Long non coding RNA in the pea aphid; iden3fica3on and compara3ve expression in sexual and asexual embryos
|
|
- Theresa Porter
- 5 years ago
- Views:
Transcription
1 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
2 Role of the long non coding RNAs HOTAIR (HOX an3sense intergenic RNA) regulates the transcrip3on of HOXD transcrip3on factor by modifying the methyla3on of the chroma3n. lincrna Cox2 : regulates immunity genes rox1 and rox2 play a key role in the dosage compensa3on in dropsophila as they are involve in the transcrip3onal upregula3on of genes on the single X chromosome in males to match the expression from the two X chromosomes COOLAIR an3sense RNAs are upregulated in response to cold temperatures and inac3ves arabidopsis flowering genes Wang K C, Chang, H Y Mol Cell 2011
3 Reads.fq lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict lncrna.fa
4 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging Align reads to genome < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict lncrna.fa Merging between condi3ons and replicates
5 Reads.fa lncrna Predic3on workflow Genome Transcriptome reconsctruc3on Mapping Predic3on Merging predic3ons Everything that is overlapping a protein coding gene in same strand is not removed from the set. < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict Only RNA larger than 200 bp are taken into account. Due to the tophat/cufflinks overpredic3on of genes, only genes with more than one exon are considered lncrna.fa
6 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Coding Poten3al Calcula3on Probability that a RNA is coding for a protein Intersec3on of 3 methods Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict z TxCPSPre dict lncrna.fa
7 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Coding Poten3al Calcula3on Probability that a RNA is coding for a protein Intersec3on of 3 methods Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict z TxCPSPre dict lncrna.fa
8 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict Coding Poten3al Calcula3on TXCDSPredict : Scoring and ranking of the RNA with criteria: ORF size Presence of an ATG Presence of a stop codo,n Number of upstream nucleo3des Kozak sequence Stop in the last exon lncrna.fa
9 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict Coding Poten3al Calcula3on : SVM based on 6 parameters 3 criteria based on the quality of the largest ORF (size, coverage, ATG) 3 criteria based on a result of a comparison to a protein databases (NR, Swissprot, ) (number of hits, mean of the e value, frameshids) lncrna.fa
10 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging < 200bp, single exon Sense to mrna predic3on Coding Poten3al Calcula3on : logis3c regression 1. ORF size 2. Coverage (ORF size/transcript size) 3. Fickee score (combina3on of the nucleo3de composi3on and codon usage) 4. Hexamer score : composi3on of adjacent amino acids Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict lncrna.fa
11 Reads.fa lncrna Predic3on workflow Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging Coding Poten3al Calcula3on < 200bp, single exon Sense to mrna predic3on genome Predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict Build of a learning set And training lncrna.fa
12 lncrna mrna Interac3ons Derrien et al. Genome Research 2012
13 Applica3on : the reproduc3ve polyphenism Jaquiéry et al. Plos 3cs 2013
14 Applica3on : the reproduc3ve polyphenism T0 2 condi3ons 3 replicates 24 samples >627 millions of RNA Seq reads (100 bp PE)
15 Applica3on : the reproduc3ve polyphenism Reads.fa Genome predic3ons Transcriptome reconsctruc3on Mapping Predic3on Merging transcrits < 200bp, single exon Sense to mrna predic3on Protein Coding Poten3al Filter Coding Poten3al Calculator ab ini&o scoring Coding Poten3al Assesment Tool TxCDSPredict lncrna 47 new mrna lncrna.fa
16 Applica3on : the reproduc3ve polyphenism Differen3ally expressed lncrna (cuffdiff, p value FDR < 0.05) interac3ons lncrna T1 T2 T3 Sexual Asexual Intergenic : «Same strand» : 1887 Divergent : 1014 Convergent : 767 Genic : Intronic : 674 Exonic : 1731 overlapping: 15 «Nested» : 743
17 Integra3on mrna (4996) lncrna (370) mirna (15)
18 Availability
19 Availability
20 Ackowledgments Gaël Le Trionnaire, Aurore Gallot Valen3n Wucher Julie Jaquiéry Audrey David, Thomas Derrien Denis Tagu
RNA- 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 informationData mining with Ensembl Biomart. Stéphanie Le Gras
Data mining with Ensembl Biomart Stéphanie Le Gras (slegras@igbmc.fr) Guidelines Genome data Genome browsers Getting access to genomic data: Ensembl/BioMart 2 Genome Sequencing Example: Human genome 2000:
More informationCharacteriza*on of Soma*c Muta*ons in Cancer Genomes
Characteriza*on of Soma*c Muta*ons in Cancer Genomes Ben Raphael Department of Computer Science Center for Computa*onal Molecular Biology Soma*c Muta*ons and Cancer Clonal Theory (Nowell 1976) Passenger
More informationVariant Classification. Author: Mike Thiesen, Golden Helix, Inc.
Variant Classification Author: Mike Thiesen, Golden Helix, Inc. Overview Sequencing pipelines are able to identify rare variants not found in catalogs such as dbsnp. As a result, variants in these datasets
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 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 informationSupplementary Figures
Supplementary Figures Supplementary Figure 1. Heatmap of GO terms for differentially expressed genes. The terms were hierarchically clustered using the GO term enrichment beta. Darker red, higher positive
More informationGene finding. kuobin/
Gene finding KUO-BIN LI, PH.D. http://www.bii.a-star.edu.sg/ kuobin/ Bioinformatics Institute 30 Medical Drive, Level 1, IMCB Building Singapore 117609 Republic of Singapore Gene finding (LSM5191) p.1
More informationSupplemental Data. Integrating omics and alternative splicing i reveals insights i into grape response to high temperature
Supplemental Data Integrating omics and alternative splicing i reveals insights i into grape response to high temperature Jianfu Jiang 1, Xinna Liu 1, Guotian Liu, Chonghuih Liu*, Shaohuah Li*, and Lijun
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 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 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 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 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 informationTranscriptome and isoform reconstruc1on with short reads. Tangled up in reads
Transcriptome and isoform reconstruc1on with short reads Tangled up in reads Topics of this lecture Mapping- based reconstruc1on methods Case study: The domes1c dog De- novo reconstruc1on method Trinity
More informationBWA alignment to reference transcriptome and genome. Convert transcriptome mappings back to genome space
Whole genome sequencing Whole exome sequencing BWA alignment to reference transcriptome and genome Convert transcriptome mappings back to genome space genomes Filter on MQ, distance, Cigar string Annotate
More informationSupplemental Figure 1. Small RNA size distribution from different soybean tissues.
Supplemental Figure 1. Small RNA size distribution from different soybean tissues. The size of small RNAs was plotted versus frequency (percentage) among total sequences (A, C, E and G) or distinct sequences
More informationBioinformatics. Sequence Analysis: Part III. Pattern Searching and Gene Finding. Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute
Bioinformatics Sequence Analysis: Part III. Pattern Searching and Gene Finding Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute Course Syllabus Jan 7 Jan 14 Jan 21 Jan 28 Feb 4 Feb 11 Feb 18
More informationNature Structural & Molecular Biology: doi: /nsmb Supplementary Figure 1
Supplementary Figure 1 Frequency of alternative-cassette-exon engagement with the ribosome is consistent across data from multiple human cell types and from mouse stem cells. Box plots showing AS frequency
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 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 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 informationDMD Genetics: complicated, complex and critical to understand
DMD Genetics: complicated, complex and critical to understand Stanley Nelson, MD Professor of Human Genetics, Pathology and Laboratory Medicine, and Psychiatry Co Director, Center for Duchenne Muscular
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 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 informationBioinformatics Laboratory Exercise
Bioinformatics Laboratory Exercise Biology is in the midst of the genomics revolution, the application of robotic technology to generate huge amounts of molecular biology data. Genomics has led to an explosion
More informationTranscriptional control in Eukaryotes: (chapter 13 pp276) Chromatin structure affects gene expression. Chromatin Array of nuc
Transcriptional control in Eukaryotes: (chapter 13 pp276) Chromatin structure affects gene expression Chromatin Array of nuc 1 Transcriptional control in Eukaryotes: Chromatin undergoes structural changes
More informationAnnotation of Chimp Chunk 2-10 Jerome M Molleston 5/4/2009
Annotation of Chimp Chunk 2-10 Jerome M Molleston 5/4/2009 1 Abstract A stretch of chimpanzee DNA was annotated using tools including BLAST, BLAT, and Genscan. Analysis of Genscan predicted genes revealed
More informationof TERT, MLL4, CCNE1, SENP5, and ROCK1 on tumor development were discussed.
Supplementary Note The potential association and implications of HBV integration at known and putative cancer genes of TERT, MLL4, CCNE1, SENP5, and ROCK1 on tumor development were discussed. Human telomerase
More informationSupplementary Figure S1. Gene expression analysis of epidermal marker genes and TP63.
Supplementary Figure Legends Supplementary Figure S1. Gene expression analysis of epidermal marker genes and TP63. A. Screenshot of the UCSC genome browser from normalized RNAPII and RNA-seq ChIP-seq data
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 informationCanadian Bioinforma1cs Workshops
5/12/16 Canadian Bioinforma1cs Workshops www.bioinforma1cs.ca Module #: Title of Module 2 1 Module 3 Introduc1on to WGBS and analysis Guillaume Bourque Learning Objec/ves of Module Know the different technologies
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 informationAnnotation of Drosophila mojavensis fosmid 8 Priya Srikanth Bio 434W
Annotation of Drosophila mojavensis fosmid 8 Priya Srikanth Bio 434W 5.1.2007 Overview High-quality finished sequence is much more useful for research once it is annotated. Annotation is a fundamental
More informationfl/+ KRas;Atg5 fl/+ KRas;Atg5 fl/fl KRas;Atg5 fl/fl KRas;Atg5 Supplementary Figure 1. Gene set enrichment analyses. (a) (b)
KRas;At KRas;At KRas;At KRas;At a b Supplementary Figure 1. Gene set enrichment analyses. (a) GO gene sets (MSigDB v3. c5) enriched in KRas;Atg5 fl/+ as compared to KRas;Atg5 fl/fl tumors using gene set
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 informationDNA codes for RNA, which guides protein synthesis.
Section 3: DNA codes for RNA, which guides protein synthesis. K What I Know W What I Want to Find Out L What I Learned Vocabulary Review synthesis New RNA messenger RNA ribosomal RNA transfer RNA transcription
More informationReporting TP53 gene analysis results in CLL
Reporting TP53 gene analysis results in CLL Mutations in TP53 - From discovery to clinical practice in CLL Discovery Validation Clinical practice Variant diversity *Leroy at al, Cancer Research Review
More informationNature Genetics: doi: /ng Supplementary Figure 1. Assessment of sample purity and quality.
Supplementary Figure 1 Assessment of sample purity and quality. (a) Hematoxylin and eosin staining of formaldehyde-fixed, paraffin-embedded sections from a human testis biopsy collected concurrently with
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 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 informationComputational Biology I LSM5191
Computational Biology I LSM5191 Aylwin Ng, D.Phil Lecture Notes: Transcriptome: Molecular Biology of Gene Expression II TRANSLATION RIBOSOMES: protein synthesizing machines Translation takes place on defined
More informationMining for disease- causing genes in the other 98% of the human genome Daniel Gautheret
Mining for disease- causing genes in the other 98% of the human genome Daniel Gautheret Ins:tut de Géné:que et Microbiologie, Orsay Plateforme Bioinforma:que ebio Orsay et Gustave Roussy 1 Gene expression
More informationSmall RNA-Seq and profiling
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,
More informationNEXT GENERATION SEQUENCING. R. Piazza (MD, PhD) Dept. of Medicine and Surgery, University of Milano-Bicocca
NEXT GENERATION SEQUENCING R. Piazza (MD, PhD) Dept. of Medicine and Surgery, University of Milano-Bicocca SANGER SEQUENCING 5 3 3 5 + Capillary Electrophoresis DNA NEXT GENERATION SEQUENCING SOLEXA-ILLUMINA
More informationIdentification of Prostate Cancer LncRNAs by RNA-Seq
DOI:http://dx.doi.org/10.7314/APJCP.2014.15.21.9439 RESEARCH ARTICLE Cheng-Cheng Hu 1, Ping Gan 1, 2, Rui-Ying Zhang 1, Jin-Xia Xue 1, Long-Ke Ran 3 * Abstract Purpose: To identify prostate cancer lncrnas
More informationMutationTaster & RegulationSpotter
MutationTaster & RegulationSpotter Pathogenicity Prediction of Sequence Variants: Past, Present and Future Dr. rer. nat. Jana Marie Schwarz Klinik für Pädiatrie m. S. Neurologie Exzellenzcluster NeuroCure
More informationSupplemental Information For: The genetics of splicing in neuroblastoma
Supplemental Information For: The genetics of splicing in neuroblastoma Justin Chen, Christopher S. Hackett, Shile Zhang, Young K. Song, Robert J.A. Bell, Annette M. Molinaro, David A. Quigley, Allan Balmain,
More informationComputational aspects of ChIP-seq. John Marioni Research Group Leader European Bioinformatics Institute European Molecular Biology Laboratory
Computational aspects of ChIP-seq John Marioni Research Group Leader European Bioinformatics Institute European Molecular Biology Laboratory ChIP-seq Using highthroughput sequencing to investigate DNA
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 informationSCIENCE CHINA Life Sciences
SCIENCE CHINA Life Sciences RESEARCH PAPER June 2013 Vol.56 No.6: 503 512 doi: 10.1007/s11427-013-4485-1 Genome-wide identification of cancer-related polyadenylated and non-polyadenylated RNAs in human
More informationPatterns of Histone Methylation and Chromatin Organization in Grapevine Leaf. Rachel Schwope EPIGEN May 24-27, 2016
Patterns of Histone Methylation and Chromatin Organization in Grapevine Leaf Rachel Schwope EPIGEN May 24-27, 2016 What does H3K4 methylation do? Plant of interest: Vitis vinifera Culturally important
More informationHao D. H., Ma W. G., Sheng Y. L., Zhang J. B., Jin Y. F., Yang H. Q., Li Z. G., Wang S. S., GONG Ming*
Comparison of transcriptomes and gene expression profiles of two chilling- and drought-tolerant and intolerant Nicotiana tabacum varieties under low temperature and drought stress Hao D. H., Ma W. G.,
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 informationGlobal Epigenetic and Transcriptional Trends among Two Rice Subspecies and Their Reciprocal Hybrids W
The Plant Cell, Vol. 22: 17 33, January 2010, www.plantcell.org ã 2010 American Society of Plant Biologists RESEARCH ARTICLES Global Epigenetic and Transcriptional Trends among Two Rice Subspecies and
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 informationSTAT1 regulates microrna transcription in interferon γ stimulated HeLa cells
CAMDA 2009 October 5, 2009 STAT1 regulates microrna transcription in interferon γ stimulated HeLa cells Guohua Wang 1, Yadong Wang 1, Denan Zhang 1, Mingxiang Teng 1,2, Lang Li 2, and Yunlong Liu 2 Harbin
More information7SK ChIRP-seq is specifically RNA dependent and conserved between mice and humans.
Supplementary Figure 1 7SK ChIRP-seq is specifically RNA dependent and conserved between mice and humans. Regions targeted by the Even and Odd ChIRP probes mapped to a secondary structure model 56 of the
More informationBacterial Gene Finding CMSC 423
Bacterial Gene Finding CMSC 423 Finding Signals in DNA We just have a long string of A, C, G, Ts. How can we find the signals encoded in it? Suppose you encountered a language you didn t know. How would
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 informationRNA-Seq Atlas of Glycine max: A guide to the Soybean Transcriptome
RNA-Seq Atlas of Glycine max: A guide to the Soybean Transcriptome How do novel structures or functions evolve? A more relevant example Symbiosis and Nitrogen Fixation Limpens & Bisseling (23) Curr. Opin.
More informationEST alignments suggest that [secret number]% of Arabidopsis thaliana genes are alternatively spliced
EST alignments suggest that [secret number]% of Arabidopsis thaliana genes are alternatively spliced Dan Morris Stanford University Robotics Lab Computer Science Department Stanford, CA 94305-9010 dmorris@cs.stanford.edu
More information3. What law of heredity explains that traits, like texture and color, are inherited independently of each other?
Section 2: Genetics Chapter 11 pg. 308-329 Part 1: Refer to the table of pea plant traits on the right. Then complete the table on the left by filling in the missing information for each cross. 6. What
More informationInfluenza Virus HA Subtype Numbering Conversion Tool and the Identification of Candidate Cross-Reactive Immune Epitopes
Influenza Virus HA Subtype Numbering Conversion Tool and the Identification of Candidate Cross-Reactive Immune Epitopes Brian J. Reardon, Ph.D. J. Craig Venter Institute breardon@jcvi.org Introduction:
More informationSelective depletion of abundant RNAs to enable transcriptome analysis of lowinput and highly-degraded RNA from FFPE breast cancer samples
DNA CLONING DNA AMPLIFICATION & PCR EPIGENETICS RNA ANALYSIS Selective depletion of abundant RNAs to enable transcriptome analysis of lowinput and highly-degraded RNA from FFPE breast cancer samples LIBRARY
More informationIntegration of high-throughput biological data
Integration of high-throughput biological data Jean Yang and Vivek Jayaswal School of Mathematics and Statistics University of Sydney Meeting the Challenges of High Dimension: Statistical Methodology,
More informationGENE EXPRESSION. Amoeba Sisters video 3pk9YVo. Individuality & Mutations
Amoeba Sisters video https://www.youtube.com/watch?v=giez 3pk9YVo GENE EXPRESSION Individuality & Mutations Complete video handout http://www.amoebasisters.com/uploads/ 2/1/9/0/21902384/video_recap_of_muta
More informationTo test the possible source of the HBV infection outside the study family, we searched the Genbank
Supplementary Discussion The source of hepatitis B virus infection To test the possible source of the HBV infection outside the study family, we searched the Genbank and HBV Database (http://hbvdb.ibcp.fr),
More informationHands-On Ten The BRCA1 Gene and Protein
Hands-On Ten The BRCA1 Gene and Protein Objective: To review transcription, translation, reading frames, mutations, and reading files from GenBank, and to review some of the bioinformatics tools, such
More informationRegulation of Gene Expression in Eukaryotes
Ch. 19 Regulation of Gene Expression in Eukaryotes BIOL 222 Differential Gene Expression in Eukaryotes Signal Cells in a multicellular eukaryotic organism genetically identical differential gene expression
More informationCONTRACTING ORGANIZATION: Johns Hopkins University, Baltimore, MD
AD Award Number: W81XWH-12-1-0480 TITLE: Molecular Characterization of Indolent Prostate Cancer PRINCIPAL INVESTIGATOR: Jun Luo, Ph.D. CONTRACTING ORGANIZATION: Johns Hopkins University, Baltimore, MD
More informationProcessing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data
Processing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data Bioinformatics methods, models and applications to disease Alex Essebier ChIP-seq experiment To
More informationFINAL ANNOTATION REPORT: Drosophila virilis Fosmid 11 (48P14) Robert Carrasquillo Bio 4342
FINAL ANNOTATION REPORT: Drosophila virilis Fosmid 11 (48P14) Robert Carrasquillo Bio 4342 2006 TABLE OF CONTENTS I. Overview... 3 II. Genes... 4 III. Clustal Analysis... 15 IV. Repeat Analysis... 17 V.
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 informationLectures 13: High throughput sequencing: Beyond the genome. Spring 2017 March 28, 2017
Lectures 13: High throughput sequencing: Beyond the genome Spring 2017 March 28, 2017 h@p://www.fejes.ca/2009/06/science- cartoons- 5- rna- seq.html Omics Transcriptome - the set of all mrnas present in
More informationThe role of noncoding RNA in hepatocellular carcinoma
Review Article The role of noncoding RNA in hepatocellular carcinoma Zhuolu Wang, Xinying Li Department of General Surgery, Xiangya Hospital, Central South University, Changsha 410008, People s Republic
More informationSec$on 8. Gene$c Muta$ons, Ribosome Structure and the Tetracyclines
Sec$on 8 Gene$c Muta$ons, Ribosome Structure and the Tetracyclines Explain the mechanism of ac$on of the transcrip$onal and transla$onal inhibitors Requires knowledge of: Sec$on 6 Sec$on 7 Sec$on 8 How
More informationMechanisms of alternative splicing regulation
Mechanisms of alternative splicing regulation The number of mechanisms that are known to be involved in splicing regulation approximates the number of splicing decisions that have been analyzed in detail.
More informationGene Finding in Eukaryotes
Gene Finding in Eukaryotes Jan-Jaap Wesselink jjwesselink@cnio.es Computational and Structural Biology Group, Centro Nacional de Investigaciones Oncológicas Madrid, April 2008 Jan-Jaap Wesselink jjwesselink@cnio.es
More informationSupplemental Information. Metabolic Maturation during Muscle Stem Cell. Differentiation Is Achieved by mir-1/133a-mediated
Cell Metabolism, Volume 27 Supplemental Information Metabolic Maturation during Muscle Stem Cell Differentiation Is Achieved by mir-1/133a-mediated Inhibition of the Dlk1-Dio3 Mega Gene Cluster Stas Wüst,
More informationCopy Number Varia/on Detec/on. Alex Mawla UCD Genome Center Bioinforma5cs Core Tuesday June 16, 2015
Copy Number Varia/on Detec/on Alex Mawla UCD Genome Center Bioinforma5cs Core Tuesday June 16, 2015 Today s Goals Understand the applica5on and capabili5es of using targe5ng sequencing and CNV calling
More informationHistone Modifications Are Associated with Transcript Isoform Diversity in Normal and Cancer Cells
Histone Modifications Are Associated with Transcript Isoform Diversity in Normal and Cancer Cells Ondrej Podlaha 1, Subhajyoti De 2,3,4, Mithat Gonen 5, Franziska Michor 1 * 1 Department of Biostatistics
More informationMasculinization of the X Chromosome in the Pea Aphid
Masculinization of the X Chromosome in the Pea Aphid Julie Jaquiéry, Claude Rispe, Denis Roze, Fabrice Legeai, Gaël Le Trionnaire, Solenn Stoeckel, Lucie Mieuzet, Corinne Da Silva, Julie Poulain, Nathalie
More informationComputational Identification and Prediction of Tissue-Specific Alternative Splicing in H. Sapiens. Eric Van Nostrand CS229 Final Project
Computational Identification and Prediction of Tissue-Specific Alternative Splicing in H. Sapiens. Eric Van Nostrand CS229 Final Project Introduction RNA splicing is a critical step in eukaryotic gene
More informationMapping by recurrence and modelling the mutation rate
Mapping by recurrence and modelling the mutation rate Shamil Sunyaev Broad Institute of M.I.T. and Harvard Current knowledge is from Comparative genomics Experimental systems: yeast reporter assays Potential
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 informationEXPression ANalyzer and DisplayER
EXPression ANalyzer and DisplayER Tom Hait Aviv Steiner Igor Ulitsky Chaim Linhart Amos Tanay Seagull Shavit Rani Elkon Adi Maron-Katz Dorit Sagir Eyal David Roded Sharan Israel Steinfeld Yossi Shiloh
More informationThe Biology and Genetics of Cells and Organisms The Biology of Cancer
The Biology and Genetics of Cells and Organisms The Biology of Cancer Mendel and Genetics How many distinct genes are present in the genomes of mammals? - 21,000 for human. - Genetic information is carried
More informationRASA: Robust Alternative Splicing Analysis for Human Transcriptome Arrays
Supplementary Materials RASA: Robust Alternative Splicing Analysis for Human Transcriptome Arrays Junhee Seok 1*, Weihong Xu 2, Ronald W. Davis 2, Wenzhong Xiao 2,3* 1 School of Electrical Engineering,
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 informationLncRNAs: The Ideal Composer of the Melody for Life
RNA and Transcription 2016; 2(2): 11-15 http://www.sciencepublishinggroup.com/j/rnat doi: 10.11648/j.rnat.20160202.11 Review Article LncRNAs: The Ideal Composer of the Melody for Life Qingyu Cheng 1, Shengwei
More informationP. Tang ( 鄧致剛 ); PJ Huang ( 黄栢榕 ) g( ); g ( ) Bioinformatics Center, Chang Gung University.
Databases and Tools for High Throughput Sequencing Analysis P. Tang ( 鄧致剛 ); PJ Huang ( 黄栢榕 ) g( ); g ( ) Bioinformatics Center, Chang Gung University. HTseq Platforms Applications on Biomedical Sciences
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 informationIPA Advanced Training Course
IPA Advanced Training Course October 2013 Academia sinica Gene (Kuan Wen Chen) IPA Certified Analyst Agenda I. Data Upload and How to Run a Core Analysis II. Functional Interpretation in IPA Hands-on Exercises
More informationMass Spectrometry and Proteomics - Lecture 4 - Matthias Trost Newcastle University
Mass Spectrometry and Proteomics - Lecture 4 - Matthias Trost Newcastle University matthias.trost@ncl.ac.uk previously Peptide fragmentation Hybrid instruments 117 The Building Blocks of Life DNA RNA Proteins
More informationTranscriptional profiling of lncrnas and novel transcribed regions across a diverse panel of archived human cancers
Genome Biology This Provisional PDF corresponds to the article as it appeared upon acceptance. Copyedited and fully formatted PDF and full text (HTML) versions will be made available soon. Transcriptional
More informationIntroduction to genetic variation. He Zhang Bioinformatics Core Facility 6/22/2016
Introduction to genetic variation He Zhang Bioinformatics Core Facility 6/22/2016 Outline Basic concepts of genetic variation Genetic variation in human populations Variation and genetic disorders Databases
More informationDevelopment and Applica0on of Real- Time Clinical Predic0ve Models
Development and Applica0on of Real- Time Clinical Predic0ve Models Ruben Amarasingham, MD, MBA Associate Professor, UT Southwestern Medical Center AHRQ- funded R24 UT Southwestern Center for Pa?ent- Centered
More informationAssignment 5: Integrative epigenomics analysis
Assignment 5: Integrative epigenomics analysis Due date: Friday, 2/24 10am. Note: no late assignments will be accepted. Introduction CpG islands (CGIs) are important regulatory regions in the genome. What
More informationEPIGENOMICS PROFILING SERVICES
EPIGENOMICS PROFILING SERVICES Chromatin analysis DNA methylation analysis RNA-seq analysis Diagenode helps you uncover the mysteries of epigenetics PAGE 3 Integrative epigenomics analysis DNA methylation
More information