the bare minimun requirement is nextflow DSL2, simply go to https://www.nextflow.io/ and follow the instruction for the installation.
It is advisable to couple a contanairization engine with nextflow for reproducibility porpuses. The ones supported by this pipeline are Docker and Singularity.
Check their respective installation guides.
Simply instal git and laungh the following command:
git clone https://github.com/alessiovignoli/TANGO2/
or downloaded the whole directory manually, every file present in this repo with the exception of the results/ subdir are necessary.
Once you have he TANGO2 directory downloaded move inside it with:
cd TANGO2
in here there is the main nextflow pipeline called align_and_evaluate.nf as well as the nextflow.config file. For this reason this is in all intents and porpuses where the pipeline should be launched from. To run the example pipeline just launch the following command :
nextflow run align_and_evaluate.nf --IN "data/*" -profile standard,docker
or using singularity:
nextflow run align_and_evaluate.nf --IN "data/*" -profile standard,singularity
or with no contenairization technology, not advisable since t_coffe has to be already installed in the system and callable as command, read Pipeline description section for more details:
nextflow run align_and_evaluate.nf --IN "data/*" -profile standard
This is how the files present in the results/ directory have been created in the first plsce. The above commands will not move the output file to the results/ subdirectory since it sees that they are alredy there (In case they are not it will).
To print an help message explaining the available flags use the following command:
nextflow run align_and_evaluate.nf --help
Make sure to edit the nextflow.config file with your necessity, under the cluster profile before running the pipeline, mainly process.executor and process.queue since they vary across cluster. For more info take a look at nextflow documentation page https://www.nextflow.io/docs/latest/config.html# .
The command syntax is the following, keep in mind that docker profile name is interchangable with singularity:
nextflow run align_and_evaluate.nf --IN "data/*" -profile cluster,docker
as said for running on cluster, the nextflow.config file has to be changed to run on cloud environment. The lines of code present in the cloud scope are indicative and should be rewritten and modified by the user, refer to the manual page indicated above.
Having said that the command is the following:
nextflow run align_and_evaluate.nf --IN "data/*" -profile cloud,singularity
This pipeline expects one or more fasta files containg the sequences that have to be alligned. A Multiple Sequence Allignment (MSA) is produced for each fasta input; so be aware to put in the same file all the sequences you want to allign toghether. The allignment is computed using the following command (t_coffee version_13.45.47.aba98c5):
t_coffee -in input.fasta-mode=regular -outfile input.aln -maxnseq=150 -maxlen=10000 -case=upper -seqnos=off -outorder=input -run_name=result -multi_core=6
To see the exact nextflow adapted command just open the align_and_evaluate.nf file.
Once the MSAs have been creted the pipeline computes for each of them the TCS (score_ascii), average percentage identity and average coverage scores, using the following commands:
t_coffee -other_pg seq_reformat -in input.aln -action +evaluate blosum62mt -output score_ascii > input.score_ascii
t_coffee -other_pg aln_compare -al1 input.aln -al2 input.aln -compare_mode sp
t_coffee -other_pg seq_reformat -in input.aln -output cov
To standard output is printed the result of the last comand reporting, filename, number of sequences, average % identity, 100% (similarity with itself) and number of columns in MSA. This is an example:
*****************************************************
seq1 seq2 Sim [ALL] Tot
TANGO2_Q6ICL3-2 5 32.6 100.0 [100.0] [ 4856]
The field described above are on the last row. For more detail on the aln_compare command take a look at https://tcoffee.readthedocs.io/en/latest/tcoffee_main_documentation.html#estimating-the-diversity-in-your-alignment . for each MSA on top of te above lines there are other five more stating the coverage values as depicted below:
Coverage
TOT 88.60
AVG 88.60
VAR 49.38
STD 7.03
The names are self explanatory.
Standard out inforamtion are also saved to --OUT_DIR option location in a file called results.scores.
The other type of output is the allignment reporing the TCS score in htnml format. This files are also present in the --OUT_DIR specified derectory (default results/) and an example is present in results/ subfolder.