Q-Fish

Q-FISH (Quantification method based on Finding the Identical Spectral set for a Homogenous peptide) is to estimate the peptide’s abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra.

Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, namely spectral counting and spectra feature analysis, have been extensively demonstrated on a wide variety of proteomes. The cornerstone of both methods is peptide identification based on protein database search and subsequent estimation of peptide retention time. However, they may suffer from restrictive database searching and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification method of the database searching or the spectral library searching algorithms such as SEQUEST or SpectraST can provide neither the best match nor high-scored matches, which are reliable indicators of protein targets. Lastly, peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.
To overcome these limitations, we propose a novel method called Q-FISH to estimate the peptide’s abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Because our proposed Q-FISH method compares all possible pairs of experimental spectra, it is possible to identify modified as well as unknown peptides.

R code for Q-FISH : Q-FISH.R

Reference : Lee S, Kwon MS, Lee HJ, Paik YK, Tang H, Lee JK and Park T, Enhanced peptide quantification using spectral count clustering and cluster abundance, BMC Bioinformatics (2011), PMCID PMC3234305.

Bis-class

Bis-Class is a tool which is made for classifying methylation status from BS-seq data. This method works best especially when whole methylation level is low and coverage is also low. This method uses bayes classifier and local methylation information to improve sensitivity given that the error rate is controlled.

Our code and example data are below.

Calling_code : Bis-Class and other functions for calling.Code_manual: Manual for Bis-Classchrexam: Data of ranges of chromosomes
examdata: Data of C and T read counts and location information

Download program and dataset : Bis-class_updated

Reference : Huh I, Yang X, Park T and Yi SV, Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information, BMC Genomics (2014), PMID 25037738.

Oct 7 2015: Program updated to ver 2

IGENT

Welcome to IGENT!

IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). IGENT supports two types of strategies to identify gene-gene interactions related with diseases in genome-wide scale. One is an exhaustive search approach for lower-order interactions such as 2nd order interaction, and the other is a stepwise selection approach for higher-order interaction. With tens of thousands of SNPs from thousands of samples, it is difficult to calculate higher-order interaction exhaustively because the computational burden is too heavy. IGENT provides a stepwise approach for higher-order interactions.

Manual

Run IGENT

igent --in [input file] --out [output file] --order [order]
ex) igent --in sample.txt --out result.csv --order 2

[Parameter]

  • –in : input file path + name
  • –out : result file path + name
  • –order : combination order (1,2,3,4,…. or 1-3, 2-5) (default : 2)
  • –mode : exhaustive search or stepwise search (exhaustive, stepwise) (default : exhaustive)
  • –shuffling : shuffling (0 : not shuffling 1: shuffling) (default : 0)

Download GWAS-GMDR

Current version

IGENT is currently at Version 1.5 (04/19/2017)

Download

   > download IGENT

Hi-Mise

Hi-Mini : A program for identification of high-order interaction

Hi-Mini allows an efficient high-order interaction analysis in genome-wide association studies using multifactor dimensionality reduction. It is a program based on character user interface (CUI), hence it does not provide graphical user interface. In order to use Hi-Mini, an understanding on command-line interface is required.

Download

Hi-Mini supports multiple operating systems, including Windows, various Linux distributions and Mac operating system. Downloading Hi-Mini is possible via below links.

Input file

Hi-Mini can load various types of SNV dataset file, including PED file, Binary PED file and transposed PED file. For the details of above file formats, please refer PLINK website. The input option should be –bed for Binary PED file, –ped for PED file, and –tped for transposed PED file, respectively. Note that the phenotype field of input file should encoded in 1 for control and 2 for case.

Usage

himini –bed sample.bed : Assigns sample.bed as an input
himini –bed sample.bed –hmdr 8 –mdr –order 2 –top 1000 : Search up to 8th-order interaction using Hi-Mini, based on top 1000 results from second-order exhaustive search
himini –bed sample.bed –hmdr 8 –mdr –order 2 –top 1000 –hmdrprior top1000_order2.txt : Search up to 8th-order interaction using Hi-Mini, based on top 1000 results from pre-computed second-order exhaustive search

In case of third example, it is required to analyze second-order exhaustive MDR using cuGWAM or other software. The prior file of third example should consists of N lines for top N search, and each line should consists of P+1 columns. In each line, the first P elements are marker IDs in the dataset, and the last element should be balanced accuaracy of that interaction pair. Note that this P should equivalent to the parameter assigned in –order. For detailed instructions on cuGWAM, refer this page.

fuzzyMDR

Fuzzy MDR

Introduction

This fold includes R source files for implementation of the numerical study in the manuscript submitted to BMC Genomics, 2016, titled “A novel multifactor dimensionality reduction method based on fuzzy set theory for detecting gene-gene interaction”.

Usage

The following functions are implemented in the source code:
mdr.fuzzycv —— Implements Fuzzy MDR with cross-validation
mdr.fuzzy —— Performs Fuzzy MDR over a specified set of combinations of variables/loci
mdr.fhr ——Estimates the accuracy of an Fuzzy MDR model given high-risk/low-risk status

Example

mdr.fuzzycv(data, K=2, cv=10, ratio = NULL, genotype = c(0,1,2),memb=”linear”,criteria_OR=2,weight=1 ) memb= “original” -> original MDR “linear”-> linear membership “sigmoid”-> sigmoid membership

Download

The above contents including the R code and sample dataset can be download from the below link.

R code download Sample dataset download

FG-MDR

Fuzzy set based approach to generalized multi-factor dimensionality reduction analysis of gene-gene interactions (FGMDR)

Introduction

This method is fuzzy generalized multifactor dimensionality reduction (FGMDR) method as an extension of GMDR method to detect gene-gene interactions associated with disease using fuzzy set theory. Details of Fuzzy MDR is explained in the following paper. More details of FGMDR will be updated as soon as published.

Citation

Jung, H.-Y., et al. (2016) A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction, Computational Biology and Chemistry.

Example

#glm fitting for residual calculation with equation as class~covariates form
fit_model <- glm(temp_data[,n]~temp_data[,n-1], data= temp_data, family = binomial(link = "logit")) 

#standard residuals for FGMDR
temp_data$res<-residuals(fit_model, type="response")

#data transformation, SNPs(9:24) and residuals(8)
data_FGMDR <- temp_data[,c(9:24, 8)]

#run FGMDR with 2-locus, 0.5 threshold and 10-fold cross validation
res_2_2<-FGMDR(data_FGMDR, 2, 0.5, 10)

#drawing result figure
plot_FMDR(res_2,2)

Download

The above R code be download from the below link.
Download

EF-MDR

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction (EFMDR)

Introduction

This method is an extension of Fuzzy MDR without tuning parameters. Details of Fuzzy MDR is explained in the following paper. In this EFMDR method, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. More details of EFMDR will be updated as soon as published.

Citation

Jung, H.-Y., et al. (2016) A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction, Computational Biology and Chemistry.

Example

# an example of 2-locus analysis using MDR sample data library(MDR) data("mdr1") 
res<-EFMDR(mdr1,2) 
plot_EFMDR(res,2)

Download

The above R and CPP code be download from the below link.

Download R code

Download CPP code zip