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A simulation tool that can simulate sequence data with user-specified disease and quantitative trait models. SeqSIMLA can efficiently simulate sequence data with disease or quantitative trait models specified by the user. It is useful for evaluating statistical properties for new study designs and new statistical methods using NGS.

iSG / indel-Seq-Gen

Simulates highly divergent DNA sequences and protein superfamilies. iSG is a method for simulating protein sequence evolution and generating realistic protein families. This is accomplished through the addition of subsequence length constraints and lineage- and site-specific evolution. iSG tracks insertion and deletion processes that occur during the simulation run. iSG records all evolutionary events and outputs the "true" multiple alignment of the sequences, and can generate a larger simulated sequence space by allowing the use of multiple related root sequences. iSG can be used to test the accuracy of multiple alignment methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein superfamily classification methods.

Provides statistical means to infer the strength of association between the rate of sequence evolution in a given genomic region and a phenotype of interest. TraitRateProp is a web server that allows testing the hypothesis of an association between the sequence evolutionary rate and the phenotypic trait. It then computes per-site predictions, where high scores indicate sequence sites whose rate is more likely to be in association with the phenotypic trait. In case where a 3D structure information of the protein is provided, the site-specific scores are mapped onto this structure, thus allowing the detection of putatively functional sequence sites that are spatially close to each other in 3D space.

Simulates multiple nearby disease SNPs on the same chromosome. The new method, HAPGEN2, retains many advantages of resampling methods and expands the range of disease models that current simulators offer.

A rapid moving-window algorithm to simulate genotype data for case-control or population samples from genomic SNP chips. For case-control data, GWAsimulator generates cases and controls according to a user-specified multi-locus disease model, and can simulate specific regions if desired. The program uses phased genotype data as input and has the flexibility of simulating genotypes for different populations and different genomic SNP chips.

RarePedSim / Rare-variant Pedigree-based Simulator

Simulates region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members' qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard linkage file (.ped) and variant call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods.

SIMLA / SIMulation of Linkage and Association

Generates data sets of families for use in Linkage and Association studies. SIMLA allows the user flexibility in specifying marker and disease placement, locus heterogeneity, disequilibrium between markers and disease loci. It allows simulation of linkage and association for multiple markers in extended pedigrees, nuclear families or in sets of unrelated cases and controls. The program will be useful for studying and comparing existing statistical tests, for developing new genetic linkage and association statistics, planning sample sizes for new studies, and interpreting genetic analysis results.

Provides a flexible simulating tool for pathway-based genome-wide association studies using real genetic data from the HapMap project or users. Pathsimu can simultaneously simulate multiple quantitative phenotypes and genome-wide genotype data under users assigned parameters, such as simulation times, genetic models (additive & epistatic genetic models), names or sizes of causal pathways, numbers and genetic effects (main & interactive effects) of disease genes, minor allele frequency ranges of causal SNPs of disease genes. Pathsimu can be used to develop novel multiple gene association study approaches, for instance evaluating the impact of genetic parameters on the power of pathway-based association testing approaches, and comparing the performance of different approaches under various parameter settings. Pathsimu is designed to output data with PLINK file format and be easily extendable.

Simulates realistic samples for genome-wide association studies (GWAS). simuGWAS simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. It can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for GWAS. Compared to other simulation methods, the tool simulates samples with existing genetic markers that resemble the human populations well in terms of marker allele frequency and linkage disequilibrium (LD) structure, with additional flexibility to simulate genomic regions with signals of natural selection.

A software tool to add a phenotype to genotypes generated in time-efficient coalescent simulations. Both qualitative and quantitative phenotypes can be generated and it is possible to partition phenotypic variation between additive effects and epistatic interactions between causal variants. The output formats of phenosim are directly usable as input for different GWAS tools. The applicability of phenosim is shown by simulating a genome-wide association study in Arabidopsis thaliana.

CC-PROMISE / canonical correlation and projection onto the most interesting evidence

Integrates two forms of molecular data with multiple clinical endpoints. CC-PROMISE is an R package that combines canonical correlation (CC), a classical method used to evaluate the association of two multivariate data sets with one another and PROMISE, a method to integrate one form of molecular data with multiple endpoints. The software also includes a probe level analysis.

DHOEM / Densification of Haplotypes by lOEss and Maximum likelihood

A simulation software that does not belong to the three aforementioned approaches. DHOEM is a statistical procedure that simulates new markers, according to statistical modeling of local data characteristics, in real SNP marker data sets. The main objective of DHOEM is to increase the marker density in a marker data set for simulation studies. For each chromosome in a marker data set, the statistical procedure defined in DHOEM models the probability distribution generating the allele frequencies and the relation between LD and physical distance between consecutive markers. DHOEM is a user-friendly and useful tool for simulation and methodological studies in quantitative genetics and breeding. The software is written in R and runs on Windows operating systems (OS), although with a few modifications, it can be extended to Linux-like OS.

Allows simulation on population genetic data. GPOPSIM is based on mutation-drift equilibrium (MDE) model. It simulates genetic and phenotypic values for each individual in the current population using the genome structure generated in the historical population, the trait and quantitative trait loci (QTL) parameters. This tool is useful for data simulation in genetic or breeding researches that needs genomic and phenotypic data from a population.

Simulates genomic data with rare variants. SimuRare is a regression-based algorithm imputing rare variants in single nucleotide polymorphism (SNP) array data. It can achieve a resampling approach on any haplotype data to simulate samples including both common and rare SNPs. This method aims to improve realistic linkage disequilibrium (LD) and minor allele frequency (MAFs) maintaining.

A simulation tool for disease/endpoint models of association studies based on single nucleotide polymorphism genotypes. SITDEM is designed to simulate disease/endpoint models based on SNP genotypes.

GAMETES / Genetic Architecture Model Emulator for Testing and Evaluating Software

Generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. It includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. The tool could be employed to pursue theoretical characterization of genetic models and epistasis.

PhenotypeSimulator

Simulates phenotypes under different models including genetic variant effects and infinitesimal genetic effects, as well as correlated, non-genetic covariates and observational noise effects. PhenotypeSimulator is an R package that combines the phenotype components into a final phenotype while controlling for the proportion of variance explained by each of the components. Users can customize, for each effect component, the number of variables, their distribution and the design of their effect across traits.

Simulates genotype and quantitative trait data in extended pedigrees. SIBSIM is as highly scalable as possible to meet any needs. It may not only be used in simulation studies, but also in the validation, verification and testing process of other applications which deal with the implementation of statistical analysis of genomic data. It had emphasis on the simulation of a quantitative trait in pedigrees of arbitrary size without monozygotic twins.

ESPRESSO / Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes

A simulation based tool, written in the R language that supports power and sample size calculations for stand-alone studies and analyses nested in cohort studies. ESPRESSO enables those designing large cohorts and biobanks to better estimate the sample size required to achieve adequate power. It can also be used to explore specific scientific questions relevant to the design and set up of large-scale association studies and biobanks.

Allows a considerable flexibility in disease models, potentially involving a large number of interacting loci. HAP-SAMPLE is a web application for simulating single nucleotide polymorphism (SNP) genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. It is a general simulation tool that is immediately available to the research community. The HapMap samples provide limited opportunity for specifying rare disease variants, because of selection bias in HapMap markers and sample size constraints.

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1 - 2 of 2 results

GESDB / Genetic Epidemiology Simulation Database

A platform for sharing simulation data and discussion of simulation techniques for human genetic studies. GESDB contains simulation scripts, simulated data, and documentations from published manuscripts. The forum provides a platform for Q&A for the simulated data and exchanging simulation ideas. GESDB aims to promote transparency and efficiency in simulation studies for human genetic studies.

A platform for sharing simulation data and discussion of simulation techniques for human genetic studies. DBSIM contains simulation scripts, simulated data, and documentations from published manuscripts. The forum provides a platform for Q&A for the simulated data and exchanging simulation ideas. DBSIM aims to promote transparency and efficiency in simulation studies for human genetic studies.

1 - 3 of 3 results

1 - 3 of 3 results

Big data scientist National institute of Plant Gneome Reseaech (NIPGR)

Research Interests Gene fusion detection in Plants Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions are well known in case of human. But, in plants, this phenomenon is not yet explored. We are planning to discover the fusion transcripts/gene fusions in different type of plants by using RNA-Seq datasets. Further, we are planning to understand the mechanism of gene fusion formation and significance of fusions in plants. Whole genome and transcriptome sequencing data analysis of Plants In this era of Next Generation Sequencing (NGS), there is huge amount of sequencing data available in the public domain. Any novel finding from these available datasets is major challenge for a computational biologist. We are interested in the analysis of whole genome and transcriptome sequencing data of different plants to fetch out the useful information from those datasets, with the help of bioinformatics tools. Currently, we are planning to study the gene clusters of secondary metabolite pathways in different plants. Development of webservers, databases and computational pipelines for plant research Development of database is necessary to compile and share the information with scientific community. We are dedicated to develop useful databases and webserver for plant research. Another area of interest is to develop automated pipelines and tools for the analysis of high throughput genomics data, generated by NGS technologies. Professional & Academic Background Staff Scientist II (May 2017- present): National Institute of Plant Genome Research (NIPGR), New Delhi, India Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India PhD Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India Awards and Fellowships Junior and Senior Research Fellowship (2009-2014): Council of Scientific and Industrial Research (CSIR), New Delhi, India GATE (Graduate Aptitude Test in Engineering): Qualified in years 2008 and 2009 Scientific Contributions/ Recognitions Associate editor: Journal of Translational Medicine. Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. Reviewer: PloS One, BMC Genomics, BMC Bioinformatics, BMC Biology, BMC Biotechnology, Frontiers in Physiology and several other journals. Web Resources/ Databases (Developed/ Contributed) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer (http://www.imtech.res.in/raghava/cancertope/) GenomeABC: A webserver for benchmarking of genome assemblers. (http://crdd.osdd.net/raghava/genomeabc/). Genomics web portal page. (http://crdd.osdd.net/raghava/genomesrs/). Map/Alignment module of CancerDr: Cancer Drug Resistance Database. (http://crdd.osdd.net/raghava/cancerdr/). Short reads and contigs alignment module of PCMDB: Pancreatic cancer methylation database. (http://crdd.osdd.net/raghava/pcmdb/). Burkholderia sp. SJ98 database. (http://crdd.osdd.net/raghava/genomesrs/burkholderia/). Rhodococcus imtechensis RKJ300 database. (http://crdd.osdd.net/raghava/genomesrs/rkj300/). Genotrick: A pipeline for whole genome assembly and annotation of Genomes (http://crdd.osdd.net/raghava/genomesrs/genotrick/) Development of Debian packages in OSDDlinux: A Customized Operating System for Drug Discovery. (http://osddlinux.osdd.net/). A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. (http://crdd.osdd.net/raghava/mtbveb/).

FusionFinder comrad

Mahantesh Biradar

‎Graduate PhD researcher focusing on Cardiovascular Epidemiology & Social Media Enthusiast Research Stash

I'm a Graduate PhD researcher in the area of Biomedical Science and focussing on the Pharmacogenomics/Pharmacogenomics of Diabetes and related complications

Mahdi Akbarzadeh

Ph.D of Biostatistics, Postdoctoral Researcher on Statistical Genetics Shahid beheshti University of Medical Sciences, Research Institute for Endocrine Sciences

ONETOOL Biopython GATK

Simulation software tools | Genome-wide association analysis

The association analysis between single nucleotide polymorphisms (SNPs) and disease or endpoint in genome-wide association studies (GWAS) has been considered as a powerful strategy for investigating genetic susceptibility and for identifying significant biomarkers. The statistical analysis approaches with simulated data have been widely used to review experimental designs and performance measurements. Source text: Oh and Deasy, 2014.

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