Variant analysis workflow in GeneSpring GX is designed for detection, management and analysis of genetic variants such as single nucleotide polymorphisms (SNPs) and InDels. The webinar showcases SNP data analysis from a public repository for identification of germline and somatic mutations. This is a simple workflow for selection of homozygous SNPs from Variant Call Format (VCF) file and for filtering based on heterozygous SNPs, multi-nucleotide polymorphisms (MNPs) and insertion-deletions (InDels). Multi-omic integration of datasets shows related attributes from different datasets in a biologically meaningful way.

GeneSpring GX’s newly introduced NGS Gene Expression Analysis workflow is designed to address the need of quickly analyzing processed RNA-Seq data by biologists. It allows expression profiling, statistical analysis and biological interpretation of quantified and normalized RNA-Seq data. The webinar will demo this workflow using lung adenocarcinoma RNA-Seq data from TCGA. It will describe steps to analyze and interpret processed RNA-Seq data, look at the biological context of significant genes in single as well as multi-omics environment and, visualize data in elastic genome browser using gene and transcript information from databases like UCSC, Ensembl, RefSeq, Gencode.

High affinity antisense oligonucleotides (ASOs) containing bicylic modifications (BNA) such as locked nucleic acid (LNA) or constrained ethyl (cEt) designed to induce target RNA cleavage have been shown to have enhanced potency along with a higher propensity to cause hepatotoxicity. In order to unravel the mechanism of this hepatotoxicity, we leveraged GeneSpring GX Analysis Software to analyze transcriptional profiles from the livers of mice treated with a panel of highly efficacious hepatotoxic or non-hepatotoxic LNA ASOs. With hierarchical clustering in GX, we demonstrated that the liver of mice treated with non-hepatotoxic LNA ASOs displayed highly selective on-target transcript knockdown, while the levels of many unintended transcripts were reduced in mice treated with hepatotoxic LNA ASOs. Using the correlation analysis option in GX, we showed that plasma ALT, a biomarker for liver toxicity, was correlated to the transcriptional signature, which was concurrent with on-target RNA reduction and preceded transaminitis. Surprisingly, mRNA transcripts commonly reduced by toxic LNA ASOs were not strongly associated with any particular biological process, cellular component or functional group, rather they tended to have much longer pre-mRNA transcripts. We also demonstrate that the off-target RNA knockdown and hepatotoxicity was dependent on RNase H1. This suggests that for a certain set of BNA ASOs, hepatotoxicity can occur as a result of unintended off-target RNase H1 dependent RNA degradation.

This webinar will focus on the below aspects of GeneSpring GX/ MPP's export / import functionality: i) storing projects in a local drive as backup or for transferring data between systems, ii) saving projects to cloud storage space (Dropbox or Google Drive), iii) Sharing of projects between collaborators using cloud storage, iv) importing projects from storage devices including local drive, Dropbox, or Google Drive, and v) options available to export associated data with a project and importing back into GeneSpring GX/MPP.

In this webinar, using protein and gene expression data, we will discuss and demonstrate how GeneSpring GX/MPP can be used for the joint analysis of individual omics data and contextualized with biological pathways. The highlights of the webinar include i) proteomics data processing, ii) discovery proteomics analysis, classification in tumor samples using clustering and correlation analysis, iii) combined analysis of protein and gene expression that identified core signatures for tumor classification, and iv) multi-omic pathway analysis.

This session will demonstrate the metadata framework and correlation functionality of GeneSpring using a case study. Using the expression values, copy number and associated metadata for 173 samples from TCGA, we will highlight and demonstrate metadata framework and correlation analysis functionality in GeneSpring 13.

This session will introduce the new integrated multiomics analysis workflow using high throughput microarray and next generation sequencing data. The webinar will demonstrate the multi-omics functionality using Strand NGS 2.1 and GeneSpring 13.

This seminar will give a broad overview of key new features, enhancements, and workflows in GeneSpring. The functionality of selected features will be demonstrated in GeneSpring, including GEO dataset importer, MeSH pathway builder, creating gene-level experiment from probe-level data, and viewing data in the genome browser.

This is an introductory eSeminar on how to use GeneSpring to analyze Agilent 1-color gene expression arrays. The goal of the eSeminar is to help users fully understand of what GeneSpring is doing to the data from import, to getting a list of statistically significantly differentially expressed genes, to getting biological contextualizations for those genes, and then to translate them to other types of experiments such as to a microRNA experiment.

This eSeminar will demonstrate the workflow for microRNA analysis using Agilent miRNA array data. We will discuss normalization methods available, filtering options, and statistical analyses to identify differentially expressed miRNAs. Biological contextualization of differentially expressed miRNAs will be demonstrated. Specifically, we will show you how to use TargetScan data to identify gene targets of the differentially expressed miRNAs and determine whether there is a significant enrichment of these targets in any GO category or biological pathways.

This seminar will explore the functionalities of the genome browser in depth. During the demonstration, you will learn how to plot, overlay, and navigate multiple data types in the genome browser. We will also demonstrate how to import new genome builds and annotation tracks. Using the custom track import function, we will show how generic data can be visualized in the genome browser. Specifically, we will explore how to import DNA methylation data into the genome browser and visualize the dataset within the context of gene expression data.

This seminar will focus on the usability of the Pathway analysis module in GeneSpring to build and analyze large biological networks. Using a particular condition as an example, we will perform pathway analysis to understand the distinct signaling pathways operating in presence and absence of estrogen signaling. The study will focus on key biological processes and known pathways, activated in each condition sub-type and identify key regulators and target marker genes of each pathway. The study will also demonstrate a query for potential molecules and inhibitors of the signaling pathways. We will also demonstrate the use of the NLP tool and the MeSH Browser, available in this module to build concept networks relevant to the condition's biology.

This eSeminar will introduce our new network analysis function in GeneSpring. We will give a description of the gene product interaction database provided in GeneSpring and demonstrate how to build biological interaction networks from your genes of interest. In addition, we will also demonstrate how to create networks using interactions mined by next-generation natural language processing (NLP) algorithms.

The Genome-wide association analysis workflow will be used, in this session, to replicate a study analyzing disorder susceptibility genes, using SNP arrays. This webinar will walk you through all the steps of analyzing a case-control GWAS study and will demonstrate the QC steps and use of filters, (i.e. the filters on Differential missingness, HWE and MAF), in order to generate an appropriate SNP list for further analysis. Complex analysis steps of the GWAS workflow, including the EIGENSTRAT correction for stratification, SNP tagging, SNP regression and LD analysis will all be explored in the context of this data set.

This session will demonstrate the use of various analytical tools in the new genomic copy number analysis workflow to identify important aberrations associated with a particular disorder. The analysis of this data set will cover topics from quality control of samples, segmentation, filtering for genomic regions of interest, and functional analysis of genes overlapping these regions. Results of the copy number analysis will be integrated with a corresponding gene expression experiment to demonstrate how integrative analysis can lead to a more comprehensive understanding of the disorder.

The joint analysis of Microarray and RNA-seq data will allow for comparison of differential gene expression across these platforms. This session will demonstrate ways to find significant SNPs and pursue an integrated approach by performing Multi- Omic Analysis in GeneSpring to find biological pathways significant to both experiment types.

This eSeminar will demonstrate the functions, workflows, and visualization tools available in GeneSpring to help you compare Affymetrix and Agilent microarray expression data.

This eSeminar will demonstrate the workflow for analysis of real-time PCR data using Applied Biosystems’ real-time PCR SDS RQ data. We will demonstrate how results from real-time PCR can be directly compared to the corresponding microarray data for cross-validation purposes.