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Gene Discovery and Annotation Using LCM-454 Transcriptome Sequencing Scott J
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Gene discovery and annotation using LCM-454 transcriptome sequencing Scott J. Emrich,1,2,6 W. Brad Barbazuk,3,6 Li Li,4 and Patrick S. Schnable1,4,5,7 1Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa 50010, USA; 2Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50010, USA; 3Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA; 4Interdepartmental Plant Physiology Graduate Major and Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50010, USA; 5Department of Agronomy and Center for Plant Genomics, Iowa State University, Ames, Iowa 50010, USA 454 DNA sequencing technology achieves significant throughput relative to traditional approaches. More than 261,000 ESTs were generated by 454 Life Sciences from cDNA isolated using laser capture microdissection (LCM) from the developmentally important shoot apical meristem (SAM) of maize (Zea mays L.). This single sequencing run annotated >25,000 maize genomic sequences and also captured ∼400 expressed transcripts for which homologous sequences have not yet been identified in other species. Approximately 70% of the ESTs generated in this study had not been captured during a previous EST project conducted using a cDNA library constructed from hand-dissected apex tissue that is highly enriched for SAMs. In addition, at least 30% of the 454-ESTs do not align to any of the ∼648,000 extant maize ESTs using conservative alignment criteria. These results indicate that the combination of LCM and the deep sequencing possible with 454 technology enriches for SAM transcripts not present in current EST collections. -
The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
Bioinformatics Study of Lectins: New Classification and Prediction In
Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr. -
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. -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
Genbank Dennis A
D48–D53 Nucleic Acids Research, 2012, Vol. 40, Database issue Published online 5 December 2011 doi:10.1093/nar/gkr1202 GenBank Dennis A. Benson, Ilene Karsch-Mizrachi, Karen Clark, David J. Lipman, James Ostell and Eric W. Sayers* National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA Received September 30, 2011; Revised November 14, 2011; Accepted November 17, 2011 ABSTRACT sequence (GSS), whole-genome shotgun (WGS) and Õ other high-throughput data from sequencing centers. GenBank is a comprehensive database that The US Office of Patents and Trademarks also contributes contains publicly available nucleotide sequences sequences from issued patents. GenBank participates with for more than 250 000 formally described species. the EMBL Nucleotide Sequence Database (EMBL-Bank), Downloaded from These sequences are obtained primarily through part of the European Nucleotide Archive (ENA) (2), and submissions from individual laboratories and batch the DNA Data Bank of Japan (DDBJ) (3) as a partner submissions from large-scale sequencing projects, in the International Nucleotide Sequence Database including whole-genome shotgun (WGS) and envir- Collaboration (INSDC). The INSDC partners exchange onmental sampling projects. Most submissions data daily to ensure that a uniform and comprehensive http://nar.oxfordjournals.org/ are made using the web-based BankIt or standalone collection of sequence information is available worldwide. Sequin programs, and accession numbers are NCBI makes the GenBank data available at no cost over the Internet, through FTP and a wide range of web-based assigned by GenBank staff upon receipt. Daily data retrieval and analysis services (4). -
A Beginner's Guide to Eukaryotic Genome Annotation
REVIEWS STUDY DESIGNS A beginner’s guide to eukaryotic genome annotation Mark Yandell and Daniel Ence Abstract | The falling cost of genome sequencing is having a marked impact on the research community with respect to which genomes are sequenced and how and where they are annotated. Genome annotation projects have generally become small-scale affairs that are often carried out by an individual laboratory. Although annotating a eukaryotic genome assembly is now within the reach of non-experts, it remains a challenging task. Here we provide an overview of the genome annotation process and the available tools and describe some best-practice approaches. Genome annotation Sequencing costs have fallen so dramatically that a sin- with some basic UNIX skills, ‘do-it-yourself’ genome A term used to describe two gle laboratory can now afford to sequence large, even annotation projects are quite feasible using present- distinct processes. ‘Structural’ human-sized, genomes. Ironically, although sequencing day tools. Here we provide an overview of the eukary- genome annotation is the has become easy, in many ways, genome annotation has otic genome annotation process, describe the available process of identifying genes and their intron–exon become more challenging. Several factors are respon- toolsets and outline some best-practice approaches. structures. ‘Functional’ genome sible for this. First, the shorter read lengths of second- annotation is the process of generation sequencing platforms mean that current Assembly and annotation: an overview attaching meta-data such as genome assemblies rarely attain the contiguity of the Assembly. The first step towards the successful annota- gene ontology terms to classic shotgun assemblies of the Drosophila mela- tion of any genome is determining whether its assem- structural annotations. -
Whole Genome Sequencing Data of Multiple Individuals of Pakistani
www.nature.com/scientificdata oPeN Whole genome sequencing data DAtA DeScriptor of multiple individuals of Pakistani descent Shahid Y. Khan1, Muhammad Ali1, Mei-Chong W. Lee2, Zhiwei Ma3, Pooja Biswas4, Asma A. Khan5, Muhammad Asif Naeem5, Saima Riazuddin 6, Sheikh Riazuddin5,7,8, Radha Ayyagari4, J. Fielding Hejtmancik 3 & S. Amer Riazuddin1 ✉ Here we report whole genome sequencing of four individuals (H3, H4, H5, and H6) from a family of Pakistani descent. Whole genome sequencing yielded 1084.92, 894.73, 1068.62, and 1005.77 million mapped reads corresponding to 162.73, 134.21, 160.29, and 150.86 Gb sequence data and 52.49x, 43.29x, 51.70x, and 48.66x average coverage for H3, H4, H5, and H6, respectively. We identifed 3,529,659, 3,478,495, 3,407,895, and 3,426,862 variants in the genomes of H3, H4, H5, and H6, respectively, including 1,668,024 variants common in the four genomes. Further, we identifed 42,422, 39,824, 28,599, and 35,206 novel variants in the genomes of H3, H4, H5, and H6, respectively. A major fraction of the variants identifed in the four genomes reside within the intergenic regions of the genome. Single nucleotide polymorphism (SNP) genotype based comparative analysis with ethnic populations of 1000 Genomes database linked the ancestry of all four genomes with the South Asian populations, which was further supported by mitochondria based haplogroup analysis. In conclusion, we report whole genome sequencing of four individuals of Pakistani descent. Background & Summary The completion of Human Genome Project ignited several large scale efforts to characterize variations in the human genome, which led to a comprehensive catalog of the common variants including single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels), across the entire human genome1,2. -
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 -
An Open-Sourced Bioinformatic Pipeline for the Processing of Next-Generation Sequencing Derived Nucleotide Reads
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.20.050369; this version posted May 28, 2020. 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 4.0 International license. An open-sourced bioinformatic pipeline for the processing of Next-Generation Sequencing derived nucleotide reads: Identification and authentication of ancient metagenomic DNA Thomas C. Collin1, *, Konstantina Drosou2, 3, Jeremiah Daniel O’Riordan4, Tengiz Meshveliani5, Ron Pinhasi6, and Robin N. M. Feeney1 1School of Medicine, University College Dublin, Ireland 2Division of Cell Matrix Biology Regenerative Medicine, University of Manchester, United Kingdom 3Manchester Institute of Biotechnology, School of Earth and Environmental Sciences, University of Manchester, United Kingdom [email protected] 5Institute of Paleobiology and Paleoanthropology, National Museum of Georgia, Tbilisi, Georgia 6Department of Evolutionary Anthropology, University of Vienna, Austria *Corresponding Author Abstract The emerging field of ancient metagenomics adds to these Bioinformatic pipelines optimised for the processing and as- processing complexities with the need for additional steps sessment of metagenomic ancient DNA (aDNA) are needed in the separation and authentication of ancient sequences from modern sequences. Currently, there are few pipelines for studies that do not make use of high yielding DNA cap- available for the analysis of ancient metagenomic DNA ture techniques. These bioinformatic pipelines are tradition- 1 4 ally optimised for broad aDNA purposes, are contingent on (aDNA) ≠ The limited number of bioinformatic pipelines selection biases and are associated with high costs. -
Identification of Transcribed Sequences in Arabidopsis Thaliana by Using High-Resolution Genome Tiling Arrays
Identification of transcribed sequences in Arabidopsis thaliana by using high-resolution genome tiling arrays Viktor Stolc*†‡§, Manoj Pratim Samanta‡§¶, Waraporn Tongprasitʈ, Himanshu Sethiʈ, Shoudan Liang*, David C. Nelson**, Adrian Hegeman**, Clark Nelson**, David Rancour**, Sebastian Bednarek**, Eldon L. Ulrich**, Qin Zhao**, Russell L. Wrobel**, Craig S. Newman**, Brian G. Fox**, George N. Phillips, Jr.**, John L. Markley**, and Michael R. Sussman**†† *Genome Research Facility, National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035; †Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520; ¶Systemix Institute, Cupertino, CA 94035; ʈEloret Corporation at National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035; and **Center for Eukaryotic Structural Genomics, University of Wisconsin, Madison, WI 53706 Edited by Sidney Altman, Yale University, New Haven, CT, and approved January 28, 2005 (received for review November 4, 2004) Using a maskless photolithography method, we produced DNA Genome-wide tiling arrays can overcome many of the shortcom- oligonucleotide microarrays with probe sequences tiled through- ings of the previous approaches by comprehensively probing out the genome of the plant Arabidopsis thaliana. RNA expression transcription in all regions of the genome. This technology has was determined for the complete nuclear, mitochondrial, and been used successfully on different organisms (5–12). A recent chloroplast genomes by tiling 5 million 36-mer probes. These study on A. thaliana reported measuring transcriptional activities probes were hybridized to labeled mRNA isolated from liquid of four different cell lines by using 25-mer-based tiling arrays that grown T87 cells, an undifferentiated Arabidopsis cell culture line. -
Orthofiller: Utilising Data from Multiple Species to Improve the Completeness of Genome Annotations Michael P
Dunne and Kelly BMC Genomics (2017) 18:390 DOI 10.1186/s12864-017-3771-x SOFTWARE Open Access OrthoFiller: utilising data from multiple species to improve the completeness of genome annotations Michael P. Dunne and Steven Kelly* Abstract Backround: Complete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction. Results: Here we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species. Conclusions: We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species. Keywords: Genome annotation, Gene prediction, Orthology, Orthogroup Background of several effective algorithms for identifying genes in Genome sequences have become fundamental to many de novo sequenced genomes [3].