CLUMP: CLUstering by Mutation Position

CLUMP (CLustering by Mutation Postion) is an unsupervised clustering of amino acid residue positions where variants occur, without any prior knowledge of their functional importance.

Citation

If you use CLUMP in your research, please cite:

• Turner TN, Douville C, Kim D, Stenson PD, Cooper DN, Chakravarti A, Karchin R. Proteins linked to autosomal dominant and autosomal recessive disorders harbor characteristic rare missense mutation distribution patterns. Hum Mol Genet. 2015 Nov 1;24(21):5995-6002. doi: 10.1093/hmg/ddv309. Epub 2015 Aug 5. PubMed PMID: 26246501.

http://www.ncbi.nlm.nih.gov/pubmed/26246501

CLUMP requires:

• R/3.0.0
• python/2.7.3
• numpy/1.8.0
• scipy/0.12.0
• the R library package 'fpc'

USAGE:

python combined.clump.py -f inputfile -p protein_lengths 

OPTIONS: -a allele_frequency(Default=1) Remove Mutations Greater than an allele frequency threshold. Default includes every variant.

-c inputfile_controls Input file for the controls. A set of controls is required to get a statistical significance with the clump score

-z number_of_permutations The number of permutations you want to perform for significance testing.

-m minimum_number_of_mutations(Default=5) The minimum number of mutations in a gene in order to perform CLUMP.

-n normalize(Default=No) Do you want to normalize based on protein length. Normalization was not used in the published results. -t Output Column Titles

Permutation CLUMP works best when the input file only contains one gene.

The input file and the control input file are in the same format:

• Column 1: GENE_HUGO_ID Required
• Column 2: PROTEIN_ID Required: Must match Protein Id's provided in the protein length file
• Column 3: STUDY_NAME Used as a column placeholder in CLUMP scripts (Can use NA if unavailable)
• Column 4: AMINO_ACID_POSITION Required: Amino Acid position of the variant
• Column 5: CHROM Used as a column placeholder in CLUMP scripts (Can use NA if unavailable)
• Column 6: POSITION Used as a column placeholder in CLUMP scripts (Can use NA if unavailable)
• Column 7: REF Allele Used as a column placeholder in CLUMP scripts (Can use NA if unavailable)
• Column 8: ALT Allele Used as a column placeholder in CLUMP scripts (Can use NA if unavailable)
• Column 9: ALLELE_FREQUENCY Required column. Need to add a value between 0 and 1. If you do not know it can just be 0 unless you are actually using the allele frequency feature of CLUMP.
• Column 10:DOMAIN Optional column (can be NA)

I have provided a set of neutral variation from 1000 Genomes in the format used by CLUMP.

The protein length file is in the format: PROTEIN_ID LENGTH

I have provided a protein length file: protein.2.length.txt

EXAMPLE 1: PERMUTATION TESTING OF A SINGLE GENE USING CLUMP

python combined.clump.py -f example.inputfile.txt -p protein.2.length.txt -z 10000 -c example.controlfile.txt -t 

OUTPUT:

Now clustering: ACADS
Now clumping
GENE    PROTEIN_ID      STUDY_NAME      CLUMP_SCORE_DIFFERENCE(Controls-Cases)  P-value(Probability Cases have a CLUMP score greater than the Controls) P-value(Probability Cases have a CLUMP score less than Controls)        CONTROL_Raw_Clump_Score
ACADS   NP_000008.1     1000Genomes     0.0     0.497   0.503   1.51153082805   1.51153082805

EXAMPLE 2: RAW CLUMP SCORE

python combined.clump.py -f example.inputfile.txt -p protein.2.length.txt

OUTPUT:

Now clustering: ACADS
Now clumping
GENE    PROTEIN_ID      STUDY_NAME      Raw_Clump_Score
ACADS   NP_000008.1     1000Genomes     1.51153082805

EXAMPLE 3: HIGH THROUGHPUT PERMUTATION CLUMP ANALYSIS

Permutation testing for clump analysis runs much faster when the input files are split among individual genes. I have created an example with a shell script to demonstrate the easiest way to perform clump permutation testing on a large set of genes.

bash runlargescaleclump.sh example3.inputfile.txt example3.controlfile.txt 100

================================================== Running high throughput CLUMP using snakemake:

To run, generate a CLUMP style mutation file as above:

• Generate the list of proteins to test:

to plot all you could use the command below or you could just make your own list

cut -f2 <mutation file> | sort | uniq > proteins_to_plot.txt

• Get the github repository

git clone https://github.com/tycheleturner/CLUMP.git
cd CLUMP/high_throughput/

• Fill out the config file with your information depending on the test you want to run

Raw CLUMP

raw.score.config.json 
raw.score.snake

Case-Control CLUMP

case.control.config.json 
case.control.snake

• Run the snakemake either locally or by submitting to cluster

Raw CLUMP

Local

snakemake -s raw.score.snake

Submit to cluster

snakemake --cluster 'qsub {params.sge_opts}' -j 100 -w 30 -k -s raw.score.snake

Case-Control CLUMP

Local

snakemake -s case.control.snake

Submit to cluster

snakemake --cluster 'qsub {params.sge_opts}' -j 100 -w 30 -k -s case.control.snake