RiPPER (RiPP Precursor Peptide Enhanced Recognition) is a customisable tool for the identification of genes encoding putative precursor peptides within RiPP (ribosomally synthesised and post-translationally modified peptide) gene clusters. This was first described in Santos-Aberturas et al. (Nucleic Acids Res., 2019, 47, 4624–4637). A new methods article (June 2021) provides a step-by-step workflow to how we search for BGCs using RiPPER 1.1 Moffat et al., (Methods Mol. Biol., 2021, 2296, 227–247).
We strongly recommend that users work with a Docker container of RiPPER. This is built from the latest version of RiPPER and includes all dependencies, so nothing needs installing apart from Docker and the RiPPER container. Details are provided below on using this in the Docker section below.
The latest version of RiPPER (1.1) now includes automatic short peptide networking using using EGN built into the processing, which also provides associated output files for network analysis in Cytoscape. The analysis and processing of this output is described in detail in Moffat et al., (Methods Mol. Biol., 2021, 2296, 227–247), which is free to access. This version is the latest version of RiPPER that is accessed as normal via Docker (described below). If a user prefers to use the original form of RiPPER (described in Santos-Aberturas et al.) then it can be accessed via Docker as a "legacy" version (use the following command to install this older legacy version: docker pull streptomyces/ripdock:legacy).
RiPPER identifies putative precursor peptides associated with a set of putative RiPP tailoring enzymes that are defined by the user. To do this, the following steps are taken:
A detailed technical description of the workflow is provided at the end of this page as Description of RiPPER workflow.
The input for RiPPER is a .txt file that contains a list of NCBI accession numbers for proteins (one on each line) that the user wants to investigate for the presence of associated precursor peptide genes. Lists can be generated manually, or via multiple routes to retrieving a list of associated protein accessions, such as a BLAST search or conserved domain retrieval.
The simplest way to work with RiPPER is to use the Docker container, which includes all dependencies and can be run on Linux/MacOSX/Windows. This all follows installation of Docker.
To install RiPPER:
docker pull streptomyces/ripdockThe default is to pull the image tagged as latest. You could pull a different image if you have a tag name. See example below where legacy is the tag name:
docker pull streptomyces/ripdock:legacyFollowing installation, run the container using the following command, where
your input accession list file is stored in /home/tom/rippwork on Linux and
MacOS systems or C:/Users/tom/rippwork on a MS Windows system. (substitute your
relevant directories in place of these):
# Example usage on Linux
docker run -it -v /home/tom/rippwork:/home/mnt streptomyces/ripdock
# Example usage on MS Windows.
docker run -it -v C:/Users/tom/rippwork:/home/mnt streptomyces/ripdockDo not change the /home/mnt part. This refers to a directory in the container and scripts in the container expect to find this directory. The host directory you mount on /home/mnt in the container is where the output directories and files are written to. You can place your input list in the mounted host directory on the host side and access it in /home/mnt/ on the container side. See the example Run on your own list below.
Following the docker run command above, to ensure RiPPER is working correctly, you can run a small test analysis on 3 accessions that are included in a test file named minitest.txt. Use the following command:
./ripper_run.shUse the following command to analyse your own list, substituting in a relevant filename for te_accessions.txt:
./ripper_run.sh /home/mnt/te_accessions.txtPeptide scoring and retrieval relies on a number of parameters that can be modified and are defined in a configuration file (local.conf, see Description of RiPPER workflow for more details). By default, settings that worked well during testing across multiple RiPP classes are used:
flankLen 17500
minPPlen 20
maxPPlen 120
maxDistFromTE 8000
sameStrandReward 5
fastaOutputLimit 3
prodigalScoreThresh 7.5flankLen refers to the size of the nucleotide region retrieved from either side of the search protein.
minPPlen and maxPPlen refer to the minimum and maximum length of peptide (AA length) that will be searched for and retrieved by RiPPER.
maxDistFromTE refers to the maximum distance from the search protein (the tailoring enzyme, TE) that will be considered for peptide retrieval. Short genes will be annotated on the Genbank file outside of this region, but not tabulated.
sameStrandReward refers to a score boost if the peptide is encoded on the same strand as the tailoring enzyme.
fastaOutputLimit refers to the number of peptides retrieved for analysis from the maxDistFromTE region. Peptides are chosen for retrieval according to their Prodigal score, and there is no lower limit.
prodigalScoreThresh is the score threshold where RiPPER will retrieve additional peptides if they are above this score and within the maxDistFromTE region.
Currently, local.conf needs to be directly modified to change the above parameters, although a future update will allow for parameters to be directly set in the command line. If Docker is used, the local.conf file is automatically present in the RiPPER container with the above defaults. To modify this, once the container is running, copy it into the host directory on your computer:
cp local.conf /home/mntModify and save the file using a text editor and then use the following command to copy it back into the working directory for RiPPER:
cp /home/mnt/local.conf /home/workThe RiPPER output consists of a series of output files and folders:
A folder containing Genbank files for all retrieved gene clusters. These files are named by the host organism and the input accession number
This is a tab-delimited file that reports peptide data for all peptides retrieved by RiPPER (as defined by the analysis parameters above) for all gene clusters. This includes various associated data for the peptides, including Pfam domains and distance from the TE.
This is a tab-delimited file that reports peptide data for all peptides retrieved by RiPPER via a precursor peptide HMM search across the full size of the retrieved gene cluster (i.e. ignores maxDistFromTE), and only reports peptides that were not identified in the original RiPPER search. Peptide identifiers are unique from out.txt, so these text files can be combined for downstream analysis.
A fasta file for peptides reported in out.txt that is formatted as an input file for similarity networking using EGN. An equivalent distant.faa is also provided.
A folder containing RODEO html output files for all retrieved gene clusters. Additional RODEO data is provided in a separate rodout folder
A bash script (ripper_run.sh, further details below) runs a series of scripts to provide a full RiPPER analysis. This uses analysis settings defined in an associated configuration file (local.conf). Where relevant, variables defined in local.conf are described below.
Using an accession number for a predicted RiPP tailoring enzyme, the rodeo2 Python script rodeo_main.py produces and output file named main_co_occur.csv that contains information about the annotated genes that flank the RiPP tailoring enzyme.
ripper.pl reads the main_co_occur.csv file produced by the rodeo2 script rodeo_main.py to determine the genbank accession to fetch and the coordinats of the tailoring enzyme that was used as the search term.
The genbank file is then fetched from GenBank and a smaller genbank file is generated from it whose length is twice the value of the variable $flankLen (default = 17500). This smaller genbank file, hereafter referred to as the sub-genbank file, is centered around the centre of the tailoring enzyme.
Determines the genbank accession to fetch based on the value in column 3.
Determines the coordinates of the tailoring enzyme by reading columns 5, 6 and 7 for the line in which the values in column 1 and column 4 are identical.
Then the genbank file is fetched from Genbank and a smaller genbank file is generated from it whose length is twice the value of the variable $flankLen. This smaller genbank file, hereafter referred to as the sub-genbank file, is centered around the centre of the tailoring enzyme.
Rodeo retrieves the entire genbank file as it exists in Genbank.
A specially built version of prodigal renamed prodigal-short is then used to find genes in the sub-genbank file. prodigal-short is configured to find genes as short as 60 nucleotides compared to 90 nucleotides in the default build.
For all the genes found by prodigal-short the following is done.
The prodigal score is enhanced by a reward if the gene is on the
same strand as the tailoring enzyme and its score before reward is
more than negative of the reward number.(variable $sameStrandReward,
default = 5).
Determine overlap with already annotated genes.
Translate to get the protein sequence.
If the length of the protein is less than $minPPlen (default = 20)
or greater than $maxPPlen; skip to the next gene found by prodigal.
If the gene has significant overlap (> 20 nucleotides) with an already annotated gene then it is skipped.
If a gene is not filtered out in steps 4 or 5, it is annotated in the genbank file and its distance from the tailoring enzyme is determined.
If the gene is within a specified distance from the TE
(variable $maxDistFromTE, default = 8000)
it is included in the list to be output and also saved in a Sqlite3
table. By default, ripper.pl includes 3 peptides per gene cluster
(variable $fastaOutputLimit) within this specified distance, as well
as any additional peptides whose prodigal-short scores are greater
than variable $prodigalScoreThresh (default = 7.5).
The proteins in this table are then searched for pfam domains using the pfam_sqlite.pl script. The results of these searches are placed in a new table in the same sqlite3 database.
mergeRidePfam.pl then merges the information contained in the two sqlite3 tables, one containing the output of prodigal-short and the other containing the output of Pfam searches on the proteins selected from the output of prodigal-short. A tab delimited file (out.txt) is then generated that contains all protein sequence and Pfam information alongside various associated data (tailoring enzyme accession, strain, peptide sequence, distance from tailoring enzyme, coding strand in relation to tailoring gene, Prodigal score). If multiple genomic regions are searched in parallel, all data are collated in this single file.
gbkNameAppendOrg.pl copies the output genbank files to a new directory with the organism names appended to the filenames for ease of identification. Files are copied to the directory specified in the configuration variable orgnamegbkdir (default = orgnamegbk).
collectFiles.pl is used to copy all rodeo result .html files into a single directory defined by the rodeohtmldir variable in the configuration file (default = rodeohtml).
A file named ripper_run.sh is included. If it is run without any arguments it reads the file minitest.txt as input. You can supply a different input file an argument to ripper_run.sh.
The starting point is a query file containing Genpept accessions for tailoring enzymes. In the example below this file is named minitest.txt. The input file should contain a list of tailoring enzyme accessions.
Contents of ripper_run.sh are listed below. There is no need to make any changes to it.
#!/bin/bash
# This file is the bash script that provides a full RiPPER analysis
# that encompasses a number of related Perl scripts. Final outputs
# are found in orgnamegbk (GenBank files featuring RiPPER annotations),
# out.txt (tab-delimited table containing retrieved peptides and
# associated data) and rodeohtml (RODEO2 html output).
# This file should be in the directory from where it
# will be run. The local.conf file (featuring any parameter
# modifications) should also be placed in this directory
# This file should be in the directory from where it
# will be run as
#
# ./ripper_run.sh <inputfilename>
#
#
# Query file name defaults to minitest.txt.
queryfn=$1;
if [[ ${#queryfn} -lt 1 ]]; then
queryfn="minitest.txt";
fi
if [[ ! -s ${queryfn} || ! -e ${queryfn} ]]; then
echo "Either $queryfn does not exists or is zero in size. Aborting.";
exit 1;
fi
ripperdir=/home/work/ripper;
rodeodir=/home/work/rodeo2;
pfamdir=/home/work/pfam
# Tab delimited output file for results including pfam hits.
outfile=/home/mnt/out.txt
# Rodeo output directory
rodoutdir=/home/mnt/rodout;
# ripper output directory. Contains gbk files.
ripoutdir=/home/mnt/ripout;
# ripper output directory. Contains gbk files where filenames
# have the organism name prepended for convenience.
orgnamegbkdir=/home/mnt/orgnamegbk;
# The html file output by rodeo2 are here.
rodeohtmldir=/home/mnt/rodeohtml;
# Below is legacy from the Linux installable version of this script.
perlbin="perl"
pythonbin="python3"
# Make a couple of symlinks to keep rodeo_main.py happy.
ln -s $pfamdir ./hmm_dir
ln -s ${rodeodir}/confs ./
# Make the various directories where output will be placed.
for hcd in $rodoutdir $ripoutdir sqlite gbkcache $orgnamegbkdir $rodeohtmldir; do
if [[ ! -d $hcd ]]; then
mkdir $hcd
fi
done
### Setup is now complete. Actual runs below. ###
# rodeo run and ripper.pl run for each query in $queryfn
for acc in $(cat $queryfn); do
echo $pythonbin ${rodeodir}/rodeo_main.py -out ${rodoutdir}/${acc} ${acc}
$pythonbin ${rodeodir}/rodeo_main.py -out ${rodoutdir}/${acc} ${acc}
echo $perlbin ${ripperdir}/ripper.pl -outdir $ripoutdir -- ${rodoutdir}/${acc}/main_co_occur.csv
$perlbin ${ripperdir}/ripper.pl -outdir $ripoutdir -- ${rodoutdir}/${acc}/main_co_occur.csv
done
# Run the postprocessing scripts
$perlbin ${ripperdir}/pfam_sqlite.pl
$perlbin ${ripperdir}/mergeRidePfam.pl -out ${outfile}
$perlbin ${ripperdir}/gbkNameAppendOrg.pl -indir $ripoutdir
$perlbin ${ripperdir}/collectFiles.pl ${rodoutdir} ${rodeohtmldir} '\.html$'A file named local.conf is included in the repository.
local.conf is a two column (space delimited) text file which is read by ripper.pl and the postprocessing scripts in the pipeline. There should be no need to make changes to this file unless default parameters are being changed (as described above).
# Lines beginning with # are comments.
# All names are case sensitive.
# Downloaded genbank files are cached here.
# ripper_run.sh automatically generates a gbkcache
# directory in the directory it is run from.
gbkcache gbkcache
# Filename for the SQLite3 database.
# ripper_run.sh automatically generates a sqlite
# directory in the directory it is run from.
sqlite3fn sqlite/ripp.sqlite3
# Location of prodigal-short binary
# prodigalshortbin /usr/local/bin/prodigal-short
# Location of hmmscan binary
# hmmscanbin /usr/local/bin/hmmscan
# Directory containing the Pfam database files.
# Should be the same as pfamdir in the ripper_run.sh file.
hmmdir pfam
# Name of the Pfam database to use.
hmmdb Pfam-A.hmm
# Name of the Pfam data file.
# Used for reading information about models.
pfamhmmdatfn Pfam-A.hmm.dat
# Name of the SQLite3 table where results of
# hmmer searches are stored.
pfamrestab pfamscan
# Name of the SQLite3 table where results of
# prodigal search are stored.
prepeptab ripper
# Directory where output genbank files are stored.
# Organism names are prefixed to the file names for
# ease of identification.
# ripper_run.sh automatically generates an orgnamegbk directory in the
# directory it is run from.
orgnamegbkdir /home/mnt/orgnamegbk
# Below are some defaults (commented out) that can also
# be specified in this file by removing the hashtag.
# The names are case sensitive!
# minPPlen 20
# maxPPlen 120
# prodigalScoreThresh 7.5
# maxDistFromTE 8000
# fastaOutputLimit 3
# sameStrandReward 5
# flankLen 17500This is for documentation only. prodigal-short is provided in the Docker version.
The following changes (shown as the output of the git diff command) were made to prodigal source files before building prodigal-short according to instructions provided with the prodigal source download.
TARGET = prodigal was changed to TARGET = prodigal-short.diff --git a/Makefile b/Makefile
index 23ffe00..6edbb53 100644
--- a/Makefile
+++ b/Makefile
@@ -24,7 +24,7 @@ CC = gcc
CFLAGS += -pedantic -Wall -O3
LFLAGS = -lm $(LDFLAGS)
-TARGET = prodigal
+TARGET = prodigal-short
SOURCES = $(shell echo *.c)
HEADERS = $(shell echo *.h)
OBJECTS = $(SOURCES:.c=.o)#define MAX_SAM_OVLP 60 was changed to #define MAX_SAM_OVLP 45diff --git a/dprog.h b/dprog.h
index d729f4c..ea7fa10 100644
--- a/dprog.h
+++ b/dprog.h
@@ -26,7 +26,7 @@
#include "sequence.h"
#include "node.h"
-#define MAX_SAM_OVLP 60
+#define MAX_SAM_OVLP 45
#define MAX_OPP_OVLP 200
#define MAX_NODE_DIST 500#define MIN_GENE 90
#define MIN_EDGE_GENE 60
#define MAX_SAM_OVLP 60
#define ST_WINDOW 60
#define OPER_DIST 60Were changed to the following lines.
#define MIN_GENE 60
#define MIN_EDGE_GENE 45
#define MAX_SAM_OVLP 45
#define ST_WINDOW 45
#define OPER_DIST 45diff --git a/node.h b/node.h
index 6c722be..551c7b8 100644
--- a/node.h
+++ b/node.h
@@ -27,11 +27,11 @@
#include "training.h"
#define STT_NOD 100000
-#define MIN_GENE 90
-#define MIN_EDGE_GENE 60
-#define MAX_SAM_OVLP 60
-#define ST_WINDOW 60
-#define OPER_DIST 60
+#define MIN_GENE 60
+#define MIN_EDGE_GENE 45
+#define MAX_SAM_OVLP 45
+#define ST_WINDOW 45
+#define OPER_DIST 45
#define EDGE_BONUS 0.74
#define EDGE_UPS -1.00
#define META_PEN 7.5pfam_sqlite.pl takes all the proteins in a specified table in a specified sqlite3 database and searches them for Pfam domains. The results of these searches are placed in a new table in the same sqlite3 database.
mergeRidePfam.pl merges the information contained in the two sqlite3 tables, one containing the output of prodigal-short and the other containing the output of Pfam searches on the proteins selected from the output of prodigal-short. It writes out a tab delimited file.
Copies the output genbank files to a new directory with the organism names appended to the filenames for ease of identification. Files are copied to the directory specified in the configuration variable orgnamegbkdir.
collectFiles.pl copies files from a specified directory (and subdirectories) to another directory if the base filename matches the specified regular expression.
perl collectFiles.pl -indir rodout -pat '\.html$' -outdir rodeohtmlThe options shown above are the defaults. -outdir may be specified in local.conf as rodeohtmldir. Value in the configuration file takes precedence.
If it does not already exists, a directory named pna is made and the
files out.faa, distance.faa, out.txt and distance.txt are
copied to it.
After changing to the pna directory EGN is run. This is because EGN
need to be run from the directory where the input fasta files are.
The Perl script make_cytoscape_attribute_file.pl is used to make
cytoattrib.txt from the two txt files.
After the above is done the working directory is changed to
/home/work