Sequencing

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11th Annual Sequencing, Finishing, and Analysis in the Future Meeting RELIABLE DETECTION OF COPY NUMBER VARIATIONS BASED ON DATA MINING APPROACHES Friday, 3rd June 14:40 La Fonda Ballroom Talk (OS‐9.03) Zbyszek Otwinowski, Maciej Puzio, Dominika Borek UT Southwestern Medical Center Copy number variations (CNVs) are important for understanding biology, and cancer development in particular. Despite significant progress in the development of tools for directly detecting CNVs in sequencing reads, no single tool detects all types of CNVs. This is because all tools analyze either explicitly or implicitly coverage variability in order to model its overdispersion, but they do not identify all sources of variations. There are many contributors to overdispersion other than CNVs, and these contributors vary significantly between experiments, resulting from biases in fragmentation and other steps of library preparation, systematic sequencing errors and artifacts of mapping. We designed a method to separate these artifactual contributions from the biological signal, i.e. the CNVs. By means of data mining, we map the patterns of artifactual variability on a genome of interest and then we correct the coverage distribution for these patterns so that the resulting distribution can be used in efficient and reliable CNV detection. The reduction of overdispersion provides a very stringent validation criterion. The method also has great potential to further the analysis of pan‐genome CNVs variations and to determine whether particular CNVs represent a population or a private variant. For the latter, on average we expect different phenotypic effect. 145
11th Annual Sequencing, Finishing, and Analysis in the Future Meeting ROBUST GENOME-WIDE TRANSCRIPTOME ENRICHMENT SEQUENCING FOR RESEARCH AND CLINICAL PLATFORMS Friday, 3rd June 15:00 La Fonda Ballroom Talk (OS‐9.04) Harsha Doddapaneni 1 , Prof. Jianhong Hu 1 , Hsu Chao 2 , Simon White 2 , Tittu Matthew 2 , Viktoriya Korchina 2 , Caitlin Nessner 2 , Sandra Lee 2 , Donald W Parsons 3 , David A Wheeler 2 , Angshumoy Roy 4 , Eric Boerwinkle 5 , Donna Muzny 1 , Richard Gibbs 2 1 Human Genome Sequencing Center Baylor College of Medicine, 2 Human Genome Sequencing Center, Baylor College of Medicine, 3 Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, 4 Department of Pathology & Immunology, Baylor College of Medicine; Department of Pediatrics, Texas Children’s Hospital, 5 University of Texas Health Science Center at Houston Transcriptome sequencing (RNA‐Seq) together with whole genome sequencing (WGS) offers an in‐ tegrated informative dataset for functional characterization of human transcriptome. Generation of high‐quality data is essential for success in RNA‐Seq studies. However, standard RNA‐Seq methods rely heavily on very high quality RNA samples, and the library preparation involves multiple enzy‐ matic manipulations as RNA is first converted to double‐stranded cDNA and then into paired‐end libraries. Therefore to increase RNA‐Seq utility in routine clinical setting, these protocols have to overcome RNA quality constraints as well as need rapid preparation protocols. Since August 2010, at BCM‐HGSC we have generated transcriptome data for 4519 samples in support of different cancer, pharmacogenomics, vascular and other disease projects. These RNA‐Seq libraries are strand‐specific and poly(A)+ enriched and use automated library preparation workflow. To monitor sample and process variability, in addition to sequence metrics, we use RNA developed by the External RNA Controls Consortium (ERCC). Our primary RNA‐Seq data analysis pipeline is built on STAR and Cuff‐Links software and we assess performance of individual RNA‐Seq libraries by 12 different metrics for library process consistency. Since 2014, we have supported exome‐capture transcriptome‐seq by combining our strand‐specific RNA‐Seq protocol with whole exome sequencing protocol as tool to handle low quality RNA and RNA extracted from FFPE specimens. Total RNA (40 ng) from control samples as well as FFPE samples from cancer patients (2‐3 RIN and DV 200 of >30%) sequenced using this protocol have shown that sensitivity of this approach for detecting fusions and somatic SNVs is comparable to that of standard poly‐A+ enriched libraries. Our recent efforts are focused on designing ways in which we can simplify the RNA‐Seq protocol to reduce sample preparation time as well as increase sample throughput. In this regard, we have developed a RNA‐Seq protocol using first strand cDNA as a template for preparing libraries using Accel‐NGS 1S DNA Library Kit from Swift Biosciences. Libraries prepared using Universal Hu‐ man Reference (UHR) RNA and human Placenta RNA with this protocol generated 80‐120 million reads/sample with high unique rates (89 % for UHR and 75 % for Placenta). Comparison of gene ex‐ pression values as Fragments Per Kilobase of transcript per Million mapped reads (FPKM) between 1S and RNA‐Seq protocol gave a correlation of 0.97 for UHR sample and 0.97 for Placenta sample. Over all, this workflow eliminates the need to generate second cDNA synthesis and also reduces the library preparation time by half over standard poly‐A+ enriched workflow. This protocol also allows to multiplex samples after ligating the molecular barcodes to the samples, but before PCR to increase sample throughput. In all, these improvements to our RNA‐Seq workflows will allows us to handle low quality RNA including RNA from FFPE specimens as well as result in fast turnaround times to have practical utility in both research and clinical settings. 146
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