biofold/snps-and-go repository overview

⁠SNPs&GO

⁠Predicting disease associated variations using GO terms

https://snps.biofold.org/snps-and-go⁠
For information and bugs contact:
emidio.capriotti (at) gmail.com

⁠Emidio Capriotti, 2012.

⁠SNPs&GO scripts are released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

The container includes the following packages and databases licensed by other researchers:

• PANTHER 6.1 Library
• blast2.2.26
• GO-TermFinder
• hmmer-2.3.2
• dsspcmbi
• UniRef90 (Nov 2009)

⁠Example 1:

    Run 3D predictions inside the docker image using the pre-calculated GO annotation: associated to SwissProt ID (TNR5_HUMAN)

    > python /home/bass/SNPs-and-GO/run-snps-3d.py /home/bass/SNPs-and-GO/Test/1cdf.pdb A /home/bass/SNPs-and-GO/Test/1cdf.mut --swiss-go TNR5_HUMAN

⁠Example 2:

    Run Sequence based prediction inside the docker image providing manually the GOTerms:

    > python /home/bass/SNPs-and-GO/run-snps.py /home/bass/SNPs-and-GO/Test/fa5_human.fasta /home/bass/SNPs-and-GO/Test/fa5_human.mut -g GO:0005507,GO:0005576,GO:0005886,GO:0007155,GO:0007596,GO:0016491,GO:0031093,GO:0046872,GO:0055114 --parents

⁠Example 3

    Run the command 1 outside the docker image mounting the personal home directory inside the docker image

   > docker run -v $HOME:/home/$USER   --rm -it  biofold/snps-and-go:full  python /home/bass/SNPs-and-GO/run-snps-3d.py /home/$USER/program/SNPs-and-GO/Test/1cdf.pdb A /home/$USER/program/SNPs-and-GO/Test/1cdf.mut --swiss-go TNR5_HUMAN -b

⁠Please cite:

• Capriotti E, Martelli PL, Fariselli P, Casadio R. (2017). Blind prediction of deleterious amino acid variations with SNPs&GO. Human Mutation. 38: 1064-1071. PMID:28102005.
• Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. 14 Suppl 3:S6. PMID:23819482.
• Capriotti E, Altman RB. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. 12 (Sup.4) S3. PMID:21992054.
• Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation. 30: 1237-1244. PMID:19514061.

⁠Disclaimer:

Maintainers have no responsibility for any damage resulting from the use of this tool.