrumb-item active">Analyzing and Modifying the ESGEMME Output</li>
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......@@ -74,18 +79,64 @@
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<section id="analyzing-the-esgemme-output">
<h1>Analyzing the ESGEMME output<a class="headerlink" href="#analyzing-the-esgemme-output" title="Permalink to this heading"></a></h1>
<p>By default, ESGEMME will output the following files: *
myProt_normPred_evolEpi.txt * myProt_normPred_evolInd.txt *
myProt_normPred_evolCombi.txt</p>
<p>The most important output file is <strong>myProt_normPred_evolCombi.txt</strong>. The
values of interest are the normalized predictions (normPred). Each file
contains a 20 x n matrix, where n is the number of positions (columns) in the
query sequence. If the user provides her/his own list of mutations, then
only the global epistatic model will be run and the output file will
contain 2 columns, the first one with the mutations, the second one with
the normalized predicted effects.</p>
<section id="analyzing-and-modifying-the-esgemme-output">
<h1>Analyzing and Modifying the ESGEMME Output<a class="headerlink" href="#analyzing-and-modifying-the-esgemme-output" title="Permalink to this heading"></a></h1>
<section id="raw-esgemme-scores-and-their-interpretation">
<h2>Raw ESGEMME Scores and Their Interpretation<a class="headerlink" href="#raw-esgemme-scores-and-their-interpretation" title="Permalink to this heading"></a></h2>
<p>There is not a hardcoded limit for raw ESGEMME scores. However, the values
range between [-12, 2] generally. The lower values mean the mutations is impactful,
while values close to 0 means the mutation does not have any significant impact.</p>
<p>We should note that most of the ‘impactful’ mutations are deleterious but
it is not always the case.</p>
</section>
<section id="entire-single-point-mutation-landscape-calculations">
<h2>Entire Single Point Mutation Landscape Calculations<a class="headerlink" href="#entire-single-point-mutation-landscape-calculations" title="Permalink to this heading"></a></h2>
<p>By default, ESGEMME will only output the combined (independent
and epistatic) scores*:</p>
<p>There are three output files:</p>
<ol class="arabic">
<li><p>myProt_normPred_evolCombi.txt</p>
<blockquote>
<div><p>myProt is the short name in the MSA file for your protein. The
‘myProt_normPred_evolCombi.txt’ file contains 20 rows (for 20 amino acids
in alphabetical order) and L columns, where L is the number of amino acids
in your protein of interest. Since this file is horizontal, it is easy to
read it in R or Python but difficult to find the mutations you are interested.</p>
</div></blockquote>
</li>
<li><p>myProt_normPred_evolCombiTransposed.txt</p>
<blockquote>
<div><p>As the name implies, this is the transposed version of the combined results.
It is easier to find the mutations you are interested in this file. Just
check the row corresponding to the mutation.</p>
</div></blockquote>
</li>
<li><p>myProt_normPred_evolCombi.Preparing</p>
<blockquote>
<div><p>This is the image file of the combined results. It selects ‘Oranges_r’
matplotlib color map by default. You can change it by adding ‘–colormap turbo_r’
for a more fancy look during the esgemme call. It –colormap argument accepts
all the color maps in matplotlib.</p>
</div></blockquote>
</li>
</ol>
<p>Note*: If you want to see epistatic and independent contributions as well,
you should add ‘–verbose true’ argument while calling esgemme.</p>
</section>
<section id="selected-single-point-or-multiple-point-mutation-calculations">
<h2>Selected Single Point or Multiple Point Mutation Calculations<a class="headerlink" href="#selected-single-point-or-multiple-point-mutation-calculations" title="Permalink to this heading"></a></h2>
<p>Since you used a mutation file to predict mutations, you will have your combined (epistatic+independent)
results in the same format, such as :</p>
<dl class="simple">
<dt><a href="#id1"><span class="problematic" id="id2">*</span></a>. myProt_normPred_evolCombi.txt:</dt><dd><p>A2C -6.23
A2D -1.23
.
.
.
D286F -0.23</p>
</dd>
</dl>
</section>
</section>
......
docs/_build/html/docker.html
......@@ -19,7 +19,7 @@
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......@@ -56,7 +56,8 @@
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<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing the ESGEMME output</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing and Modifying the ESGEMME Output</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html#raw-esgemme-scores-and-their-interpretation">Raw ESGEMME Scores and Their Interpretation</a></li>
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......@@ -210,7 +211,7 @@ mutation and its predicted effect, separated by a space.</p>
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<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing the ESGEMME output</a></li>
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......@@ -44,7 +44,7 @@
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<li class="toctree-l1"><a class="reference internal" href="docker.html">Using ESGEMME via Docker</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing the ESGEMME output</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing and Modifying the ESGEMME Output</a></li>
<li class="toctree-l1"><a class="reference internal" href="input-preparation.html">Preparing Your Own Input</a></li>
<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
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......@@ -86,7 +86,12 @@
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<li class="toctree-l2"><a class="reference internal" href="analysis.html#raw-esgemme-scores-and-their-interpretation">Raw ESGEMME Scores and Their Interpretation</a></li>
<li class="toctree-l2"><a class="reference internal" href="analysis.html#entire-single-point-mutation-landscape-calculations">Entire Single Point Mutation Landscape Calculations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="docker.html">Using ESGEMME via Docker</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing the ESGEMME output</a></li>
<li class="toctree-l1"><a class="reference internal" href="analysis.html">Analyzing and Modifying the ESGEMME Output</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Preparing Your Own Input</a><ul>
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......@@ -141,7 +142,7 @@ II. An input PDB: myprotein.pdb</p>
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<li class="toctree-l1"><a class="reference internal" href="docker.html">Using ESGEMME via Docker</a></li>
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"Getting the example input data": [[1, "getting-the-example-input-data"]], "Single point mutation calculations": [[1, "single-point-mutation-calculations"]], "Obtaining the entire single point mutation landscape": [[1, "obtaining-the-entire-single-point-mutation-landscape"]], "Predicting the effect of a subset of single point mutations": [[1, "predicting-the-effect-of-a-subset-of-single-point-mutations"]], "Multiple point mutation calculations": [[1, "multiple-point-mutation-calculations"]], "Running several jobs using docker": [[1, "running-several-jobs-using-docker"]], "Welcome to esgemme\u2019s documentation!": [[2, "welcome-to-esgemme-s-documentation"]], "Contents:": [[2, null]], "Indices and tables": [[2, "indices-and-tables"]], "Preparing Your Own Input": [[3, "preparing-your-own-input"]], "Preparing Your Input MSA and PDB with Colabfold": [[3, "preparing-your-input-msa-and-pdb-with-colabfold"]], "Installation": [[4, "installation"]], "Installing the dependencies:": [[4, 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Chapter 2.
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[]
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Chapter 3.
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Chapter 4.
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Chapter 5.
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docs/_build/latex/esgemme.tex
......@@ -64,7 +64,7 @@
\title{esgemme}
\date{May 30, 2023}
\date{Jun 08, 2023}
\release{1.3.0}
\author{Mustafa Tekpinar}
\newcommand{\sphinxlogo}{\vbox{}}
......@@ -265,21 +265,90 @@ mutation and its predicted effect, separated by a space.
\sphinxstepscope
\chapter{Analyzing the ESGEMME output}
\label{\detokenize{analysis:analyzing-the-esgemme-output}}\label{\detokenize{analysis::doc}}
\chapter{Analyzing and Modifying the ESGEMME Output}
\label{\detokenize{analysis:analyzing-and-modifying-the-esgemme-output}}\label{\detokenize{analysis::doc}}
\section{Raw ESGEMME Scores and Their Interpretation}
\label{\detokenize{analysis:raw-esgemme-scores-and-their-interpretation}}
\sphinxAtStartPar
There is not a hardcoded limit for raw ESGEMME scores. However, the values
range between {[}\sphinxhyphen{}12, 2{]} generally. The lower values mean the mutations is impactful,
while values close to 0 means the mutation does not have any significant impact.
\sphinxAtStartPar
We should note that most of the ‘impactful’ mutations are deleterious but
it is not always the case.
\section{Entire Single Point Mutation Landscape Calculations}
\label{\detokenize{analysis:entire-single-point-mutation-landscape-calculations}}
\sphinxAtStartPar
By default, ESGEMME will only output the combined (independent
and epistatic) scores*:
\sphinxAtStartPar
There are three output files:
\begin{enumerate}
\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
\item {}
\sphinxAtStartPar
By default, ESGEMME will output the following files: *
myProt\_normPred\_evolEpi.txt * myProt\_normPred\_evolInd.txt *
myProt\_normPred\_evolCombi.txt
\begin{quote}
\sphinxAtStartPar
The most important output file is \sphinxstylestrong{myProt\_normPred\_evolCombi.txt}. The
values of interest are the normalized predictions (normPred). Each file
contains a 20 x n matrix, where n is the number of positions (columns) in the
query sequence. If the user provides her/his own list of mutations, then
only the global epistatic model will be run and the output file will
contain 2 columns, the first one with the mutations, the second one with
the normalized predicted effects.
myProt is the short name in the MSA file for your protein. The
‘myProt\_normPred\_evolCombi.txt’ file contains 20 rows (for 20 amino acids
in alphabetical order) and L columns, where L is the number of amino acids
in your protein of interest. Since this file is horizontal, it is easy to
read it in R or Python but difficult to find the mutations you are interested.
\end{quote}
\item {}
\sphinxAtStartPar
myProt\_normPred\_evolCombiTransposed.txt
\begin{quote}
\sphinxAtStartPar
As the name implies, this is the transposed version of the combined results.
It is easier to find the mutations you are interested in this file. Just
check the row corresponding to the mutation.
\end{quote}
\item {}
\sphinxAtStartPar
myProt\_normPred\_evolCombi.Preparing
\begin{quote}
\sphinxAtStartPar
This is the image file of the combined results. It selects ‘Oranges\_r’
matplotlib color map by default. You can change it by adding ‘\textendash{}colormap turbo\_r’
for a more fancy look during the esgemme call. It \textendash{}colormap argument accepts
all the color maps in matplotlib.
\end{quote}
\end{enumerate}
\sphinxAtStartPar
Note*: If you want to see epistatic and independent contributions as well,
you should add ‘\textendash{}verbose true’ argument while calling esgemme.
\section{Selected Single Point or Multiple Point Mutation Calculations}
\label{\detokenize{analysis:selected-single-point-or-multiple-point-mutation-calculations}}
\sphinxAtStartPar
Since you used a mutation file to predict mutations, you will have your combined (epistatic+independent)
results in the same format, such as :
\begin{description}
\sphinxlineitem{{\color{red}\bfseries{}*}. myProt\_normPred\_evolCombi.txt:}
\sphinxAtStartPar
A2C \sphinxhyphen{}6.23
A2D \sphinxhyphen{}1.23
.
.
.
D286F \sphinxhyphen{}0.23
\end{description}
\sphinxstepscope
......
docs/_build/latex/esgemme.toc
......@@ -7,7 +7,10 @@
\contentsline {subsection}{\numberline {1.3.2}Predicting the effect of a subset of single point mutations}{2}{subsection.1.3.2}%
\contentsline {section}{\numberline {1.4}Multiple point mutation calculations}{3}{section.1.4}%
\contentsline {section}{\numberline {1.5}Running several jobs using docker}{3}{section.1.5}%
\contentsline {chapter}{\numberline {2}Analyzing the ESGEMME output}{5}{chapter.2}%
\contentsline {chapter}{\numberline {2}Analyzing and Modifying the ESGEMME Output}{5}{chapter.2}%
\contentsline {section}{\numberline {2.1}Raw ESGEMME Scores and Their Interpretation}{5}{section.2.1}%
\contentsline {section}{\numberline {2.2}Entire Single Point Mutation Landscape Calculations}{5}{section.2.2}%
\contentsline {section}{\numberline {2.3}Selected Single Point or Multiple Point Mutation Calculations}{6}{section.2.3}%
\contentsline {chapter}{\numberline {3}Preparing Your Own Input}{7}{chapter.3}%
\contentsline {section}{\numberline {3.1}Preparing Your Input MSA and PDB with Colabfold}{7}{section.3.1}%
\contentsline {chapter}{\numberline {4}Installation}{9}{chapter.4}%
......
docs/analysis.rst
Analyzing the ESGEMME output
----------------------------
By default, ESGEMME will output the following files: \*
myProt_normPred_evolEpi.txt \* myProt_normPred_evolInd.txt \*
myProt_normPred_evolCombi.txt
The most important output file is **myProt_normPred_evolCombi.txt**. The
values of interest are the normalized predictions (normPred). Each file
contains a 20 x n matrix, where n is the number of positions (columns) in the
query sequence. If the user provides her/his own list of mutations, then
only the global epistatic model will be run and the output file will
contain 2 columns, the first one with the mutations, the second one with
the normalized predicted effects.
Analyzing and Modifying the ESGEMME Output
==========================================
Raw ESGEMME Scores and Their Interpretation
-------------------------------------------
There is not a hardcoded limit for raw ESGEMME scores. However, the values
range between [-12, 2] generally. The lower values mean the mutations is impactful,
while values close to 0 means the mutation does not have any significant impact.
We should note that most of the 'impactful' mutations are deleterious but
it is not always the case.
Entire Single Point Mutation Landscape Calculations
---------------------------------------------------
By default, ESGEMME will only output the combined (independent
and epistatic) scores*:
There are three output files:
#. myProt_normPred_evolCombi.txt
myProt is the short name in the MSA file for your protein. The
'myProt_normPred_evolCombi.txt' file contains 20 rows (for 20 amino acids
in alphabetical order) and L columns, where L is the number of amino acids
in your protein of interest. Since this file is horizontal, it is easy to
read it in R or Python but difficult to find the mutations you are interested.
#. myProt_normPred_evolCombiTransposed.txt
As the name implies, this is the transposed version of the combined results.
It is easier to find the mutations you are interested in this file. Just
check the row corresponding to the mutation.
#. myProt_normPred_evolCombi.Preparing
This is the image file of the combined results. It selects 'Oranges_r'
matplotlib color map by default. You can change it by adding '--colormap turbo_r'
for a more fancy look during the esgemme call. It --colormap argument accepts
all the color maps in matplotlib.
Note*: If you want to see epistatic and independent contributions as well,
you should add '--verbose true' argument while calling esgemme.
Selected Single Point or Multiple Point Mutation Calculations
-------------------------------------------------------------
Since you used a mutation file to predict mutations, you will have your combined (epistatic+independent)
results in the same format, such as :
*. myProt_normPred_evolCombi.txt:
A2C -6.23
A2D -1.23
.
.
.
D286F -0.23
computePred.R → esgemme/computePred.R
......@@ -6,15 +6,15 @@
# Usage: Rscript --save computePred.R XXXX
library("seqinr")
source(paste(Sys.getenv("ESGEMME_PATH"),"/pred.R",sep=""))
#source(paste(Sys.getenv("ESGEMME_PATH"),"/pred.R",sep=""))
source("pred.R")
# amino acid full alphabet
aa = c("a","c","d","e","f","g","h","i","k","l","m","n","p","q","r","s","t","v","w","y")
# BLOSUM62 matrix
blosum62p = as.matrix(read.table(paste0(Sys.getenv("ESGEMME_PATH"),"/data/blosum62p.txt"),row.names=1))
bgp = blosum62p[order(rownames(blosum62p)),order(colnames(blosum62p))]
bg = apply(bgp,1,sum)
# # BLOSUM62 matrix
# blosum62p = as.matrix(read.table(paste0(Sys.getenv("ESGEMME_PATH"),"/data/blosum62p.txt"),row.names=1))
# bgp = blosum62p[order(rownames(blosum62p)),order(colnames(blosum62p))]
# bg = apply(bgp,1,sum)
# get arguemnts
args = commandArgs(trailingOnly = TRUE)
......
esgemme/esgemme.py
......@@ -310,10 +310,25 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
# Run Rscript to compute predictions
def launchPred(prot,inAli,mutFile, normWeightMode, alphabet):
# I don't want to have a path dependency here.
# Removing this dependency makes the code more portable.
# Therefore, I copy the R scripts that are saved as the
# package resources to the current working directory.
source = files('esgemme').joinpath('pred.R')
with as_file(source) as esgemme_pred_R_path:
shutil.copy2(esgemme_pred_R_path, os.getcwd())
source = files('esgemme').joinpath('computePred.R')
with as_file(source) as esgemme_computePred_R_path:
shutil.copy2(esgemme_computePred_R_path, os.getcwd())
if (mutFile!=''):
rcmd="Rscript --save $ESGEMME_PATH/computePred.R "+prot+" "+inAli+" FALSE "+mutFile+" "+normWeightMode+" "+alphabet
# rcmd="Rscript --save $ESGEMME_PATH/computePred.R "+prot+" "+inAli+" FALSE "+mutFile+" "+normWeightMode+" "+alphabet
rcmd="Rscript --save computePred.R "+prot+" "+inAli+" FALSE "+mutFile+" "+normWeightMode+" "+alphabet
else:
rcmd="Rscript --save $ESGEMME_PATH/computePred.R "+prot+" "+inAli+" TRUE none "+normWeightMode+" "+alphabet
# rcmd="Rscript --save $ESGEMME_PATH/computePred.R "+prot+" "+inAli+" TRUE none "+normWeightMode+" "+alphabet
rcmd="Rscript --save computePred.R "+prot+" "+inAli+" TRUE none "+normWeightMode+" "+alphabet
print("\nRunning: \n"+rcmd)
reCode=subprocess.call(rcmd,shell=True)
......@@ -363,8 +378,8 @@ def cleanTheMess(prot,bFile,fFile, chainID, verbosity=False):
if os.path.isfile("caracTest.dat"):
os.remove("caracTest.dat")
if os.path.isfile("default.conf"):
os.remove("default.conf")
if os.path.isfile("default.conf"):
os.remove("default.conf")
if verbosity == False:
if os.path.isfile(prot+"_normPred_evolInd.txt"):
......
examples/example-esgemme-script.sh
......@@ -48,7 +48,7 @@ then
esgemme ../data/aliBLAT.fasta -r input -f ../data/aliBLAT.fasta \
--pdbfile ../data/blat-af2.pdb --normweightmode sstjetormax \
-m ../data/Stiffler_2015_BLAT_ECOLX.mut
#demust compare -i ../data/BLAT_ECOLX_Stiffler_2015_experimental.dat --itype singleline -j BLAT_normPred_evolCombi.txt --jtype singleline
demust compare -i ../data/BLAT_ECOLX_Stiffler_2015_experimental.dat --itype singleline -j BLAT_normPred_evolCombi.txt --jtype singleline
elif [ "$1" == "tjetormax" ]
then
......
setup.py
......@@ -45,7 +45,6 @@ setup(name='esgemme',
'Environment :: Console',
'Operating System :: POSIX',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
......@@ -65,5 +64,5 @@ setup(name='esgemme',
]
},
include_package_data=True,
package_data={'esgemme':['default.conf']},
package_data={'esgemme':['default.conf', 'computePred.R', 'pred.R']},
)
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