DOCSLIB.ORG

A Standard Variation File Format for Human Genome Sequences

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Standard Variation File Format for Human Genome Sequences Reese et al. Genome Biology 2010, 11:R88 http://genomebiology.com/2010/11/8/R88 METHOD Open Access A standard variation file format for human genome sequences Martin G Reese1*, Barry Moore2, Colin Batchelor3, Fidel Salas1, Fiona Cunningham4, Gabor T Marth5, Lincoln Stein6, Paul Flicek4, Mark Yandell2, Karen Eilbeck7* Abstract Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for com- munity analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon’s EC2 cloud computing environment. Background GVF [14] is an extension of the widely used Generic With the advent of personalized genomics we have seen Feature Format version 3 (GFF3) standard for describing the first examples of fully sequenced individuals [1-9]. genome annotation data. The GFF3 format [15] was Now, next generation sequencing technologies promise developed to permit the exchange and comparison of to radically increase the number of human sequences in gene annotations between different model organism thepublicdomain.Thesedatawillcomenotjustfrom databases [16]. GFF3 is based on the General Feature large sequencing centers, but also from individual Format (GFF), which was originally developed during laboratories. For reasons of resource economy, ‘variant the human genome project to compare human genome files’ rather than raw sequence reads or assembled gen- annotations [17]. Importantly, GFF3, unlike GFF, is omes are rapidly emerging as the common currency for typed using an ontology. This means that the terminol- exchange and analysis of next generation whole genome ogy being used to describe the data is standardized, and re-sequencing data. Several data formats have emerged organized by pre-specified relationships. The attribute recently for sequencing reads (SRF) [10], read align- specification structure of GFF3 files allows extensibility ments (SAM/BAM) [11], genotype likelihoods/posterior in specifying feature-specific data for different types of SNP probabilities (GLF) [12], and variant calling (VCF) features and it is this extensibility that GVF capitalizes [13]. However, the resulting variant files of single on in defining sequence alteration specific data types. nucleotide variants (SNVs) and structural variants (SVs) Annotation databases have historically developed differ- are still distributed as non-standardized tabular text ent in-house schemas; thus, such standardization is files, with each sequence provider producing its own required to ensure interoperability between databases idiomatic data files [1-9]. The lack of a standard format and for comparative analyses. complicates comparisons of data from multiple sources While there are richer ways of representing genomic and across projects and sequencing platforms, tremen- features using XML (Extensible Markup Language) and dously slowing the progress of comparative personal relational database schemas, simple text-based, tab- genome analysis. In response we have developed GVF, delimited files have persisted in bioinformatics because the Genome Variation Format. they balance human with computer readability. Since its adoption as the basic exchange format, two aspects of GFF3 have emerged as essential for success. First, it must * Correspondence: [email protected]; [email protected] 1Omicia, 2200 Powell Street, Suite 525, Emeryville, CA 94608, USA be simple for software to produce and parse; second, its 7Department of Biomedical Informatics, Health Sciences Education Building, contents need to be typed using terms drawn from an Suite 5700, 26 South 2000 East, University of Utah, Salt Lake City, UT 84112, ontology. The first aspect means that humans can easily USA Full list of author information is available at the end of the article read and edit files with a text editor and perform simple © 2010 Reese et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Reese et al. Genome Biology 2010, 11:R88 Page 2 of 9 http://genomebiology.com/2010/11/8/R88 analyses with command-line software tools. The second sequence alteration specific data (Table 1). Effectively aspect not only constrains different database curators to describing sequence variants in this fashion has three use the same terminologies, but also, because of the for- prerequisites. First, a standard vocabulary is required for mal structure of the ontology, allows automated reason- additional tags and values. Second, the vocabulary must ing on the contents of such a file. It therefore prevents be defined in a machine-readable fashion. And finally - in ambiguities and conflicting terminologies. GVF builds order to facilitate downstream analyses - the relationships upon these strengths of GFF3, adopting GFF3’s simple, between terms used must be formally specified using an tab-delimited format; and like GFF3, the contents of GVF ontology. In addition to SO, GVF also allows, but does files are described using the Sequence Ontology (SO) - not require, the use of other ontologies such as the an ontology developed by the Gene Ontology Consor- PATO, an ontology of phenotypic qualities [22] and the tium [18] to describe the parts of genomic annotations, Human Phenotype Ontology (HPO) [23] to categorize and how these parts relate to each other [19,20]. Using the phenotype of the individual. SO to type both the features and the consequences of a The SO has been extended in order to describe both variation gives GVF files the flexibility necessary to cap- the nature of the observed variants and the effects that ture a wide variety of variation data, while still maintain- the variants might have. SO is part of the Open Biologi- ing unified semantics and a simple file format. For cal and Biomedical Ontologies (OBO) Library [24], and example, GVF files can contain both re-sequencing and follows the recommendations and formalisms of the DNA genotyping microarray experiment data. In addi- OBO Foundry [25]. This enables machine reasoning tion, GVF capitalizes on the extensibility of GFF3 to spe- across GVF data files using the rich collection of soft- cify a rich set of attributes specific to sequence ware tools and libraries developed for use with OBO. alterations in a structured way. An added benefit of The key top-level terms are shown in Figure 1. The GVF’s compliance with GFF3 is that existing parsers, logicandstructureimposedbyanupperlevelontology visualization and validation software, such as those devel- means that existing and novel feature annotations are oped by the Generic Model Organism Database (GMOD) easily added and then immediately computable. project to operate on GFF3 files can be used to manipu- late and view GVF files. Thus, the GVF complements GVF: a specification for genome variant description existing gene and variant nomenclature efforts [21], and Figure 2 shows several lines from a typical GVF file. As provides a simple ontology-based sequence-centric gen- in GFF3, there are three types of lines: those beginning ome file format linking variants to genome positions and with ‘##’ specify file-wide pragmas - global features of genome annotations. the genome as a whole; lines beginning with ‘#’ are BelowwedescribetheGVFstandardandthevarious unstructured comments; and all remaining lines additions we have made to GFF3 and SO to support it. described features of the sequence. We also briefly describe the conversion of the first ten GVF provides nine new pragmas to describe the refer- publicly available personal genomes into GVF format. ence sequence and the methods used to call variants. These GVF files are available for download and for These pragmas are described in detail in Table 2. The cloud computation. We will refer to these data as the existing genome-build pragma of GFF is mandatory, as 10Gen dataset. This is provided as a service to the bio- all GVF files are dependent on a reference sequence to medical community as a reference dataset for whole specify variant positions. While most of the examples genome comparative analyses and software develop- discussed here are human genome sequence variants, ment. This dataset will hopefully foster the development GVF is a truly generic format. A GVF file can contain of new tools for the analyses of personal genome sequence variants identified in other organisms as well sequences. as identified by DNA microarrays (see example on 10Gen web site for NA_19240). GVF files can also con- Results tain variants identified in collections of individuals, as We have extended both the GFF3 specification and SO to well as population data. The GFF3 species pragma is allow the rigorous description of sequence variations with used to specify other organisms. If one wants to specify respect to a reference genome. The first eight columns of multiple individuals in the same file, it is denoted using a GFF3 file specify the type and source of a feature, its the source field, and the population_freq tag is provided location on a reference sequence, and optionally a score, to describe the frequency of a variant within a popula-
Load more

Recommended publications
  • Sequence Alignment/Map) Is a Text Format for Storing Sequence Alignment Data in a Series of Tab Delimited ASCII Columns
    NGS FILE FORMATS SEQUENCE FILE FORMATS FASTA FORMAT FASTA Single sequence example: >HWI-ST398_0092:1:1:5372:2486#0/1 TTTTTCGTTCTTTTCATGTACCGCTTTTTGTTCGGTTAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGAT ACGTAGCAGCAGCATCAGTACGACTACGACGACTAGCACATGCGACGATCGATGCTAGCTGACTATCGATG Multiple sequence example: >Sequence Name 1 TTTTTCGTTCTTTTCATGTACCGCTTTTTGTTCGGTTAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGAT ACGTAGCAGCAGCATCAGTACGACTACGACGACTAGCACATGCGACGATCGATGCTAGCTGACTATCGATG >Sequence Name 2 ACGTAGACACGACTAGCATCAGCTACGCATCGATCAGCATCGACTAGCATCACACATCGATCAGCATCACGACTAGCAT AGCATCGACTACACTACGACTACGATCCACGTACGACTAGCATGCTAGCGCTAGCTAGCTAGCTAGTCGATCGATGAGT AGCTAGCTAGCTAGC >Sequence Name 3 ACTCAGCATGCATCAGCATCGACTACGACTACGACATCGACTAGCATCAGCAT SEQUENCE FILE FORMATS FASTQ FORMAT FASTQ Text based format for storing sequence data and corresponding quality scores for each base. To enable a one-one correspondence between the base sequence and the quality score the score is stored as a single one letter/number code using an offset of the standard ASCII code. Quality scores range from 0 to 40 and represent a log10 score for the probability of being wrong. E.g. score of 30 => 1:1000 chance of error SEQUENCE FILE FORMATS FASTQ FORMAT FASTQ Each fastq file contain multiple entries and each entry consists of 4 lines: 1. header line beginning with “@“ and sequence name 2. sequence line 3. header line beginning with “+” which can have the name but rarely does 4. quality score line SEQUENCE FILE FORMATS FASTQ FORMAT FASTQ @HWI-ST398_0092:6:73:5372:2486#0/1 TTTTTCGTTCTTTTCATGTACCGCTTTTTGTTCGGTTAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGAT
    [Show full text]
  • Alternate-Locus Aware Variant Calling in Whole Genome Sequencing Marten Jäger1,2, Max Schubach1, Tomasz Zemojtel1,Knutreinert3, Deanna M
    Jäger et al. Genome Medicine (2016) 8:130 DOI 10.1186/s13073-016-0383-z RESEARCH Open Access Alternate-locus aware variant calling in whole genome sequencing Marten Jäger1,2, Max Schubach1, Tomasz Zemojtel1,KnutReinert3, Deanna M. Church4 and Peter N. Robinson1,2,3,5,6* Abstract Background: The last two human genome assemblies have extended the previous linear golden-path paradigm of the human genome to a graph-like model to better represent regions with a high degree of structural variability. The new model offers opportunities to improve the technical validity of variant calling in whole-genome sequencing (WGS). Methods: We developed an algorithm that analyzes the patterns of variant calls in the 178 structurally variable regions of the GRCh38 genome assembly, and infers whether a given sample is most likely to contain sequences from the primary assembly, an alternate locus, or their heterozygous combination at each of these 178 regions. We investigate 121 in-house WGS datasets that have been aligned to the GRCh37 and GRCh38 assemblies. Results: We show that stretches of sequences that are largely but not entirely identical between the primary assembly and an alternate locus can result in multiple variant calls against regions of the primary assembly. In WGS analysis, this results in characteristic and recognizable patterns of variant calls at positions that we term alignable scaffold-discrepant positions (ASDPs). In 121 in-house genomes, on average 51.8 ± 3.8 of the 178 regions were found to correspond best to an alternate locus rather than the primary assembly sequence, and filtering these genomes with our algorithm led to the identification of 7863 variant calls per genome that colocalized with ASDPs.
    [Show full text]
  • Galaxy Platform for NGS Data Analyses
    Galaxy Platform For NGS Data Analyses Weihong Yan [email protected] Collaboratory Web Site http://qcb.ucla.edu/collaboratory Collaboratory Workshops Workshop Outline ü Day 1 § UCLA galaxy and user account § Galaxy web interface and management § Tools for NGS analyses and their application § Data formats § Build/share workflow and history § Q and A ü Day 2 § Galaxy Tools for RNA-seq analysis § Galaxy Tools for ChIP-seq analysis § Galaxy Tools for annotation. § Q and A *** Published datasets/results will be used in the tutorial UCLA Galaxy http://galaxy.hoffman2.idre.ucla.edu ü Hardware – Headnode (1) 96Gb memory, 12 core – Computing nodes (8) 48Gb memory, 12 core – Storage 100 Tb disk space ü Galaxy Resource Management － Hoffman2 grid engine Default: 1 core/job bowtie, bwa, tophat, cuffdiff, cufflinks, gatk programs: 4 core/job UCLA Galaxy http://galaxy.hoffman2.idre.ucla.edu ü galaxy login account: login: your email associated with ucla ü Disk quota: 1 Tb/user Galaxy Account Management Installed tools Launch analysis and view result History of execu7on and results Raw Reads *_qseq.txt, *.fastq Upload to Galaxy File transfer protocol (ftp) deMultiplex Barcode splitter, deMultiplex workflow fastqc, compute quality statistics, Quality Assessment draw quality score boxplot, draw nuclotides distribution Process Reads Trim sequences, sickle, scythe Alignment to bwa, bowtie, bowtie2, tophat Reference Format Conversion Text manipulation toolkit, BEDTools, SAM Results (sam/bam) Tools, java genomics toolkit, picard toolkit Downstream Analyses BS-Seeker2, cufflinks, cuffdiff, macs, macs2, GATK, CEAS Visualization Genome browser, IGV Repositories of Galaxy Tools https://toolshed.g2.bx.psu.edu ü History panel contains all datasets that are uploaded and results derived from certain analyses ü A history can be organized, annotated, and managed as a project ü History is sharable.
    [Show full text]
  • BMC Bioinformatics Biomed Central
    BMC Bioinformatics BioMed Central Database Open Access Atlas – a data warehouse for integrative bioinformatics Sohrab P Shah, Yong Huang, Tao Xu, Macaire MS Yuen, John Ling and BF Francis Ouellette* Address: UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada Email: Sohrab P Shah - [email protected]; Yong Huang - [email protected]; Tao Xu - [email protected]; Macaire MS Yuen - [email protected]; John Ling - [email protected]; BF Francis Ouellette* - [email protected] * Corresponding author Published: 21 February 2005 Received: 04 September 2004 Accepted: 21 February 2005 BMC Bioinformatics 2005, 6:34 doi:10.1186/1471-2105-6-34 This article is available from: http://www.biomedcentral.com/1471-2105/6/34 © 2005 Shah et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. Description: The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs).
    [Show full text]
  • An Online Visualization Tool for Functional Features of Human Fusion Genes Pora Kim 1,*,†,Keyiya2,*,† and Xiaobo Zhou 1,3,4,*
    Published online 18 May 2020 Nucleic Acids Research, 2020, Vol. 48, Web Server issue W313–W320 doi: 10.1093/nar/gkaa364 FGviewer: an online visualization tool for functional features of human fusion genes Pora Kim 1,*,†,KeYiya2,*,† and Xiaobo Zhou 1,3,4,* 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA, 2College of Electronic and Information Engineering, Tongji University, Shanghai, China, 3McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA and 4School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA Received March 16, 2020; Revised April 17, 2020; Editorial Decision April 27, 2020; Accepted April 27, 2020 ABSTRACT broken gene context provided the aberrant functional clues to study disease genesis and there are many fusion genes Among the diverse location of the breakpoints (BPs) that have been recognized as biomarkers and therapeutic of structural variants (SVs), the breakpoints of fu- targets in cancer (1). Many tumorigenic FGs have retained sion genes (FGs) are located in the gene bodies. or lost oncogenic protein-domains or regulatory features This broken gene context provided the aberrant func- (2). The maintenance or loss of the functional features tional clues to study disease genesis. Many tumori- directly impacts on tumor initiation, progression, and genic fusion genes have retained or lost functional evolution. To date, there are multiple representative func- or regulatory domains and these features impacted tional mechanisms of fusion genes studied as shown in tumorigenesis. Full annotation of fusion genes aided Figure 1.
    [Show full text]
  • Gffread and Gffcompare[Version 1; Peer Review: 3 Approved]
    F1000Research 2020, 9:304 Last updated: 10 SEP 2020 SOFTWARE TOOL ARTICLE GFF Utilities: GffRead and GffCompare [version 1; peer review: 3 approved] Geo Pertea1,2, Mihaela Pertea 1,2 1Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21218, USA 2Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA v1 First published: 28 Apr 2020, 9:304 Open Peer Review https://doi.org/10.12688/f1000research.23297.1 Latest published: 09 Sep 2020, 9:304 https://doi.org/10.12688/f1000research.23297.2 Reviewer Status Abstract Invited Reviewers Summary: GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to 1 2 3 represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and version 2 GffCompare are open source programs that provide extensive and (revision) efficient solutions to manipulate files in a GTF or GFF format. While 09 Sep 2020 GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different version 1 gene annotations. 28 Apr 2020 report report report Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT 1. Andreas Stroehlein , The University of license (https://github.com/gpertea/gffread, https://github.com/gpertea/gffcompare). Melbourne, Parkville, Australia Keywords 2. Michael I. Love , University of North gene annotation, transcriptome analysis, GTF and GFF file formats Carolina-Chapel Hill, Chapel Hill, USA 3. Rob Patro, University of Maryland, College This article is included in the International Park, USA Society for Computational Biology Community Any reports and responses or comments on the Journal gateway.
    [Show full text]
  • Next-Generation DNA Sequencing Informatics, 2Nd Edition
    This is a free sample of content from Next-Generation DNA Sequencing Informatics, 2nd edition. Click here for more information on how to buy the book. Index Page references followed by f denote figures. Page references followed by t denote tables. A Needleman–Wunsch (NW) algorithm, 49, 54, 110–113 overview, 109–110 Abeel, Thomas, 103 – – – ABI. See Applied Biosystems Inc. Smith Waterman (SW) algorithm, 38, 49, 62 63, 111 113 Ab initio genome annotation, 172, 178, 180t–181t Splign, 182 – TopHat, 43, 182 ab1PeakReporter software, 52 53 – A-Bruijn graph, 133–134 Alignment score, FASTA, 64 65 ABySS (Assembly by Short Sequencing), 134, 142, 147–153 Allele, 52, 354 Allele frequency, 76, 94, 193 effect of k-mer size and minimum pair number on assembly, fi 148–149, 149f Allele-speci c expression, 155, 298 overview of, 147–148 ALLPATHS, 134 quality of assembly, 149–153, 150t, 151f–152f ALN format, 92 α transcriptome assembly (Trans-ABySS), 158t, 160–161, 166 -diversity indices, 319 – – AceView database, 294, 295f Alternative splicing, 182, 293 296, 294f 295f Acrylamide gels Altschul, Stephen, 65 capillary tube, 4 Amazon Elastic Compute Cloud (EC2), 43, 254, 300, 315, – Sanger sequencing and, 2, 3–4 362 364, 366, 369 – ACT, 179t Amino acids, pairwise comparisons, 48 49 Adapter removal, 37–39, 39f, 43 Amplicons, 8, 30, 89, 204, 309, 312 Adapter Removal program, 38 Amplicon Variant Analyzer, 101 Affine gaps, 42, 110, 111–112 AmpliSeq Cancer Panel (Ion Torrent), 206 Algorithms Annotation, 75. See also Genome annotation – – – alignment, 49, 109–124, 129, 223, 338, 344 ChIP-seq peak, 240 242, 255, 259, 262 263, 262f 263f – assembly, 59, 127–129, 133–134, 338 proteogenomics and, 327 328, 328f – database searching, 113–115 of variants, 208 212 development, 364 ANNOVAR, 211 DNA fragment/genome assembly, 127–129, 133–134, 142 Anthrax, 141 dynamic programming, 110–124 Anti-sense RNA, 281 file compression, 79 Application programming interface (API), 368 Golay error-correcting, 31 Applied Biosystems Inc.
    [Show full text]
  • Identifying Disease Genes
    Genomics for today • Cancer genomics • Reproductive health • Forensic genomics • Agrigenomics • Complex disease genomics • Microbial genomics • Genomics in Drug and development • and more …omics Data/File formats • File format, a format for encoding data for storage in a computer file which is a standardized file format • Storage, access, sharing, interpretation, security, etc. http://en.wikipedia.org/wiki/Data_format Bioinformatics for dummies http://www.dummies.com/how-to/content/bioinformatics-data-formats.html Scientific data formats 23andMe microarray track data Browser Extensible Data Format AB1 (Chromatogram files used by DNA sequencing instruments from Applied Biosystems) MINiML (MIAME Notation in Markup Language) ABCD (Access to Biological Collection Data) mini Protein Data Bank Format ABCDDNA (Access to Biological Collection Data DNA extension) MIQAS-TAB (Minimal Information for QTLs and Association Studies Tabular) ABCDEFG (Access to Biological Collection Data Extension For Geosciences) MITAB ACE (Sequence assembly format) mmCIF (macromolecular Crystallographic Information File) Affymetrix Raw Intensity Format Multiple Alignment Forma ARLEQUIN Project Format mzData (deprecated) Axt Alignment Format mzIdentML BAM (Binary compressed SAM format) mzML BED (Browser extensible display format describing genes and other features of DNA sequences) mzQuantML BEDgraph mzXML (deprecated) Big Browser Extensible Data Format NCD (Natural Collections Descriptions) Big Wiggle Format NDTF (Neurophysiology Data Translation Format) Binary Alignement
    [Show full text]
  • A Resource Optimized GATK 4 Based Open Source Variant Calling Workflow
    Bathke and Lühken BMC Bioinformatics (2021) 22:402 https://doi.org/10.1186/s12859-021-04317-y SOFTWARE Open Access OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow Jochen Bathke* and Gesine Lühken *Correspondence: [email protected] Abstract giessen.de Background: The advent of next generation sequencing has opened new avenues Institute of Animal Breeding and Genetics, for basic and applied research. One application is the discovery of sequence vari- Justus Liebig University ants causative of a phenotypic trait or a disease pathology. The computational task Gießen, Ludwigstraße 21, of detecting and annotating sequence diferences of a target dataset between a 35390 Gießen, Germany reference genome is known as "variant calling". Typically, this task is computationally involved, often combining a complex chain of linked software tools. A major player in this feld is the Genome Analysis Toolkit (GATK). The "GATK Best Practices" is a com- monly referred recipe for variant calling. However, current computational recommen- dations on variant calling predominantly focus on human sequencing data and ignore ever-changing demands of high-throughput sequencing developments. Furthermore, frequent updates to such recommendations are counterintuitive to the goal of ofering a standard workfow and hamper reproducibility over time. Results: A workfow for automated detection of single nucleotide polymorphisms and insertion-deletions ofers a wide range of applications in sequence annotation of model and non-model organisms. The introduced workfow builds on the GATK Best Practices, while enabling reproducibility over time and ofering an open, generalized computational architecture. The workfow achieves parallelized data evaluation and maximizes performance of individual computational tasks.
    [Show full text]
  • Tools and Algorithms in Bioinformatics GCBA815, Fall 2013
    Tools and Algorithms in Bioinformatics GCBA815, Fall 2013 Week-13: NextGen Sequence Analysis Demonstrators: Suleyman Vural, Li You, Sanjit Pandey Babu Guda Department of Genetics, Cell Biology and Anatomy University of Nebraska Medical Center __________________________________________________________________________________________________ 11/22/2013 GCBA 815 Published Genome-Wide Associations through 12/2012 Published GWA at p≤5X10-8 for 17 trait categories NHGRI GWA Catalog www.genome.gov/ GWAStudies www.ebi.ac.uk/fgpt/gwas/ 1 __________________________________________________________________________________________________ 11/22/2013 GCBA 815 Allele Frequency vs Effect size of rare and common variants __________________________________________________________________________________________________ 11/22/2013 GCBA 815 2 IUPAC codes for nucleotides __________________________________________________________________________________________________ 11/22/2013 GCBA 815 NGS Applications in Biosciences • Genome • Exome sequencing • Clinical sequencing, personalized medicine • Targeted genome sequencing (Ex: Ion torrent amplicons) • Whole genome sequencing • Transcriptome • Whole transcriptome analysis • Small RNA analysis (siRNA, lncRNA, miRNA) • Gene expression profiling for selected target genes • Metagenome • Sequencing together the genomes of a mixture of species • Example: Human gut microbiota or environmental samples • Epigenome • Chromatin Immunoprecipitation Sequencing (ChIP-Seq) • Methylation and chromatin remodeling studies __________________________________________________________________________________________________
    [Show full text]
  • An Efficient General-Purpose Program for Assigning Sequence Reads To
    featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features Yang Liao 1;2, Gordon K Smyth 1;3 and Wei Shi 1;2∗ 1Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2Department of Computing and Information Systems, 3Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia Abstract Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key in- formation required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great vari- ety of genomic analyses but has so far received relatively little attention in the liter- ature. We present featureCounts, a read summarization program suitable for count- ing reads generated from either RNA or genomic DNA sequencing experiments. fea- tureCounts implements highly efficient chromosome hashing and feature blocking tech- niques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with ei- ther single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Pub- lic License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages. 1 Introduction Next-generation (next-gen) sequencing technologies are revolution-izing biology by pro- arXiv:1305.3347v2 [q-bio.GN] 14 Nov 2013 viding the ability to sequence DNA at unprecendented speed (Schuster, 2007; Metzker, 2009).
    [Show full text]
  • Riboreport - Benchmarking Tools for Ribosome Profiling-Based Identification of Open Reading Frames in Bacteria
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.08.447495; this version posted June 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. RiboReport - Benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria Rick Gelhauseny,1,*, Teresa M¨ullery,1,*, Sarah L. Svenssony,2, Omer Alkhnbashi1, Cynthia M. Sharma2, Florian Eggenhoferz,1, Rolf Backofen1,3,4,z,* 1 Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany 2 Chair of Molecular Infection Biology II, Institute of Molecular Infection Biology (IMIB), Josef-Schneider-Str. 2 / Bau D15, University of W¨urzburg, Germany 3 ZBSA Centre for Biological Systems Analysis, University of Freiburg, Hauptstr. 1, 79104 Freiburg, Germany 4 Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany * Corresponding authors: [email protected], [email protected], [email protected] y, z These authors contributed equally to this work Abstract Small proteins, those encoded by open reading frames, with less than or equal to 50 codons, are emerging as an important class of cellular macromolecules in all kingdoms of life. However, they are recalcitrant to detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation of sORFs in diverse species, and this has driven the development of ORF detection tools using Ribo-seq read signals.
    [Show full text]