PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.


INTRODUCTION
Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. The PredictSNP is a consensus classifier combining six best performing prediction methods to provide more accurate and robust alternative to the predictions delivered by individual integrated tools. The predictions from the computational tools are supplemented by experimental annotations from two databases. The web server is freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. 6

INPUT PAGE
The process of submitting the job to PredictSNP server consists of following steps: 1. INPUT section -paste an amino acid sequence of a query protein in FASTA format and press "Load" button. NOTE: the input sequence will be translated into interactive sequence into the section MUTATIONS.

MUTATIONS section
a. select mutations using interactive sequence -select position and then define required mutations, e.g., selection of wild-type Leucine at position 3, and mutant variants Cystein, Histidine and Asparagine. NOTE: To mutate the selected position to all variants, use option "ALL". 7 b. submit a list of mutations in the text format -press "Manual input" button. The required format of the text consists of one character abbreviation of wild-type residue, residue position, and one character abbreviation of mutant residue, e.g., L3C, L3H and L3N.

RESULTS section -once the job is finished, the prediction of each tool and PredictSNP is
provided for all selected mutations. The predicted effect is color-coded: neutral mutations are in green, while deleterious mutations in red. The "-" symbol indicates that the respective mutation was not evaluated by a given tool. The normalized confidence of the tools is represented as a percentage. The symbol in the first column indicates that a relevant annotation was found in the database for a given mutation. By clicking on this symbol, users can show/hide the annotation. The annotations provide description of experimentally observed effects of a given mutation as well as the links to the original database records.
NOTE: To show/hide all annotations at once use "Expand all annotations" or "Collapse all annotations" button.

RAW RESULTS
The content of the zip package with results of a calculated job is described in the following

RIGHT BANNER
The right banner provides following information and services: 1. JOB CONTROL section -pressing "Submit job" button redirects the user to the INPUT page (see chapter 2) enabling submission of a new job. "Find job" button can be used to access an output page of a job with provided Job ID.
2. REFERENCE section -provides access to the article describing the methodology behind the PredictSNP consensus classifier.
3. CONTACT section -provides means to contact the developers.

EXAMPLE
The PredictSNP server provides embedded example, which can be raised by pressing "Load example" button in the INPUT section at the input page. This loads the sequence of the alpha subunit of Human Hemoglobin and pre-selects 16 mutations at 13 positions.
Once the evaluation of the mutations is finished, users can analyze the results in the result table. 15 The results of such analysis provided by PredictSNP are summarized in following Prerequisites: Python (successfully tested on python2.6 and python2.7)

Installation:
1) Download and install prerequisites described above.

Content of the archive:
predictsnp.py -Executable script.
testInput.txt -Input file for testing the PredictSNP consensus.

$ python predictsnp.py [options]
Options: -h, --help show help message and exit -i, --input input file path; it contains confidence scores of integrated tools of PredictSNP (required) -o, --output output file path (required)

Running PredictSNP standalone version with the test case:
Test case analyzes 16 mutations at 13 positions of the alpha subunit of Human Hemoglobin: $ python predictsnp.py -i testInput.txt -o testOutput.txt