ReliefSeq has been deprecated and reimplemented in inbix on github.
ReliefSeq 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.
ReliefSeq is developed by the In Silico Research Group at the Tandy School of Computer Science of the University of Tulsa. Our research is sponsored by the NIH and William K. Warren foundation. For more details, visit our research website.
- GNU Scientific library (libgsl)
- Boost system, filesystem, and program-options libraries
- OpenMP is required to take advantage of the parallelized distance matrix calculations for ReliefF. This library is typically installed alongside the compiler toolchain.
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 ReliefSeq on:
- Linux (64-bit Ubuntu) (gcc-4.6)
- Linux (64-bit) gcc (Debian 4.4.5-8) 4.4.5
To build ReliefSeq, first run the bootstrap script:
./bootstrap.sh
Ignore any extraneous warnings. This calls autoreconf and generates the configure script.
Set any OS or other system-sepcific environment variables. For example OSX:
export BOOST_ROOT="/usr/local" export CXXFLAGS="-I/usr/local/Cellar/gcc/4.9.2_1/lib/gcc/4.9/gcc/x86_64-apple-darwin13.4.0/4.9.2/include $CXXFLAGS" export LDFLAGS="-L/usr/local/Cellar/gcc/4.9.2_1/lib/gcc/4.9 $LDFLAGS"
From this point, the standard build procedure:
./configure && make && sudo make install
will generate the Makefile, compile and link the code, and copy the
executable to the installation directory prefix (default of /usr/local).
reliefseq:
--help produce help message
--verbose verbose output
--convert convert data set to data set - does not
run reliefseq
--write-best-k optimize k, write best k's
--write-each-k-scores optimize k, write best scores for each
k
-c [ --config-file ] arg read configuration options from file -
command line overrides these
-s [ --snp-data ] arg read SNP attributes from genotype
filename: txt, ARFF, plink (map/ped,
binary, raw)
--snp-file-type arg Ignore file extension and use type:
textwhitesp, wekaarff, plinkped,
plinkbed, plinkraw, dge, birdseed
-n [ --numeric-data ] arg read continuous attributes from
PLINK-style covar file
-X [ --numeric-transform ] arg perform numeric transformation:
normalize, standardize, zscore, log,
sqrt, anscombe
-a [ --alternate-pheno-file ] arg specifies an alternative
phenotype/class label file; one value
per line
-g [ --algorithm-mode ] arg (=relieff)
Relief algorithm mode
(relieff|reliefseq)
--seq-algorithm-mode arg (=snr) Relief algorithm mode (snr|tstat)
--seq-snr-mode arg (=snr) Seq interaction algorithm SNR mode
(snr|relieff)
--seq-tstat-mode arg (=pval) Seq interaction algorithm t-statistic
mode (pval|abst|rawt)
--seq-algorithm-s0 arg (=0.050000000000000003)
Seq interaction algorithm s0 (0.0 <= s0
<= 1.0)
-t [ --num-target ] arg target number of attributes to keep
after backwards selection
-r [ --iter-remove-n ] arg number of attributes to remove per
iteration of backwards selection
-p [ --iter-remove-percent ] arg percentage of attributes to remove per
iteration of backwards selection
--normalize-scores arg (=0) normalize ReliefF scores? (0|1)
-O [ --out-dataset-filename ] arg write a new tab-delimited data set with
EC filtered attributes
-o [ --out-files-prefix ] arg (=reliefseq_default)
use prefix for all output files
--snp-metric arg (=gm) metric for determining the difference
between subjects (gm|am|nca|nca6)
-B [ --snp-metric-nn ] arg (=gm) metric for determining the difference
between subjects (gm|am|nca|nca6|km)
-W [ --snp-metric-weights ] arg (=gm) metric for determining the difference
between SNPs (gm|am|nca|nca6)
-N [ --numeric-metric ] arg (=manhattan)
metric for determining the difference
between numeric attributes
(manhattan=|euclidean)
-x [ --snp-exclusion-file ] arg file of SNP names to be excluded
-k [ --k-nearest-neighbors ] arg (=10)
set k nearest neighbors (0=optimize k)
--kopt-begin arg (=1) optimize k starting with kopt-begin
--kopt-end arg (=1) optimize k ending with kopt-end
--kopt-step arg (=1) optimize k incrementing with kopt-step
-m [ --number-random-samples ] arg (=0)
number of random samples (0=all|1 <= n
<= number of samples)
-b [ --weight-by-distance-method ] arg (=equal)
weight-by-distance method
(equal|one_over_k|exponential)
--weight-by-distance-sigma arg (=2) weight by distance sigma
-d [ --diagnostic-tests ] arg performs diagnostic tests and sends
output to filename without running EC
-D [ --diagnostic-levels-file ] arg write diagnostic attribute level counts
to filename
--dge-counts-data arg read digital gene expression counts
from text file
--dge-norm-factors arg read digital gene expression
normalization factors from text file
--birdseed-snps-data arg read SNP data from a birdseed formatted
file
--birdseed-phenos-data arg read birdseed subjects phenotypes from
a text file
--birdseed-subjects-labels arg read subject labels from filename to
override names from data file
--birdseed-include-snps arg include the SNP IDs listed in the text
file
--birdseed-exclude-snps arg exclude the SNP IDs listed the text
file
--distance-matrix arg create a distance matrix for the loaded
samples and exit
--gain-matrix arg create a GAIN matrix for the loaded
samples and exit
--dump-titv-file arg file for dumping SNP
transition/transversion information
All commands will include an input file (-s/--snp-data), and, optionally,
an output file prefix (-o/--output-files-prefix).
To perform a standard, all-default-parameters analysis,
./reliefseq -s snpdata.ped -o result
This will use genotype/phenotype information from snpdata.ped, a PLINK
plaintext GWAS file, in the feature selection. All of the output files
produced will be prepended with 'result'.
This produces a file called result.reliefseq, in which the SNPs are ranked
in descending order.
For additional examples, see the ReliefSeq page on our website.
See AUTHORS file.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent