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schloro - Prediction of protein sub-chloroplastic localization

Publication

Savojardo C., Martelli P.L., Fariselli P., Casadio R. SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments, Bioinformatics (2017) 33(3): 347-353.

The SChloro Docker image

Image availbale on DockerHub https://hub.docker.com/r/bolognabiocomp/schloro

Usage of the image

The first step to run SChloro Docker container is the pull the container image. To do so, run:

$ docker pull bolognabiocomp/schloro

Now the SChloro Docker image is installed in your local Docker environment and ready to be used. To show SChloro help page run:

$ docker run bolognabiocomp/schloro -h

usage: schloro.py [-h] {multi-fasta,pssm} ...

SChloro: Predictor of sub-chloroplastic localization

optional arguments:
  -h, --help          show this help message and exit

subcommands:
  valid subcommands

  {multi-fasta,pssm}  additional help
    multi-fasta       Multi-FASTA input module
    pssm              PSSM input module (one sequence at a time)

The program can be run in two different modes:

  • multi-fasta mode, accepting a FASTA file in input containing one or more sequences. In this mode, SChloro internally computes a sequence profile using PSIBLAST for each sequence in the input file and then predicts sub-chloroplastic localization.
  • pssm mode, accepting a FASTA file containing a single protein sequence and a pre-computed PSSM file obtained by PSI-BLAST (using -out_ascii_pssm option). In this case, the computation of the sequence profile i skipped. The provided PSSM must be generated from the input sequence (an exception is raised otherwise). Only a single protein sequence can be processed in this mode.

Multi-fasta mode

The show the SChloro help in multi-fasta mode run:

$ docker run bolognabiocomp/schloro multi-fasta -h

usage: schloro.py multi-fasta [-h] -f FASTA -d DBFILE -o OUTF

SChloro: Multi-FASTA input module.

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        The input multi-FASTA file name
  -d DBFILE, --dbfile DBFILE
                        The PSIBLAST DB file
  -o OUTF, --outf OUTF  The output GFF3 file

Three arguments are accepted:

  • The full path of the input FASTA file containing protein sequences to be predicted;
  • The output GFF3 file where predictions will be stored;
  • The database used to generate sequence profiles using PSI-BLAST.

Let's now try a concrete example. First of all, let's download an example sequence from UniProtKB, e.g. the Preprotein translocase subunit SCY2, chloroplastic from Arabidopsis thaliana (accession F4IQV7):

$ wget https://www.uniprot.org/uniprot/F4IQV7.fasta

Secondly, we need to obtain a protein sequence database for sequence profile generation. We can use either a large one such as the UniRef90 sequence database or a smaller one like the UniProtKB/SwissProt. In the former case, the computation of the sequence profile can be very slow. For simplicity, let's download and unzip the UniProtKB/SwissProt database:

$ wget ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
$ unzip uniprot_sprot.fasta.gz

Now, we are ready to run SChloro on our input protein. Run:

$ docker run -v $(pwd):/data/ -v $(pwd):/seqdb/ bolognabiocomp/schloro -f F4IQV7.fasta -o F4IQV7.gff -d uniprot_sprot.fasta

In the example above, we are mapping the current program working directory ($(pwd)) to the /data/ folder inside the container. This will allow the container to see the external FASTA file F4IQV7.fasta and the database file uniprot_sprot.fasta.

After running SChloro, a database index is generated (using makeblastdb) for the input database, if not present.

The file F4IQV7.gff now contains the SChloro prediction in GFF3 format:

$ cat F4IQV7.gff

##gff-version 3
sp|F4IQV7|SCY2_ARATH	SChloro	Chloroplast thylakoid membrane	1	575	0.629293	.	.	Ontology_term:GO:0009535;evidence=ECO:0000256

Columns are as follows:

  • Column 1: the protein ID/accession as reported in the FASTA input file;
  • Column 2: the name of tool performing the annotation (i.e. SChloro)
  • Column 3: the annotated feature along the sequence. Here, the complete input sequence is annotated with the corresponding subcellular localization.
  • Column 4: start position of the feature (always 1);
  • Column 5: end position of the feature (always the sequence length);
  • Column 6: feature annotation score as assigned by SChloro;
  • Columns 7,8: always empty, reported for compliance with GFF3 format
  • Column 9: Description field. Gene Ontology Cellular Component terms and evidence codes are reported.

PSSM mode

The show the SChloro help in pssm mode run:

$ docker run bolognabiocomp/schloro pssm -h

usage: schloro.py pssm [-h] -f FASTA -p PSIBLAST_PSSM -o OUTF

SChloro: PSSM input module.

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        The input FASTA file name (one sequence)
  -p PSIBLAST_PSSM, --pssm PSIBLAST_PSSM
                        The PSIBLAST PSSM file
  -o OUTF, --outf OUTF  The output GFF3 file

Three arguments are accepted:

  • The full path of the input FASTA file containing protein sequences to be predicted;
  • The output GFF3 file where predictions will be stored;
  • A PSSM file previously generated with PSI-BLAST.

With the protein in the example above (F4IQV7) and the sequence database (uniprot_sprot.fasta), we can create a PSSM file using PSI-BLAST:

$ psiblast -query F4IQV7.fasta -db uniprot_sprot.fasta -out_ascii_pssm F4IQV7.pssm -evalue 0.001 -num_iterations 3

The generated PSSM can be now used as input to Schloro in pssm mode:

$ docker run -v $(pwd):/data/ -f F4IQV7.fasta -p F4IQV7.pssm -o F4IQV7.gff

In pssm mode, since no sequence database is used to generate the profile, we can skip the mounting of the /seqdb/ folder in the container.

The file F4IQV7.gff now contains the SChloro prediction in GFF3 format as detailed above.

Install and use SChloro from source

Source code available on GitHub at https://github.com/BolognaBiocomp/schloro.

Installation and configuration

SChloro is designed to run on Unix/Linux platforms. The software was written using the Python programming language and it was tested under the Python version 3.

To obtain SChloro, clone the repository from GitHub:

$ git clone https://github.com/BolognaBiocomp/schloro

This will produce a directory schloro. Before running schloro you need to set and export a variable named SCHLORO_ROOT to point to the schloro installation dir:

$ export SCHLORO_ROOT='/path/to/schloro'

Before running the program, you need to install SChloro dependencies. We suggest to use Conda (we suggest Miniconda3) create a Python virtual environment and activate it.

To create a conda env for schloro:

$ conda create -n schloro

To activate the environment:

$ conda activate schloro

The following Python libraries/tools are required:

  • biopython
  • blast
  • libsvm

To install all requirements run the followgin commands:

$ conda install blast -c bioconda
$ conda install libsvm -c conda-forge
$ conda install biopython

Now you are able to use schloro (see next Section). Remember to keep the environment active. If you wish, you can copy the “schloro.py” script to a directory in the users' PATH.

Usage

To show SChloro help page run:

$ ./schloro.py -h

usage: schloro.py [-h] {multi-fasta,pssm} ...

SChloro: Predictor of sub-chloroplastic localization

optional arguments:
  -h, --help          show this help message and exit

subcommands:
  valid subcommands

  {multi-fasta,pssm}  additional help
    multi-fasta       Multi-FASTA input module
    pssm              PSSM input module (one sequence at a time)

The program can be run in two different modes:

  • multi-fasta mode, accepting a FASTA file in input containing one or more sequences. In this mode, SChloro internally computes a sequence profile using PSIBLAST for each sequence in the input file and then predicts sub-chloroplastic localization.
  • pssm mode, accepting a FASTA file containing a single protein sequence and a pre-computed PSSM file obtained by PSI-BLAST (using -out_ascii_pssm option). In this case, the computation of the sequence profile i skipped. The provided PSSM must be generated from the input sequence (an exception is raised otherwise). Only a single protein sequence can be processed in this mode.

Multi-fasta mode

The show the SChloro help in multi-fasta mode run:

$ schloro.py multi-fasta -h

usage: schloro.py multi-fasta [-h] -f FASTA -d DBFILE -o OUTF

SChloro: Multi-FASTA input module.

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        The input multi-FASTA file name
  -d DBFILE, --dbfile DBFILE
                        The PSIBLAST DB file
  -o OUTF, --outf OUTF  The output GFF3 file

Three arguments are accepted:

  • The full path of the input FASTA file containing protein sequences to be predicted;
  • The output GFF3 file where predictions will be stored;
  • The database used to generate sequence profiles using PSI-BLAST.

Let's now try a concrete example. First of all, let's download an example sequence from UniProtKB, e.g. the Preprotein translocase subunit SCY2, chloroplastic from Arabidopsis thaliana (accession F4IQV7):

$ wget https://www.uniprot.org/uniprot/F4IQV7.fasta

Secondly, we need to obtain a protein sequence database for sequence profile generation. We can use either a large one such as the UniRef90 sequence database or a smaller one like the UniProtKB/SwissProt. In the former case, the computation of the sequence profile can be very slow. For simplicity, let's download and unzip the UniProtKB/SwissProt database:

$ wget ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
$ unzip uniprot_sprot.fasta.gz

Now, we are ready to run SChloro on our input protein. Run:

$ ./schloro.py -f F4IQV7.fasta -o F4IQV7.gff -d uniprot_sprot.fasta

After running SChloro, a database index is generated (using makeblastdb) for the input database, if not present.

The file F4IQV7.gff now contains the SChloro prediction in GFF3 format as detailed above:

$ cat F4IQV7.gff

##gff-version 3
sp|F4IQV7|SCY2_ARATH	SChloro	Chloroplast thylakoid membrane	1	575	0.629293	.	.	Ontology_term:GO:0009535;evidence=ECO:0000256

PSSM mode

The show the SChloro help in pssm mode run:

$ ./schloro.py pssm -h

usage: schloro.py pssm [-h] -f FASTA -p PSIBLAST_PSSM -o OUTF

SChloro: PSSM input module.

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        The input FASTA file name (one sequence)
  -p PSIBLAST_PSSM, --pssm PSIBLAST_PSSM
                        The PSIBLAST PSSM file
  -o OUTF, --outf OUTF  The output GFF3 file

Three arguments are accepted:

  • The full path of the input FASTA file containing protein sequences to be predicted;
  • The output GFF3 file where predictions will be stored;
  • A PSSM file previously generated with PSI-BLAST.

With the protein in the example above (F4IQV7) and the sequence database (uniprot_sprot.fasta), we can create a PSSM file using PSI-BLAST:

$ psiblast -query F4IQV7.fasta -db uniprot_sprot.fasta -out_ascii_pssm F4IQV7.pssm -evalue 0.001 -num_iterations 3

The generated PSSM can be now used as input to Schloro in pssm mode:

$ ./schloro.py -f F4IQV7.fasta -p F4IQV7.pssm -o F4IQV7.gff

The file F4IQV7.gff now contains the SChloro prediction in GFF3 format as detailed above.

Please, reports bugs to: [email protected]