Mapping RNA Sequence Data (Part 1: Using Pathogen Portal’S Rnaseq Pipeline) Exercise 3
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File Formats Exercises
Introduction to high-throughput sequencing file formats – advanced exercises Daniel Vodák (Bioinformatics Core Facility, The Norwegian Radium Hospital) ( [email protected] ) All example files are located in directory “/data/file_formats/data”. You can set your current directory to that path (“cd /data/file_formats/data”) Exercises: a) FASTA format File “Uniprot_Mammalia.fasta” contains a collection of mammalian protein sequences obtained from the UniProt database ( http://www.uniprot.org/uniprot ). 1) Count the number of lines in the file. 2) Count the number of header lines (i.e. the number of sequences) in the file. 3) Count the number of header lines with “Homo” in the organism/species name. 4) Count the number of header lines with “Homo sapiens” in the organism/species name. 5) Display the header lines which have “Homo” in the organism/species names, but only such that do not have “Homo sapiens” there. b) FASTQ format File NKTCL_31T_1M_R1.fq contains a reduced collection of tumor read sequences obtained from The Sequence Read Archive ( http://www.ncbi.nlm.nih.gov/sra ). 1) Count the number of lines in the file. 2) Count the number of lines beginning with the “at” symbol (“@”). 3) How many reads are there in the file? c) SAM format File NKTCL_31T_1M.sam contains alignments of pair-end reads stored in files NKTCL_31T_1M_R1.fq and NKTCL_31T_1M_R2.fq. 1) Which program was used for the alignment? 2) How many header lines are there in the file? 3) How many non-header lines are there in the file? 4) What do the non-header lines represent? 5) Reads from how many template sequences have been used in the alignment process? c) BED format File NKTCL_31T_1M.bed holds information about alignment locations stored in file NKTCL_31T_1M.sam. -
EMBL-EBI Powerpoint Presentation
Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each -
Choudhury 22.11.2018 Suppl
The Set1 complex is dimeric and acts with Jhd2 demethylation to convey symmetrical H3K4 trimethylation Rupam Choudhury, Sukdeep Singh, Senthil Arumugam, Assen Roguev and A. Francis Stewart Supplementary Material 9 Supplementary Figures 1 Supplementary Table – an Excel file not included in this document Extended Materials and Methods Supplementary Figure 1. Sdc1 mediated dimerization of yeast Set1 complex. (A) Multiple sequence alignment of the dimerizaton region of the protein kinase A regulatory subunit II with Dpy30 homologues and other proteins (from Roguev et al, 2001). Arrows indicate the three amino acids that were changed to alanine in sdc1*. (B) Expression levels of TAP-Set1 protein in wild type and sdc1 mutant strains evaluated by Westerns using whole cell extracts and beta-actin (B-actin) as loading control. (C) Spp1 associates with the monomeric Set1 complex. Spp1 was tagged with a Myc epitope in wild type and sdc1* strains that carried TAP-Set1. TAP-Set1 was immunoprecipitated from whole cell extracts and evaluated by Western with an anti-myc antibody. Input, whole cell extract from the TAP-set1; spp1-myc strain. W303, whole cell extract from the W303 parental strain. a Focal Volume Fluorescence signal PCH Molecular brightness comparison Intensity 25 20 Time 15 10 Frequency Brightness (au) 5 0 Intensity Intensity (counts /ms) GFP1 GFP-GFP2 Time c b 1x yEGFP 2x yEGFP 3x yEGFP 1 X yEGFP ADH1 yEGFP CYC1 2 X yEGFP ADH1 yEGFP yEGFP CYC1 3 X yEGFP ADH1 yEGFP yEGFP yEGFP CYC1 d 121-165 sdc1 wt sdc1 sdc1 Swd1-yEGFP B-Actin Supplementary Figure 2. Molecular analysis of Sdc1 mediated dimerization of Set1C by Fluorescence Correlation Spectroscopy (FCS). -
Sequence Alignment/Map Format Specification
Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345. -
RNA-‐Seq: Revelation of the Messengers
Supplementary Material Hands-On Tutorial RNA-Seq: revelation of the messengers Marcel C. Van Verk†,1, Richard Hickman†,1, Corné M.J. Pieterse1,2, Saskia C.M. Van Wees1 † Equal contributors Corresponding author: Van Wees, S.C.M. ([email protected]). Bioinformatics questions: Van Verk, M.C. ([email protected]) or Hickman, R. ([email protected]). 1 Plant-Microbe Interactions, Department of Biology, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands 2 Centre for BioSystems Genomics, PO Box 98, 6700 AB Wageningen, The Netherlands INDEX 1. RNA-Seq tools websites……………………………………………………………………………………………………………………………. 2 2. Installation notes……………………………………………………………………………………………………………………………………… 3 2.1. CASAVA 1.8.2………………………………………………………………………………………………………………………………….. 3 2.2. Samtools…………………………………………………………………………………………………………………………………………. 3 2.3. MiSO……………………………………………………………………………………………………………………………………………….. 4 3. Quality Control: FastQC……………………………………………………………………………………………………………………………. 5 4. Creating indeXes………………………………………………………………………………………………………………………………………. 5 4.1. Genome IndeX for Bowtie / TopHat alignment……………………………………………………………………………….. 5 4.2. Transcriptome IndeX for Bowtie alignment……………………………………………………………………………………… 6 4.3. Genome IndeX for BWA alignment………………………………………………………………………………………………….. 7 4.4. Transcriptome IndeX for BWA alignment………………………………………………………………………………………….7 5. Read Alignment………………………………………………………………………………………………………………………………………… 8 5.1. Preliminaries…………………………………………………………………………………………………………………………………… 8 5.2. Genome read alignment with Bowtie……………………………………………………………………………………………… -
Introduction to Bioinformatics (Elective) – SBB1609
SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. -
Supplementary Materials
Supplementary Materials Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening 1,† 1,† 2,3 1,3 Gabriele Picco , Elisabeth D Chen , Luz Garcia Alonso , Fiona M Behan , Emanuel 1 1 1 1 1 Gonçalves , Graham Bignell , Angela Matchan , Beiyuan Fu , Ruby Banerjee , Elizabeth 1 1 4 1,7 5,3 Anderson , Adam Butler , Cyril H Benes , Ultan McDermott , David Dow , Francesco 2,3 5,3 1 1 6,2,3 Iorio E uan Stronach , Fengtang Yang , Kosuke Yusa , Julio Saez-Rodriguez , Mathew 1,3,* J Garnett 1 Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK. 2 European Molecular Biology Laboratory – European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK. 3 Open Targets, Wellcome Genome Campus, Cambridge CB10 1SA, UK. 4 Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA. 5 GSK, Target Sciences, Gunnels Wood Road, Stevenage, SG1 2NY, UK and Collegeville, 25 Pennsylvania, 19426-0989, USA. 6 Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, Heidelberg 69120, Germany. 7 AstraZeneca, CRUK Cambridge Institute, Cambridge CB2 0RE * Correspondence to: [email protected] † Equally contributing authors Supplementary Tables Supplementary Table 1: Annotation of 1,034 human cancer cell lines used in our study and the source of RNA-seq data. All cell lines are part of the GDSC cancer cell line project (see COSMIC IDs). Supplementary Table 2: List and annotation of 10,514 fusion transcripts found in 1,011 cell lines. Supplementary Table 3: Significant results from differential gene expression for recurrent fusions. Supplementary Table 4: Annotation of gene fusions for aberrant expression of the 3 prime end gene. -
Software List for Biology, Bioinformatics and Biostatistics CCT
Software List for biology, bioinformatics and biostatistics v CCT - Delta Software Version Application short read assembler and it works on both small and large (mammalian size) ALLPATHS-LG 52488 genomes provides a fast, flexible C++ API & toolkit for reading, writing, and manipulating BAMtools 2.4.0 BAM files a high level of alignment fidelity and is comparable to other mainstream Barracuda 0.7.107b alignment programs allows one to intersect, merge, count, complement, and shuffle genomic bedtools 2.25.0 intervals from multiple files Bfast 0.7.0a universal DNA sequence aligner tool analysis and comprehension of high-throughput genomic data using the R Bioconductor 3.2 statistical programming BioPython 1.66 tools for biological computation written in Python a fast approach to detecting gene-gene interactions in genome-wide case- Boost 1.54.0 control studies short read aligner geared toward quickly aligning large sets of short DNA Bowtie 1.1.2 sequences to large genomes Bowtie2 2.2.6 Bowtie + fully supports gapped alignment with affine gap penalties BWA 0.7.12 mapping low-divergent sequences against a large reference genome ClustalW 2.1 multiple sequence alignment program to align DNA and protein sequences assembles transcripts, estimates their abundances for differential expression Cufflinks 2.2.1 and regulation in RNA-Seq samples EBSEQ (R) 1.10.0 identifying genes and isoforms differentially expressed EMBOSS 6.5.7 a comprehensive set of sequence analysis programs FASTA 36.3.8b a DNA and protein sequence alignment software package FastQC -
Rnaseq2017 Course Salerno, September 27-29, 2017
RNASeq2017 Course Salerno, September 27-29, 2017 RNA-seq Hands on Exercise Fabrizio Ferrè, University of Bologna Alma Mater ([email protected]) Hands-on tutorial based on the EBI teaching materials from Myrto Kostadima and Remco Loos at https://www.ebi.ac.uk/training/online/ Index 1. Introduction.......................................2 1.1 RNA-Seq analysis tools.........................2 2. Your data..........................................3 3. Quality control....................................6 4. Trimming...........................................8 5. The tuxedo pipeline...............................12 5.1 Genome indexing for bowtie....................12 5.2 Insert size and mean inner distance...........13 5.3 Read mapping using tophat.....................16 5.4 Transcriptome reconstruction and expression using cufflinks...................................19 5.5 Differential Expression using cuffdiff........22 5.6 Transcriptome reconstruction without genome annotation........................................24 6. De novo assembly with Velvet......................27 7. Variant calling...................................29 8. Duplicate removal.................................31 9. Using STAR for read mapping.......................32 9.1 Genome indexing for STAR......................32 9.2 Read mapping using STAR.......................32 9.3 Using cuffdiff on STAR mapped reads...........34 10. Concluding remarks..............................35 11. Useful references...............................36 1 1. Introduction The goal of this -
Integrative Analysis of Transcriptomic Data for Identification of T-Cell
www.nature.com/scientificreports OPEN Integrative analysis of transcriptomic data for identifcation of T‑cell activation‑related mRNA signatures indicative of preterm birth Jae Young Yoo1,5, Do Young Hyeon2,5, Yourae Shin2,5, Soo Min Kim1, Young‑Ah You1,3, Daye Kim4, Daehee Hwang2* & Young Ju Kim1,3* Preterm birth (PTB), defned as birth at less than 37 weeks of gestation, is a major determinant of neonatal mortality and morbidity. Early diagnosis of PTB risk followed by protective interventions are essential to reduce adverse neonatal outcomes. However, due to the redundant nature of the clinical conditions with other diseases, PTB‑associated clinical parameters are poor predictors of PTB. To identify molecular signatures predictive of PTB with high accuracy, we performed mRNA sequencing analysis of PTB patients and full‑term birth (FTB) controls in Korean population and identifed diferentially expressed genes (DEGs) as well as cellular pathways represented by the DEGs between PTB and FTB. By integrating the gene expression profles of diferent ethnic groups from previous studies, we identifed the core T‑cell activation pathway associated with PTB, which was shared among all previous datasets, and selected three representative DEGs (CYLD, TFRC, and RIPK2) from the core pathway as mRNA signatures predictive of PTB. We confrmed the dysregulation of the candidate predictors and the core T‑cell activation pathway in an independent cohort. Our results suggest that CYLD, TFRC, and RIPK2 are potentially reliable predictors for PTB. Preterm birth (PTB) is the birth of a baby at less than 37 weeks of gestation, as opposed to the usual about 40 weeks, called full term birth (FTB)1. -
NGS Raw Data Analysis
NGS raw data analysis Introduc3on to Next-Generaon Sequencing (NGS)-data Analysis Laura Riva & Maa Pelizzola Outline 1. Descrip7on of sequencing plaorms 2. Alignments, QC, file formats, data processing, and tools of general use 3. Prac7cal lesson Illumina Sequencing Single base extension with incorporaon of fluorescently labeled nucleodes Library preparaon Automated cluster genera3on DNA fragmenta3on and adapter ligaon Aachment to the flow cell Cluster generaon by bridge amplifica3on of DNA fragments Sequencing Illumina Output Quality Scores Sequence Files Comparison of some exisng plaGorms 454 Ti RocheT Illumina HiSeqTM ABI 5500 (SOLiD) 2000 Amplificaon Emulsion PCR Bridge PCR Emulsion PCR Sequencing Pyrosequencing Reversible Ligaon-based reac7on terminators sequencing Paired ends/sep Yes/3kb Yes/200 bp Yes/3 kb Read length 400 bp 100 bp 75 bp Advantages Short run 7mes. The most popular Good base call Longer reads plaorm accuracy. Good improve mapping in mulplexing repe7ve regions. capability Ability to detect large structural variaons Disadvantages High reagent cost. Higher error rates in repeat sequences The Illumina HiSeq! Stacey Gabriel Libraries, lanes, and flowcells Flowcell Lanes Illumina Each reaction produces a unique Each NGS machine processes a library of DNA fragments for single flowcell containing several sequencing. independent lanes during a single sequencing run Applications of Next-Generation Sequencing Basic data analysis concepts: • Raw data analysis= image processing and base calling • Understanding Fastq • Understanding Quality scores • Quality control and read cleaning • Alignment to the reference genome • Understanding SAM/BAM formats • Samtools • Bedtools Understanding FASTQ file format FASTQ format is a text-based format for storing a biological sequence and its corresponding quality scores. -
RNA-Seq Lecture 2
Intermediate RNA-Seq Tips, Tricks and Non-Human Organisms Kevin Silverstein PhD, John Garbe PhD and Ying Zhang PhD, Research Informatics Support System (RISS) MSI September 25, 2014 Slides available at www.msi.umn.edu/tutorial-materials RNA-Seq Tutorials • Tutorial 1 – RNA-Seq experiment design and analysis – Instruction on individual software will be provided in other tutorials • Tutorial 2 – Thursday Sept. 25 – Advanced RNA-Seq Analysis topics • Hands-on tutorials - – Analyzing human and potato RNA-Seq data using Tophat and Cufflinks in Galaxy – Human: Thursday Oct. 2 – Potato: Tuesday Oct. 14 RNA-seq Tutorial 2 Tips, Tricks and Non-Human Organisms Part I: Review and Considerations for Different Goals and Biological Systems (Kevin Silverstein) Part II: Read Mapping Statistics and Visualization (John Garbe) Part III: Post-Analysis Processing – Exploring the Data and Results (Ying Zhang) Part I Review and Considerations for Different Goals and Biological Systems Typical RNA-seq experimental protocol and analysis Sample mRNA isolation Fragmentation RNA -> cDNA PairedSingle End (PE)(SE) Sequencing Map reads Genome A B Reference Transcriptome Steps in RNA-Seq data analysis depend on your goals and biological system Step 1: Quality Control KIR HLA Step 2: Data prepping Step 3: Map Reads to Reference Genome/Transcriptome Step 4: Assemble Transcriptome “microarray simulation” Discovery mode Other applications: Identify Differentially De novo Assembly Expressed Genes Refine gene models Programs used in RNA-Seq data analysis depend on your goals and biological system FastQC Step 1: Quality Control Trimmomatic, cutadapt Step 2: Data prepping TopHat, GSNAP Step 3: Map Reads to Reference Genome/Transcriptome Cufflinks, Cuffmerge Step 4: Assemble Transcriptome IGV Cuffdiff Other applications: Identify Differentially Refine gene models Expressed Genes Specific Note for Prokaryotes • Cufflinks developer: “We don’t recommend assembling bacteria transcripts using Cufflinks at first.