JMP Genomics（英語） (PDF)
農業、ゲノム薬理学、バイオテクノロジーなど、ゲノム研究のさまざまな分野で、JMP Genomics はデータの探索や理解、解析結果の共有において役立ちます。JMP Genomics は、希少変異や一般的変異の解析、発現パターンの差の検出、NGS データの解析、信頼できるバイオマーカー プロファイルの発見、さらに解析ワークフローへのパスウェイ情報の取り込みなどに必要なツールを搭載しています。
他に類を見ない JMP Genomics は、対話的な JMP のグラフィックと、頑健な SAS® Analytics を組み合わせることで、研究者が大容量のゲノムデータをあらゆる角度から探索し、解析結果を他の研究者と共有することを可能にします。
JMP Genomics serves as your experimental design and analysis platform for crop-breeding programs. Biologists and breeders can employ linkage mapping and QTL analysis for experimental populations, and complex association methods for diversity populations to identify markers for pest and pathogen resistance.
Comprehensive predictive modeling reviews perform genomic selection with multiple traits of interest to improve breeding selection and accuracy, allowing insights that aren’t possible with classical phenotypic selection. These genomic selection tools uncover optimal combinations of markers to produce desirable traits, which in turn can be used to design and evaluate future crosses to produce healthier foods.
JMP Genomics allows you to import variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, Plink text and binary files, and common output formats from SNP arrays. Once imported, choose from extensive association analysis options from simple case-control association to complex linear models supporting covariates, interactions and random effects.
Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, or identify genomic regions shared identical by state (IBS) between related or unrelated individuals. You can also analyze patterns of linkage disequilibrium, correct for population structure and discover SNP-SNP interactions. Create, compress and easily integrate relationship matrices into association tests to simultaneously correct for population structure and relatedness with Q-K mixed model analysis.
With support for intensity, aligned read, and count data formats, JMP Genomics lets biologists and biostatisticians normalize and analyze both array data and summaries from next-gen studies, providing several scaling and normalization methods tailored for count data sets. Point-and-click workflows simplify gene and exon expression, and RNA-seq analysis for new users.
JMP Genomics also supports more complex expression analyses, such as screening for allele-specific expression, filtering intensities or counts, performing batch normalization, and applying sample and gene filters to quickly reanalyze subsets of your data.
Gene Expression Analysis for Absolute Beginners
JMP Genomics provides exceptional tools for statistical geneticists, from simple case-control association and linkage disequilibrium analyses to complex linear models supporting covariates, interactions and random effects. The software seamlessly imports variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, PLINK text and binary files, and common output formats from SNP arrays.
With comprehensive GWAS capabilities, JMP Genomics lets you examine associations between SNPs and multiple continuous traits, correct for population structure, and explore SNP-SNP interactions. Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, and find identical by state genomic regions between related or unrelated individuals.
Point-and-click Q-K mixed model analysis simplifies the creation, compression and integration of relationship matrices into association tests, making it easy to simultaneously correct for population structure and relatedness.
Next-generation sequencing experiments yield a vast amount of information, from variant genotypes to RNA expression levels. JMP Genomics provides downstream statistical analysis capabilities for aligned reads from state-of-the-art sequence analysis pipelines.
Import counts from text formats, or summarize counts from SAM and BAM files to take advantage of normalization and generalized linear modeling methods tailored for count data. Basic workflows for RNA-seq and miRNA-seq streamline steps in statistical analysis.
Load genotypes directly from a variety of text formats or VCF files, or call variants from a set of BAM files using a reference genome. JMP Genomics supports a wide range of methods for association analysis of rare and common SNP variants and can identify regions identical by state (IBS) between related or unrelated individuals.
JMP Genomics lets researchers take advantage of the rich information within sequencing experiments by screening for significant correlations between different data types. The JMP Genomics Browser provides comprehensive views of next-gen data, showing counts or statistical analysis results, and overlaying histogram and heat plot tracks with individual- or group-level summaries to complement known SNP and gene track.
Considerations with mRNA-seq Data Analysis
JMP Genomics provides a suite of interactive processes for the construction, optimization, and visualization of genetic marker linkage maps used to improve agronomic crops and guide animal breeding efforts.
Geneticists can identify linkage groups automatically or interactively using genetic distance, recombination fraction, or genotypes from experimental inbred crosses. JMP Genomics includes advanced optimization algorithms to order markers within linkage groups, with options to designate consensus groups and framework markers. Intuitive graphics let you visualize and filter newly created or imported marker maps, or create high quality multichromosome views.
JMP Genomics 7 provides new algorithms to allow larger marker data sets in linkage mapping analysis. Additional support for outcrossing populations is also available, both in linkage mapping and downstream QTL analysis.
Newly constructed marker maps can be readily used to explore genotype-environment interactions in multienvironment trials, summarize phenotype information with interactive graphics, and perform QTL analysis. Haley-Knott regression and permutation options expand capabilities for interval and composite interval mapping of QTLs. Now in JMP Genomics 7, multiple interval mapping methods allow more extensive QTL analyses.
Easily explore copy number and loss of heterozygosity (LOH) data between groups or within individuals using JMP Genomics. Data quality assessment functions identify outlier samples and data points, and fast circular binary segmentation (CBS) performs partition analysis to visualize shared patterns across samples. Built-in functions allow you to easily adjust copy number or LOH data sets using paired or grouped reference samples.
ANOVA-based approaches are also available to find statistically significant differences between experimental groups, or to compare individual samples to a reference group. Interactive graphical displays and the JMP Genomics Browser make it simple to identify shared regions of interest as well as unique variations in copy number.
Biomarker Discovery and Predictive Modeling
JMP Genomics excels at predictive modeling, offering a broad and robust array of methods, as well as options for predictor filtering, predictor lock-in, and cross-validation. The software guides biologists and biostatisticians through comprehensive exploratory analyses of separate and paired data types and allows combining multiple predictor types to build, test and cross-validate biomarker signatures with a choice of hold-out methods.
JMP Genomics 7 provides improvements in computational speed with high performance logistic regression. Predictive modeling capabilities now include Ridge regression, elastic net regularization, and additional methods for predictor optimization and variable selection – a significant advantage for high-dimensional genomics data. The new predictive modeling review tool allows users to easily build and compare multiple models for multiple traits.
Extensive participation of JMP developers in the MicroArray Quality Control consortium has influenced the development of predictive modeling functions in JMP Genomics. Replication and iteration strategies implemented in the software seek to reduce bias, with honest cross-validation approaches that can accurately assess the relative performance of hundreds of different models at a time.
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