DOCSLIB.ORG

RNA Next Generation Sequencing Resources Available at the Experimental and Computational Genomics Core (ECGC)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
RNA Next Generation Sequencing Resources Available at the Experimental and Computational Genomics Core (ECGC) RNA next generation sequencing resources available at the Experimental and Computational Genomics Core (ECGC) Kornel Schuebel, PhD ECGC Resource Director [email protected] Telephone 410-614-0445 CRB2 Rm 131 (lab) CRB2 Rm 1m44 (office) What’s our mission? To facilitate easy access to genomic technologies and bioinformatics expertise, including experimental design, sample processing, and data analysis. To build educational and training opportunities for genomics analysis. Next Generation Microarray Sequencing Experimental and Computational Genomics Biostatistics and Genomics Bioinformatics Education Analysis The ECGC team Faculty Directors Staff Leslie Cope Michael Considine Sarah Wheelan Anuj Gupta Vasan Yegnasubramanian Jennifer Meyers Alyza Skaist Resource Director Hai Xu Kornel Schuebel Coordinators Lauren Ciotti Luda Danilova Daniel Vellucci Core faculty IT support Rob Scharpf Greg Smith Elana Fertig Dominic King How do I start my project? Let us know a little (a Lauren Sarah sentence or two is fine) Vasan Contact us at ecgc.jhmi.edu about your project Leslie Kornel Schedule and attend a consultation Lauren Meet with us to establish an Sarah experimental plan, discuss Vasan costs and a timeline Leslie Set up an iLab project report Kornel and drop off samples Drop off times are generally Lauren on Tuesdays and Thursdays Jennifer Kornel We will contact you to verify types of data analysis you want Anuj, Alyza, Michael Schedule a meeting to look We confirm with you the Sarah comparisons your iLabs Vasan at the data together report showed Leslie Kornel Followup Finding us is easy! Just fill out the form. ecgc.jhmi.edu What type of RNA seq should I do? Depends on your project… Enrichment-------------- Three popular options Library construction 1)Bulk RNA seq Sequencing-------------- -500ng-1ug of high quality total RNA 2) Low input Mapping------------------- -Minimum input is 100 cells 3) FFPE/degraded RNA Analysis-------------------------------- Step 1: Total RNA quality assessment with the Agilent BioAnalyzer /Tapestation Step 2: Library construction for bulk RNA seq rRNA depletion with Ribo Zero beads followed by fragmentation A) polyA selection with magnetic beads C) End repair B) cDNA synthesis D) Adapter ligation and amplification Advantages of the Illumina Tru Seq approach - Retains strand specific information on RNA transcript - Library capture of both coding RNA, as well as multiple forms of non-coding RNA - Degraded RNA can be used with minor adjustments to fragmentation procedures - Can be automated and scaled up to 96 well format - Is compatible with no indexing or a lower indexing pooling level (<24). - The libraries do not require PCR amplification to enable cluster generation although PCR is recommended to meet the yield requirements of most standard applications. Step 2: Library construction for problematic RNA samples Illumina TruSeq RNA Exome To perform RNA seq analysis using FFPE and other low-quality samples we use the Illumina TruSeq RNA Exome workflow. TruSeq RNA Exome features a highly optimized probe set that delivers comprehensive coverage of coding RNA sequences. TruSeq RNA Exome includes 425,437 probes designed to capture 210,000 targets, spanning 21,415 genes of interest qnd covering 98.3% of the RefSeq exome. This method adds unique oligonucleotides to each library, tagging them for downstream pooling into one lane (Figure 2A). This multisample pooling step allows more samples to be loaded in a single sequencing run, making high-throughput studies feasible. After libraries are pooled, they undergo a capture step that produces a targeted library, depleted of ribosomal RNA and intronic or intergenic regions. Pooled libraries are hybridized to biotin-labeled probes specific for coding RNA regions (Figure 2B). Specific targets within the pool are then captured by adding streptavidin beads that bind to the biotinylated probes (Figure 2C). Magnets pull the bound RNA fragments from the solution (Figure 2D). Captured RNA fragments are eluted from the beads and hybridized for a second enrichment reaction. After amplification, a targeted library is ready for cluster generation and subsequent sequencing. Advantages of the RNA exome approach - Requires very little RNA (>10 ng) - Can be used with degraded FFPE samples - Retains strand specificity - Only available for human RNA transcriptome analysis Low input RNA sequencing Step1. cDNA is prepared from total RNA using the chimeric DNA/RNA primer mix and reverse transcriptase. The chimeric primers have a 3' DNA section that hybridizes to the mRNA and a unique 5' RNA sequence that does not hybridize to the mRNA. Step2. After partial fragmentation of mRNA in the cDNA/mRNA complex, DNA polymerase generates a second strand, including DNA complementary to the unique 5' RNA sequence resulting in a double-stranded cDNA with an RNA/DNA heteroduplex at one end. Step3. RNase H removes the unique RNA sequence in the double-stranded cDNA revealing a site for binding the second DNA/RNA chimeric primer. DNA polymerase synthesizes cDNA starting at the 3' end of the primer, displacing the existing forward strand. RNA at the 5' end of the newly synthesized strand is again cleaved by RNase H, exposing the priming site for initiation of the next round of cDNA synthesis. The process of SPIA DNA/RNA primer binding, cDNA synthesis, strand displacement and RNA cleavage is repeated in a highly processive manner, resulting in rapid accumulation of micrograms of amplified cDNA from picograms of total RNA. Advantages of the SPIA approach Highly reproducible -The SPIA amplification process is highly reproducible over a wide range of input amounts, both in terms of the relative abundance of transcripts as well as the distribution of sequencing reads in an NGS experiment. -As with any enzymological amplification method, there is some level of bias with regard to the degree of amplification of various sequences or molecules. In the case of SPIA, this bias is highly reproducible, with biological information being consistently maintained thereby enabling correlative comparisons between samples processed with the same SPIA-based protocols. High Fidelity The SPIA amplification method produces sequence copies with a high degree of accuracy thereby preservating biological differences in the transcriptome. Robust! Disadvantage that it does not retain strand specificity. Step 3: Sequencing through third party vendors Novagen and Psomagen. Sequencing with “in house” ECGC NGS sequencers Installed in May 2019, the Ion GeneStudio S5 Prime System can sequence 150 million molecules in 3 hours. Installed in September 2019, the Illumina NovaSeq 6000 is able to the sequence up to twenty billion DNA fragments at the same time. As an example, this allows us to sequence forty-eight human genomes at 30x coverage in a forty hour run. Typical sequencing parameters -50,000,000 reads per sample -paired end 150x150 bp Illumina sequencing Step 4: RNA seq analysis- from sequencing reads to differential expression Raw Sequence Data QC by FASTQ Files FastQC Reads Mapping RSEM-STAR Mapped Reads Expression Quantification SAM/BAM Files Expected Counts, FPKM, TPM etc. DE testing DEseq2, EBSeq, edgeR etc. List of DE Functional Interpretation genes Pathway Explanatory Integrate with analysis plots other data SAM/BAM format Two section: header section, alignment section http://samtools.sourceforge. net/SAM1.pdf How we quantitate gene expression Sample level DE level Expression quantification viewed by IGV Gene expression analysis using arrays Illumina microarrays Agilent microarrays - gene and microRNA expression - custom Educational activities: (partnered with the Center for Computational Genomics) Short courses, including: - statistics and data analysis using R - introduction to UNIX - sed, awk and shell scripting - inferring phylogenies in cancer - visualizing sequencing data Practical Genomics Workshop (annual, late August) Bioinformatics and Genomics Symposium (annual, October) Future directions: single cell RNA seq using the 10x Genomics Chromium platform Clustering of approximately 5,000 CITE-seq single-cell expression profiles of PBMCs reveals distinct cell populations based on transcriptome analysis. The left panel shows a two-dimensional representation (tSNE) of global gene expression relationships among all cells. Major cell types in peripheral blood can be discerned based on marker gene expression as indicated. The right panels show mRNA (blue) and corresponding Antibody-Derived Tag (ADT, green) signal for the CITE-seq antibody panel projected on the tSNE plot. Darker shading corresponds to higher levels measured. Future Directions • Continually update and enhance the diversity and utility of the services offered through the Core • Support research on technologies for early detection, treatment monitoring and risk stratification • Adopt and generate new technologies, equipment, workflows and analytical pipelines • Continue and update genomics educational activities • Strive to provide exceptional services with optimal pricing • Establish discount pricing with 3rd party sequencing vendors for certain applications. Contact info: Kornel Schuebel [email protected] Lauren Ciotti [email protected] Our website: ecgc.jhmi.edu Thanks for your attention!.
Load more

Recommended publications
  • Next-Gen Sequencing Identifies Non-Coding Variation Disrupting
    OPEN Molecular Psychiatry (2018) 23, 1375–1384 www.nature.com/mp ORIGINAL ARTICLE Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders P Devanna1, XS Chen2,JHo1,2, D Gajewski1, SD Smith3, A Gialluisi2,4, C Francks2,5, SE Fisher2,5, DF Newbury6,7 and SC Vernes1,5 Understanding the genetic factors underlying neurodevelopmental and neuropsychiatric disorders is a major challenge given their prevalence and potential severity for quality of life. While large-scale genomic screens have made major advances in this area, for many disorders the genetic underpinnings are complex and poorly understood. To date the field has focused predominantly on protein coding variation, but given the importance of tightly controlled gene expression for normal brain development and disorder, variation that affects non-coding regulatory regions of the genome is likely to play an important role in these phenotypes. Herein we show the importance of 3 prime untranslated region (3'UTR) non-coding regulatory variants across neurodevelopmental and neuropsychiatric disorders. We devised a pipeline for identifying and functionally validating putatively pathogenic variants from next generation sequencing (NGS) data. We applied this pipeline to a cohort of children with severe specific language impairment (SLI) and identified a functional, SLI-associated variant affecting gene regulation in cells and post-mortem human brain. This variant and the affected gene (ARHGEF39) represent new putative risk factors for SLI. Furthermore, we identified 3′UTR regulatory variants across autism, schizophrenia and bipolar disorder NGS cohorts demonstrating their impact on neurodevelopmental and neuropsychiatric disorders. Our findings show the importance of investigating non-coding regulatory variants when determining risk factors contributing to neurodevelopmental and neuropsychiatric disorders.
    [Show full text]
  • Whole Exome and Whole Genome Sequencing
    UnitedHealthcare® Community Plan Medical Policy Whole Exome and Whole Genome Sequencing Policy Number: CS150.J Effective Date: October 1, 2021 Instructions for Use Table of Contents Page Related Community Plan Policies Application ..................................................................................... 1 • Chromosome Microarray Testing (Non-Oncology Coverage Rationale ....................................................................... 1 Conditions) Definitions ...................................................................................... 2 • Molecular Oncology Testing for Cancer Diagnosis, Applicable Codes .......................................................................... 3 Prognosis, and Treatment Decisions Description of Services ................................................................. 4 • Preimplantation Genetic Testing Clinical Evidence ........................................................................... 4 U.S. Food and Drug Administration ........................................... 22 Commercial Policy References ................................................................................... 22 • Whole Exome and Whole Genome Sequencing Policy History/Revision Information ........................................... 26 Medicare Advantage Coverage Summaries Instructions for Use ..................................................................... 26 • Genetic Testing • Laboratory Tests and Services Application This Medical Policy does not apply to the states listed below; refer to
    [Show full text]
  • Clinical Exome Sequencing Tip Sheet – Medicare Item Numbers 73358/73359
    Clinical exome sequencing Tip sheet – Medicare item numbers 73358/73359 Glossary Chromosome microarray (CMA or molecular Monogenic conditions (as opposed karyotype): CMA has a Medicare item number to polygenic or multifactorial conditions) are for patients presenting with intellectual caused by variants in a single gene. Variants disability, developmental delay, autism, or at may be inherited (dominant or recessive least two congenital anomalies. CMA is the fashion), or may occur spontaneously (de recommended first line test in these cases as novo) showing no family history. it can exclude a chromosome cause of disease which is unlikely to be detected by Whole exome sequence – sequencing only exome. the protein coding genes (exons). The exome is ~2% of the genome and contains ~85% of Gene panel is a set of genes that are known to disease-causing gene variants. be associated with a phenotype or disorder. They help narrow down the search Whole genome sequence – sequencing the for variants of interest to genes with evidence entire genome (all genes, including coding linking them to particular phenotypes and noncoding regions) Human phenotype ontology (HPO) terms Singleton – Analysis of the child only. describe a phenotypic abnormality using a Trio – analysis of the child and both biological standard nomenclature. Ideally, all clinicians parents. and scientists are using the same terms. Variant - A change in the DNA code that Mendeliome refers to the ~5,000 genes (out of differs from a reference genome. about 20,000 protein coding genes) that are known to be associated with monogenic disease. As variants in new genes are identified with evidence linking them with human disease, they are added to the Mendeliome.
    [Show full text]
  • The Genomics Era: the Future of Genetics in Medicine - Glossary
    The Genomics Era: the Future of Genetics in Medicine - Glossary The glossary below provides a list of key terms used throughout the course. You do not need to read them all now; we’ll be linking back to the main glossary step wherever these terms appear, so you may refer back to this list if you are unsure of the terminology being used. Term Definition The process of matching reads back to their original Alignment position in the reference genome. An allele is one of a number of alternative forms of the same gene or genetic locus. We inherit one copy Allele of our genetic code from our mother and one copy of our genetic code from our father. Each copy is known as an allele. Microarray based genomic comparative hybridisation. This is a technique used to detect chromosome imbalances by comparing patient and control DNA and comparing differences between the two sets. It is Array CGH a useful technique for detecting small chromosome deletions and duplications which would not have been detected with more traditional karyotyping techniques. A unit of DNA. There are four bases which form the Base cross links (or rungs) of the DNA double helix: adenine (A), thymine (T), guanine (G) and cytosine (C). Capture see Target enrichment. The process by which a cell becomes specialized in Cell differentiation order to perform a specific function. Centromere The point at which the sister chromatids are joined. #1 FutureLearn A structure located in the nucleus all living cells, comprised of DNA bound around proteins called histones. The normal number of chromosomes in each Chromosome human cell nucleus is 46 and is composed of 22 pairs of autosomes and a pair of sex chromosomes which determine gender: males have an X and a Y chromosome whilst females have two X chromosomes.
    [Show full text]
  • Whole Exome Sequencing (WES)
    Whole Exome Sequencing (WES) Turn Around Time: 30 Days TEST METHODOLOGY CPT Codes: Proband – 81415, Family Member – 81416 DNA will be extracted from whole blood or Test Includes: DNA Extraction other specimen types. Extracted DNA is Library Prep quantified and sheared to the correct size. The Exome Capture sample then undergoes library preparation and Library QC the exome is captured. After quality assurance, Illumina Platform Sequencing the captured library is then subjected to next Data Analysis generation DNA sequencing on the Illumina Sanger Variant Confirmation (if requested) platform. The reads from this sequencing are Interpreted Clinical Report aligned to a reference sequence and variations from this reference are identified. The sequence variants are then loaded into a commercial software package that contains data sources and Expedited WES testing is available. algorithms allowing for the evaluation of whole Contact the lab for more information. exome sequencing variants for evolutionary conservation, predicted impact on protein TEST DESCRIPTION structure and function (including Polyphen2 (5) and SIFT (6)), ability to disrupt conserved Whole Exome Sequencing (WES) is used to detect variants in a patient’s exome splice sites, and presence in databases including in order to determine the role of genomic variants in disease outcomes. The OMIM, dbSNP, and HGMD (1,2,3). The exome is a little more than 1% of the genome that codes for protein. The patient’s software annotates variants with this data, exome will be sequenced to an average depth of 100X with a minimum depth of considering both the reference gene model and coverage of 85X. Over 97% of the exome will be sequenced to a depth of 10X.
    [Show full text]
  • Genomic Technologies for Cancer Research
    Genomic Technologies for Cancer Research www.illumina.com/applications/cancer.html Table of Contents I. Introduction: Genomic Technologies for Cancer Research 3 II. Approaches for Detecting Somatic Mutations 4 Targeted Sequencing Solutions for Somatic Mutation Detection 4 Exome Sequencing 4 Focused Sequencing Panels 4 Custom Targeted Sequencing 4 Whole-Genome Sequencing Solutions 4 Data Analysis Tools for Somatic Variant Detection 5 III. Evaluating Germline Mutations in Cancer 6 Targeted Sequencing to Detect Common Germline Mutations 7 Microarray-Based Approaches 7 IV. Structural Variant Detection in Cancer 7 DNA and RNA Sequencing for Translocation Detection 8 Copy Number Variation Arrays 8 V. Investigating Gene Regulation in Cancer 8 DNA–Protein Interactions 8 DNA Methylation 9 RNA Sequencing 9 Targeted RNA Sequencing 9 Small RNA Sequencing 10 Data Analysis Tools for the Study of Gene Regulation 11 VI. Summary 11 For Research Use Only. Not for use in diagnostic procedures. I. Introduction: Genomic Technologies for Cancer Research In recent years, genomic technologies have emerged as invaluable tools in cancer research (Figure 1). International projects such as the International Cancer Genome Consortium (ICGC)1 and The Cancer Genome Atlas (TCGA)2, tasked with mapping the biology of dozens of tumor types, would not have been possible without these tools. Next-generation sequencing (NGS) and high-density microarrays are used to study the biology of cancer. Both provide the cancer research community with a growing body of knowledge that may lead to more effective drug design, better patient treatment options, and more accurate prognoses.3 Normal Neoplastic Changes Tumor Treatment Response Recurrence PROGRESSION Somatic Mutations Germline Gene Expression & Mutations Epigenetic Changes Additional Mutations Chromosomal Abnormalities HETEROGENEITY Figure 1: The Tumor Progression Pathway—Genomic technologies are helping researchers achieve a deeper understanding of the tumor progression pathway.
    [Show full text]
  • Guide to Interpreting Genomic Reports: a Genomics Toolkit
    Guide to Interpreting Genomic Reports: A Genomics Toolkit A guide to genomic test results for non-genetics providers Created by the Practitioner Education Working Group of the Clinical Sequencing Exploratory Research (CSER) Consortium Genomic Report Toolkit Authors Kelly East, MS, CGC, Wendy Chung MD, PhD, Kate Foreman, MS, CGC, Mari Gilmore, MS, CGC, Michele Gornick, PhD, Lucia Hindorff, PhD, Tia Kauffman, MPH, Donna Messersmith , PhD, Cindy Prows, MSN, APRN, CNS, Elena Stoffel, MD, Joon-Ho Yu, MPh, PhD and Sharon Plon, MD, PhD About this resource This resource was created by a team of genomic testing experts. It is designed to help non-geneticist healthcare providers to understand genomic medicine and genome sequencing. The CSER Consortium1 is an NIH-funded group exploring genomic testing in clinical settings. Acknowledgements This work was conducted as part of the Clinical Sequencing Exploratory Research (CSER) Consortium, grants U01 HG006485, U01 HG006485, U01 HG006546, U01 HG006492, UM1 HG007301, UM1 HG007292, UM1 HG006508, U01 HG006487, U01 HG006507, R01 HG006618, and U01 HG007307. Special thanks to Alexandria Wyatt and Hugo O’Campo for graphic design and layout, Jill Pope for technical editing, and the entire CSER Practitioner Education Working Group for their time, energy, and support in developing this resource. Contents 1 Introduction and Overview ................................................................ 3 2 Diagnostic Results Related to Patient Symptoms: Pathogenic and Likely Pathogenic Variants . 8 3 Uncertain Results
    [Show full text]
  • Examples of How ENCODE Facilitates Biomedical Research
    Examples of how ENCODE facilitates biomedical research ENCODE Users Meeting Potomac, MD Richard M. Myers [email protected] July 1, 2015 http://www.hudsonalpha.org/ Richard M. Myers Disclosure Our group has been part of the ENCODE Consortium since it began in 2003 Rick Myers Barbara Wold Ross Hardison pasdfasdf Eric Mendenhall Ali Mortazavi Tim Reddy Richard M. Myers 2 Goals of ENCODE Annotate the human genome Disseminate data to researchers everywhere Richard M. Myers 3 5 examples of how we use ENCODE data to help in our research on human diseases Richard M. Myers 4 1. Discovering the causes of undiagnosed genetic diseases Richard M. Myers 5 Childhood genetic disorders 1.5-3% of kids worldwide are born with 1 or more of: • intellectual disability • developmental delay • heart defects • craniofacial and skeletal abnormalities • severe autism • seizures The vast majority of these problems have genetic causes Richard M. Myers 6 Diagnostic challenges for childhood genetic disorders Inaccurate or undetermined causes (i.e., diagnoses) are a major hardship: Years of expensive, invasive, and futile testing Impossible to predict disease progression, symptoms Treatment decisions are complicated Slows research into developing new therapies Impacts family planning Results in feelings of parental guilt and lack of control Thus, identifying the root genetic causes is essential Richard M. Myers 7 HudsonAlpha Pediatric Genetics Project Sequence whole genomes of 500 children with developmental/ intellectual delay of unknown etiology (and both parents’ genomes too) North Alabama Univ. of Louisville Children’s Specialists HudsonAlpha Richard M. Myers 8 Exome results so far Exome sequencing completed for 171 families Definitive genetic diagnosis in 25% of the children Pitt-Hopkins syndrome Dravet syndrome Rett syndrome Rubinstein-Taybi syndrome Noonan-like syndrome Many never-described causes 25%25%! ! >20% of families receive uncertain genetic findings that 56%! will likely be definitively diagnostic in the future 56%! 19%19%! ! Richard M.
    [Show full text]
  • Combining Whole-Exome and RNA-Seq Data Improves the Quality of PDX Mutation Profiles
    Combining whole-exome and RNA-Seq data improves the quality of PDX mutation profiles Manuel Landesfeind 1, Bruno Zeitouni 1, Anne-Lise Peille 1, Vincent Vuaroqueaux 1 1 Oncotest - Charles River, Am Flughafen 12-14, 79108 Freiburg, Germany 1 Introduction 2 Mutation profiling technologies and data analysis 3 Evaluation of WES mutation detection pipeline Patient-derived xenograft tumor models (PDX) are an important tool for anti- Materials: Methods: PDX-specific bioinformatics pipelines (Fig. 3) comprises mapping of • Mutation screening using Sequenom Oncocarta panels 1-3 paired-end reads to human and mouse reference genomes using BWA and cancer agent testing. Oncotest has developed a unique collection of models SEQUENCING DATA (FASTQ) QC • We investigated as reference data, 25 genes in 284 models Figure 4: Venn- WES targeting mutations in 39 genes, validation of mutations by TopHat for WES and RNA-Seq data respectively. Reads that map better to covering all major lineages (Fig. 1). In the context of targeted therapy containing 500 mutations assessed by Sanger sequencing diagram of 500 Sanger sequencing in 25 genes in 284 models mouse are removed. Human-specific duplicate reads are discarded. After base Read Mapping on Human Read Mapping on Mouse mutations from Sanger development, an accurate and comprehensive molecular characterization is • WES data for 300 models, DNA extraction using Agilent quality score recalibration, WES Variant detection is performed utilizing GATK- • Our WES analysis retrieved > 95% of these mutations (Fig. 4) hg19 genome mm10 genome Sanger and 988 crucial for model selection and for biomarker identification. We recently SureSelect All Human Exon kits (v1, v4, or v5) and Lite, Samtools, Freebayes.
    [Show full text]
  • Report 103356
    Patient: DoB: 1 /10 Sample report as of Feb 1st, 2021. Regional differences may apply. For complete and up-to-date test methodology description, please see your report in Nucleus online portal. Accreditation and certification information available at blueprintgenetics.com/certifications Whole Exome Plus REFERRING HEALTHCARE PROFESSIONAL NAME HOSPITAL PATIENT NAME DOB AGE GENDER ORDER ID 1 Male PRIMARY SAMPLE TYPE SAMPLE COLLECTION DATE CUSTOMER SAMPLE ID SUMMARY OF RESULTS PRIMARY FINDINGS Analysis of whole exome sequence variants in previously established disease genes The patient is heterozygous for MPZ c.532del, p.(Val178Trpfs*74), which is likely pathogenic. Del/Dup (CNV) analysis Negative. SECONDARY FINDINGS The patient is heterozygous for PMS2 c.1297A>T, p.(Lys433*), which is likely pathogenic. Please see APPENDIX 3: Secondary Findings for further details PRIMARY FINDINGS: SEQUENCE ALTERATIONS IN ESTABLISHED DISEASE GENES GENE TRANSCRIPT NOMENCLATURE GENOTYPE CONSEQUENCE INHERITANCE CLASSIFICATION MPZ NM_000530.7 c.532del, p.(Val178Trpfs*74) HET frameshift_variant AD Likely pathogenic ASSEMBLY POS REF/ALT ID GRCh37/hg19 1:161276170 AC/A PHENOTYPE gnomAD AC/AN POLYPHEN SIFT MUTTASTER Charcot-Marie-Tooth disease, 0/0 N/A N/A N/A Dejerine-Sottas disease SEQUENCING PERFORMANCE METRICS SAMPLE MEDIAN COVERAGE PERCENT >= 20X Index 162 99.25 Blueprint Genetics Oy, Keilaranta 16 A-B, 02150 Espoo, Finland 1 / 10 VAT number: FI22307900, CLIA ID Number: 99D2092375, CAP Number: 9257331 Patient: DoB: 2 / 10 TEST INFORMATION Blueprint Genetics Whole Exome Plus Test (version 2, Feb 9, 2018) consists of sequence analysis of all protein coding genes in the genome for the proband, coupled with Whole Exome Deletion/Duplication (CNV) Analysis.
    [Show full text]
  • Whole Exome and Transcriptome Sequencing Solutions for Solid Tumor and Hematological Cancers
    Coming Sema4 Signal WES/WTS Soon Sema4 Signal PanCancer Whole exome and transcriptome sequencing solutions for solid tumor and hematological cancers We are excited to announce the upcoming launch of Sema4 Signal WES/WTS (~20,000 genes) and Sema4 Signal PanCancer (>2,000 cancer-related genes), two comprehensive testing solutions designed for all solid tumor and hematological malignancies. These advanced offerings integrate whole exome sequencing (WES), whole transcriptome sequencing (WTS), and tumor-normal matched analysis to deliver insights across both somatic and germline* mutations to help providers: Determine diagnoses and prognoses for solid tumor or hematological cancers Identify and use available targeted therapies Make decisions regarding suitability of current clinical trials Learn about certain hereditary contributions to various cancer types* Gain insight regarding secondary findings per ACMG guidelines** (with consent) Powerful insights to help personalize cancer treatment Our tumor-normal matched DNA analysis and RNA-seq (tumor only) analysis cover these alterations and features based on 250x tumor coverage and 100x normal coverage across all genes, and 100M RNA reads: • Single nucleotide variants (SNVs) • Insertion/Deletions (INDELs) • Copy number variants (CNVs) • Fusions • Certain splice variants • Tumor mutational burden (TMB) • Microsatellite instability (MSI) • Arm- and chromosome-level aneuploidies Samples accepted: FFPE Fresh frozen Bone marrow Blood Saliva Buccal swab * Please note that while some inherited genetic changes may be detected, these tests do not replace comprehensive germline testing. If an inherited condition is suspected in the patient or their family member(s), a different test with the purpose of examining germline genetic changes based on family and/or personal history may be appropriate.
    [Show full text]
  • Glossary of Terms
    GLOSSARY OF TERMS Table of Contents A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z A Amino acids: any of a class of 20 molecules that are combined to form proteins in living things. The sequence of amino acids in a protein and hence protein function are determined by the genetic code. From http://www.geneticalliance.org.uk/glossary.htm#C • The building blocks of proteins, there are 20 different amino acids. From https://www.yourgenome.org/glossary/amino-acid Antisense: Antisense nucleotides are strings of RNA or DNA that are complementary to "sense" strands of nucleotides. They bind to and inactivate these sense strands. They have been used in research, and may become useful for therapy of certain diseases (See Gene silencing). From http://www.encyclopedia.com/topic/Antisense_DNA.aspx. Antisense and RNA interference are referred as gene knockdown technologies: the transcription of the gene is unaffected; however, gene expression, i.e. protein synthesis (translation), is lost because messenger RNA molecules become unstable or inaccessible. Furthermore, RNA interference is based on naturally occurring phenomenon known as Post-Transcriptional Gene Silencing. From http://www.ncbi.nlm.nih.gov/probe/docs/applsilencing/ B Biobank: A biobank is a large, organised collection of samples, usually human, used for research. Biobanks catalogue and store samples using genetic, clinical, and other characteristics such as age, gender, blood type, and ethnicity. Some samples are also categorised according to environmental factors, such as whether the donor had been exposed to some substance that can affect health.
    [Show full text]