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资源说明：GWAS and biological data analysis tool
Encore
========
#### A computational tool for integrative analysis of GWAS and other biological data ####
### Description ###
Encore is a free, open-source command-line tool for analysis of GWAS (SNP) and
other types of biological data. Several modes are available for various types
of analysis, including:
* Regression of SNPs and quantitative data in epistasis interaction networks
(Regression [GAIN](http://insilico.utulsa.edu/gain)/reGAIN)
* Eigenvector centrality ranking of top SNPs/quantitative attributes
([SNPrank](http://insilico.utulsa.edu/snprank))
* Machine learning feature selection algorithms useful for filtering initially
large data sets to the top few thousand attributes for subsequent analysis
([Evaporative Cooling](http://insilico.utulsa.edu/evaporative-cooling)/EC)
* Data formats and encoding of SNP and quantitative data types
(PLINK)
* Several additional options ported from the PLINK library
Many libraries are used to provide various functionality. The most
prominent is PLINK, a ubiquitous third-party GWAS analysis tool. We have
modified the latest stable source release of PLINK into a library used to handle
GWAS formats, filter data, and provide statistical/association tests. See our
PLINK project page for more details.
Evaporative Cooling (EC) is another key library, and provides feature selection
of SNPs and quantitative data, using ReliefF and Random Forest/Jungle for
interactions and main effects, respectively. EC is also available as a
standalone [tool](http://insilico.utulsa.edu/evaporative-cooling).
Encore is being developed by the McKinney Lab In Silico Research Group at the
Tandy School of Computer Science of the
[University of Tulsa](http://www.utulsa.edu). Our research is sponsored by the
NIH and William K. Warren foundation. For more details, visit our research
[website](http://insilico.utulsa.edu).
If you downloaded a binary release, skip down to Usage for examples of running
Encore.
### Dependencies ###
* EC library, available as a source release on the EC project page, and its
dependencies:
* [Random Jungle](http://github.com/insilico/randomjungle)
* gfortran, sometimes installed alongside compiler tools
* GNU Scientific library (libgsl)
* libxml2
* [Boost](http://www.boost.org) system, filesystem, and program-options libraries
* The libz/zlib compression library is required, but this is installed by default
on most Unix systems. In MinGW libz is installed via mingw-get.
* [Armadillo](http://arma.sourceforge.net), LAPACK and BLAS for the optimized
linear algebra code
* OpenMP is required to take advantage of the parallelized regression GAIN
analysis code. This is another library typically installed alongside the
compiler toolchain.
### Compilation Environment and Instructions ###
To compile this code, a GNU toolchain and suitable environment are required.
GNU g++ has been used to successfully compile the code.
We have successfully built and run Encore on:
* Linux (64-bit Ubuntu) (gcc-4.6)
* Mac (10.6 - 10.7) (gcc-4.2.1)
* Windows 7 (32-bit) using the [MinGW](http://www.mingw.org) compiler system
(gcc-4.6)
To build Encore, first run the bootstrap script
./bootstrap.sh
This calls autoreconf and generates the configure script. From this point, a
standard
./configure && make && sudo make install
will generate the `Makefile`, compile and link the code, and copy the objects to
the installation directory (default of `/usr/local`). As is convention, headers
are installed in `$PREFIX/include`, binary in `$PREFIX/bin`, and libraries in
`$PREFIX/lib`.
The resulting binary encore/encore.exe will run as a command-line tool.
### Usage ###
To see the help screen,
./encore -h
Encore - a tool for analysis of GWAS and other biological data.
Usage: encore -i snpdata.ped [mode] -o output-prefix:
-i [ --input-file ] arg Input GWAS file (.bed or .ped) or GAIN/reGAIN
matrix (tab- or comma-separated)
-o [ --output-prefix ] arg Prefix to use for all output files
-n [ --numeric ] arg Numeric file for quantitative data (uses PLINK
covariate file format)
-d [ --data-summary ] Simply print input file stats (for PLINK
.ped/.bed files)
-s [ --snprank ] Perform SNPrank analysis *mode*
--gamma arg (=0.85) SNPrank algorithm damping factor
-g [ --gain ] Calculate GAIN *mode*
-r [ --regain ] Calculate regression GAIN *mode*
--component-matrices Write component reGAIN matrices for integrative
analyses
--compress-matrices Write binary (compressed) reGAIN matrices
--sif-threshold arg (=0.05) Numerical cutoff for SIF file (generated by
reGAIN) interaction scores
--fdr-prune FDR prune reGAIN interaction terms
--fdr arg (=0.5) FDR value for BH method applied to reGAIN
-e [ --ec ] Perform Evaporative Cooling (EC) analysis
*mode*
--ec-algorithm arg EC ML algorithm (all|rf|rj)
--ec-snp-metric arg EC SNP metric (gm|am)
--ec-num-target arg EC target number of attributes to keep
--extract arg Extract list of SNPs from specified file
--remove arg Remove list of individuals from specified file
--keep arg Keep list of individuals from specified file
--exclude arg Exclude list of SNPs
--prune Remove individuals with missing phenotypes
--covar arg Include covariate file in analysis
--pheno arg Include alternate phenotype file in analysis
--make-bed Make .bed, .fam and .bim
--ci arg (=0.95) Confidence interval for CMH odds ratios
--assoc Case/control, QTL association *mode*
--linear Test for quantitative traits and multiple
covariates *mode*
--logistic Test for disease traits and multiple covariates
*mode*
--model Cochran-Armitage and full-model C/C association
*mode*
--model-trend Use CA-trend test from model *mode*
--model-gen Use genotypic test from model *mode*
--model-dom Use dominant test from model *mode*
--model-rec Use recessive test from model *mode*
--freq Allele frequencies *mode*
--counts Modifies --freq to report actual allele counts
--missing Missing rates (per individual, per SNP) *mode*
--missing-genotype arg (="0") Missing genotype code
--maf arg (=0.0) Minor allele frequency
--geno arg (=1) Maximum per-SNP missing
--mind arg (=1) Maximum per-person missing
--hwe arg (=0.001) Hardy-Weinberg disequilibrium p-value (exact)
--hwe2 arg (=0.001) Hardy-Weinberg disequilibrium p-value
(asymptotic)
--1 0/1 unaffected/affected coding
--filter-founders Include only founders
--map3 Specify 3-column MAP file format
--no-sex PED file does not contain column 5 (sex)
--allow-no-sex Do not set ambiguously-sexed individuals
missing
--no-parents PED file does not contain columns 3,4 (parents)
--no-fid PED file does not contain columns 1 (family ID)
--r Pairwise SNPxSNP LD (r)
--r2 Pairwise SNPxSNP LD (r^2)
-l [ --ld-prune ] Linkage disequilibrium (LD) pruning *mode*
-h [ --help ] display this help screen
All commands will include an input file (`-i/--input-file`), and, optionally,
an output file prefix (`-o/--output-prefix`). Each command must include a
single mode, denoted in the help screen with `*mode*`.
To perform a regression GAIN (reGAIN) analysis,
./encore -i snpdata.ped --regain -o result
This will use genotype/phenotype information from `snpdata.ped`, a PLINK
plaintext GWAS file, in the regression. All of the output files produced will
be prepended with 'result'.
Once a `.regain` file is produced, this can be used as input for eigenvector
centrality ranking with SNPrank.
./encore -i result.regain --snprank -o rankings
This produces a file called `rankings.snprank`, in which the SNPs are ranked
in descending order, along with their associated SNPrank and information gain
(IG) scores.
To invoke EC to filter the original SNP data,
./encore -i snpdata.ped --ec -o filtered
Additional EC-based options can be specified, such as `--ec-algorithm` and
`--ec-snp-metric` to select the EC algorithm and SNP metric, respectively.
For additional examples, see the [Encore](http://insilico.utulsa.edu/encore)
page on our research website.
### Contributors ###
See AUTHORS file.
### References ###
N.A. Davis, J.E. Crowe, Jr., N.M. Pajewski, and B.A. McKinney. Surfing a
genetic association interaction network to identify modulators of antibody
response to smallpox vaccine. Genes and Immunity, 2010,
doi: 10.1038/gene.2010.3.
B.A. McKinney, J.Guo, J.E. Crowe, Jr., and D. Tian. Capturing the spectrum of
interaction effects in genetic association studies by simulated evaporative
cooling network analysis. PLoS Genetics 2009, 5(3): e1000432.
doi:10.1371/journal.pgen.1000432.
B.A. McKinney, D. M. Reif, B. C. White, J. E. Crowe Jr., J. H. Moore.
"Evaporative cooling feature selection for genotypic data involving
interactions." Bioinformatics 2007, 23: 2113-2120
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