1. The document discusses variant calling from NGS data and prioritizing variants. It covers calling variants, identifying somatic mutations by comparing tumor and normal samples, and identifying inherited variants using trio analysis.
2. Key steps include calling variants, filtering, identifying somatic mutations as variants present in tumor but not normal, and identifying inherited variants by applying models of inheritance to family trio data.
3. Prioritization considers functional impact, population frequencies, and visual inspection to select candidates for follow up.
The document discusses challenges in computational genotyping of structural variations for clinical diagnosis. It describes assessing accuracy of structural variation calls, challenges in functional interpretation, and how to call structural variations in routine scans. It also discusses using simulated and benchmark data to evaluate structural variation genotypers and their ability to sensitively and precisely genotype deletions. A new graph-based genotyper called Paragraph is highlighted for its ability to genotype multiple structural variation types with high accuracy based on benchmarking.
Digital RNAseq Technology Introduction: Digital RNAseq Webinar Part 1QIAGEN
QIAseq RNA is a revolutionary turnkey solution for digital gene expression analysis by NGS. From 10 genes to 1000, from one sample to 100, QIAseq RNA delivers precise results on both ION and Illumina sequencing platforms. The data from QIAseq RNA is directly comparable to expression analysis derived from whole transcriptome sequencing or by qRTPCR, only better, cheaper, faster, and more flexible. This webinar will describe the principles of digital expression analysis by NGS, and review the features and benefits of the QIAseq system, options available, and the integrated data analysis package.
1) The document discusses different whole genome amplification techniques for obtaining DNA from single cells, including PCR-based and PCR-free methods.
2) It provides comparisons of different whole genome amplification kits, finding that QIAGEN's REPLI-g Single Cell Kit has the highest genome coverage, lowest duplication rates, and best performance for detecting copy number variations and single nucleotide variations, making it optimal for single cell sequencing applications.
3) Case studies demonstrate that the REPLI-g Single Cell Kit provides more uniform coverage and significantly fewer sequencing errors compared to the MALBAC method.
Profile Multiple Cytokines and Chemokines Simultaneously with Very High Sensi...QIAGEN
Learn how to profile multiple cytokines and chemokines simultaneously with very high sensitivity and specificity using the standard ELISA reader. Available in different formats to suit your research needs such as single-analyte, multi-analyte or custom mix-n-match format for human, mouse and rat.
Innovative NGS Library Construction TechnologyQIAGEN
Next-generation sequencing (NGS) is a driving force for numerous new and exciting applications, including cancer research, stem cell research, metagenomics, population genetics, medical research and single cell analysis. While NGS technology is continuously improving, library preparation remains one of the biggest bottlenecks in the NGS workflow and includes several time-consuming steps that can result in considerable sample loss and the potential to introduce handling errors. Moreover, conducting single-cell genomic analysis using NGS methods has traditionally been challenging since the amount of genomic DNA present in a single cell is very limited.
VarMatch is a tool for comparing small variant datasets that uses a flexible scoring scheme and branch-and-bound algorithm. It identifies separators in the reference genome to break the problem into smaller independent parts that require less memory and time than other tools. VarMatch matches more equivalent variants than other methods, especially in low-complexity regions and dense regions with multiple variants close together. It also allows customizable scoring to maximize different goals, such as number of variants matched or edit distance.
The document discusses using NCBI databases to design quantitative PCR (qPCR) assays. It describes several NCBI tools that can be used:
1) The NCBI Nucleotide and Gene databases to obtain sequence information for the gene of interest.
2) NCBI BLAST to perform sequence searches and check primer specificity against relevant databases.
3) NCBI dbSNP to search for single nucleotide polymorphisms (SNPs) in the primer binding sites that could affect assay performance.
The document provides guidance on how to use these NCBI tools at various steps of the qPCR assay design process.
1. The document discusses variant calling from NGS data and prioritizing variants. It covers calling variants, identifying somatic mutations by comparing tumor and normal samples, and identifying inherited variants using trio analysis.
2. Key steps include calling variants, filtering, identifying somatic mutations as variants present in tumor but not normal, and identifying inherited variants by applying models of inheritance to family trio data.
3. Prioritization considers functional impact, population frequencies, and visual inspection to select candidates for follow up.
The document discusses challenges in computational genotyping of structural variations for clinical diagnosis. It describes assessing accuracy of structural variation calls, challenges in functional interpretation, and how to call structural variations in routine scans. It also discusses using simulated and benchmark data to evaluate structural variation genotypers and their ability to sensitively and precisely genotype deletions. A new graph-based genotyper called Paragraph is highlighted for its ability to genotype multiple structural variation types with high accuracy based on benchmarking.
Digital RNAseq Technology Introduction: Digital RNAseq Webinar Part 1QIAGEN
QIAseq RNA is a revolutionary turnkey solution for digital gene expression analysis by NGS. From 10 genes to 1000, from one sample to 100, QIAseq RNA delivers precise results on both ION and Illumina sequencing platforms. The data from QIAseq RNA is directly comparable to expression analysis derived from whole transcriptome sequencing or by qRTPCR, only better, cheaper, faster, and more flexible. This webinar will describe the principles of digital expression analysis by NGS, and review the features and benefits of the QIAseq system, options available, and the integrated data analysis package.
1) The document discusses different whole genome amplification techniques for obtaining DNA from single cells, including PCR-based and PCR-free methods.
2) It provides comparisons of different whole genome amplification kits, finding that QIAGEN's REPLI-g Single Cell Kit has the highest genome coverage, lowest duplication rates, and best performance for detecting copy number variations and single nucleotide variations, making it optimal for single cell sequencing applications.
3) Case studies demonstrate that the REPLI-g Single Cell Kit provides more uniform coverage and significantly fewer sequencing errors compared to the MALBAC method.
Profile Multiple Cytokines and Chemokines Simultaneously with Very High Sensi...QIAGEN
Learn how to profile multiple cytokines and chemokines simultaneously with very high sensitivity and specificity using the standard ELISA reader. Available in different formats to suit your research needs such as single-analyte, multi-analyte or custom mix-n-match format for human, mouse and rat.
Innovative NGS Library Construction TechnologyQIAGEN
Next-generation sequencing (NGS) is a driving force for numerous new and exciting applications, including cancer research, stem cell research, metagenomics, population genetics, medical research and single cell analysis. While NGS technology is continuously improving, library preparation remains one of the biggest bottlenecks in the NGS workflow and includes several time-consuming steps that can result in considerable sample loss and the potential to introduce handling errors. Moreover, conducting single-cell genomic analysis using NGS methods has traditionally been challenging since the amount of genomic DNA present in a single cell is very limited.
VarMatch is a tool for comparing small variant datasets that uses a flexible scoring scheme and branch-and-bound algorithm. It identifies separators in the reference genome to break the problem into smaller independent parts that require less memory and time than other tools. VarMatch matches more equivalent variants than other methods, especially in low-complexity regions and dense regions with multiple variants close together. It also allows customizable scoring to maximize different goals, such as number of variants matched or edit distance.
The document discusses using NCBI databases to design quantitative PCR (qPCR) assays. It describes several NCBI tools that can be used:
1) The NCBI Nucleotide and Gene databases to obtain sequence information for the gene of interest.
2) NCBI BLAST to perform sequence searches and check primer specificity against relevant databases.
3) NCBI dbSNP to search for single nucleotide polymorphisms (SNPs) in the primer binding sites that could affect assay performance.
The document provides guidance on how to use these NCBI tools at various steps of the qPCR assay design process.
This document summarizes data from the Genome in a Bottle Consortium SV Data Jamboree. It discusses several approaches to integrating structural variant calls from multiple technologies to establish a benchmark set of high-confidence SVs:
1) Finding deletions supported by multiple technologies with concordant breakpoints and filtering questionable calls. This approach identified 524 deletions supported by 2+ technologies ranging in size from 20bp to over 3kb.
2) A method called svcompare that compares SV calls across technologies and outputs a multi-sample VCF with variant details from each caller. This identified over 2,000 regions with structural variants called by multiple technologies.
3) A method called svviz that analyzes read support for
The Importance of Quality Control Steps in ExperimentsQIAGEN
From starting material to final results, every analysis workflow is a journey to unlock the biological information within your sample without altering it, and high-quality results are only achieved from high-quality samples.
Within each step, lie challenges directly related to the sample type and analysis technologies, and at each step, there is potential for multiple things to go wrong, jeopardizing your experiments, results and reputation. Therefore, standardizing samples and performing relevant quality control after critical steps is of utmost importance to ensure the quality and reproducibility of results, as well as reliable interpretation.
In this webinar, we will introduce you to the main sample quality parameters and their respective impact on downstream applications, discuss how to monitor them and cover the advantages of automating quality control along complex workflows.
This document summarizes the Genome in a Bottle (GIAB) Consortium's efforts to characterize structural variants in human genomes to serve as benchmarks. The GIAB Consortium has generated structural variant calls for 7 human genomes using diverse data types and analysis methods. The document describes the GIAB Consortium's process for integrating these data to identify high-confidence structural variant calls to include in version 0.6 of the structural variant benchmark set. It provides examples of different types of structural variants characterized and evaluates the trustworthiness of the benchmark calls based on independent validation. The document also discusses ongoing efforts to further improve structural variant characterization using emerging long-read technologies.
This document summarizes the development and application of using genetic segregation patterns in a large family to establish a "ground truth" set of variants for evaluating variant calling pipelines. The author analyzed whole genome sequencing data from a 14-person CEPH pedigree, identifying over 680 recombination crossovers to phase variants into maternal and paternal haplotypes. Over 99% of called variants were found to segregate in a manner consistent with genetic inheritance, establishing this set as a high-confidence "ground truth" for variant calling assessment. The analysis also identified areas for improvement in structural variant and small indel calling.
This document summarizes QIAGEN's products for sample preparation, targeted sequencing, and single-cell analysis across various areas of biomedical research including liquid biopsy, circulating tumor cells, and gene expression profiling. Key products mentioned are the QIAseq cfDNA All-in-One Kits for streamlined library preparation from plasma/serum, QIAseq Targeted DNA/RNA Panels for digital sequencing of genomic regions or genes, and QIAseq FX Single Cell DNA/RNA Library Kits for cell-to-library workflows from isolated single cells.
Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2QIAGEN
Traditional RNA sequencing (RNA-Seq) is a powerful tool for expression profiling, but is hindered by PCR amplification bias and inaccuracy at low expressing genes. QIAseq RNA is a flexible and precise tool developed for mitigating these complications, allowing digital gene expression analysis. In this webinar we will cover, in depth, the sample requirements, experimental design, NGS platform specific challenges, and workflow for gene enrichment, library prep and sequencing. The applications of QIASeq RNA Panels in cancer research, stem cell differentiation and elucidating the effects small molecules on signaling pathways will be highlighted.
This document discusses targeted sequencing of Genome in a Bottle (GIAB) reference materials using commercial panels to further characterize the materials. Seven individuals from various GIAB materials will be sequenced in triplicate using various targeted panels, including cancer and inherited disease panels. The results will be compared to the high confidence GIAB regions to identify any discordant variants and potentially add new information. Overlap between different panels and to the GIAB regions will also be examined.
Genome in a bottle for ashg grc giab workshop 181016GenomeInABottle
Genome in a Bottle (GIAB) provides benchmark genomes to evaluate the accuracy of variant calling from whole genome sequencing. GIAB has characterized 7 human genomes to date, including difficult variants. The benchmark calls continue to evolve as new data and methods are integrated. While current benchmarks enable validation of "easier" variants, GIAB is working to characterize more difficult variants and regions. This will allow validation of clinical tests focused on difficult sites. GIAB data and analyses are openly available to support method development and technology optimization.
Accelerate Your Discovery with QIAGEN Service Solutions for Biomarker Researc...QIAGEN
This slidedeck will highlight QIAGEN’s service capabilities in sample isolation, microarray and NGS-sequencing, qPCR panel and custom assay development and bioinformatics as we look at the identification of potential biomarkers and gene signatures. The applications of QIAGEN Service Core in microRNA discovery for toxicology markers in serum and plasma and in identification of RNA signatures for tumor stratification are featured. Learn how you can accelerate your research with QIAGEN service solutions.
GENCODE provides manual gene annotation of the human genome based on cDNA, EST, genomic and protein sequences, as well as publication and comparative analysis data. It is a merge of annotation from HAVANA and Ensembl. GENCODE v23 contains over 60,000 genes made up of protein coding, long non-coding RNA and pseudogenes. It is the reference gene set for the ENCODE project and can be viewed on the UCSC genome browser and Ensembl. HAVANA annotation of new patches and alternative loci is ongoing to improve GENCODE.
1. The document discusses a cost-effective approach for diploid genome assembly using sequence graphs. It presents a pipeline for diploid assembly using long reads from single individuals or trios.
2. The pipeline has advantages over previous methods as it can assemble genomes of any complexity accounting for different heterozygous rates and repeat contents, and can phase complex structural variants.
3. Results show the pipeline can assemble diploid genomes with as low as 15x coverage of long reads from each individual in a trio.
This document describes rtgTools, a toolkit for comparing and analyzing variant call format (VCF) files. It includes tools for VCF evaluation (vcfeval), generating receiver operating characteristic (ROC) curves from evaluation data (rocplot), counting Mendelian inheritance errors (medelian), basic VCF statistics (vcfstats), VCF filtering (vcffilter), VCF annotation (vcfannotate), and VCF merging (vcfmerge). The document focuses on the vcfeval tool and explains how it compares calls to a baseline by creating paths that maximize true positives and minimize errors using weighting and dynamic programming.
This document provides information about variation resources available from the National Center for Biotechnology Information (NCBI). It lists the staff members who work on variation resources and key collaborators. It describes some of the major databases hosted by NCBI that contain genetic variation data, including dbSNP, dbVar, ClinVar and GTR. It also summarizes some of the tools and viewers available for exploring genetic variation data from NCBI.
How to cluster and sequence an ngs library (james hadfield160416)James Hadfield
A presentation for people intersted in understanding how Illumina adapter ligation, clustering ands SBS sequencing work. Follow core-genomics http://core-genomics.blogspot.co.uk/
Next-Generation Sequencing an Intro to Tech and Applications: NGS Tech Overvi...QIAGEN
This slidedeck provides a technical overview of DNA/RNA preprocessing, template preparation, sequencing and data analysis. It covers the applications for NGS technologies, including guidelines for how to select the technology that will best address your biological question.
Analyzing Fusion Genes Using Next-Generation SequencingQIAGEN
Fusion genes are hybrid genes formed by the fusion of two separate genes. Translocation, interstitial deletion and chromosomal inversions are some of the genetic events that can lead to the formation of fusion genes. The occurrence of fusion genes and its implications in cancer have already been known, but the emergence of NGS technology – especially RNA sequencing – offers the potential to detect novel gene fusions. You can learn more about fusion genes and applying NGS to detect them at our upcoming webinar, presented by Raed Samara, Ph.D., QIAGEN’s Global Product Manager for NGS technologies.
In this webinar, Dr. Raed Samara will cover:
1. Fusion genes: what they are and a historical perspective
2. Fusion gene detection: the current status
3. RNA sequencing vs. digital RNA sequencing
4. How to detect and accurately quantify novel fusion genes in your sample
Diversity affects genome assembly in several ways. Complex diversity can lead to assembly errors and missing haplotype information when using old consensus models. It is important to represent diversity in genome assemblies to avoid missing sequences, haplotype configurations, gene models and overall knowledge. Improving genome assemblies with new reference models and techniques like linked-reads can help address these issues and improve individual genome analysis by better resolving allelic diversity, alternate loci and medically important genes.
Precision medicine for oncology requires accurate and sensitive molecular characterization. However, sample degradation, polymerase errors, and sequencing errors reduce accuracy for sequencing genetic variants. By incorporating molecular tagged adapters in target enrichment, and using DNA probes that deliver extremely even and deep coverage, we are able to demonstrate a 300-fold reduction in false positives at or above 0.25% variant frequency. In this presentation, Dr Mirna Jarosz discusses these methods and how they can significantly reduce error rates in your sequencing data.
This document discusses oncogenomics and cancer genomics technologies. It provides an overview of oncogenomics, the types of DNA biomarkers studied including mutations, and experimental strategies used for cancer genomics research. Key techniques discussed are next-generation sequencing, quantitative PCR (qPCR), and mass spectrometry. The document compares different technologies for mutation detection and profiling and their sensitivities. It also outlines the specifications and pipeline for developing a qPCR-based somatic mutation assay.
This document summarizes the work of the Genome in a Bottle Consortium to develop reference materials and methods for benchmarking genome sequencing and variant calling. The Consortium has extensively characterized the genome of the NA12878 sample to create a "gold standard" set of variant calls that can be used to evaluate sequencing platforms and bioinformatics pipelines. They are also analyzing additional samples to generate reference materials that will enable standardized testing and validation of clinical genome sequencing. The Consortium aims to develop benchmarking tools and strategies with the Global Alliance for Genomics and Health to facilitate regulatory approval and clinical application of genome sequencing.
Introducing VSWarehouse - A Scalable Genetic Data Warehouse for VarSeqGolden Helix Inc
1) The document discusses a genetic data warehousing solution called VSWarehouse from Golden Helix that allows users to store, query, and analyze large genomic datasets.
2) VSWarehouse uses a column-based storage approach that is more scalable than traditional row-based databases and allows efficient querying and joining of variant call and sample data.
3) Golden Helix is launching an early adopter program for VSWarehouse, offering interested customers the chance to influence the product roadmap in exchange for a discounted license and commitment through March 2016.
Golden Helix's End-to-End Solution for Clinical LabsGolden Helix
In this webcast, we provide an overview of our complete end-to-end clinical stack. Initially, we walk through our powerful secondary analysis pipeline which allows you to call SNVs and CNVs. We then demonstrate how various types of CNVs are called and discuss metrics that express the confidence associated with each call.
From there, we show you our powerful tertiary analysis capabilities for gene panels, exome, and whole genome data. We show how our users can move seamlessly from the variant interpretation stage to a clinical report. Lastly, we demonstrate how our genetic data warehouse, VSWarehouse, can be used in the clinic. We also demonstrate various use cases and show how a comprehensive assessment catalog can be utilized to ensure consistent analysis across multiple labs.
We hope you enjoy our first presentation on Golden Helix's entire end-to-end solution for clinical labs!
This document summarizes data from the Genome in a Bottle Consortium SV Data Jamboree. It discusses several approaches to integrating structural variant calls from multiple technologies to establish a benchmark set of high-confidence SVs:
1) Finding deletions supported by multiple technologies with concordant breakpoints and filtering questionable calls. This approach identified 524 deletions supported by 2+ technologies ranging in size from 20bp to over 3kb.
2) A method called svcompare that compares SV calls across technologies and outputs a multi-sample VCF with variant details from each caller. This identified over 2,000 regions with structural variants called by multiple technologies.
3) A method called svviz that analyzes read support for
The Importance of Quality Control Steps in ExperimentsQIAGEN
From starting material to final results, every analysis workflow is a journey to unlock the biological information within your sample without altering it, and high-quality results are only achieved from high-quality samples.
Within each step, lie challenges directly related to the sample type and analysis technologies, and at each step, there is potential for multiple things to go wrong, jeopardizing your experiments, results and reputation. Therefore, standardizing samples and performing relevant quality control after critical steps is of utmost importance to ensure the quality and reproducibility of results, as well as reliable interpretation.
In this webinar, we will introduce you to the main sample quality parameters and their respective impact on downstream applications, discuss how to monitor them and cover the advantages of automating quality control along complex workflows.
This document summarizes the Genome in a Bottle (GIAB) Consortium's efforts to characterize structural variants in human genomes to serve as benchmarks. The GIAB Consortium has generated structural variant calls for 7 human genomes using diverse data types and analysis methods. The document describes the GIAB Consortium's process for integrating these data to identify high-confidence structural variant calls to include in version 0.6 of the structural variant benchmark set. It provides examples of different types of structural variants characterized and evaluates the trustworthiness of the benchmark calls based on independent validation. The document also discusses ongoing efforts to further improve structural variant characterization using emerging long-read technologies.
This document summarizes the development and application of using genetic segregation patterns in a large family to establish a "ground truth" set of variants for evaluating variant calling pipelines. The author analyzed whole genome sequencing data from a 14-person CEPH pedigree, identifying over 680 recombination crossovers to phase variants into maternal and paternal haplotypes. Over 99% of called variants were found to segregate in a manner consistent with genetic inheritance, establishing this set as a high-confidence "ground truth" for variant calling assessment. The analysis also identified areas for improvement in structural variant and small indel calling.
This document summarizes QIAGEN's products for sample preparation, targeted sequencing, and single-cell analysis across various areas of biomedical research including liquid biopsy, circulating tumor cells, and gene expression profiling. Key products mentioned are the QIAseq cfDNA All-in-One Kits for streamlined library preparation from plasma/serum, QIAseq Targeted DNA/RNA Panels for digital sequencing of genomic regions or genes, and QIAseq FX Single Cell DNA/RNA Library Kits for cell-to-library workflows from isolated single cells.
Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2QIAGEN
Traditional RNA sequencing (RNA-Seq) is a powerful tool for expression profiling, but is hindered by PCR amplification bias and inaccuracy at low expressing genes. QIAseq RNA is a flexible and precise tool developed for mitigating these complications, allowing digital gene expression analysis. In this webinar we will cover, in depth, the sample requirements, experimental design, NGS platform specific challenges, and workflow for gene enrichment, library prep and sequencing. The applications of QIASeq RNA Panels in cancer research, stem cell differentiation and elucidating the effects small molecules on signaling pathways will be highlighted.
This document discusses targeted sequencing of Genome in a Bottle (GIAB) reference materials using commercial panels to further characterize the materials. Seven individuals from various GIAB materials will be sequenced in triplicate using various targeted panels, including cancer and inherited disease panels. The results will be compared to the high confidence GIAB regions to identify any discordant variants and potentially add new information. Overlap between different panels and to the GIAB regions will also be examined.
Genome in a bottle for ashg grc giab workshop 181016GenomeInABottle
Genome in a Bottle (GIAB) provides benchmark genomes to evaluate the accuracy of variant calling from whole genome sequencing. GIAB has characterized 7 human genomes to date, including difficult variants. The benchmark calls continue to evolve as new data and methods are integrated. While current benchmarks enable validation of "easier" variants, GIAB is working to characterize more difficult variants and regions. This will allow validation of clinical tests focused on difficult sites. GIAB data and analyses are openly available to support method development and technology optimization.
Accelerate Your Discovery with QIAGEN Service Solutions for Biomarker Researc...QIAGEN
This slidedeck will highlight QIAGEN’s service capabilities in sample isolation, microarray and NGS-sequencing, qPCR panel and custom assay development and bioinformatics as we look at the identification of potential biomarkers and gene signatures. The applications of QIAGEN Service Core in microRNA discovery for toxicology markers in serum and plasma and in identification of RNA signatures for tumor stratification are featured. Learn how you can accelerate your research with QIAGEN service solutions.
GENCODE provides manual gene annotation of the human genome based on cDNA, EST, genomic and protein sequences, as well as publication and comparative analysis data. It is a merge of annotation from HAVANA and Ensembl. GENCODE v23 contains over 60,000 genes made up of protein coding, long non-coding RNA and pseudogenes. It is the reference gene set for the ENCODE project and can be viewed on the UCSC genome browser and Ensembl. HAVANA annotation of new patches and alternative loci is ongoing to improve GENCODE.
1. The document discusses a cost-effective approach for diploid genome assembly using sequence graphs. It presents a pipeline for diploid assembly using long reads from single individuals or trios.
2. The pipeline has advantages over previous methods as it can assemble genomes of any complexity accounting for different heterozygous rates and repeat contents, and can phase complex structural variants.
3. Results show the pipeline can assemble diploid genomes with as low as 15x coverage of long reads from each individual in a trio.
This document describes rtgTools, a toolkit for comparing and analyzing variant call format (VCF) files. It includes tools for VCF evaluation (vcfeval), generating receiver operating characteristic (ROC) curves from evaluation data (rocplot), counting Mendelian inheritance errors (medelian), basic VCF statistics (vcfstats), VCF filtering (vcffilter), VCF annotation (vcfannotate), and VCF merging (vcfmerge). The document focuses on the vcfeval tool and explains how it compares calls to a baseline by creating paths that maximize true positives and minimize errors using weighting and dynamic programming.
This document provides information about variation resources available from the National Center for Biotechnology Information (NCBI). It lists the staff members who work on variation resources and key collaborators. It describes some of the major databases hosted by NCBI that contain genetic variation data, including dbSNP, dbVar, ClinVar and GTR. It also summarizes some of the tools and viewers available for exploring genetic variation data from NCBI.
How to cluster and sequence an ngs library (james hadfield160416)James Hadfield
A presentation for people intersted in understanding how Illumina adapter ligation, clustering ands SBS sequencing work. Follow core-genomics http://core-genomics.blogspot.co.uk/
Next-Generation Sequencing an Intro to Tech and Applications: NGS Tech Overvi...QIAGEN
This slidedeck provides a technical overview of DNA/RNA preprocessing, template preparation, sequencing and data analysis. It covers the applications for NGS technologies, including guidelines for how to select the technology that will best address your biological question.
Analyzing Fusion Genes Using Next-Generation SequencingQIAGEN
Fusion genes are hybrid genes formed by the fusion of two separate genes. Translocation, interstitial deletion and chromosomal inversions are some of the genetic events that can lead to the formation of fusion genes. The occurrence of fusion genes and its implications in cancer have already been known, but the emergence of NGS technology – especially RNA sequencing – offers the potential to detect novel gene fusions. You can learn more about fusion genes and applying NGS to detect them at our upcoming webinar, presented by Raed Samara, Ph.D., QIAGEN’s Global Product Manager for NGS technologies.
In this webinar, Dr. Raed Samara will cover:
1. Fusion genes: what they are and a historical perspective
2. Fusion gene detection: the current status
3. RNA sequencing vs. digital RNA sequencing
4. How to detect and accurately quantify novel fusion genes in your sample
Diversity affects genome assembly in several ways. Complex diversity can lead to assembly errors and missing haplotype information when using old consensus models. It is important to represent diversity in genome assemblies to avoid missing sequences, haplotype configurations, gene models and overall knowledge. Improving genome assemblies with new reference models and techniques like linked-reads can help address these issues and improve individual genome analysis by better resolving allelic diversity, alternate loci and medically important genes.
Precision medicine for oncology requires accurate and sensitive molecular characterization. However, sample degradation, polymerase errors, and sequencing errors reduce accuracy for sequencing genetic variants. By incorporating molecular tagged adapters in target enrichment, and using DNA probes that deliver extremely even and deep coverage, we are able to demonstrate a 300-fold reduction in false positives at or above 0.25% variant frequency. In this presentation, Dr Mirna Jarosz discusses these methods and how they can significantly reduce error rates in your sequencing data.
This document discusses oncogenomics and cancer genomics technologies. It provides an overview of oncogenomics, the types of DNA biomarkers studied including mutations, and experimental strategies used for cancer genomics research. Key techniques discussed are next-generation sequencing, quantitative PCR (qPCR), and mass spectrometry. The document compares different technologies for mutation detection and profiling and their sensitivities. It also outlines the specifications and pipeline for developing a qPCR-based somatic mutation assay.
This document summarizes the work of the Genome in a Bottle Consortium to develop reference materials and methods for benchmarking genome sequencing and variant calling. The Consortium has extensively characterized the genome of the NA12878 sample to create a "gold standard" set of variant calls that can be used to evaluate sequencing platforms and bioinformatics pipelines. They are also analyzing additional samples to generate reference materials that will enable standardized testing and validation of clinical genome sequencing. The Consortium aims to develop benchmarking tools and strategies with the Global Alliance for Genomics and Health to facilitate regulatory approval and clinical application of genome sequencing.
Introducing VSWarehouse - A Scalable Genetic Data Warehouse for VarSeqGolden Helix Inc
1) The document discusses a genetic data warehousing solution called VSWarehouse from Golden Helix that allows users to store, query, and analyze large genomic datasets.
2) VSWarehouse uses a column-based storage approach that is more scalable than traditional row-based databases and allows efficient querying and joining of variant call and sample data.
3) Golden Helix is launching an early adopter program for VSWarehouse, offering interested customers the chance to influence the product roadmap in exchange for a discounted license and commitment through March 2016.
Golden Helix's End-to-End Solution for Clinical LabsGolden Helix
In this webcast, we provide an overview of our complete end-to-end clinical stack. Initially, we walk through our powerful secondary analysis pipeline which allows you to call SNVs and CNVs. We then demonstrate how various types of CNVs are called and discuss metrics that express the confidence associated with each call.
From there, we show you our powerful tertiary analysis capabilities for gene panels, exome, and whole genome data. We show how our users can move seamlessly from the variant interpretation stage to a clinical report. Lastly, we demonstrate how our genetic data warehouse, VSWarehouse, can be used in the clinic. We also demonstrate various use cases and show how a comprehensive assessment catalog can be utilized to ensure consistent analysis across multiple labs.
We hope you enjoy our first presentation on Golden Helix's entire end-to-end solution for clinical labs!
This document summarizes the process used to benchmark large deletion calls from multiple sequencing technologies and bioinformatics pipelines. Researchers merged deletion calls from 14 datasets into regions and evaluated call size accuracy. Calls supported by two or more technologies were identified as draft benchmark calls. Sensitivity to these calls was calculated for each method. The results provide insight into strengths and weaknesses of different approaches to structural variant detection.
VS-CNV Annotations from the User's PerspectiveGolden Helix
Next-generation sequencing has enabled clinicians and researchers alike to identify novel genetic variants associated with rare Mendelian Diseases across the human genome. To help enable researchers and clinicians understand the role of CNVs in human health and disease, Golden Helix has a fully integrated CNV annotations to provide clinicians and researchers with more effective methods to identify pathogenic CNVs for heritable diseases. In this webcast, we will present our comprehensive clinical workflows that integrates the annotating and reporting of high-quality CNV alongside their existing NGS variants.