Genomic DNA ASSEMBLY BY REMAPPING. Course overview
|
|
- Anthony Stone
- 6 years ago
- Views:
Transcription
1 ASSEMBLY BY REMAPPING Laurent Falquet, The Bioinformatics Unravelling Group, UNIFR & SIB UniFr Group SIB Course overview Genomic DNA PacBio Illumina methylation de novo remapping Annotation Indels calling SNP calling Virulence/ Resistance genes VCF annotation Comparative genomics roary Comparative genomics SNP diff
2 What is remapping? Originally "mapping" is the process of finding the location of genes on each chromosome, but in NGS context, "remapping" means identify (by aligning) all possible locations of a read on a reference sequence (genome). AGCTGATGTGCCGCCTCACTTCGGTGGTGAGGTG Reference sequence! CTGATGTGCCGCCTCACTTCGGTGGT Short read 1! TGATGTGCCGCCTCACTACGGTGGTG Short read 2! GATGTGCCGCCTCACTTCGGTGGTGA Short read 3! GCTGATGTGCCGCCTCACTACGGTG Short read 4! GCTGATGTGCCGCCTCACTACGGTG Short read 5 Next Generation Sequencing and remapping: an easy task? Remapping reads onto an existing genome: Current tools are fast by using the Burrows-Wheeler Transform Success depends on the degree of similarity of the reference Detectable variations: SNPs and small insertions or deletions Variations difficult to identify: large insertions/deletions, inversions and translocations reference target
3 Quality Control of the data First step after receiving the data Sometimes already done by the sequencing center (e.g., chastity) Objective: Remove bad quality reads Remove contaminants Trim ends of reads Remove orphans (if possible or desirable) FastQC ( FastX toolkit ( PrinSeq ( 5 Phred quality score, a measure of base call quality Q sanger = -10 log 10 p Phred quality scores are logarithmically linked to error probabilities" Phred Quality Score!Probability of incorrect call!base call accuracy" 10 "1 in 10 "90%" 20 "1 in 100 "99%" 30 "1 in 1000 "99.9%" 40 "1 in "99.99%" 50 "1 in "99.999%" The quality score is ASCII encoded in the FASTQ format" FASTQ is a FASTA with score
4 Example of FASTA >C3PO_0001:2:1:17:1499#0/1! TGAATTCATTGACCATAACAATCATATGCATGATGCAAATTATAATATCATT TTTGTTTGAGCAAATGATTCATAATAATGTATTTCAATATTTTTAGGAATAT CTCCCAATATTGCGCGTGCTGAATTCCATCCGGAATTTTTGACGTCCCCCCC CGAANGGANGNGANNNNGNNGNNNTNTNNAAANGNNNNN!! Example of FASTQ Illumina 1:N:0:51! AACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGTTGTTGGGTGCTTTTTG! 1:N:0:51! AACCGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGTCTACTAGTTGTTGGTGGAGTAAAA! 1:N:0:51! AACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAG! +! read 1 read 2 read 3
5 Warning: various FASTQ formats }~!! ! ! ! ! ! ! S - Sanger Phred+33, raw reads typically (0, 40)! X - Solexa Solexa+64, raw reads typically (-5, 40)! I - Illumina 1.3+ Phred+64, raw reads typically (0, 40)! J - Illumina 1.5+ Phred+64, raw reads typically (3, 40)! with 0=unused, 1=unused, 2=Read Segment Quality Control Indicator (bold)! (Note: See discussion above).! L - Illumina 1.8+ Phred+33, raw reads typically (0, 41)!!!!! Quality control examples Forward Reverse Forward
6 Quality Control example 11 Quality Control example 12
7 Read trimming or filtering Trimming remove 5' and/or 3' ends of reads (bad quality or adapter) Filtering remove full reads (e.g., contaminants) Tools: FastX toolkit ( PrinSeq ( Sickle ( ea-utils ( Trimmomatic ( cutadapt ( Error correction For substitutions (mainly Illumina) Quake Reptile ECHO HiTEC For insertions and deletions (454, IonTorrent, PacBio, ONP) Coral HSHREC Quiver Arrow 14
8 Remapping methods By sequence comparison with Smith-Waterman much too slow By sequence indexing (e.g., BLAST or BLAT) Conventional tools like Blast or Blat do not work well with short sequence reads. -> Modification of existing alignment algorithms to handle short reads. Indexing methods Suffix tree Suffix array Seed hash tables BWT (Burrows-Wheeler Transform) Suffix tree The suffix tree for a string S is a tree whose edges are labelled with strings. Suffix trees also provided one of the first linear-time solutions for the longest common substring problem. These speedups come at a cost: storing a string's suffix tree typically requires significantly more space than storing the string itself. 35Gb for the human genome
9 Suffix array: a sorted array of all suffixes of a string Consider the string BANANA$ of length 7. It has 7 suffixes: index suffix 0 BANANA$ 1 ANANA$ 2 NANA$ 3 ANA$ 4 NA$ 5 A$ 6 $ sort à index suffix 6 $ 5 A$ 3 ANA$ 1 ANANA$ 0 BANANA$ 4 NA$ 2 NANA$ The suffix array is the array of indices: {6,5,3,1,0,4,2} 12Gb for the human genome Seed hash table Given the string ACGTACGTAAG of length 10, extract all substrings length 4 (seeds) and store their starting positions. index seed 0,4 ACGT 1,5 CGTA 2 GTAC 3 TACG 6 GTAA 7 TAAG sort à index seed 0,4 ACGT 1,5 CGTA 6 GTAA 2 GTAC 7 TAAG 3 TACG The size of the hash table depends on the length of the seed and the complexity of the input string 12Gb for the human genome
10 Spaced seed hash table indexing (MAQ) (original algorithm for remapping short reads with 2 mismatches) MAQ builds 6 hash tables, each indexing 14 of the first 28 bases Hence, Maq finds all alignments with at most 2 mismatches in the first 28 bases. Why Burrows-Wheeler? BWT very compact Approximately ½ byte per base As large as the original text(sequence), plus a few extras Can fit onto a standard computer with 2GB of memory Linear-time search algorithm proportional to length of query for exact matches
11 Burrows-Wheeler Transform (BWT) acaacg$ all rotations $acaacg g$acaac cg$acaa acg$aca aacg$ac caacg$a acaacg$ sort $acaacg aacg$ac acaacg$ acg$aca caacg$a cg$acaa g$acaac BW Matrix gc$aaac Langmead et al Genome Biology Burrows-Wheeler Matrix $acaacg aacg$ac acaacg$ acg$aca caacg$a cg$acaa g$acaac See the hidden suffix array?
12 Burrows-Wheeler Transform LF mapping property: The i th occurrence of character X in the Last column corresponds to the same text character as the i th occurrence of X in the First column acaacg$ 2 nd $acaacg aacg$ac acaacg$ acg$aca caacg$a cg$acaa g$acaac 2 nd Burrows-Wheeler Transform LF mapping property: Using LF the UNPERMUTE algorithm can recreate the original string
13 Burrows-Wheeler Transform LF mapping property Using LF the EXACTMATCH algorithm from Ferragina and Manzini can find occurrence of a substring from right to left (! greedy) Mapping tools history DNA mappers in blue RNA mappers in red mirna mappers in green bisulfite mappers in purple
14 Example of output formats a) alignment b) SAM c) pileup Li H et al. Bioinformatics 2009;25: MAQ Pileup example BA A BA A BA T BA T BA A BA G BA T BA C BA T BA A BA T BA C BA A BA A BA A BA A BA G BA A BA A BA A BA G BA G BA G BA G BA T BA T BA G BA G BA G BA A BA C BA T BA G BA T BA T
15 SAM/BAM formats Here is an example of an SAM SN:chr20 ID:L1 PU:SC_1_10 LB:SC_1 ID:L2 PU:SC_2_12 LB:SC_2 SM:NA12891! read_28833_29006_ chr M1D25M = AGCTTAGCTAGCTACCTATATCTTGGTCTTGGCCG <<<<<<<<<<<<<<<<<<<<<:<9/,&,22;;<<< NM:i:1 RG:Z:L1! read_28701_28881_323b 147 chr M = ACCTATATCTTGGCCTTGGCCGATGCGGCCTTGCA <<<<<;<<<<7;:<<<6;<<<<<<<<<<<<7<<<< MF:i:18 RG:Z:L2!!! BAM is the binary compressed version of the same data More details: Visualization tools for mapping (non-exhaustive list) Tool Windows Linux Mac Input format BAMview Y Y Y BAM Consed/Gap5 N Y (X11) Y (X11) ACE, MAQ, BAM Eagleview Y Y Y ACE Gambit Y Y Y BAM Hawkeye Y (cygwin) Y (Y) afg (AMOS) IGViewer Y Y Y BAM, SAM, GFF, BED, VCF Tablet Y Y Y ACE, MAQ, BAM, afg, SAM, IGBrowser Y Y Y BAM, SAM, GFF, BED...
16 Text based with Samtools 34 Tablet visualization of the mapping and the SNPs Mapping of the reads of a Staphylococcus aureus sequencing, showing 2 SNPs vs the reference genome.
17 IGV Integrative Genome Viewer Summary Lessons from the remapping Easy to map reads onto a closely related reference (always better than de novo) Less easy to find non-matching reads and what they are (plasmids, insertion sequences, phages, virus, other) Repeats are a nightmare in any case Paired-ends help SNPs, CNVs, and phasing Next courses
NEXT GENERATION SEQUENCING. Farhat Habib
NEXT GENERATION SEQUENCING HISTORY HISTORY Sanger Dominant for last ~30 years 1000bp longest read Based on primers so not good for repetitive or SNPs sites HISTORY Sanger Dominant for last ~30 years 1000bp
More informationC3BI. VARIANTS CALLING November Pierre Lechat Stéphane Descorps-Declère
C3BI VARIANTS CALLING November 2016 Pierre Lechat Stéphane Descorps-Declère General Workflow (GATK) software websites software bwa picard samtools GATK IGV tablet vcftools website http://bio-bwa.sourceforge.net/
More informationMapping Next Generation Sequence Reads. Bingbing Yuan Dec. 2, 2010
Mapping Next Generation Sequence Reads Bingbing Yuan Dec. 2, 2010 1 What happen if reads are not mapped properly? Some data won t be used, thus fewer reads would be aligned. Reads are mapped to the wrong
More informationDATA FORMATS AND QUALITY CONTROL
HTS Summer School 12-16th September 2016 DATA FORMATS AND QUALITY CONTROL Romina Petersen, University of Cambridge (rp520@medschl.cam.ac.uk) Luigi Grassi, University of Cambridge (lg490@medschl.cam.ac.uk)
More informationRead Quality Assessment & Improvement. UCD Genome Center Bioinformatics Core Tuesday 14 June 2016
Read Quality Assessment & Improvement UCD Genome Center Bioinformatics Core Tuesday 14 June 2016 QA&I should be interactive Error modes Each technology has unique error modes, depending on the physico-chemical
More informationHigh-Throughput Bioinformatics: Re-sequencing and de novo assembly. Elena Czeizler
High-Throughput Bioinformatics: Re-sequencing and de novo assembly Elena Czeizler 13.11.2015 Sequencing data Current sequencing technologies produce large amounts of data: short reads The outputted sequences
More informationIllumina (Solexa) Throughput: 4 Tbp in one run (5 days) Cheapest sequencing technology. Mismatch errors dominate. Cost: ~$1000 per human genme
Illumina (Solexa) Current market leader Based on sequencing by synthesis Current read length 100-150bp Paired-end easy, longer matepairs harder Error ~0.1% Mismatch errors dominate Throughput: 4 Tbp in
More informationNGS in Pathology Webinar
NGS in Pathology Webinar NGS Data Analysis March 10 2016 1 Topics for today s presentation 2 Introduction Next Generation Sequencing (NGS) is becoming a common and versatile tool for biological and medical
More informationSNP calling and VCF format
SNP calling and VCF format Laurent Falquet, Oct 12 SNP? What is this? A type of genetic variation, among others: Family of Single Nucleotide Aberrations Single Nucleotide Polymorphisms (SNPs) Single Nucleotide
More informationSanger vs Next-Gen Sequencing
Tools and Algorithms in Bioinformatics GCBA815/MCGB815/BMI815, Fall 2017 Week-8: Next-Gen Sequencing RNA-seq Data Analysis Babu Guda, Ph.D. Professor, Genetics, Cell Biology & Anatomy Director, Bioinformatics
More informationBioinformatics in next generation sequencing projects
Bioinformatics in next generation sequencing projects Rickard Sandberg Assistant Professor Department of Cell and Molecular Biology Karolinska Institutet May 2013 Standard sequence library generation Illumina
More informationData Basics. Josef K Vogt Slides by: Simon Rasmussen Next Generation Sequencing Analysis
Data Basics Josef K Vogt Slides by: Simon Rasmussen 2017 Generalized NGS analysis Sample prep & Sequencing Data size Main data reductive steps SNPs, genes, regions Application Assembly: Compare Raw Pre-
More informationGenome 373: Mapping Short Sequence Reads II. Doug Fowler
Genome 373: Mapping Short Sequence Reads II Doug Fowler The final Will be in this room on June 6 th at 8:30a Will be focused on the second half of the course, but will include material from the first half
More informationFrancisco García Quality Control for NGS Raw Data
Contents Data formats Sequence capture Fasta and fastq formats Sequence quality encoding Quality Control Evaluation of sequence quality Quality control tools Identification of artifacts & filtering Practical
More informationEcole de Bioinforma(que AVIESAN Roscoff 2014 GALAXY INITIATION. A. Lermine U900 Ins(tut Curie, INSERM, Mines ParisTech
GALAXY INITIATION A. Lermine U900 Ins(tut Curie, INSERM, Mines ParisTech How does Next- Gen sequencing work? DNA fragmentation Size selection and clonal amplification Massive parallel sequencing ACCGTTTGCCG
More informationVariation detection based on second generation sequencing data. Xin LIU Department of Science and Technology, BGI
Variation detection based on second generation sequencing data Xin LIU Department of Science and Technology, BGI liuxin@genomics.org.cn 2013.11.21 Outline Summary of sequencing techniques Data quality
More informationAlignment methods. Martijn Vermaat Department of Human Genetics Center for Human and Clinical Genetics
Alignment methods Martijn Vermaat Department of Human Genetics Center for Human and Clinical Genetics Alignment methods Sequence alignment Assembly vs alignment Alignment methods Common issues Platform
More informationAlignment. J Fass UCD Genome Center Bioinformatics Core Wednesday December 17, 2014
Alignment J Fass UCD Genome Center Bioinformatics Core Wednesday December 17, 2014 From reads to molecules Why align? Individual A Individual B ATGATAGCATCGTCGGGTGTCTGCTCAATAATAGTGCCGTATCATGCTGGTGTTATAATCGCCGCATGACATGATCAATGG
More informationRead Mapping and Variant Calling. Johannes Starlinger
Read Mapping and Variant Calling Johannes Starlinger Application Scenario: Personalized Cancer Therapy Different mutations require different therapy Collins, Meredith A., and Marina Pasca di Magliano.
More informationAlignment & Variant Discovery. J Fass UCD Genome Center Bioinformatics Core Tuesday June 17, 2014
Alignment & Variant Discovery J Fass UCD Genome Center Bioinformatics Core Tuesday June 17, 2014 From reads to molecules Why align? Individual A Individual B ATGATAGCATCGTCGGGTGTCTGCTCAATAATAGTGCCGTATCATGCTGGTGTTATAATCGCCGCATGACATGATCAATGG
More informationReference genomes and common file formats
Reference genomes and common file formats Overview Reference genomes and GRC Fasta and FastQ (unaligned sequences) SAM/BAM (aligned sequences) Summarized genomic features BED (genomic intervals) GFF/GTF
More informationReference genomes and common file formats
Reference genomes and common file formats Dóra Bihary MRC Cancer Unit, University of Cambridge CRUK Functional Genomics Workshop September 2017 Overview Reference genomes and GRC Fasta and FastQ (unaligned
More informationBST 226 Statistical Methods for Bioinformatics David M. Rocke. March 10, 2014 BST 226 Statistical Methods for Bioinformatics 1
BST 226 Statistical Methods for Bioinformatics David M. Rocke March 10, 2014 BST 226 Statistical Methods for Bioinformatics 1 NGS Technologies Illumina Sequencing HiSeq 2500 & MiSeq PacBio Sequencing PacBio
More informationChIP-seq analysis. adapted from J. van Helden, M. Defrance, C. Herrmann, D. Puthier, N. Servant
ChIP-seq analysis adapted from J. van Helden, M. Defrance, C. Herrmann, D. Puthier, N. Servant http://biow.sb-roscoff.fr/ecole_bioinfo/training_material/chip-seq/documents/presentation_chipseq.pdf A model
More informationIntroduction to Short Read Alignment. UCD Genome Center Bioinformatics Core Tuesday 14 June 2016
Introduction to Short Read Alignment UCD Genome Center Bioinformatics Core Tuesday 14 June 2016 From reads to molecules Why align? Individual A Individual B ATGATAGCATCGTCGGGTGTCTGCTCAATAATAGTGCCGTATCATGCTGGTGTTATAATCGCCGCATGACATGATCAATGG
More informationNGS sequence preprocessing. José Carbonell Caballero
NGS sequence preprocessing José Carbonell Caballero jcarbonell@cipf.es Contents Data Format Quality Control Sequence capture Fasta and fastq formats Sequence quality encoding Evaluation of sequence quality
More informationRNA-seq Data Analysis
Lecture 3. Clustering; Function/Pathway Enrichment analysis RNA-seq Data Analysis Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University Lecture 1. Map RNA-seq read to genome Lecture
More informationIntroduction to Next Generation Sequencing
The Sequencing Revolution Introduction to Next Generation Sequencing Dena Leshkowitz,WIS 1 st BIOmics Workshop High throughput Short Read Sequencing Technologies Highly parallel reactions (millions to
More informationShort Read Alignment to a Reference Genome
Short Read Alignment to a Reference Genome Shamith Samarajiwa CRUK Summer School in Bioinformatics Cambridge, September 2018 Aligning to a reference genome BWA Bowtie2 STAR GEM Pseudo Aligners for RNA-seq
More informationBioinformatics Support of Genome Sequencing Projects. Seminar in biology
Bioinformatics Support of Genome Sequencing Projects Seminar in biology Introduction The Big Picture Biology reminder Enzyme for DNA manipulation DNA cloning DNA mapping Sequencing genomes Alignment of
More informationUAB DNA-Seq Analysis Workshop. John Osborne Research Associate Centers for Clinical and Translational Science
+ UAB DNA-Seq Analysis Workshop John Osborne Research Associate Centers for Clinical and Translational Science ozborn@uab.,edu + Thanks in advance You are the Guinea pigs for this workshop! At this point
More informationTranscriptomics analysis with RNA seq: an overview Frederik Coppens
Transcriptomics analysis with RNA seq: an overview Frederik Coppens Platforms Applications Analysis Quantification RNA content Platforms Platforms Short (few hundred bases) Long reads (multiple kilobases)
More informationIntroduction to RNA sequencing
Introduction to RNA sequencing Bioinformatics perspective Olga Dethlefsen NBIS, National Bioinformatics Infrastructure Sweden November 2017 Olga (NBIS) RNA-seq November 2017 1 / 49 Outline Why sequence
More informationBioinformatics small variants Data Analysis. Guidelines. genomescan.nl
Next Generation Sequencing Bioinformatics small variants Data Analysis Guidelines genomescan.nl GenomeScan s Guidelines for Small Variant Analysis on NGS Data Using our own proprietary data analysis pipelines
More informationCNV and variant detection for human genome resequencing data - for biomedical researchers (II)
CNV and variant detection for human genome resequencing data - for biomedical researchers (II) Chuan-Kun Liu 劉傳崑 Senior Maneger National Center for Genome Medican bioit@ncgm.sinica.edu.tw Abstract Common
More informationQuantifying gene expression
Quantifying gene expression Genome GTF (annotation)? Sequence reads FASTQ FASTQ (+reference transcriptome index) Quality control FASTQ Alignment to Genome: HISAT2, STAR (+reference genome index) (known
More informationL3: Short Read Alignment to a Reference Genome
L3: Short Read Alignment to a Reference Genome Shamith Samarajiwa CRUK Autumn School in Bioinformatics Cambridge, September 2017 Where to get help! http://seqanswers.com http://www.biostars.org http://www.bioconductor.org/help/mailing-list
More informationIntroduction to transcriptome analysis using High Throughput Sequencing technologies. D. Puthier 2012
Introduction to transcriptome analysis using High Throughput Sequencing technologies D. Puthier 2012 Transcriptome: the old school Cyanine 5 (Cy5) Cy-3: - Excitation 550nm - Emission 570nm Cy-5: - Excitation
More informationNext Generation Sequencing. Tobias Österlund
Next Generation Sequencing Tobias Österlund tobiaso@chalmers.se NGS part of the course Week 4 Friday 13/2 15.15-17.00 NGS lecture 1: Introduction to NGS, alignment, assembly Week 6 Thursday 26/2 08.00-09.45
More informationLecture 7. Next-generation sequencing technologies
Lecture 7 Next-generation sequencing technologies Next-generation sequencing technologies General principles of short-read NGS Construct a library of fragments Generate clonal template populations Massively
More informationISO/IEC JTC 1/SC 29/WG 11 N15527 Warsaw, CH June Introduction
INTERNATIONAL ORGANISATION FOR STANDARDISATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO/IEC JTC 1/SC 29/WG 11 CODING OF MOVING PICTURES AND AUDIO ISO/IEC JTC 1/SC 29/WG 11 N15527 Warsaw, CH June
More informationNormal-Tumor Comparison using Next-Generation Sequencing Data
Normal-Tumor Comparison using Next-Generation Sequencing Data Chun Li Vanderbilt University Taichung, March 16, 2011 Next-Generation Sequencing First-generation (Sanger sequencing): 115 kb per day per
More informationExperimental Design. Sequencing. Data Quality Control. Read mapping. Differential Expression analysis
-Seq Analysis Quality Control checks Reproducibility Reliability -seq vs Microarray Higher sensitivity and dynamic range Lower technical variation Available for all species Novel transcript identification
More informationMapping of Next Generation Sequencing Data
Mapping of Next Generation Sequencing Data Agnes Hotz-Wagenblatt Bioinformatik (HUSAR) Next Generation Sequencers Next (or 3 rd ) generation sequencers came onto the scene in the early 2000 s General characteristics
More informationBasic Bioinformatics: Homology, Sequence Alignment,
Basic Bioinformatics: Homology, Sequence Alignment, and BLAST William S. Sanders Institute for Genomics, Biocomputing, and Biotechnology (IGBB) High Performance Computing Collaboratory (HPC 2 ) Mississippi
More informationDe Novo Assembly of High-throughput Short Read Sequences
De Novo Assembly of High-throughput Short Read Sequences Chuming Chen Center for Bioinformatics and Computational Biology (CBCB) University of Delaware NECC Third Skate Genome Annotation Workshop May 23,
More information14 March, 2016: Introduction to Genomics
14 March, 2016: Introduction to Genomics Genome Genome within Ensembl browser http://www.ensembl.org/homo_sapiens/location/view?db=core;g=ensg00000139618;r=13:3231547432400266 Genome within Ensembl browser
More informationChallenging algorithms in bioinformatics
Challenging algorithms in bioinformatics 11 October 2018 Torbjørn Rognes Department of Informatics, UiO torognes@ifi.uio.no What is bioinformatics? Definition: Bioinformatics is the development and use
More informationReads to Discovery. Visualize Annotate Discover. Small DNA-Seq ChIP-Seq Methyl-Seq. MeDIP-Seq. RNA-Seq. RNA-Seq.
Reads to Discovery RNA-Seq Small DNA-Seq ChIP-Seq Methyl-Seq RNA-Seq MeDIP-Seq www.strand-ngs.com Analyze Visualize Annotate Discover Data Import Alignment Vendor Platforms: Illumina Ion Torrent Roche
More informationFast, Accurate and Sensitive DNA Variant Detection from Sanger Sequencing:
Fast, Accurate and Sensitive DNA Variant Detection from Sanger Sequencing: Patented, Anti-Correlation Technology Provides 99.5% Accuracy & Sensitivity to 5% Variant Knowledge Base and External Annotation
More informationIllumina Sequencing Error Profiles and Quality Control
Illumina Sequencing Error Profiles and Quality Control RNA-seq Workflow Biological samples/library preparation Sequence reads FASTQC Adapter Trimming (Optional) Splice-aware mapping to genome Counting
More informationQuality assessment and control of sequence data
Quality assessment and control of sequence data Naiara Rodríguez-Ezpeleta Workshop on Genomics 2015 Cesky Krumlov fastq format fasta Most basic file format to represent nucleotide or amino-acid sequences
More informationIntroduction to NGS analyses
Introduction to NGS analyses Giorgio L Papadopoulos Institute of Molecular Biology and Biotechnology Bioinformatics Support Group 04/12/2015 Papadopoulos GL (IMBB, FORTH) IMBB NGS Seminar 04/12/2015 1
More informationNext Generation Sequencing: An Overview
Next Generation Sequencing: An Overview Cavan Reilly November 13, 2017 Table of contents Next generation sequencing NGS and microarrays Study design Quality assessment Burrows Wheeler transform Next generation
More informationCourse Presentation. Ignacio Medina Presentation
Course Index Introduction Agenda Analysis pipeline Some considerations Introduction Who we are Teachers: Marta Bleda: Computational Biologist and Data Analyst at Department of Medicine, Addenbrooke's Hospital
More informationIntroduc)on to Bioinforma)cs of next- genera)on sequencing. Sequence acquisi)on and processing; genome mapping and alignment manipula)on
Introduc)on to Bioinforma)cs of next- genera)on sequencing Sequence acquisi)on and processing; genome mapping and alignment manipula)on Ruslan Sadreyev Director of Bioinformatics Department of Molecular
More informationGalaxy for Next Generation Sequencing 初探次世代序列分析平台 蘇聖堯 2013/9/12
Galaxy for Next Generation Sequencing 初探次世代序列分析平台 蘇聖堯 2013/9/12 What s Galaxy? Bringing Developers And Biologists Together. Reproducible Science Is Our Goal An open, web-based platform for data intensive
More informationGenomic Dark Matter: The limitations of short read mapping illustrated by the Genome Mappability Score (GMS)
Genomic Dark Matter: The limitations of short read mapping illustrated by the Genome Mappability Score (GMS) Hayan Lee Advised by Prof. Michael Schatz Sep. 28, 2011 Quantitative Biology Seminar 1 Outline
More informationRNA-Seq de novo assembly training
RNA-Seq de novo assembly training Training session aims Give you some keys elements to look at during read quality check. Transcriptome assembly is not completely a strait forward process : Multiple strategies
More informationIntroduction to bioinformatics (NGS data analysis)
Introduction to bioinformatics (NGS data analysis) Alexander Jueterbock 2015-06-02 1 / 45 Got your sequencing data - now, what to do with it? File size: several Gb Number of lines: >1,000,000 @M02443:17:000000000-ABPBW:1:1101:12675:1533
More informationDNASeq: Analysis pipeline and file formats Sumir Panji, Gerrit Boha and Amel Ghouila
DNASeq: Analysis pipeline and file formats Sumir Panji, Gerrit Boha and Amel Ghouila Bioinforma>cs analysis and annota>on of variants in NGS data workshop Cape Town, 4th to 6th April 2016 DNA Sequencing:
More informationQuality assessment and control of sequence data. Naiara Rodríguez-Ezpeleta
Quality assessment and control of sequence data Naiara Rodríguez-Ezpeleta Workshop on Genomics 2014 Quality control is important Some of the artefacts/problems that can be detected with QC Sequencing Sequence
More informationGene Expression analysis with RNA-Seq data
Gene Expression analysis with RNA-Seq data C3BI Hands-on NGS course November 24th 2016 Frédéric Lemoine Plan 1. 2. Quality Control 3. Read Mapping 4. Gene Expression Analysis 5. Splicing/Transcript Analysis
More informationAbout Strand NGS. Strand Genomics, Inc All rights reserved.
About Strand NGS Strand NGS-formerly known as Avadis NGS, is an integrated platform that provides analysis, management and visualization tools for next-generation sequencing data. It supports extensive
More informationshort read genome assembly Sorin Istrail CSCI1820 Short-read genome assembly algorithms 3/6/2014
1 short read genome assembly Sorin Istrail CSCI1820 Short-read genome assembly algorithms 3/6/2014 2 Genomathica Assembler Mathematica notebook for genome assembly simulation Assembler can be found at:
More informationRNA-Seq Module 2 From QC to differential gene expression.
RNA-Seq Module 2 From QC to differential gene expression. Ying Zhang Ph.D, Informatics Analyst Research Informatics Support System (RISS) MSI Apr. 24, 2012 RNA-Seq Tutorials Tutorial 1: Introductory (Mar.
More informationRNAseq and Variant discovery
RNAseq and Variant discovery RNAseq Gene discovery Gene valida5on training gene predic5on programs Gene expression studies Paris japonica Gene discovery Understanding physiological processes Dissec5ng
More informationAnalysis of RNA-seq Data. Feb 8, 2017 Peikai CHEN (PHD)
Analysis of RNA-seq Data Feb 8, 2017 Peikai CHEN (PHD) Outline What is RNA-seq? What can RNA-seq do? How is RNA-seq measured? How to process RNA-seq data: the basics How to visualize and diagnose your
More informationFrom reads to results. Dr Torsten Seemann
From reads to results Dr Torsten Seemann AGRF/EMBL Introduction to Bioinformatics - Monash University - Wed 1 Aug 2012 What I will cover * NGS Applications Sequences Sequence quality Read file formats
More informationSequencing technologies. Jose Blanca COMAV institute bioinf.comav.upv.es
Sequencing technologies Jose Blanca COMAV institute bioinf.comav.upv.es Outline Sequencing technologies: Sanger 2nd generation sequencing: 3er generation sequencing: 454 Illumina SOLiD Ion Torrent PacBio
More informationNext-Generation Sequencing in practice
Next-Generation Sequencing in practice Bioinformatics analysis techniques and some medical applications Salvatore Alaimo, MSc. Email: alaimos@dmi.unict.it Overview Next Generation Sequencing: how it works
More informationNGS, Cancer and Bioinformatics. 5/3/2015 Yannick Boursin
NGS, Cancer and Bioinformatics 5/3/2015 Yannick Boursin 1 NGS and Clinical Oncology NGS in hereditary cancer genome testing BRCA1/2 (breast/ovary cancer) XPC (melanoma) ERCC1 (colorectal cancer) NGS for
More information10/06/2014. RNA-Seq analysis. With reference assembly. Cormier Alexandre, PhD student UMR8227, Algal Genetics Group
RNA-Seq analysis With reference assembly Cormier Alexandre, PhD student UMR8227, Algal Genetics Group Summary 2 Typical RNA-seq workflow Introduction Reference genome Reference transcriptome Reference
More informationGenomics AGRY Michael Gribskov Hock 331
Genomics AGRY 60000 Michael Gribskov gribskov@purdue.edu Hock 331 Computing Essentials Resources In this course we will assemble and annotate both genomic and transcriptomic sequence assemblies We will
More informationIntroduction. CS482/682 Computational Techniques in Biological Sequence Analysis
Introduction CS482/682 Computational Techniques in Biological Sequence Analysis Outline Course logistics A few example problems Course staff Instructor: Bin Ma (DC 3345, http://www.cs.uwaterloo.ca/~binma)
More informationSequence Assembly and Alignment. Jim Noonan Department of Genetics
Sequence Assembly and Alignment Jim Noonan Department of Genetics james.noonan@yale.edu www.yale.edu/noonanlab The assembly problem >>10 9 sequencing reads 36 bp - 1 kb 3 Gb Outline Basic concepts in genome
More informationBIOINFORMATICS ORIGINAL PAPER
BIOINFORMATICS ORIGINAL PAPER Vol. 27 no. 20 2011, pages 2790 2796 doi:10.1093/bioinformatics/btr477 Sequence analysis Advance Access publication August 19, 2011 Comparative analysis of algorithms for
More informationALGORITHMS IN BIO INFORMATICS. Chapman & Hall/CRC Mathematical and Computational Biology Series A PRACTICAL INTRODUCTION. CRC Press WING-KIN SUNG
Chapman & Hall/CRC Mathematical and Computational Biology Series ALGORITHMS IN BIO INFORMATICS A PRACTICAL INTRODUCTION WING-KIN SUNG CRC Press Taylor & Francis Group Boca Raton London New York CRC Press
More informationIntroduction to metagenome assembly. Bas E. Dutilh Metagenomic Methods for Microbial Ecologists, NIOO September 18 th 2014
Introduction to metagenome assembly Bas E. Dutilh Metagenomic Methods for Microbial Ecologists, NIOO September 18 th 2014 Sequencing specs* Method Read length Accuracy Million reads Time Cost per M 454
More informationDisclosing the nature of computational tools for the analysis of Next Generation Sequencing data.
Disclosing the nature of computational tools for the analysis of Next Generation Sequencing data. Francesca Cordero 1,2, Marco Beccuti 1, Susanna Donatelli 1 and Raffaele A Calogero 2 (1) Department of
More informationIntroduction to DNA-Sequencing
informatics.sydney.edu.au sih.info@sydney.edu.au The Sydney Informatics Hub provides support, training, and advice on research data, analyses and computing. Talk to us about your computing infrastructure,
More informationPrioritization: from vcf to finding the causative gene
Prioritization: from vcf to finding the causative gene vcf file making sense A vcf file from an exome sequencing project may easily contain 40-50 thousand variants. In order to optimize the search for
More informationBioinformatics Core Facility IDENTIFYING A DISEASE CAUSING MUTATION
IDENTIFYING A DISEASE CAUSING MUTATION MARCELA DAVILA 2/03/2017 Core Facilities at Sahlgrenska Academy www.cf.gu.se 5 statisticians, 3 bioinformaticians Consultation 7-8 Courses / year Contact information
More informationNature Biotechnology: doi: /nbt Supplementary Figure 1. Read Complexity
Supplementary Figure 1 Read Complexity A) Density plot showing the percentage of read length masked by the dust program, which identifies low-complexity sequence (simple repeats). Scrappie outputs a significantly
More informationData Retrieval from GenBank
Data Retrieval from GenBank Peter J. Myler Bioinformatics of Intracellular Pathogens JNU, Feb 7-0, 2009 http://www.ncbi.nlm.nih.gov (January, 2007) http://ncbi.nlm.nih.gov/sitemap/resourceguide.html Accessing
More informationIntroduction to RNA-Seq in GeneSpring NGS Software
Introduction to RNA-Seq in GeneSpring NGS Software Dipa Roy Choudhury, Ph.D. Strand Scientific Intelligence and Agilent Technologies Learn more at www.genespring.com Introduction to RNA-Seq In a few years,
More informationProcessing Ion AmpliSeq Data using NextGENe Software v2.3.0
Processing Ion AmpliSeq Data using NextGENe Software v2.3.0 July 2012 John McGuigan, Megan Manion, Kevin LeVan, CS Jonathan Liu Introduction The Ion AmpliSeq Panels use highly multiplexed PCR in order
More informationRapid Parallel Genome Indexing using MapReduce
Rapid Parallel Genome Indexing using MapReduce Rohith Menon, Goutham Bhat & Michael Schatz* June 8, 2011 HPDC 11/MapReduce Outline 1. Brief Overview of DNA Sequencing 2. Genome Indexing Serial, Basic MR,
More informationEuropean Union Reference Laboratory for Genetically Modified Food and Feed (EURL GMFF)
Guideline for the submission of DNA sequences derived from genetically modified organisms and associated annotations within the framework of Directive 2001/18/EC and Regulation (EC) No 1829/2003 European
More informationIDENTIFYING A DISEASE CAUSING MUTATION
IDENTIFYING A DISEASE CAUSING MUTATION Targeted resequencing MARCELA DAVILA 3/MZO/2016 Core Facilities at Sahlgrenska Academy Statistics Software bioinformatics@gu.se www.cf.gu.se/english// Increasing
More informationResolution of fine scale ribosomal DNA variation in Saccharomyces yeast
Resolution of fine scale ribosomal DNA variation in Saccharomyces yeast Rob Davey NCYC 2009 Introduction SGRP project Ribosomal DNA and variation Computational methods Preliminary Results Conclusions SGRP
More informationNext-Generation Sequencing. Technologies
Next-Generation Next-Generation Sequencing Technologies Sequencing Technologies Nicholas E. Navin, Ph.D. MD Anderson Cancer Center Dept. Genetics Dept. Bioinformatics Introduction to Bioinformatics GS011062
More informationEucalyptus gene assembly
Eucalyptus gene assembly ACGT Plant Biotechnology meeting Charles Hefer Bioinformatics and Computational Biology Unit University of Pretoria October 2011 About Eucalyptus Most valuable and widely planted
More informationIntroduction to human genomics and genome informatics
Introduction to human genomics and genome informatics Session 1 Prince of Wales Clinical School Dr Jason Wong ARC Future Fellow Head, Bioinformatics & Integrative Genomics Adult Cancer Program, Lowy Cancer
More informationRNAseq Applications in Genome Studies. Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford
RNAseq Applications in Genome Studies Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford RNAseq Protocols Next generation sequencing protocol cdna, not RNA sequencing
More informationSequencing technologies. Jose Blanca COMAV institute bioinf.comav.upv.es
Sequencing technologies Jose Blanca COMAV institute bioinf.comav.upv.es Outline Sequencing technologies: Sanger 2nd generation sequencing: 3er generation sequencing: 454 Illumina SOLiD Ion Torrent PacBio
More informationMatch the Hash Scores
Sort the hash scores of the database sequence February 22, 2001 1 Match the Hash Scores February 22, 2001 2 Lookup method for finding an alignment position 1 2 3 4 5 6 7 8 9 10 11 protein 1 n c s p t a.....
More informationTruSPAdes: analysis of variations using TruSeq Synthetic Long Reads (TSLR)
tru TruSPAdes: analysis of variations using TruSeq Synthetic Long Reads (TSLR) Anton Bankevich Center for Algorithmic Biotechnology, SPbSU Sequencing costs 1. Sequencing costs do not follow Moore s law
More informationData Analysis with CASAVA v1.8 and the MiSeq Reporter
Data Analysis with CASAVA v1.8 and the MiSeq Reporter Eric Smith, PhD Bioinformatics Scientist September 15 th, 2011 2010 Illumina, Inc. All rights reserved. Illumina, illuminadx, Solexa, Making Sense
More informationBioinformatics for NGS projects. Guidelines. genomescan.nl
Next Generation Sequencing Bioinformatics for NGS projects Guidelines genomescan.nl GenomeScan s Guidelines for Bioinformatics Services on NGS Data Using our own proprietary data analysis pipelines Dear
More information