Utilities for running the predector pipeline.
This is where the models and more extensive utility scripts are developed.
All command line tools are accessible under the main command predutils.
Load a line-delimited JSON into an SQLite database.
Basic usage:
predutils load_db results.db results.ldjsonOptions:
--replace-name
Replace record names with the string 'd'.
--drop-null-dbversion
Don't enter records requiring a database but have no database version specified.
--mem <float>
Use this much RAM for the SQLite cache. Set this to at most half of the total ram available.
--include ANALYSIS [ANALYSIS ...]
Only include these analyses, specify multiple with spaces.
--exclude ANALYSIS [ANALYSIS ...]
Exclude these analyses, specify multiple with spaces. Overrides analyses specified in --include.
If you are using this to set up a pre-computed database, specify the --replace-name, --drop-null-dbversion flags which will make sure any duplicate entries are excluded.
It is also recommended that you exclude results that are faster to recompute than to enter and fetch from the database, including pepstats, kex2_cutsite, and rxlr_like_motif.
This will be relatively fast for small to medium datasets, but can take several hours for many millions of entries.
Setting the --mem option is also a good idea to speed up inserting larger datasets.
Convert the output of one of the analyses into a common line delimited JSON format. The json records retain all information from the original output files, but are much easier to parse because each line is just JSON.
Basic usage:
predutils r2js \
-o outfile.ldjson \
--software-version 1.0 \
--database-version 1.0 \
--pipeline-version 0.0.1 \
pfamscan \
pfamscan_results.txt \
in.fastaAnalyses available to parse in place of pfamscan are:
signalp3_nn, signalp3_hmm, signalp4, signalp5, deepsig, phobius, tmhmm,
deeploc, targetp_plant (v2), targetp_non_plant (v2), effectorp1, effectorp2,
apoplastp, localizer, pfamscan, dbcan (HMMER3 domtab output), phibase *, pepstats,
effectordb, kex2_cutsite, and rxlr_like_motif.
* assumes search with MMseqs with tab delimited output format columns: query, target, qstart, qend, qlen, tstart, tend, tlen, evalue, gapopen, pident, alnlen, raw, bits, cigar, mismatch, qcov, tcov.
Preprocess some fasta files.
- Strips trailing
*amino acids from sequences, removes-characters, replaces internal*s and other redundant/non-standard amino acids withX, and converts sequences to uppercase. - removes duplicate sequence using a checksum, saving the mapping table to recover the duplicates at the end of the analysis.
- Replace the names of the deduplicated sequences with a short simple one.
Basic usage:
predutils encode \
output.fasta \
output_mapping.tsv \
input_fastas/*Note that it can take multiple input fasta files, and the filename is saved alongside the sequences in the output mapping table to recover that information.
By default, the temporary names will be SR[A-Z0-9]5 e.g. SR003AB.
You can change the prefix (default SR) with the --prefix flag, and the number of id characters (default 5) with the --length parameter.
Splits a fasta files into several files each with a maximum of n sequences.
Basic usage:
predutils split_fasta --template 'chunk{index}.fasta' --size 100 in.fastaThe --template parameter accepts python .format style string formatting, and
is provided the variables fname (the input filename) and index (the chunk number starting at 1).
To pad the numbers with zeros for visual ordering in directories, use the something like --template '{fname}.{index:0>4}.fasta'.
Directories in the template will be created for you if they don't exist.
Takes a database and some sequences and uses the sequence checksums to decide what has already been computed.
Outputs the precomputed results if -o is set.
Writes fasta for the remaining sequences to be computed.
The analyses and software versions to check for in the database are specified as a tab separated file to analyses.
usage: predutils precomputed [-h] [-o OUTFILE] [-t TEMPLATE] [--mem MEM] db analyses infasta
positional arguments:
db Where the sqlite database is
analyses A 3 column tsv file, no header. 'analysis<tab>software_version<tab>database_version'. database_version should be empty string if None.
infasta The fasta file to parse as input. Cannot be stdin.
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the precomputed ldjson results to.
-t TEMPLATE, --template TEMPLATE
A template for the output filenames. Can use python `.format` style variable analysis. Directories will be created.
--mem MEM The amount of RAM in gibibytes to let SQLite use for cache.
The other end of predutils encode.
Takes the common line delimited format from analyses and separates them back
out into the original filenames.
predutils decode [-h] [-t TEMPLATE] [--mem MEM] db map
positional arguments:
db Where the sqlite database is
map Where to save the id mapping file.
optional arguments:
-h, --help show this help message and exit
-t TEMPLATE, --template TEMPLATE
What to name the output files.
--mem MEM The amount of RAM in gibibytes to let SQLite use for cache.
predutils decode \
--template 'decoded/{filename}.ldjson' \
results.db \
output_mapping.tsvWe use the template flag to indicate what the filename output should be, using python format
style replacement. Available values to --template are filename and filename_noext.
The latter is just filename without the last extension.
Take the common line delimited output from predutils r2js and recover a tabular version of the raw data.
Output filenames are controlled by the --template parameter, which uses python format style replacement.
Currently, analysis is the only value available to the template parameter.
Directories in the template will be created automatically.
predutils tables [-h] [-t TEMPLATE] [--mem MEM] db
positional arguments:
db Where to store the database
optional arguments:
-h, --help show this help message and exit
-t TEMPLATE, --template TEMPLATE
A template for the output filenames. Can use python `.format` style variable analysis. Directories will be created.
--mem MEM The amount of RAM in gibibytes to let SQLite use for cache.
predutils tables \
--template "my_sample-{analysis}.tsv" \
results.db
Take the common line-delimited json output from predutils r2js and get a GFF3 formatted
set of results for analyses with a positional component (e.g. signal peptides, transmembrane domains, alignment results).
predutils gff \
--outfile "my_sample.gff3" \
results.ldjson
By default, mmseqs and HMMER search results will be filtered by the built in significance thresholds.
To include all matches in the output (and possibly filter by your own criterion) supply the flag --keep-all.
Take a database of results entered by load_db and get a summary table
that includes all of the information commonly used for effector prediction, as well as
a scoring column to prioritise candidates.
predutils rank \
--outfile my_samples-ranked.tsv \
results.db
To change that Pfam or dbCAN domains that you consider to be predictive of effectors,
supply a text file with each pfam or dbcan entry on a new line (do not include pfam version number or .hmm in the ids) to the parameters --dbcan or --pfam.
You can also change the weights for how the score columns are calculated.
See predutils rank --help for a full list of parameters.
predutils regex [-h] [-o OUTFILE] [-l] [-k {kex2_cutsite,rxlr_like_motif,custom}] [-r REGEX] INFILE
positional arguments:
INFILE The input fasta file.
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the output to. Default: stdout
-l, --ldjson Write the output as ldjson rather than tsv.
-k {kex2_cutsite,rxlr_like_motif,custom}, --kind {kex2_cutsite,rxlr_like_motif,custom}
Which regular expressions to search for. If custom you must specify a regular expression to --regex. Default: custom.
-r REGEX, --regex REGEX
The regular expression to search for. Ignored if --kind is not custom.
predutils dump_db [-h] [-o OUTFILE] [--mem MEM] db
positional arguments:
db The database to dump results from.
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the output to. Default: stdout
--mem MEM The amount of RAM in gibibytes to let SQLite use for cache.
This script projects the protein GFF file from the results into genome coordinates based on the GFF used to extract the proteins. This is intended to support visualisation and selection of candidates in genome browsers like JBrowse or Apollo.
usage: predutils map_to_genome [-h] [-o OUTFILE] [--split {source,type}] [--no-filter-kex2] [--id ID_FIELD] genes annotations
positional arguments:
genes Gene GFF to use.
annotations Annotation GFF to use (i.e. the GFF3 output of predector)
options:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the output to. If using the --split parameter this
becomes the prefix. Default: stdout
--split {source,type}
Output distinct GFFs for each analysis or type of feature.
--no-filter-kex2 Output kex2 cutsites even if there is no signal peptide
--id ID_FIELD What GFF attribute field corresponds to your protein feature seqids?
Default uses the Parent field. Because some fields (like Parent)
can have multiple values, we'll raise an error if there is more
than 1 unique value. Any CDSs missing the specified
field (e.g. ID) will be skipped.
Pay attention to the --id parameter. This determines how the protein ID from the predector output will be matched to the genome GFF3.
By default it looks at the Parent attribute, as this is the default name from many GFF protein extraction tools.
But any attribute in the 9th column of the GFF can be used, e.g. to use the CDS feature ID, use --id ID.
This script will only consider records with type CDS, and matches names exactly (no partial matches).
--split provides a convenience function to split the GFFs into multiple GFFs based on type or analysis.
This is to facilitate loading the different features/analyses as separate tracks in the genome browser.
This script plots numeric scores from the predector ranked table at the CDS coordinates from the GFF used to extract the proteins. This is intended to support visualisation and selection of candidates from genome browsers like JBrowse or Apollo.
usage: predutils scores_to_genome [-h] [-o OUTFILE] [--target TARGET [TARGET ...]] \
[--id ID_FIELD] [--reducer {min,max,mean,median}] genes annotations
positional arguments:
genes Gene GFF to use.
annotations ranking table to use (i.e. the -ranked.tsv output of predector)
options:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the output to. Default: stdout
--target TARGET [TARGET ...]
Only output these columns into the bedgraph file.
By default writes a multi bedgraph with all columns.
--id ID_FIELD What GFF attribute field corresponds to your protein feature seqids?
Default uses the Parent field. Because some fields (like Parent)
can have multiple values, we'll raise an error if there is more
than 1 unique value. Any CDSs missing the specified
--reducer {min,max,mean,median}
How should we combine scores if two features overlap?
As with the map_to_genome command, you should ensure that the protein names (in the first column of the -ranked.tsv table), match with the attribute in the 9th column of the GFF3 file.
Because multiple CDS features can overlap (e.g. if there are alternative splicing patterns), we must combine those scores somehow so that a single value is associated with the genomic region.
This can be specified with the --reducer parameter which takes the maximum value by default (e.g. the highest effectorP score). But if instead you wanted the average use --reducer mean.
By default this script outputs a multi-bedgraph file with all numeric columns.
But if you only wish to output a subset of those columns, you can provide them as a space separated list to the --target parameter.
Note that an error will be raised if you try to access a non-numeric column.
If the --target parameter comes directly before the two positional arguments, you need to indicate where the space separated list stops with a --.
e.g.
predutils scores_go_genome --id ID --target effector_score apoplastp effectorp2 -- mygenes.gff3 mygenes-ranked.tsv
To split the output into single column bedgraphs you can use the following to extract one bedgraph per value column.
#!/usr/bin/env bash
set -euo pipefail
# OPTIONAL
# BGTOBW_EXE="bedGraphToBigWig" # This comes from the UCSC toolkit, or here: http://hgdownload.soe.ucsc.edu/admin/exe/
INFILE=$1
FAI=$2
PREFIX=$3
COLUMNS=( $(head -n 1 "${INFILE}" | cut -f4-) )
for i in "${!COLUMNS[@]}"
do
idx=$(( ${i} + 4 ))
col="${COLUMNS[${i}]}"
cut -f1,2,3,${idx} "${INFILE}" | tail -n+2 | awk '$4 != "NA"' > "${PREFIX}${col}.bedgraph"
# OPTIONAL
# ${BGTOBW_EXE} "${PREFIX}${col}.bedgraph" "${FAI}" "${PREFIX}${col}.bw"
done
This is useful for converting the tracks into bigwig files (which only support a single value), suitable for use with Jbrowse or Apollo.
This program creates a GFF3 file in protein coordinates based on an InterProScan (5+) xml output file. While interproscan can output a GFF3 file of it's own, it doesn't include the domain names or InterPro/GO term annotations etc. This program gives you a much richer output.
usage: predutils ipr_to_gff [-h] [-o OUTFILE] [--namespace NAMESPACE] xml
positional arguments:
xml Interproscan XML file.
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the GFF output to. Default: stdout
This program is like map_to_genome but it doesn't do anything that is specialised to handling Predector output.
This should be able to map any protein-coordinate GFF onto a genome, but it was developed to transfer interproscan results output by the ipr_to_gff tool.
usage: predutils prot_to_genome [-h] [-o OUTFILE] [--split] [--id ID_FIELD] genes annotations
positional arguments:
genes Gene GFF to use.
annotations Annotation GFF in protein coordinates
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Where to write the output to. If using the --split parameter this becomes the prefix. Default: stdout
--split Output separate GFFs for each "source" in the GFF.
--id ID_FIELD What GFF attribute field corresponds to your protein feature seqids? Default uses the Parent field. Because some fields (like Parent) can have multiple values, we'll raise an error if there is more than 1 unique value. Any CDSs missing the specified
field (e.g. ID) will be skipped.
Note that we don't sort the output.
If you're using tools downstream that require a sorted file, e.g. TABIX, you can mix and match from the following. This just happens to create suitable inputs for the GFF3+TABIX input type for Jbrowse.
FNAME="mapped.gff3"
(grep "^#" "${FNAME}"; grep -v "^#" "${FNAME}" | sort -k1,1 -k4,4n) | bgzip > "${FNAME}.gz"
bgzip --keep --reindex "${FNAME}.gz"
tabix -p gff "${FNAME}.gz"