A pipeline for functional annotation of proteins from non-model organisms implemented in Nextflow engine.
The pipeline uses a set of well characterised software to assign functional information to the proteins of interests, i.e. domains, GO terms annotation, putative name and some other features.
The software used in this pipeline is free software for academic users. For the software from the Center for Biological Sequence (CBS), i.e. signalP, a suitable license agreement should be obtained.
The pipeline is build on Nextflow as a woking engine, so it need to be installed first
wget -qO- get.nextflow.io | bash
The detailed procedure is described in the Nextflow documentation
Predictions of the KEGG orthology groups (KO) should be obtained outside of the pipeline, i.e. via KAAS server.
Note: for the downstream processing of the KO file it is very important to store information about species used for predictions. Species are encoded in three lellters abbreviations, and the list can be copied from the 'Selected organisms' field in the kaas_main form.
The pipeline require as an input the configuration file with specified parameters, such as path to the input files, specie name, KEGG specie abbreviations used to obtain KO groups, and some more.
The example of configuration file is included into this repository with name main_configuration.config
The annotation itself, when various software is excuted and the results are stored in a internal database.
Result files, including main annotation file in gff format and annotation report in pdf format, are generated at the end of the pipeline.
The annotation step can be launched by using the following command:
./nextflow run -bg pipeline.nf --config configuration_file.config &> logfile
This Nextflow build-in parameter allow to re-execute processes that has changed or crashed during the pipeline run. Only processes that not finished will be executed. More information can be found in the Nextflow documentation
- blast: it perfoms BLAST search against defined database from input files
- diamond: the same as above but using DIAMOND (
diamond = "true"in config file ) - ipscn: it performs InterProScan analyses from input files
- signalP: it performs signalP analyses from input files
- targetP: it performs targetP analyses from input files
- blast_annotator: it retrieves GO terms from BLAST hits
- blastDef: it suggest a definition to input entries based on BLAST hits
- cdSearchHit: it performs a NCBI CDSearch Hit query
- cdSearchFeat: it performs a NCBI CDSearch Feature query
- initDB: it initialitzes the Database used for gathering data from different analyses and later generating the reports
- definition_upload: it uploads definitions derived from BLAST into the DB
- signalP_upload: it uploads signalP analyses into the DB
- targetP_upload: it uploads targetP analyses into the DB
- CDSearch_hit_upload: : it uploads NCBI CDSearch Hit analyses into the DB
- CDSearch_feat_upload: it uploads NCBI CDSearch Feature analyses into the DB
- blast_annotator_upload: it uploads GO terms from BLAST hits into the DB
- kegg_upload: it retrieves and uploads KEGG data into the DB
- generateResultFiles: it generates report files
- generateGFF3File: if GFF provided as input, it provides a modified GFF with additional information
### Formatted databases
- For BLAST:
blastDbPath = "/path/to/db"It looks for formatted database files (normally named db.p* for protein type based ones), otherwise it will try to format FASTA file with that name - For DIAMOND:
blastDbPath = "/path/to/db"It looks for a single formatted database file (normally named db.dmnd), otherwise it will try to format the FASTA file with that name (gzip compressed files accepted)
Retrieval of GO terms from BLAST results can be performed either from BLAST2GO results or from other methods as far as a BLAST2GO-compatible output format is provided.
As a example, in our case we are using a web API providing this information from UniProt GOA database imported into a MySQL and Neo4j.
We recommend installing either Docker of Singularity (the latter preferred).
The software used all along this pipeline is encapsulated in, at least, 4 containers:
As written down in nextflow.config file, whenever possible, we try to provide necessary images in a public repository (e.g. Docker hub or quay.io from Biocontainers). However, for some software that includes privative components, we suggest to build the container image by yourself.
- SignalP and TargetP (user needs to build this)
- Interproscan and 3rd party tools (user needs to build this)
docker build -t fa-nf .
sudo singularity build fa-nf.sif docker-daemon://fa-nf:latest
We offer a convenience wrapper script for running the pipeline in MySQL mode either in SGE-compatible clusters or in local.
nohup perl run_pipeline_mysql.pl -conf ./main_configuration.config &> log.mysql &
This is convenient for checking results database once analyses are finished. NO further analyses are run.
nohup perl run_pipeline_mysql.pl -mysqlonly -conf ./main_configuration.config &> log.mysql.only &
for further options or details, run:
perl run_pipeline_mysql.pl -h