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The following list highlights software developed by the NIEHS Biostatistics Branch. The software is open to the public and may be downloaded for free.


    ACANA is an alignment tool for DNA sequences.
    Reference: Huang W, Umbach DM, Li L (2006). Accurate anchoring alignment of divergent sequences. Bioinformatics 22: 29-34.
  • Analysis of Genetic Associations for Shared Controls Design
    ("/Rhythmyx/assembler/render?sys_contentid=35154&sys_revision=8&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35154" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35154" sys_siteid="" sys_folderid="")When controls are shared between studies that utilize cases of different diseases, the resulting genetic association P-values are correlated. This software adjusts or combines P-values while taking into account that correlation.
    Reference: Zaykin DV, Kozbur DO (2010). P-value based analysis for shared controls design in genome-wide association studies. Genet Epidemiol. 34: 725-738.
  • ART ("/Rhythmyx/assembler/render?sys_contentid=34834&sys_revision=9&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34834" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34834" sys_siteid="" sys_folderid="")
    ART is a set of simulation tools that can generate synthetic next-generation sequencing reads of three sequencing platforms: Illumina, 454, and SOLiD.
    Reference: Huang W, Li L, Myers JR, Marth GT (2012). ART: a next-generation sequencing read simulator. Bioinformatics 28: 593-594.
  • Case Control("/Rhythmyx/assembler/render?sys_contentid=56407&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56407" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56407" sys_siteid="" sys_folderid="")
    This archive provides information for fitting log-linear models and carrying out statistical tests for a design that includes two samples from the same population: one sample of affected individuals and their mothers and a second sample of unaffected individuals and their mothers.
    Reference: Shi M, Umbach DM, Vermeulen SH, Weinberg CR (2008). Making the most of case-mother/control-mother studies. Am J Epidemiol 168: 541-547.
  • Circular FSA
    ("/Rhythmyx/assembler/render?sys_contentid=51492&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="51492" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="51492" sys_siteid="" sys_folderid="")For a given set of angular data, this program tests whether the corresponding angular parameters satisfy a pre-specified order.
    Reference: Fernandez M, Rueda C, Peddada SD (2012). Identification of a core set of signature cell-cycle genes whose relative order of time to peak expression is conserved across species. Nucleic Acids Research 40: 2823-2832.
  • coMotif ("/Rhythmyx/assembler/render?sys_contentid=34836&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34836" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34836" sys_siteid="" sys_folderid="")
    Software for fitting a three-component mixture framework for ChIP-seq data to model the joint distribution of two motifs, allowing for the situation where sequences may contain either one, both, or neither of the motifs.
    Reference: Xu M, Weinberg CR, Umbach DM, Li L (2011). coMOTIF: A mixture framework for identifying transcription factor and a co-regulator motif in ChIP-seq data. Bioinformatics 27: 2625-2632.
  • Correlation-Based Tests
    ("/Rhythmyx/assembler/render?sys_contentid=35146&sys_revision=5&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35146" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35146" sys_siteid="" sys_folderid="")Software for correlation-based inference for assessing linkage disequilibrium between loci with more than two alleles.
    Reference: Zaykin DV, Pudovkin AI, Weir BS (2008). Correlation-based inference for linkage disequilibrium with multiple alleles. Genetics 180: 533-545.
  • Ctrl-mom-hybrid
    ("/Rhythmyx/assembler/render?sys_contentid=56408&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56408" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56408" sys_siteid="" sys_folderid="")This archive provides information for fitting log-linear models and carrying out statistical tests for a hybrid design that includes a sample of affected individuals and their parents and a sample of unaffected individuals and their mothers (case-parent triad/control-mother dyad design).
    Reference: Vermeulen SH, Shi M, Weinberg CR, Umbach DM (2009). A hybrid design: case-parent triads supplemented by control-mother dyads. Genet Epidemiol 33: 136-144.
  • DOMINE: Database of protein domain interactions 
    DOMINE is a database of known and predicted protein domain (domain-domain) interactions. It contains interactions inferred from PDB entries, and those that are predicted by 13 different computational approaches using Pfam domain definitions.
    Reference: Yellaboina S, Tasneem A, Zaykin DV, Raghavachari B, Jothi R (2011). DOMINE: A comprehensive collection of known and predicted domain-domain interactions. Nucleic Acids Research 39 (Database Issue): D730-735.
    Reference: Raghavachari B, Tasneem A, Przytycka T, Jothi R (2008). DOMINE: A database of protein domain interactions. Nucleic Acids Research 36 (Database Issue): D656-661.
  • EagleView
    EagleView is an information-rich viewer for next-generation genome assembles with data integration capability.
    Reference: Huang W, Marth GT (2008). EagleView: a genome assembly viewer for next-generation sequencing technologies. Genome Research 18: 1538-1543.
  • EpiCenter
    ("/Rhythmyx/assembler/render?sys_contentid=34838&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34838" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34838" sys_siteid="" sys_folderid="")EpiCenter is a powerful tool for analyzing genome-wide mRNA-seq or ChIP-seq data to detect differentially expressed genes or to identify changes in epigenetic modifications.
    Reference: Huang W, Umbach DM, Vincent JN, Abell AN, Johnson GL, Li L (2011). Efficiently identifying genome-wide changes with next-generation sequencing data. Nucleic Acids Research 39: e130.
  • Expression Predictor (ExP)
    ("/Rhythmyx/assembler/render?sys_contentid=34881&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34881" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34881" sys_siteid="" sys_folderid="")A desktop application developed with the Java programming language for classifying and predicting samples based on gene expression data using a simplified fuzzy adaptive resonance theory map (SFAM) neural network architecture.
    Reference: Bushel PR, Heinloth AN, Li J, Huang L, Chou JW, Boorman GA, Malarkey DE, Houle CD, Ward SM, Wilson RE, Fannin RD, Russo MW, Watkins PB, Tennant RW, Paules RS (2007). Blood gene expression signatures predict exposure levels. Proc Natl Acad Sci 104: 18211-18216.
  • Extracting Patterns and Identifying co-expressed Genes (EPIG)
    ("/Rhythmyx/assembler/render?sys_contentid=34840&sys_revision=5&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34840" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34840" sys_siteid="" sys_folderid="")A method for extracting microarray gene expression patterns and identifying co-expressed genes.
    Reference: Chou JW, Zhou T, Kaufmann WK, Paules RS, Bushel PR (2007). Extracting gene expression patterns and identifying co-expressed genes from microarray data. BMC Bioinformatics 8: 427.
  • fdrMotif
    ("/Rhythmyx/assembler/render?sys_contentid=34887&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34887" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34887" sys_siteid="" sys_folderid="")For finding motif instances while controlling false discovery rates.
    Reference: Li L, Bass RL, Liang Y (2008). fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control. Bioinformatics 24: 629-636.
  • GA/KNN
    ("/Rhythmyx/assembler/render?sys_contentid=34892&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34892" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34892" sys_siteid="" sys_folderid="")This software selects the most discriminative variables for sample classification and can be used for analysis of microarray gene expression data, proteomic data or other high-dimensional data.
    Reference: Li L, Weinberg CR, Darden TA, Pedersen LG (2001). Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17: 1131-1142.
  • GADEM ("/Rhythmyx/assembler/render?sys_contentid=34890&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34890" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34890" sys_siteid="" sys_folderid="")
    An unbiased de novo motif discovery tool that implements an expectation-maximization (EM) algorithm.
    Reference: Li L (2009). GADEM: A genetic algorithm guided formation of spaced dyads coupled with an EM algorithm for motif discovery. J Comput Biol 16: 317-329.
    ("/Rhythmyx/assembler/render?sys_contentid=56417&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56417" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56417" sys_siteid="" sys_folderid="")This program provides tests for multiplicative gene-environment interaction effects using multiple markers from case-parent triad families.
    Reference: Shi M, Umbach DM, Weinberg CR (2010). Testing haplotype-environment interactions using case-parent triads. Hum Hered 70: 23-33.
  • Genetic Algorithm Method for Optimizing a Position Weight Matrix
    ("/Rhythmyx/assembler/render?sys_contentid=34945&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34945" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34945" sys_siteid="" sys_folderid="")Implements a method to improve a poorly estimated position weight matrix using chromatin immunoprecipitation (ChIP) data.
    Reference: Li L, Liang Y, Bass RL (2007). GAPWM: a genetic algorithm method for optimizing a position weight matrix. Bioinformatics 23: 1188-1194.
  • Hill Viewer
    ("/Rhythmyx/assembler/render?sys_contentid=34951&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34951" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34951" sys_siteid="" sys_folderid="")This program is used to visualize dose-response curves and relative potency functions based on two sets of Hill model parameters.
    Reference: Dinse GE, Umbach DM (2011). Characterizing non-constant relative potency. Regulatory Toxicology and Pharmacology 60: 342-353.
  • Hybrid Design
    ("/Rhythmyx/assembler/render?sys_contentid=34981&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34981" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34981" sys_siteid="" sys_folderid="")Provides information for fitting log-linear models and carrying out statistical tests for a design where a sample of case-parent triads is augmented by a sample of genotyped control parents from the same population.
    Reference: Weinberg CR, Umbach DM (2005). A hybrid design for studying genetic influences on risk of diseases with onset early in life. Am J Hum Genet 77: 627-636.
  • LEM scripts ("/Rhythmyx/assembler/render?sys_contentid=56421&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56421" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56421" sys_siteid="" sys_folderid="") - for preterm birth study
    This archive provides information for fitting log-linear models and carrying out statistical tests for a preterm birth study using the LEM software provided by Vermunt.
    Reference: Weinberg CR, Shi M (2009). The genetics of preterm birth: using what we know to design better association studies. Am J Epidemiol 170: 1373-1381.
  • LEM scripts - case-siblings
    This package contains LEM scripts for analyzing case-sibling data using a missing-parents approach.
    Reference: Shi M, Umbach DM, Weinberg CR (2012). Case-sibling studies that acknowledge unstudied parents and permit enrollment of unmatched individuals (in review).
  • Modk-Prototypes
    ("/Rhythmyx/assembler/render?sys_contentid=35062&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35062" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35062" sys_siteid="" sys_folderid="")Clusters biological samples by simultaneously considering microarray gene expression data and classes of known phenotypic variables such as clinical chemistry evaluations and histopathologic observations.
    Reference: Bushel PR, Wolfinger RD, Gibson G (2006). Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes. BMC Systems Biology 1: 15.
  • OMiMa
    The OMiMa System is a computational tool for identifying functional motifs in DNA or protein sequences.
    Reference: Huang W, Umbach DM, Ohler U, Li L (2006). Optimized mixed Markov models for motif identification. BMC Bioinformatics 7: 279.
  • ORIOGEN v 3 - Order Restricted Inference for Ordered Gene Expression
    ("/Rhythmyx/assembler/render?sys_contentid=35065&sys_revision=8&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35065" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35065" sys_siteid="" sys_folderid="")This software analyzes gene expression data obtained from time-course/dose-response studies, where only order-restricted alternative hypotheses are allowed.
    Reference: Peddada SD, Harris S, Zajd J, Harvey E (2005). ORIOGEN: Order Restricted Inference for Ordered Gene Expression data. Bioinformatics 21: 3933-3934.
  • PCA-based Gene Filtering
    ("/Rhythmyx/assembler/render?sys_contentid=35076&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35076" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35076" sys_siteid="" sys_folderid="")A new filtering statistic for Affymetrix GeneChips, based on principal components analysis (PCA) of probe-level gene expression data.
    Reference: Lu J, Kerns RT, Peddada S, Bushel PR (2010). PCA-based filtering improves detection for Affymetrix gene expression arrays (in preparation).
  • Phase-shifted Analysis of Gene Expression (PAGE)
    ("/Rhythmyx/assembler/render?sys_contentid=35110&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35110" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35110" sys_siteid="" sys_folderid="")An interactive tool that uses a line graph to dynamically illustrate the phase-shifted patterns of gene expressions based on the q-Cluster selected by the user.
    Reference: Leung E, Bushel PR (2006). PAGE: phase-shifted analysis of gene expression. Bioinformatics 22: 367-368.
  • Poisson Hidden Markov Model (PHMM)
    Model used to estimate (hidden) states of gene expression levels in terminal exon 3’ UTRs, infer shortening of the region and demonstrate alternative polyadenylation.
  • Principal Variance Component Analysis ("/Rhythmyx/assembler/render?sys_contentid=35299&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35299" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35299" sys_siteid="" sys_folderid="")
    A hybrid approach using principal components analysis and variance component analysis as a methodology to determine and quantify sources of variability most prominent in microarray gene expression data.
    Reference: Boedigheimer MJ, Wolfinger RD, Bass MB, Bushel PR, Chou JW, Cooper M, Corton JC, Fostel J, Hester S, Lee JS, Liu F, Liu J, Qian HR, Quackenbush J, Pettit S, Thompson KL (2008). Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics 9: 285.
  • PSPE
    PSPE is a computational tool specifically designed for simulating evolution of non-coding DNA sequences, in particular promoter sequences.
    Reference: Huang W, Nevins JR, Ohler U (2007). Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools. Genome Biology 8: R225.
  • R code for fitting Random Periods Model ("/Rhythmyx/assembler/render?sys_contentid=51500&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="51500" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="51500" sys_siteid="" sys_folderid="")
    This software is for fitting the Random Periods Model (RPM) for cyclic expression levels in gene expression data from cell-cycle genes.
    Reference: Liu D, Umbach D, Peddada SD, Li L, Crockett P, Weinberg C (2004). A random-periods model for expression of cell-cycle genes. Proc Natl Acad Sci 101: 7240-7245.
  • Rankings of causal loci in GWAS
    ("/Rhythmyx/assembler/render?sys_contentid=35144&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35144" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35144" sys_siteid="" sys_folderid="")These scripts evaluate the ranking probability Pxy, defined as the probability of capturing at least x "true associations," while taking y smallest P-values from an experiment with many statistical tests (such as GWAS). The scripts also evaluate the proportion of true associations among a user-specified number of smallest P-values, expressed via a sum of Pxy, as well as a posterior probability that the association is real for any particular P-value.
    Reference: Kuo C-L, Zaykin DV (2011). Novel rank-based approaches for discovery and replication in genome wide association studies. Genetics 189: 329-340.
    ("/Rhythmyx/assembler/render?sys_contentid=56430&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56430" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56430" sys_siteid="" sys_folderid="")This package contains two R scripts for generating scenarios 1-4 described in the article by Shi and Weinberg (2011).
    Reference: Shi M, Weinberg CR (2011). How much are we missing in SNP-by-SNP analyses of genome-wide association studies? Epidemiology 22: 845-847.
  • SA-Modk-Prototypes
    ("/Rhythmyx/assembler/render?sys_contentid=35617&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35617" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35617" sys_siteid="" sys_folderid="")For simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations using simulated annealing.
    Reference: Bushel PR (2009). Clustering of gene expression data and End-point measurements by simulated annealing. Journal of Bioinformatics and Computational Biology 7: 193-215.
  • SAS Code to Fit Genetic Models
    ("/Rhythmyx/assembler/render?sys_contentid=56439&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56439" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56439" sys_siteid="" sys_folderid="")Various SAS codes for identifying genes related to a quantitative trait, incorporating multiple siblings and missing parents.
    Reference: Weinberg CR, Umbach DM (2005). A hybrid design for studying genetic influences on risk of diseases with early onset in life. Am J Hum Genet 77: 627-636.
    Reference: Kistner EO, Weinberg CR (2004). Method for using complete and incomplete trios to identify genes related to a quantitative trait. Genet Epidemiol 27: 33-42.
    Reference: Kistner EO, Weinberg CR (2005). A method for identifying genes related to a quantitative trait, incorporating multiple siblings and missing parents. Genet Epidemiol 29: 155-165.
    Reference: Kistner EO, Infante-Rivard C, Weinberg CR (2006). A method for using incomplete triads to test maternally-mediated genetic effects and parent-of-origin effects in relation to a quantitative trait. Am J Epidemiol 163: 255-261.
  • SISSRs: ChIP-Seq peak finder 
    SISSRs is a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments.
    Reference: Jothi R, Cuddapah S, Barski A, Cui K, Zhao K (2008). Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Research 36: 5221-5231.
    Reference: Narlikar L, Jothi R (2012). ChIP-Seq data analysis: identification of protein-DNA binding sites with SISSRs peak-finder. Methods in Molecular Biology 802: 305-322.
  • Sparse Multiclass SVM: Multiclass SVM with Variable Selection (SMS)
    Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. Multiclass support vector machines (MSVMs) provide a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. Spare MC-SVM (SMS) improves the procedure by imposing shrinkage penalties in learning to enforce solution sparsity.
    Reference: Lingkang Huang, Hao Helen Zhang, Zhao-Bang Zeng and Pierre R. Bushel. (2013). Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer Informatics.
  • Systematic Variation Normalization (SVN)
    ("/Rhythmyx/assembler/render?sys_contentid=35622&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35622" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35622" sys_siteid="" sys_folderid="")A procedure for removing systematic variation in microarray gene expression data.
    Reference: Chou JW, Paules RS, Bushel PR (2005). Systematic variation normalization in microarray data to get gene expression comparison unbiased. J Bioinform Comput Biol 3: 225-241.
  • TRIad Multi-Marker
    ("/Rhythmyx/assembler/render?sys_contentid=35906&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35906" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35906" sys_siteid="" sys_folderid="")This program performs association tests for a child's or mother's genetic effects using multiple markers from case-parent triad families.
    Reference: Shi M, Umbach DM, Weinberg CR (2007). Identification of risk-related haplotypes with the use of multiple SNPs from nuclear families. Am J Hum Genet 81: 53-66.
    ("/Rhythmyx/assembler/render?sys_contentid=56435&sys_revision=1&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="56435" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="56435" sys_siteid="" sys_folderid="")This package contains programs that fit log-linear models to estimate the relative risk associated with a candidate risk haplotype in a triad-based association study.
    Reference: Shi M, Umbach DM, Weinberg CR (2009). Using case-parent triads to estimate relative risks associated with a candidate haplotype. Ann Hum Genet 73: 346-359.

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