Small RNA-Seq and profiling
|
|
- Christian Barber
- 5 years ago
- Views:
Transcription
1 Small RNA-Seq and profiling Y. Hoogstrate 1,2 1 Department of Bioinformatics & Department of Urology ErasmusMC, Rotterdam 2 CTMM Translational Research IT (TraIT) BioSB: 5th RNA-seq data analysis course, Leiden
2 Overview Small RNA-Seq - primary target: micrornas Characterization of the Melanoma mirnaome by Deep Sequencing Beyond micrornas
3 Main application of Small RNA-Seq profiling MicroRNAs (mirnas) 18 23bp [4]
4 Main application of Small RNA-Seq profiling MicroRNAs (mirnas) Mature Mature-star [2]
5 Main application of Small RNA-Seq profiling MicroRNAs (mirnas) Mature Mature-star mornas [1]
6 Obtaining small RNA-seq data (Illumina.com) No fragmentation Size selection Read size often 30bp or 35bp Part of primer / adapters present in most reads! Often stranded protocol
7 Characterization of the Melanoma mirnaome by Deep Sequencing Case study: Characterization of mirnas Characterization of the Melanoma mirnaome by Deep Sequencing[9] 12 samples (human pigment cells) Goal: find existing + novel mirnas mirna expression profiles to see if samples group by cell type (validation)
8 Characterization of the Melanoma mirnaome by Deep Sequencing MiRNA characterization workflow (miranalyzer) QA/QC Adapter removal (very important) If mirna=22bp and read=30bp, 8 bases are adapter Trimming low-q bases? I would not do this, because this might predict shorter mirnas Profiling Alignment Spliced alignment? (mirtrons, some trnas; nt) Align to: reference genome or known mirnas (e.g. mirbase) Reference genome / transcriptome if you want to find novel Max multimaps? Minimum read count? Predict novel mirnas
9 Characterization of the Melanoma mirnaome by Deep Sequencing Profiling novel mirnas (miranalyzer) [3] Followed by classification, including features based on Secondary structure of pre-mirna Expression / read count Alignment
10 Characterization of the Melanoma mirnaome by Deep Sequencing Profiling novel mirnas - quanitification
11 Characterization of the Melanoma mirnaome by Deep Sequencing Profiling novel mirnas - quanitification
12 Characterization of the Melanoma mirnaome by Deep Sequencing Profiling novel mirnas - quanitification
13 Small RNA-Seq - primary target: micrornas Beyond micrornas Characterization of the Melanoma mirnaome by Deep Sequencing Profiling novel mirnas [9] I Count reads of all mirnas I Validation: check if clustering confirms biological subtypes RNA-Seq analysis in Galaxy 8 September 2015 References
14 Characterization of the Melanoma mirnaome by Deep Sequencing Next steps with a full repertoire of mirnas Differential mirna expression analysis Similar to RNA-Seq (use pre-mirnas or allow offset) Order on gene count, en see if Let-7 is at the top Be careful with multi-maps (many homologue mirnas) mirna target analysis Discover targets: e.g. Correlation analysis with mrna-seq data Discover targets: find matches in genome Use known targets: Gene onthology and text mining
15 Beyond micrornas: RNA in pieces many different RNA fragments derived from small RNA species other than microrna [8] [10]
16 Beyond micrornas: characterizing all small RNAs Adapter removal Aligning with non-splicing aligners (bowtie, bwa, etc.) Except for a few trnas and mirtrons, small RNAs are usually not spliced Alignment to: 1 Database of small RNAs of interest, e.g. mirbase no novel mirnas etc. 2 Reference genome Will introduce more multi-map reads 3 First 1, then 2, then merge More complicated, but solves both issues Reads are small, changes on multi-map are high, use stranded protocol if possible
17 Beyond micrornas: characterizing all small RNAs
18 Beyond micrornas: RNA in pieces QC: alignment - read counts per host [7]
19 Analysis: detection of novel small RNAs Classical strategy 1: predict mirna folding (energy) + overlapping reads X Very specific for mirnas [2]
20 Analysis: detection of novel small RNAs Classical strategy 2: derive normal distribution from read density V Focus on mornas (not 2D-structure specific) X Assumes read density over small RNA to be symmetrical X If variance is not correct, predicted small RNAs may become huge - A small RNA is usually cleft at 5 and 3 independently: two independent events [6]
21 Analysis: detection of novel small RNAs Detect both 5 and 3 individually and join back together (FlaiMapper) [5]
22 FlaiMapper on Human dataset 1 Adapter removal 2 Align reads to database of all annotated ncrnas 3 Detect novel small RNAs Classify by type of precursor (pre-mirna, snorna, trna, etc.) All types of ncrnas seem to produce small RNAs
23 Novel small ncrnas All types of ncrnas seem to produce small RNAs [7]
24 Novel small ncrnas QC: length distribution of predicted small RNAs [7]
25 Small RNA-Seq workflow 1 Adapter removal 2 Align reads to database of all annotated ncrnas 3 Detect novel small RNAs QC: length distribution of predicted small RNAs 4 Quantify expression levels (known + detected novel small RNAs) Because of the small size of ncrnas, add an offset of 3 5bp to annotations on both sides Be careful with conclusions: many multi-mappers in mirnas, snorna- and trna (many homologues) 5 Diffential gene expression analysis, identical in classical RNA-seq, (EdgeR, DeSeq, CLC Bio, etc.) 6 Interpretation of DE small RNAs
26 Interpretation of DE small RNAs Sounds intuitive If small RNA is up- or down-regulated, relation to precursor is important (trf up, trna also up?) mirnas expression in relation to target mrnas novel Small RNAs: location in precursor (2D structure?) Little is known about Small RNA derived fragments (their roles, their function, their structure, etc.)
27 SnoRNAs [7] Multiple and overlapping sdrnas Multiple degradation mechanisms? Correlation sdrnas from same host snorna? Correlation with host snorna itself?
28 References I [1] Stefania Bortoluzzi, Marta Biasiolo, and Andrea Bisognin. Micrornaoffset {RNAs} (mornas): by-product spectators or functional players? Trends in Molecular Medicine, 17(9): , [2] Marc R. Friedlander, Wei Chen, Catherine Adamidi, Jonas Maaskola, Ralf Einspanier, Signe Knespel, and Nikolaus Rajewsky. Discovering micrornas from deep sequencing data using mirdeep. Nat Biotech, 26(4): , Apr [3] Michael Hackenberg, Naiara Rodrguez-Ezpeleta, and Ana M. Aransay. miranalyzer: an update on the detection and analysis of micrornas in high-throughput sequencing experiments. Nucleic Acids Research, 39(suppl 2):W132 W138, [4] Lin He and Gregory J. Hannon. Micrornas: small rnas with a big role in gene regulation. Nat Rev Genet, 5(7): , Jul [5] Youri Hoogstrate, Guido Jenster, and Elena S. Martens-Uzunova. Flaimapper: computational annotation of small ncrna-derived fragments using rna-seq high-throughput data. Bioinformatics, 31(5): , [6] David Langenberger, Clara Bermudez-Santana, Jana Hertel, Steve Hoffmann, Philipp Khaitovich, and Peter F. Stadler. Evidence for human microrna-offset rnas in small rna sequencing data. Bioinformatics, 25(18): , [7] Elena Martens-Uzunova, Youri Hoogstrate, Anton Kalsbeek, Bas Pigmans, Mirella den Berg, Natasja Dits, Sren Nielsen, Adam Baker, Tapio Visakorpi, Chris Bangma, and Guido Jenster. C/d-box snorna-derived rna production is associated with malignant transformation and metastatic progression in prostate cancer. Oncotarget, 6(19), [8] Elena S. Martens-Uzunova, Michael Olvedy, and Guido Jenster. Beyond microrna novel {RNAs} derived from small non-coding {RNA} and their implication in cancer. Cancer Letters, 340(2): , Next Generation Sequencing Applications in Cancer Research. [9] Mitchell S. Stark, Sonika Tyagi, Derek J. Nancarrow, Glen M. Boyle, Anthony L. Cook, David C. Whiteman, Peter G. Parsons, Christopher Schmidt, Richard A. Sturm, and Nicholas K. Hayward. Characterization of the melanoma mirnaome by deep sequencing. PLoS ONE, 5(3):e9685, [10] Alex C. Tuck and David Tollervey. {RNA} in pieces. Trends in Genetics, 27(10): , 2011.
Small RNAs and how to analyze them using sequencing
Small RNAs and how to analyze them using sequencing RNA-seq Course November 8th 2017 Marc Friedländer ComputaAonal RNA Biology Group SciLifeLab / Stockholm University Special thanks to Jakub Westholm for
More informationEukaryotic small RNA Small RNAseq data analysis for mirna identification
Eukaryotic small RNA Small RNAseq data analysis for mirna identification P. Bardou, C. Gaspin, S. Maman, J. Mariette, O. Rué, M. Zytnicki INRA Sigenae Toulouse INRA MIA Toulouse GenoToul Bioinfo INRA MaIAGE
More informationArabidopsis thaliana small RNA Sequencing. Report
Arabidopsis thaliana small RNA Sequencing Report September 2015 Project Information Client Name Client Company / Institution Macrogen Order Number Order ID Species Arabidopsis thaliana Reference UCSC hg19
More informationAmbient temperature regulated flowering time
Ambient temperature regulated flowering time Applications of RNAseq RNA- seq course: The power of RNA-seq June 7 th, 2013; Richard Immink Overview Introduction: Biological research question/hypothesis
More informationP. Tang ( 鄧致剛 ); PJ Huang ( 黄栢榕 ) g( 鄧致剛 ); g ( 黄栢榕 ) Bioinformatics Center, Chang Gung University.
Small RNA High Throughput Sequencing Analysis I P. Tang ( 鄧致剛 ); PJ Huang ( 黄栢榕 ) g( 鄧致剛 ); g ( 黄栢榕 ) Bioinformatics Center, Chang Gung University. Prominent members of the RNA family Classic RNAs mediating
More informationObstacles and challenges in the analysis of microrna sequencing data
Obstacles and challenges in the analysis of microrna sequencing data (mirna-seq) David Humphreys Genomics core Dr Victor Chang AC 1936-1991, Pioneering Cardiothoracic Surgeon and Humanitarian The ABCs
More informationSmall RNAs and how to analyze them using sequencing
Small RNAs and how to analyze them using sequencing Jakub Orzechowski Westholm (1) Long- term bioinforma=cs support, Science For Life Laboratory Stockholm (2) Department of Biophysics and Biochemistry,
More informationomiras: MicroRNA regulation of gene expression
omiras: MicroRNA regulation of gene expression Sören Müller, Goethe University of Frankfurt am Main Molecular Bioinformatics Group, Institute of Computer Science Plant Molecular Biology Group, Institute
More informationmicrornas (mirna) and Biomarkers
micrornas (mirna) and Biomarkers Small RNAs Make Big Splash mirnas & Genome Function Biomarkers in Cancer Future Prospects Javed Khan M.D. National Cancer Institute EORTC-NCI-ASCO November 2007 The Human
More informationProfiling of MicroRNA Expression in Obese and Diabetic-Induced Mice for Biomarker Discovery
Transactions on Science and Technology Vol. 4, No. 3-3, 391-395, 2017 Profiling of MicroRNA Expression in Obese and Diabetic-Induced Mice for Biomarker Discovery Janan N. Hadi, Mohammad Iqbal, Vijay Kumar
More informationAnalysis of Massively Parallel Sequencing Data Application of Illumina Sequencing to the Genetics of Human Cancers
Analysis of Massively Parallel Sequencing Data Application of Illumina Sequencing to the Genetics of Human Cancers Gordon Blackshields Senior Bioinformatician Source BioScience 1 To Cancer Genetics Studies
More informationRNA-seq Introduction
RNA-seq Introduction DNA is the same in all cells but which RNAs that is present is different in all cells There is a wide variety of different functional RNAs Which RNAs (and sometimes then translated
More informationCan melanoma treatment be guided by a panel of predictive and prognostic microrna Biomarkers?
Can melanoma treatment be guided by a panel of predictive and prognostic microrna Biomarkers? Mitchell Stark Research Fellow Dermatology Research Centre WCCS2016 Vienna, Austria Survival is stage dependant
More informationTranscriptome Analysis
Transcriptome Analysis Data Preprocessing Sample Preparation Illumina Sequencing Demultiplexing Raw FastQ Reference Genome (fasta) Reference Annotation (GTF) Reference Genome Analysis Tophat Accepted hits
More informationLong non-coding RNAs
Long non-coding RNAs Dominic Rose Bioinformatics Group, University of Freiburg Bled, Feb. 2011 Outline De novo prediction of long non-coding RNAs (lncrnas) Genome-wide RNA gene-finding Intrinsic properties
More informationLecture 8 Understanding Transcription RNA-seq analysis. Foundations of Computational Systems Biology David K. Gifford
Lecture 8 Understanding Transcription RNA-seq analysis Foundations of Computational Systems Biology David K. Gifford 1 Lecture 8 RNA-seq Analysis RNA-seq principles How can we characterize mrna isoform
More informationa) List of KMTs targeted in the shrna screen. The official symbol, KMT designation,
Supplementary Information Supplementary Figures Supplementary Figure 1. a) List of KMTs targeted in the shrna screen. The official symbol, KMT designation, gene ID and specifities are provided. Those highlighted
More informationPost-transcriptional regulation of an intronic microrna
Post-transcriptional regulation of an intronic microrna Carl Novina Dana-Farber Cancer Institute Harvard Medical School Broad Institute of Harvard and MIT Qiagen Webinar 05-17-11 Outline 1. The biology
More informationgenomics for systems biology / ISB2020 RNA sequencing (RNA-seq)
RNA sequencing (RNA-seq) Module Outline MO 13-Mar-2017 RNA sequencing: Introduction 1 WE 15-Mar-2017 RNA sequencing: Introduction 2 MO 20-Mar-2017 Paper: PMID 25954002: Human genomics. The human transcriptome
More informationHigh AU content: a signature of upregulated mirna in cardiac diseases
https://helda.helsinki.fi High AU content: a signature of upregulated mirna in cardiac diseases Gupta, Richa 2010-09-20 Gupta, R, Soni, N, Patnaik, P, Sood, I, Singh, R, Rawal, K & Rani, V 2010, ' High
More informationmirna Dr. S Hosseini-Asl
mirna Dr. S Hosseini-Asl 1 2 MicroRNAs (mirnas) are small noncoding RNAs which enhance the cleavage or translational repression of specific mrna with recognition site(s) in the 3 - untranslated region
More informationDNA Sequence Bioinformatics Analysis with the Galaxy Platform
DNA Sequence Bioinformatics Analysis with the Galaxy Platform University of São Paulo, Brazil 28 July - 1 August 2014 Dave Clements Johns Hopkins University Robson Francisco de Souza University of São
More informationMulti-omics data integration colon cancer using proteogenomics approach
Dept. of Medical Oncology Multi-omics data integration colon cancer using proteogenomics approach DTL Focus meeting, 29 August 2016 Thang Pham OncoProteomics Laboratory, Dept. of Medical Oncology VU University
More informationmicrorna analysis Merete Molton Worren Ståle Nygård
microrna analysis Merete Molton Worren Ståle Nygård Help personnel: Daniel Vodak Background Dysregulation of mirna expression has been connected to progression and development of atherosclerosis The hypothesis:
More informationSmall RNA Sequencing. Project Workflow. Service Description. Sequencing Service Specification BGISEQ-500 SERVICE OVERVIEW SAMPLE PREPARATION
BGISEQ-500 SERVICE OVERVIEW Small RNA Sequencing Service Description Small RNAs are a type of non-coding RNA (ncrna) molecules that are less than 200nt in length. They are often involved in gene silencing
More informationV16: involvement of micrornas in GRNs
What are micrornas? V16: involvement of micrornas in GRNs How can one identify micrornas? What is the function of micrornas? Elisa Izaurralde, MPI Tübingen Huntzinger, Izaurralde, Nat. Rev. Genet. 12,
More informationAccessing and Using ENCODE Data Dr. Peggy J. Farnham
1 William M Keck Professor of Biochemistry Keck School of Medicine University of Southern California How many human genes are encoded in our 3x10 9 bp? C. elegans (worm) 959 cells and 1x10 8 bp 20,000
More informationGene-microRNA network module analysis for ovarian cancer
Gene-microRNA network module analysis for ovarian cancer Shuqin Zhang School of Mathematical Sciences Fudan University Oct. 4, 2016 Outline Introduction Materials and Methods Results Conclusions Introduction
More informationChip Seq Peak Calling in Galaxy
Chip Seq Peak Calling in Galaxy Chris Seward PowerPoint by Pei-Chen Peng Chip-Seq Peak Calling in Galaxy Chris Seward 2018 1 Introduction This goals of the lab are as follows: 1. Gain experience using
More informationRNA - protein interactions in mrna decay A case study on TTP and HuR in AMD
RNA - protein interactions in mrna decay A case study on TTP and HuR in AMD Jörg Fallmann Institute for Theoretical Biochemistry University of Vienna 17.2.214 Research Platform Decoding mrna decay in inflammation
More informationProfiles of gene expression & diagnosis/prognosis of cancer. MCs in Advanced Genetics Ainoa Planas Riverola
Profiles of gene expression & diagnosis/prognosis of cancer MCs in Advanced Genetics Ainoa Planas Riverola Gene expression profiles Gene expression profiling Used in molecular biology, it measures the
More informationdeveloping new tools for diagnostics Join forces with IMGM Laboratories to make your mirna project a success
micrornas developing new tools for diagnostics Join forces with IMGM Laboratories to make your mirna project a success Dr. Carola Wagner IMGM Laboratories GmbH Martinsried, Germany qpcr 2009 Symposium
More informationZhao et al. BMC Bioinformatics (2017) 18:180 DOI /s
Zhao et al. BMC Bioinformatics (2017) 18:180 DOI 10.1186/s12859-017-1601-4 SOFTWARE QuickMIRSeq: a pipeline for quick and accurate quantification of both known mirnas and isomirs by jointly processing
More informationRNA- seq Introduc1on. Promises and pi7alls
RNA- seq Introduc1on Promises and pi7alls DNA is the same in all cells but which RNAs that is present is different in all cells There is a wide variety of different func1onal RNAs Which RNAs (and some1mes
More informationMODULE 4: SPLICING. Removal of introns from messenger RNA by splicing
Last update: 05/10/2017 MODULE 4: SPLICING Lesson Plan: Title MEG LAAKSO Removal of introns from messenger RNA by splicing Objectives Identify splice donor and acceptor sites that are best supported by
More informationTranscript reconstruction
Transcript reconstruction Summary I Data types, file formats and utilities Annotation: Genomic regions Genes Peaks bedtools Alignment: Map reads BAM/SAM Samtools Aggregation: Summary files Wig (UCSC) TDF
More informationPrediction of novel precursor mirnas using a. context-sensitive hidden Markov model (CSHMM)
Prediction of novel precursor mirnas using a context-sensitive hidden Markov model (CSHMM) Sumeet Agarwal 1, Candida Vaz 2, Alok Bhattacharya 2,3 and Ashwin Srinivasan 4,5,6 1. Systems Biology Doctoral
More informationMicroRNA in Cancer Karen Dybkær 2013
MicroRNA in Cancer Karen Dybkær RNA Ribonucleic acid Types -Coding: messenger RNA (mrna) coding for proteins -Non-coding regulating protein formation Ribosomal RNA (rrna) Transfer RNA (trna) Small nuclear
More informationhe micrornas of Caenorhabditis elegans (Lim et al. Genes & Development 2003)
MicroRNAs: Genomics, Biogenesis, Mechanism, and Function (D. Bartel Cell 2004) he micrornas of Caenorhabditis elegans (Lim et al. Genes & Development 2003) Vertebrate MicroRNA Genes (Lim et al. Science
More informationAssembly and Annotation of
Assembly and Annotation of Mycobacterium avium subsp. paratuberculosis Typ-III Martin Hölzer RNA Bioinformatics and High Throughput Analysis Friedrich-Schiller-University Jena 14. Februar 2014 Schedule
More informationCharacterization of the Melanoma mirnaome by Deep Sequencing
Characterization of the Melanoma mirnaome by Deep Sequencing Mitchell S. Stark 1, Sonika Tyagi 1, Derek J. Nancarrow 1, Glen M. Boyle 2, Anthony L. Cook 4, David C. Whiteman 3, Peter G. Parsons 2, Christopher
More informationProfiling micrornas in lung tissue from pigs infected with Actinobacillus pleuropneumoniae
Podolska et al. BMC Genomics 2012, 13:459 RESEARCH ARTICLE Open Access Profiling micrornas in lung tissue from pigs infected with Actinobacillus pleuropneumoniae Agnieszka Podolska 1,2, Christian Anthon
More informationDeploying the full transcriptome using RNA sequencing. Jo Vandesompele, CSO and co-founder The Non-Coding Genome May 12, 2016, Leuven
Deploying the full transcriptome using RNA sequencing Jo Vandesompele, CSO and co-founder The Non-Coding Genome May 12, 2016, Leuven Roadmap Biogazelle the power of RNA reasons to study non-coding RNA
More informationTranscriptome-wide analysis of microrna expression in the malaria mosquito Anopheles gambiae
Biryukova et al. BMC Genomics 2014, 15:557 RESEARCH ARTICLE Open Access Transcriptome-wide analysis of microrna expression in the malaria mosquito Anopheles gambiae Inna Biryukova 1*, Tao Ye 2 and Elena
More informationMicro RNA Research. Ken Kosik. Harriman Professor, Department of Molecular, Cellular & Developmental Biology and Biomolecular Sciences & Engr.
Ken Kosik Harriman Professor, Department of Molecular, Cellular & Developmental Biology and Biomolecular Sciences & Engr. Program Co-Director, Neurosciences Research Institute Micro RNA Research Neuroscience
More informationRNA- seq Introduc1on. Promises and pi7alls
RNA- seq Introduc1on Promises and pi7alls RNA gives informa1on on which genes that are expressed How DNA get transcribed to RNA (and some1mes then translated to proteins) varies between e. g. - Tissues
More informationStudying Alternative Splicing
Studying Alternative Splicing Meelis Kull PhD student in the University of Tartu supervisor: Jaak Vilo CS Theory Days Rõuge 27 Overview Alternative splicing Its biological function Studying splicing Technology
More informationAnalyse de données de séquençage haut débit
Analyse de données de séquençage haut débit Vincent Lacroix Laboratoire de Biométrie et Biologie Évolutive INRIA ERABLE 9ème journée ITS 21 & 22 novembre 2017 Lyon https://its.aviesan.fr Sequencing is
More informationComputational Analysis of UHT Sequences Histone modifications, CAGE, RNA-Seq
Computational Analysis of UHT Sequences Histone modifications, CAGE, RNA-Seq Philipp Bucher Wednesday January 21, 2009 SIB graduate school course EPFL, Lausanne ChIP-seq against histone variants: Biological
More informationMetabolomic and Proteomics Solutions for Integrated Biology. Christine Miller Omics Market Manager ASMS 2015
Metabolomic and Proteomics Solutions for Integrated Biology Christine Miller Omics Market Manager ASMS 2015 Integrating Biological Analysis Using Pathways Protein A R HO R Protein B Protein X Identifies
More informationHuman breast milk mirna, maternal probiotic supplementation and atopic dermatitis in offsrping
Human breast milk mirna, maternal probiotic supplementation and atopic dermatitis in offsrping Melanie Rae Simpson PhD candidate Department of Public Health and General Practice Norwegian University of
More informationSignatures (of Response) to Environmental Exposures
Gene-expression Profiles as Signatures (of Response) to Environmental Exposures Smoking and the airway field of injury as a novel paradigm for the exposome Avrum Spira, M.D., M.Sc. Section of Computational
More informationCRS4 Seminar series. Inferring the functional role of micrornas from gene expression data CRS4. Biomedicine. Bioinformatics. Paolo Uva July 11, 2012
CRS4 Seminar series Inferring the functional role of micrornas from gene expression data CRS4 Biomedicine Bioinformatics Paolo Uva July 11, 2012 Partners Pharmaceutical company Fondazione San Raffaele,
More informationIdentification of mirnas in Eucalyptus globulus Plant by Computational Methods
International Journal of Pharmaceutical Science Invention ISSN (Online): 2319 6718, ISSN (Print): 2319 670X Volume 2 Issue 5 May 2013 PP.70-74 Identification of mirnas in Eucalyptus globulus Plant by Computational
More informationBIMM 143. RNA sequencing overview. Genome Informatics II. Barry Grant. Lecture In vivo. In vitro.
RNA sequencing overview BIMM 143 Genome Informatics II Lecture 14 Barry Grant http://thegrantlab.org/bimm143 In vivo In vitro In silico ( control) Goal: RNA quantification, transcript discovery, variant
More informationRNA-Seq Preparation Comparision Summary: Lexogen, Standard, NEB
RNA-Seq Preparation Comparision Summary: Lexogen, Standard, NEB CSF-NGS January 22, 214 Contents 1 Introduction 1 2 Experimental Details 1 3 Results And Discussion 1 3.1 ERCC spike ins............................................
More informationmirna seq of mouse brain regions
mirna seq of mouse brain regions Iiris Hovatta, PhD University of Helsinki Research Program of Molecular Neurology and Department of Medical Genetics, Faculty of Medicine i National Network of Molecular
More informationInferring condition-specific mirna activity from matched mirna and mrna expression data
Inferring condition-specific mirna activity from matched mirna and mrna expression data Junpeng Zhang 1, Thuc Duy Le 2, Lin Liu 2, Bing Liu 3, Jianfeng He 4, Gregory J Goodall 5 and Jiuyong Li 2,* 1 Faculty
More informationOasis 2: improved online analysis of small RNA-seq data
Rahman et al. BMC Bioinformatics (2018) 19:54 https://doi.org/10.1186/s12859-018-2047-z SOFTWARE Open Access Oasis 2: improved online analysis of small RNA-seq data Raza-Ur Rahman 1,2, Abhivyakti Gautam
More informationA comprehensive repertoire of trna-derived fragments in prostate cancer
/, Vol. 7, No. 17 A comprehensive repertoire of trna-derived fragments in prostate cancer Michael Olvedy 1,4,5,*, Mauro Scaravilli 2,3,*, Youri Hoogstrate 1, Tapio Visakorpi 2,3,#, Guido Jenster 1,#, Elena
More informationA Practical Guide to Integrative Genomics by RNA-seq and ChIP-seq Analysis
A Practical Guide to Integrative Genomics by RNA-seq and ChIP-seq Analysis Jian Xu, Ph.D. Children s Research Institute, UTSW Introduction Outline Overview of genomic and next-gen sequencing technologies
More informationCURRICULUM VITA OF Xiaowen Chen
Xiaowen Chen College of Bioinformatics Science and Technology Harbin Medical University Harbin, 150086, P. R. China Mobile Phone:+86 13263501862 E-mail: hrbmucxw@163.com Education Experience 2008-2011
More informationRNA-Seq profiling of circular RNAs in human colorectal Cancer liver metastasis and the potential biomarkers
Xu et al. Molecular Cancer (2019) 18:8 https://doi.org/10.1186/s12943-018-0932-8 LETTER TO THE EDITOR RNA-Seq profiling of circular RNAs in human colorectal Cancer liver metastasis and the potential biomarkers
More informationSynthetic microrna Reference Standards Genomics Research Group ABRF 2015
Synthetic microrna Reference Standards Genomics Research Group ABRF 2015 Don A. Baldwin, Ph.D. support@signalbiology.com Reference samples for Platform evaluation Protocol development Assay service improvement
More informationEpigenetic Principles and Mechanisms Underlying Nervous System Function in Health and Disease Mark F. Mehler MD, FAAN
Epigenetic Principles and Mechanisms Underlying Nervous System Function in Health and Disease Mark F. Mehler MD, FAAN Institute for Brain Disorders and Neural Regeneration F.M. Kirby Program in Neural
More informationIdentifying mirnas in RNA viruses
1 / 13 Identifying mirnas in RNA viruses Kevin Lamkiewicz Friedrich Schiller Universität Jena 16.02.2017 32nd TBI Winterseminar Bled 2 / 13 mirnas and their function U C A C A G G U C A A G C G U U G G
More informationMature microrna identification via the use of a Naive Bayes classifier
Mature microrna identification via the use of a Naive Bayes classifier Master Thesis Gkirtzou Katerina Computer Science Department University of Crete 13/03/2009 Gkirtzou K. (CSD UOC) Mature microrna identification
More informationChIP-seq data analysis
ChIP-seq data analysis Harri Lähdesmäki Department of Computer Science Aalto University November 24, 2017 Contents Background ChIP-seq protocol ChIP-seq data analysis Transcriptional regulation Transcriptional
More informationPeak-calling for ChIP-seq and ATAC-seq
Peak-calling for ChIP-seq and ATAC-seq Shamith Samarajiwa CRUK Autumn School in Bioinformatics 2017 University of Cambridge Overview Peak-calling: identify enriched (signal) regions in ChIP-seq or ATAC-seq
More informationMODULE 3: TRANSCRIPTION PART II
MODULE 3: TRANSCRIPTION PART II Lesson Plan: Title S. CATHERINE SILVER KEY, CHIYEDZA SMALL Transcription Part II: What happens to the initial (premrna) transcript made by RNA pol II? Objectives Explain
More informationBi 8 Lecture 17. interference. Ellen Rothenberg 1 March 2016
Bi 8 Lecture 17 REGulation by RNA interference Ellen Rothenberg 1 March 2016 Protein is not the only regulatory molecule affecting gene expression: RNA itself can be negative regulator RNA does not need
More informationNot IN Our Genes - A Different Kind of Inheritance.! Christopher Phiel, Ph.D. University of Colorado Denver Mini-STEM School February 4, 2014
Not IN Our Genes - A Different Kind of Inheritance! Christopher Phiel, Ph.D. University of Colorado Denver Mini-STEM School February 4, 2014 Epigenetics in Mainstream Media Epigenetics *Current definition:
More informationChIP-seq hands-on. Iros Barozzi, Campus IFOM-IEO (Milan) Saverio Minucci, Gioacchino Natoli Labs
ChIP-seq hands-on Iros Barozzi, Campus IFOM-IEO (Milan) Saverio Minucci, Gioacchino Natoli Labs Main goals Becoming familiar with essential tools and formats Visualizing and contextualizing raw data Understand
More informationCircular RNAs (circrnas) act a stable mirna sponges
Circular RNAs (circrnas) act a stable mirna sponges cernas compete for mirnas Ancestal mrna (+3 UTR) Pseudogene RNA (+3 UTR homolgy region) The model holds true for all RNAs that share a mirna binding
More informationMicroRNA roles in signaling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data
MicroRNA roles in signaling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data Duy N. Do 1, 2, Ran Li 1, 3, Pier-Luc Dudemaine 1 and Eveline
More information38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16
38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16 PGAR: ASD Candidate Gene Prioritization System Using Expression Patterns Steven Cogill and Liangjiang Wang Department of Genetics and
More informationGene Regulation Part 2
Michael Cummings Chapter 9 Gene Regulation Part 2 David Reisman University of South Carolina Other topics in Chp 9 Part 2 Protein folding diseases Most diseases are caused by mutations in the DNA that
More informationLong non coding RNA in the pea aphid; iden3fica3on and compara3ve expression in sexual and asexual embryos
Long non coding RNA in the pea aphid; iden3fica3on and compara3ve expression in sexual and asexual embryos Fabrice Legeai, Thomas Derrien, Valen3n Wucher, Audrey David, Gael Le Trionnaire and Denis Tagu
More informationSupplementary information for: Human micrornas co-silence in well-separated groups and have different essentialities
Supplementary information for: Human micrornas co-silence in well-separated groups and have different essentialities Gábor Boross,2, Katalin Orosz,2 and Illés J. Farkas 2, Department of Biological Physics,
More informationIntroduction to Systems Biology of Cancer Lecture 2
Introduction to Systems Biology of Cancer Lecture 2 Gustavo Stolovitzky IBM Research Icahn School of Medicine at Mt Sinai DREAM Challenges High throughput measurements: The age of omics Systems Biology
More informationASMS 2015 ThP 459 Glioblastoma Multiforme Subtype Classification: Integrated Analysis of Protein and Gene Expression Data
ASMS 2015 ThP 459 Glioblastoma Multiforme Subtype Classification: Integrated Analysis of Protein and Gene Expression Data Durairaj Renu 1, Vadiraja Bhat 2, Mona Al-Gizawiy 3, Carolina B. Livi 2, Stephen
More informationAnalysis of small RNAs from Drosophila Schneider cells using the Small RNA assay on the Agilent 2100 bioanalyzer. Application Note
Analysis of small RNAs from Drosophila Schneider cells using the Small RNA assay on the Agilent 2100 bioanalyzer Application Note Odile Sismeiro, Jean-Yves Coppée, Christophe Antoniewski, and Hélène Thomassin
More informationPrediction of micrornas and their targets
Prediction of micrornas and their targets Introduction Brief history mirna Biogenesis Computational Methods Mature and precursor mirna prediction mirna target gene prediction Summary micrornas? RNA can
More informationSimple, rapid, and reliable RNA sequencing
Simple, rapid, and reliable RNA sequencing RNA sequencing applications RNA sequencing provides fundamental insights into how genomes are organized and regulated, giving us valuable information about the
More informationBreast and ovarian cancer in Serbia: the importance of mutation detection in hereditary predisposition genes using NGS
Breast and ovarian cancer in Serbia: the importance of mutation detection in hereditary predisposition genes using NGS dr sc. Ana Krivokuća Laboratory for molecular genetics Institute for Oncology and
More informationThe RNA revolution: rewriting the fundamentals of genetics
RCH Grand Rounds - June 4 The RNA revolution: rewriting the fundamentals of genetics Ken Pang Overview 1. Genetics 101 2. Recent lessons from genomics 3. The expanding world of noncoding RNAs 4. Long noncoding
More informationA Statistical Framework for Classification of Tumor Type from microrna Data
DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2016 A Statistical Framework for Classification of Tumor Type from microrna Data JOSEFINE RÖHSS KTH ROYAL INSTITUTE OF TECHNOLOGY
More informationSebastian Jaenicke. trnascan-se. Improved detection of trna genes in genomic sequences
Sebastian Jaenicke trnascan-se Improved detection of trna genes in genomic sequences trnascan-se Improved detection of trna genes in genomic sequences 1/15 Overview 1. trnas 2. Existing approaches 3. trnascan-se
More informationUniversity of Pittsburgh Cancer Institute UPMC CancerCenter. Uma Chandran, MSIS, PhD /21/13
University of Pittsburgh Cancer Institute UPMC CancerCenter Uma Chandran, MSIS, PhD chandran@pitt.edu 412-648-9326 2/21/13 University of Pittsburgh Cancer Institute Founded in 1985 Director Nancy Davidson,
More informationNext Generation Cancer Diagnostics For First Time Right Therapy Choice. Anja van de Stolpe
Next Generation Cancer Diagnostics For First Time Right Therapy Choice Anja van de Stolpe Paradigm shift in cancer treatment towards personalized treatment Chemotherapy for all therapy targeting cancer
More informationTrinity: Transcriptome Assembly for Genetic and Functional Analysis of Cancer [U24]
Trinity: Transcriptome Assembly for Genetic and Functional Analysis of Cancer [U24] ITCR meeting, June 2016 The Cancer Transcriptome A window into the (expressed) genetic and epigenetic state of a tumor
More informationHigh Throughput Sequence (HTS) data analysis. Lei Zhou
High Throughput Sequence (HTS) data analysis Lei Zhou (leizhou@ufl.edu) High Throughput Sequence (HTS) data analysis 1. Representation of HTS data. 2. Visualization of HTS data. 3. Discovering genomic
More informationVirusDetect pipeline - virus detection with small RNA sequencing
VirusDetect pipeline - virus detection with small RNA sequencing CSC webinar 16.1.2018 Eija Korpelainen, Kimmo Mattila, Maria Lehtivaara Big thanks to Jan Kreuze and Jari Valkonen! Outline Small interfering
More informationCross species analysis of genomics data. Computational Prediction of mirnas and their targets
02-716 Cross species analysis of genomics data Computational Prediction of mirnas and their targets Outline Introduction Brief history mirna Biogenesis Why Computational Methods? Computational Methods
More informationMicroRNA expression profiling and functional analysis in prostate cancer. Marco Folini s.c. Ricerca Traslazionale DOSL
MicroRNA expression profiling and functional analysis in prostate cancer Marco Folini s.c. Ricerca Traslazionale DOSL What are micrornas? For almost three decades, the alteration of protein-coding genes
More informationThe Epigenome Tools 2: ChIP-Seq and Data Analysis
The Epigenome Tools 2: ChIP-Seq and Data Analysis Chongzhi Zang zang@virginia.edu http://zanglab.com PHS5705: Public Health Genomics March 20, 2017 1 Outline Epigenome: basics review ChIP-seq overview
More informationProfiling of the Exosomal Cargo of Bovine Milk Reveals the Presence of Immune- and Growthmodulatory Non-coding RNAs (ncrna)
Animal Industry Report AS 664 ASL R3235 2018 Profiling of the Exosomal Cargo of Bovine Milk Reveals the Presence of Immune- and Growthmodulatory Non-coding RNAs (ncrna) Eric D. Testroet Washington State
More informationRIP-seq of BmAgo2-associated small RNAs reveal various types of small non-coding RNAs in the silkworm, Bombyx mori
Nie et al. BMC Genomics 2013, 14:661 RESEARCH ARTICLE Open Access RIP-seq of BmAgo2-associated small RNAs reveal various types of small non-coding RNAs in the silkworm, Bombyx mori Zuoming Nie 1, Fang
More informationHigh-Throughput Sequencing Course
High-Throughput Sequencing Course Introduction Biostatistics and Bioinformatics Summer 2017 From Raw Unaligned Reads To Aligned Reads To Counts Differential Expression Differential Expression 3 2 1 0 1
More informationNGS in Cancer Pathology After the Microscope: From Nucleic Acid to Interpretation
NGS in Cancer Pathology After the Microscope: From Nucleic Acid to Interpretation Michael R. Rossi, PhD, FACMG Assistant Professor Division of Cancer Biology, Department of Radiation Oncology Department
More information