QIAGEN Bioinformatics
Jason Garner Neha Jalan, Ph.D. Account Development Manager Field Applications Scientist Qiagen Advanced Genomics Qiagen Advanced Genomics [email protected] [email protected] 210-219-5152
Sample to Insight 1 QIAGEN Bioinformatics NGS Application Support
Sample Targeted Library Data Sample NGS Run Insight Isolation Enrichment Construction Analysis Interpretation
NGS Platform: Application: Solution: Expression Analysis & Interpretation Expression • Biomarker Discovery Profiling RNA-Seq ü Biomedical Genomics Workbench • Toxicogenomics ü Ingenuity Pathway Analysis • Transcription regulation ü TRANSFAC
Variant id / • Gene expression regulatory analysis Prioritization DNA-Seq • Downstream effects & proteomics Diagnostics / Clinical Variant Detection & Interpretation
• Hereditary Disease ü Biomedical Genomics Workbench Expression Small RNA-Seq. Methyl-Seq, • Biomarker Discovery & Stratification ü QIAGEN Clinical Insight ChIP-Seq Regulation • Oncology ü Ingenuity Variant Analysis
• Rare & Undiagnosed Disease ü HGMD and PGMD
• Clinical sequencing and gene panels
Sample to Insight 2 End to end analysis and interpretation of NGS data
Multifactorial analysis 2D hierarchical clustering, PCA and Volcano plots, Venn diagram
Identify significant isoforms and understand the biology Quantify and Statistical Normalize Expression Analysis
Identify Generate Import Data Sequence Pathway Upstream Causal and Alignment Analysis Metadata Regulators Hypotheses Annotate Call and Variants Prioritize Variant
Understand variant impact on upstream regulators and pathways
Visualize expression changes and Prioritize variants on the variants together biology; Path to Phenotype
Sample to Insight 3 RNA-seq Explorer Solution
Transcriptome analysis of Pancreatic cancer exosomes reveal pathways and biological processes involved in metastatic progression
Sample to Insight 4 Pancreatic Ductal Adenocarcinoma (PDAC)
Pancreatic cancer is the fourth-leading cause of cancer-related death in the USA
• PDAC: more > 90% of pancreatic cancer cases, highly lethal. All stages combined, 1 and 5 year relative survival rates are 27% and 7%, respectively. • More than half of patients are diagnosed at a late stage, for which 5-year survival is 2%. • More than 53,000 people in USA diagnosed each year and more than 40,000 will die. Worldwide, more than 337,000 will be diagnosed, and more > 330,000 will die. • The incidence is rising, the number of new cases and deaths predicted to double by 2030. • Detection/Diagnosis: • Very difficult to diagnose at early stages of disease, develops without early symptoms • Treatment: • Only curative treatment is complete surgical resection, but fewer than 20% of patients are candidates • Patients with localized disease and small cancers with no lymph node metastases and contained within the capsule of the pancreas, complete surgical resection can yield 5-year survival rates of 18% to 24%. • Approximately 80% of patients who undergo surgery eventually relapse and die from the disease • Risk factors: • Age (> 60 yr), Smoking, Diabetes, Alcoholism, Obesity, Pancreatitis • Genetic Predisposition (hereditary disorders: pancreatitis, FAMMM, familial BrCa and OVCa, Fanconi Anemia, Ataxia-telangiectasia, Cystic fibrosis, …)
Sample to Insight 5 The main areas of Liquid Biopsy
Non-invasive method of sampling biofluids.
Free circulating Nucleic acids: DNA, mRNA, miRNA, lncRNA
• Allows early disease detection
• Allows evaluation of metastasis in real-time and monitoring of the actual treatment response Circulating Tumor Cells • Enables investigation of primary tumors and metastases through simple, non-invasive blood tests
• Enables assessment of tumor heterogeneity and monitoring of tumor dynamics Exosomes; Total RNA, mRNA, miRNA, lncRNA, DNA • Enables study of the “tumor dormancy” phenomenon
• Is much faster than classical biopsy testing
• Can be cheaper than classical biopsy testing
Sample to Insight 6 Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver. Nat Cell Biol. 2015,816-26. Costa-Silva B et al. (upenn.edu, mskcc.org, cornell.edu)
“Cancer cells are known to secrete exosomes with pro-metastatic effects. Pancreatic- cancer-derived exosomes are now shown to promote liver metastasis by eliciting pre- metastatic niche formation through a multi-step process. This involves uptake of exosome- derived factors by liver Kupffer cells and hepatic stellate cell activation to generate a fibrotic microenvironment with immune cell infiltrates that favours metastasis.”
Sample to Insight 7 Description of the study: SRP058375
Analyses of total RNA from Kupffer cells (KC) + BxPC-3 (PDAC) exosomes
• Human Kupffer cells (KC) • Treatment: BxPC-3/ BxPC-3 Beta5KD exosomes or PBS • Treatment protocol: Cells were treated in 6 wells plates with 5ug/mL of exosomes every other day for 14 days • Extraction total RNA using RNeasy Mini Kit (QIAGEN) • Library source: transcriptomic, Library selection: cDNA • Illumina HiSeq 2500
Sample to Insight 8 Simple to use RNA-Seq solution (FASTQ to Insight)
RNA-Seq Sample FASTQ files Metadata
Import
Variants
Filtered variant loss/gain and Transcript-level ACMG classification data expression data
Sample to Insight 9 Introduction to Biomedical Genomics Workbench (BxWB), data analysis
Fast and Easy Analysis
• Accurate results
ü Whole Genome Sequencing ü Whole Exome Sequencing ü Targeted or Whole Transcriptome Sequencing ü ChIP-Seq data
• Simplified analysis
ü Comprehensive end-to-end analysis workflows for single samples or cohort studies ü One-click analysis of QIAGEN GeneRead DNASeq Amplicon Panels ü Streamlined integration with Ingenuity Pathway Analysis (IPA) & Ingenuity Variant Analysis
• Flexible & extensible
ü Ready-to-use workflows can be customized ü Build your own workflows
Sample to Insight 10 BxWB to IPA: data analysis to interpretation, FASTQ to Tracks
Selection of Dataset (SRP058375)
1. Import FASTQ data (from SRA and convert to FASTQ) 2. Create metadata as Excel spreadsheet, import and match to the samples 3. Trim sequences (by quality and to remove adapter sequence) 4. Run RNA-Seq analysis tool to map the reads using Ensembl or RefSeq model:
5. Expression tracks and reports are created at Gene or Transcript level
Sample to Insight 11
BxWB to IPA: data analysis to interpretation, Tracks to STATS
From Tracks (GE or TE) to Differential Expression, Heat Map, PCA Plot, Venn Diagram
Sample to Insight 12 Analyze Expression Data and Upload Comparisons to IPA
Venn Diagram (GE) Venn Diagram (TE)
Heat Map PCA plot
Volcano Plot
Sample to Insight 13 Differential expression for RNA-Seq analysis
Kupffer Cells treated with BxPC-3 Exosomes vs Kupffer Cells treated with PBS (TE)
Sample to Insight 14 What is Ingenuity Pathway Analysis?
Sample to Insight Title, Location, Date 15 Introduction to Ingenuity Pathway Analysis (IPA), Interpretation
Gene View, Chem View, and Disease/Function View
Human and Mouse Isoform Views
Canonical Pathways/Molecule Activity Predictor Upstream Analysis Upstream Regulators/Mechanistic Network/Causal Networks Diseases & Functions Downstream Effects Analysis
Regulator Effects microRNA Target Filter
BioProfiler
IsoProfiler
Interaction Networks, Build and Overlay tools
Sample to Insight 16 How can IPA help you?
n Biological understanding of large data sets o Differential gene expression, array and RNAseq (transcriptomics) o Differential protein expression (proteomics) o Metabolomics o miRNA expression o Gene List – Chip-on-chip / chip seq – siRNA screening o Methylation o Protein phosphorylation
n Deep pathway understanding of a single gene/protein o Drug/therapeutic target discovery
Sample to Insight 17 Species Supported
Species Supported
n Human, Mouse, Rat in full content n IPA uses HomoloGene to map other identifiers to human/mouse/rat orthologs (though supporting content for the additional species will be specific to human, mouse, and rat) o Arabidopsis thaliana o Bos taurus (bovine) o Caenorhabditis elegans o Gallus gallus (chicken) o Pan troglodytes (chimpanzee) o Danio rerio (zebrafish) o Canis lupus familiaris (canine) o Drosophila melanogaster o Macaca mulatta (Rhesus Monkey) o Saccharomyces cerevisiae o Schizosaccharomyces pombe
Sample to Insight Introduction to QIAGEN Ingenuity & IPA - www.ingenuity.com 18 Data upload
n Three file formats:
o Excel spreadsheet (single sheet only)
o tab delimited text file
o Cuffdiff file n One ID column and header row n Multiple observation or single observation
Sample to Insight 19 Core Data Analysis- Summary
§ Canonical Pathway Analysis q Predicts pathways that are changing based on your dataset q Predict directional effects on the pathway molecules not in dataset (MAP overlay tool)
§ Upstream Regulator Analysis q Predicts activated/inhibited regulators responsible for observed data q Predicts master regulators
§ Diseases and Functions Analysis q Predicts the directional biological effects (cellular processes, biological function) of gene/protein set – “Increase in cell cycle” – “Decrease in apoptosis”
§ Regulator Effects q Identifies specific hypothesis: upstream regulator pathways leading to a downstream phenotype.
§ Networks q Identifies gene networks within dataset.
Sample to Insight 20 IPA Core Analysis
Summary of BxPC-3 exosomes vs PBS , TE level , FC1.5 p<0.05, RPKM>5
Sample to Insight 21 Canonical Pathways and their Pathway Activity Analysis
Hepatic Fibrosis and Diapedesis Pathways are involved after KC have taken up BxPC-3 exosomes
BxPC-3 vs PBS (TE)
Sample to Insight 22 Canonical Pathways and their Pathway Activity Analysis
Inflammatory response, actin cytoskeleton pathways are induced by BxPC-3 exosomes
BxPC-3 vs PBS (TE)
Sample to Insight 23 Comparison of Canonical Pathways across the conditions w/wo ITGB5
IL6 signaling is predicted to be activated in BxPC-3 vs PBS and BxPC-3 vs BxPC-3 Beta5KD
Sample to Insight 24 IL6 signaling is involved in differentiation of leukocytes
Using Molecule Activity Predictor (MAP), differentiation is predicted to be activated after uptake of BxPC-3
BxPC-3 vs BxPC-3 Beta5KD (TE)
Differentiation of leukocytes
Sample to Insight 25 Upstream Regulator Analysis of Kupffer Cells w BxPC-3 vs PBS (TE)
Top Upstream Regulators (Cytokine, Growth Factor) predicted to be activated highlights Inflammatory Response
Sample to Insight 26 IL6 is involved in tumor progression induced by BxPC-3 exosomes
IL6-driven network is predicted to be activated and contributes to increase invasion and adhesion of tumor cell lines and contributes to liver tumor
adhesion of tumor cell lines
Liver tumor
Invasion of tumor cell lines
Sample to Insight 27 Causal Network linking IL6R and Metastasis
IL6R is predicted to be activated and to increase metastasis after uptake of BxPC-3 by KC
Master Regulator
Regulators (12)
Targets (20/152)
Sample to Insight 28 Hypothesis to inhibit induced metastasis after uptake of BxPC-3 by KC
Potential immunotherapy to prevent metastasis: addition of Tocilizumab (approved mAb for RA)
Sample to Insight 29 Causal Network linking IL11RA and Metastasis
IL11RA is predicted to be activated and to increase metastasis after uptake of BxPC-3 by KC
Master Regulator
Regulators (4)
Targets (20/238)
Sample to Insight 30 Hypothesis: inhibition of IL11RA
Inhibiting IL11RA is tested in Castration Resistant Prostate Cancer (CRPC) and has shown success in an Endometrial Cancer in vivo model in mouse. Inhibiting IL11RA would inhibit metastasis induced after uptake of BxPC-3 by KC
Sample to Insight 31 Transcriptome in induced metastasis after PDAC exosomes uptake
Using QIAGEN Bioinformatics “RNA-Seq Explorer solution” two immune proteins were identified as potential therapeutic targets in metastatic pancreatic cancer
We were able to highlight important parameters and to explore transcriptome of Kupffer cells after uptake of pancreatic tumor (pancreatic ductal adenocarcinoma e.g. “PDAC”) exosomes in vitro:
ü Determined which and how signaling cascades are involved in the pre-metastatic niche (IL6 signaling, …) (Canonical Pathways)
ü Proposed new hypotheses that visualize which immune components could be targeted to inhibit metastasis processes after uptake of tumor exosomes by liver cells (IL6R, IL11RA) (Upstream Analysis, Causal Network, Regulator Effects)
Sample to Insight 32 End to end analysis and interpretation of NGS data
Multifactorial analysis 2D hierarchical clustering, PCA and Volcano plots, Venn diagram
Identify significant isoforms and understand the biology Quantify and Statistical Normalize Expression Analysis
Identify Generate Import Data Sequence Pathway Upstream Causal and Alignment Analysis Metadata Regulators Hypotheses Annotate Call and Variants Prioritize Variant
Understand variant impact on upstream regulators and pathways
Visualize expression changes and Prioritize variants on the variants together biology; Path to Phenotype
Sample to Insight 33 Finding Rare/Causal Variants Using Ingenuity Variant Analysis (IVA)
Sample to Insight 34 Finding a rare disease-causing variant
n Hugh Reinhoff’s daughter was born with features usually associated with Marfan’s and Loeys-Dietz syndromes, but was not affected by these syndromes n Hugh Reinhoff spent a decade sequencing whole exomes of his family members and analyzing the data to find the mutation responsible for his daughter’s rare genetic disease n This tutorial uses a subset of the whole- exome data, BxWB, and the IVA plugin to recapitulate in minutes the identification of a unique deleterious variant within a family of four
Nature 498, 418–419 (27 June 2013) doi:10.1038/498418a Sample to Insight 35 Variant Calling Overview
Sample WES reads Reference genome Targeted region file
Restrict calling to target Map reads to reference regions
InDels & Structural Variants, Reads track Local Realignment
Variant Detection
Remove false positives
Ingenuity Variant Statistics and Variant track Analysis (IVA) coverage tracks
Sample to Insight 36 Steps for Variant Calling
1. Upload sample reads, reference, and targeted regions file
2. Map reads to reference
3. Detect variants
4. Upload variants to IVA to identify unique deleterious variants
Sample to Insight 37 Ingenuity Variant Analysis
Biological interpretation of human whole genome, exome, and targeted panel samples
Sample to Insight 38 A knowledgebase (KB) that’s 15 years in the making
Unprecedented Access to Literature Knowledge
Literature findings The Ingenuity MD/PhD level Biomedical Ontology Knowledge Base curators
The Ingenuity Ontology
Sample to Insight 39 Variant Analysis Content
Quality, Context, Coverage, and Timeliness of Content (ca.1/2014)
Mouse Genome Database
Additionally • 349,748+ Ph.D./M.D. expert-curated human phenotype-associated mutation findings • ~3M+ manual literature findings • 21,458+ curated disease models • 185,310+ curated pharmacogenetic (PGx) findings Sample to Insight 40 Allele Frequency Community Data www.allelefrequencycommunity.org n Leverage the world’s largest pool of anonymized allele frequency data n Reduces false positives in analyses by removing variants that are commonly seen in the general population n Contains Whole Exome AND Whole Genome data n Better representation of Insertions and Deletions n Larger than ANY other public resource o AFC launched with 70,000 samples with >8,000 as whole genomes o 12x larger than Exome Variant Server data n The initial launch version of the database already provides a 43% average false positive rate reduction in a benchmarking set of whole-genome Diagnostic Odyssey cases n AFC will grow as more people opt-in o Launched on 25th February 2015 with 70,000 Samples including 8,000 Whole Genomes o Currently at over 100,000 samples, including over 14,000 Whole Genome samples
Sample to Insight 41 Ingenuity Variant Analysis: Identify and Filter to Causal Variants
Linking variants and the transcriptome Genetic and structural filters reduce search space from over 500,000 to ~2,000 cancer driver variants
Remove Poor Quality Variant and Usual Suspects
Remove variants found in healthy populations.
Assessment (Pathogenic, etc) and Gain or Loss of function
Compare variants found in cases vs control
Path to Phenotype™ to identify biologically relevant variants.
Biological, pharmacogenomics and other statistical filters have not been used here
Sample to Insight 42 Filter Cascade: Add/Rearrange Filters
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Sample to Insight Refine and Dynamically Filter Variants
Use biological associations and molecular interactions Regulators of…
Pathways
Phenotype Disease
Variants Diseases / Phenotypes / Genes / Signalling pathways / Biological processes / Protein domains / Protein families
Your gene Protein list domain
Sample to Insight QIAGEN Sample to Insight | www.qiagenbioinformatics.com | www.qiagen.com 44 Results: The Short List
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Sample to Insight Results: The Short List
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Sample to Insight Results: The Short List
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Sample to Insight IVA export to IPA
Sample to Insight 48 End to end analysis and interpretation of NGS data
Multifactorial analysis 2D hierarchical clustering, PCA and Volcano plots, Venn diagram
Identify significant isoforms and understand the biology Quantify and Statistical Normalize Expression Analysis
Identify Generate Import Data Sequence Pathway Upstream Causal and Alignment Analysis Metadata Regulators Hypotheses Annotate Call and Variants Prioritize Variant
Understand variant impact on upstream regulators and pathways
Visualize expression changes and Prioritize variants on the variants together biology; Path to Phenotype
Sample to Insight 49 Biomedical Genomics Workbench
Cross-platform desktop application with a graphical user-interface
Sample to Insight 50 Key design elements
Research continuity through comprehensive toolbox
GENOMICS METAGENOMICS & TYPING TRANSCRIPTOMICS Microbial Gx , Microbiome Pathogen typing MOL. BIO. Food Safety EPIGENOMICS Analysis Outbreak analysis
Meta De Novo Genome SNP and InDels GeneMark Assembly
MetaGeneMark Meta MGM plugin GeneMark RNA-Seq Structural MGM Small RNA-Seq Variants MetaGeneMark CLC Microbial plugin Genomics Module CLC CLC CLC Microbial CLC Biomedical Genomics Genomics Module Biomedical GxS Workbench Server Workbench
ChIP-Seq TF binding site prediction BLAST, CLC Genome Primer Design, Finishing Cloning Module GFM
GFM Metagenomics rd Bisulfite Seq + 3 party extensibility Microbial Typing (CosmosID CLC Genome Finishing Module GeneProbe Inc.)
Sample to Insight 51 CLC Workbench – Complex Tasks, Simply Done
Streamlined workflows and a rich toolbox to efficiently process data
Customize workflows
QC reports
History
Visualization and Validation
Sample to Insight 52 Ready to Use Workflows
Very easy customization of ready-to-use workflows
• Add new tool with drag & drop • Remove tools • Add new outputs (e.g. all large deletions) • Change parameters and lock them down
Sample to Insight 53 Flexible workflows tailored for end-users
Lock key parameters of workflow to standardise processing
Sample to Insight 54 CLC Biomedical Workbench Solutions Biomedical Workbench • DNA-seq applications for clinical teams • Tools most relevant to clinical applications on human data • Variant Calling and analysis • Structural Variation • Automation and Reference Management geared towards Human research • CNV Analysis • Ready-to-use work flows • Gene expression analysis: • RNAseq • Microarray • Epigenomics Analysis • Cloning and Sanger Sequencing • QIAGEN GeneRead Panel Analysis • Microbial Genomics
Sample to Insight Microbial Genomics Pro Suite
Integration of tools into single NGS analysis platform
Applications areas Microbial Gx , Microbiome Pathogen typing Food Safety Analysis Outbreak analysis Microbiome profiling o Function o Taxonomy o Comparative Analytics Meta GeneMark
MGM MetaGeneMark plugin Genome/Metagenome Assembly
CLC Microbial o De novo assembly Genomics Module CLC CLC (incl. PacBio) Genomics Genomics Workbench Server o Metagenome- assembly o Gene finding, annotation
NGS-based isolate typing GFM o Taxonomy o Anti-microbial resistance CLC Genome Finishing Module o Epidemiology
Sample to Insight 56 Metagenomics Features – Taxonomic profiling
Microbiome Analysis (taxonomic profiling)
o Analyze 16S-, 18S rRNA or other amplicon data. From raw reads to interactive visualization in four steps.
o De novo or reference based OTU-clustering
o Supports common OTU Databases Greengenes, Silva, and UNITE.
o Visualization: Stacked bar charts, area charts and zoomable sunburst diagrams, Venn diagrams and 2D heatmaps to explore and compare the composition of microbiomes across samples, or sample groups.
Statistical Analysis
o Estimate Alpha- and Beta diversities.
o Principal Coordinate Analysis (PCoA) results in the context of the sample metadata.
o Perform PERMANOVA analysis
o Testing for differential abundance • Measure normalized fold change of OTU abundance. Statistics use generalized linear model (GLM) assuming a negative binomial distribution of OTUs. • Estimate statistical significance. • Explore correlations between sample groups. Sample to Insight 57 CLC Microbial Genomics Module - Microbiome Profiling Explore the composition of microbial communities: Take (16s) sequences from sample
Pre-process and QC sequences: trim primers, etc.
Cluster to identify Operational Taxonomic Units (OTU’s)
TCGCTACTATATCTTAAGGCTTAGCG TCGCGAGTATATCTTAAGGCTTAGCA Derive consensus OTU sequence to identify the TCGCTAGTATATCTTCAGGCTTAGCG species TCGCTAGTATATCTTAAGGCTTACCG
TCGCTAGTATATCTTAAGGCTTAGCG Use cluster size (read depth) to estimate OTU (species) abundance
Compare abundance of specific Use abundances of species as a biomarker species • Forensics • Causal to a phenotype • Clinical biomarker • Public health • Unique identifier • Clinical intervention
Sample to Insight 58 Key design elements
Preconfigured Ease of use Example Workflows
Reduce the training intuitive user interface. Interactivity between tables and graphics
Bridging the gap between data and biological insights Scientist-friendly Complex analyses made accessible to interface
bioinformatics experts and non-experts alike Data interpretation in through preconfigured example workflows. context of metadata
Shift the focus from data analysis to the biological interpretation of NGS analysis results through comparative multi-sample analysis in the context of sample metadata.
Sample to Insight 59 Microbial Genomics Pro Suite
Integration of tools into single NGS 60 analysis platform
Applications areas Microbial Gx , Microbiome Pathogen typing Food Safety Analysis Outbreak analysis Microbiome profiling o Function o Taxonomy o Comparative Analytics Meta GeneMark
MGM MetaGeneMark plugin Genome/Metagenome Assembly
CLC Microbial o De novo assembly Genomics Module CLC CLC (incl. PacBio) Genomics Genomics Workbench Server o Metagenome- assembly o Gene finding, annotation
NGS-based isolate typing GFM o Taxonomy o AM resistance CLC Genome Finishing Module o Epidemiology
Sample to Insight Metagenomics Features - Pathogen typing
NGS-based typing and whole genome analysis of microbial isolates.
Workflows o Confirm the identity and characteristics of pathogens or starter cultures, in one easy to use analysis workflow, revealing: • Taxonomy • Closest known reference genome (k-mer spectrum matching) • Multi locus sequence type (MLST) • Antimicrobial resistance genes • QC metrics (e.g. contamination) o Create phylogenetic trees, including K-mer and whole-genome SNP trees.
Reference data o Direct access to NCBI RefSeq and NCBI Pathogen detection db for foodborne illness-inducing pathogens. o Direct access to ResFinder database of antimicrobial resistance (AMR) genes. o Build your own custom reference genome or AMR gene databases.
Outbreak investigation Advantages o Investigate outbreak trees in the context of typing results or other sample information, to: o Time spent on tracking pathogen outbreaks to • establish an association between an outbreak and its source. source costs lives. • monitor pathogens outbreaks. Our predefined workflows simplify and o Manage samples, meta-information and results all from a convenient accelerate analysis steps for source tracking Analysis Dashboard. and outbreak analysis.
o Assemble PacBio reads (or other NGS data types) into high quality o We provide the NGS data analysis solution reference genomes that is most trusted in Public Health. Sample to Insight 61 Pathogen typing features
Added benefits with latest Microbial Genomics Module (MGM) 1.5 release
Typing can now be extended to all microbes (including viruses) and not just a limited set of bacteria.
Convenient access to food-safety relevant pathogens in NCBI Pathogen Detection database: o Salmonella enterica o Listeria monocytogenes o Escherichia coli and Shigella o Campylobacter jejuni o Acinetobacter baumannii o Klebsiella pneumoniae
Contamination control for each sample is now immediately apparent.
Custom gene list can now be used for AMR typing
Sample to Insight 62 Use case: NGS based Typing and Outbreak Analysis
Preconfigured workflow. Get in a single step from NGS data to typing results
One stop shop for typing/ phylogeny needs!
Sample to Insight 63 Use case: Typing Among Multiple Species
47 food borne outbreak strains sequenced
18 isolates from six outbreaks and 16 epidemiologically unrelated background strains. 8 S. Enteritidis and 5 S. Derby were also sequenced and used for comparison
Sample to Insight 64 Use case: Typing Among Multiple Species
Kmer tree to find the closest reference – S. enterica subsp. arizonae
NC_010067
Sample to Insight 65 Use case: Typing Among Multiple Species
Sample to Insight 66 Analysis example Association with outbreak 67 information
• When you click the node of the SNP tree, details of SNPs associated with each clusters are available.
Sample to Insight Pathogen typing – outbreak analysis at highest resolution
Accurate whole genome SNP phylogeny
Interactive tree visualization showing isolates in the context of o sample meta information o and typing results (labels and color codes)
Sample to Insight 68 In CLC Workbenches you can….
• QC and preprocess NGS data (RNAseq, smRNA, and DNAseq reads)
• Perform RNAseq, Microarrays, Statistical Expression Analysis
• Resequencing, Variant detection & comparison analysis
• De Novo genome assembly, genome finishing, & phylogeny
• ChIP-Seq, Bisulfite Sequencing
• Facilitate analysis with interactive visualization
• Construct automated workflows in user friendly interface
• Genome assembly and finishing
• Microbial Metagenomics, typing
• Can scale to organization’s needs
Sample to Insight 69 What can IPA do?
β P53 MYC TGF SNAI1
Upstream Regulators
gene gene gene genegene
gene gene gene
genegene
gene gene gene gene gene gene gene
gene gene IPA Cancer
Invasion Cell Death Cellular Movement
gene
gene gene gene gene Biological Processes
gene
gene gene gene
gene gene gene
gene gene gene
gene
gene gene gene gene
gene gene
gene gene gene gene
gene gene gene gene
gene gene
gene gene
gene gene gene ILK pathwayILK CT pathway CT Cell-Junction
gene
gene gene Canonical Pathways
gene
Large RNA seq dataset in Methodical analysis by IPA in form of a huge pile of papers form of organized binders on QUESTIONS? a bookshelf CONTACTS: Sample to Insight General: [email protected]
Ingenuity Variant Analysis
Rapidly identify and prioritize variants
Sample to Insight 71 Questions?
Thank You!!
Jason Garner Neha Jalan, Ph.D. Account Development Manager Field Applications Scientist Qiagen Advanced Genomics Qiagen Advanced Genomics [email protected] [email protected] 210-219-5152 Sample to Insight 72