1
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Lang OW, Srivastava D, Pugh BF, Lai WKM. GenoPipe: identifying the genotype of origin within (epi)genomic datasets. Nucleic Acids Res 2023; 51:12054-12068. [PMID: 37933851 PMCID: PMC10711449 DOI: 10.1093/nar/gkad950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/19/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e. cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g. indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism's genome (i.e. epitope insertions, gene deletions and SNPs).
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Affiliation(s)
- Olivia W Lang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Divyanshi Srivastava
- Department of Biochemistry & Molecular Biology, Pennsylvania State University, University Park, PA, 16801, USA
| | - B Franklin Pugh
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - William K M Lai
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
- Cornell Institute of Biotechnology, Cornell University, Ithaca, NY 14850, USA
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2
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Wong ED, Miyasato SR, Aleksander S, Karra K, Nash RS, Skrzypek MS, Weng S, Engel SR, Cherry JM. Saccharomyces genome database update: server architecture, pan-genome nomenclature, and external resources. Genetics 2023; 224:iyac191. [PMID: 36607068 PMCID: PMC10158836 DOI: 10.1093/genetics/iyac191] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 11/16/2022] [Accepted: 12/21/2022] [Indexed: 01/07/2023] Open
Abstract
As one of the first model organism knowledgebases, Saccharomyces Genome Database (SGD) has been supporting the scientific research community since 1993. As technologies and research evolve, so does SGD: from updates in software architecture, to curation of novel data types, to incorporation of data from, and collaboration with, other knowledgebases. We are continuing to make steps toward providing the community with an S. cerevisiae pan-genome. Here, we describe software upgrades, a new nomenclature system for genes not found in the reference strain, and additions to gene pages. With these improvements, we aim to remain a leading resource for students, researchers, and the broader scientific community.
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Affiliation(s)
- Edith D Wong
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Stuart R Miyasato
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Suzi Aleksander
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Kalpana Karra
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Robert S Nash
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Marek S Skrzypek
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shuai Weng
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Stacia R Engel
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - J Michael Cherry
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
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3
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Lang O, Srivastava D, Pugh BF, Lai WK. GenoPipe: identifying the genotype of origin within (epi)genomic datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532660. [PMID: 36993164 PMCID: PMC10055126 DOI: 10.1101/2023.03.14.532660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e., cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g., indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism’s genome (i.e., epitope insertions, gene deletions, and SNPs).
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4
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Khanal DD, Tasharofi S, Azizi M, Khaledi MG. Improved Protein Coverage in Bottom-Up Proteomes Analysis Using Fluoroalcohol-Mediated Supramolecular Biphasic Systems With Mixed Amphiphiles for Sample Extraction, Fractionation, and Enrichment. Anal Chem 2021; 93:7430-7438. [PMID: 33970614 DOI: 10.1021/acs.analchem.1c00030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A new class of supramolecular biphasic systems containing fluoroalcohol-induced coacervates (FAiC) provides concomitant fractionation of complex protein mixtures, high solubilizing power for extraction of various types of proteins, especially those with high hydrophobicity (such as membrane proteins), and enrichment of low-abundance proteins. Subsequently, the use of FAiC biphasic systems (BPS) in the bottom-up proteomics workflow resulted in significantly higher coverage for the whole proteome, various subproteomes, especially those embedded or associated with membranes, post-translationally modified proteins, and low-abundance proteins (LAPs) as compared to the conventional methodologies. In this work, we used a new type of FAiC-BPS composed of mixed amphiphiles, a zwitterionic surfactant 3-(N,N-dimethylmyristyl ammonia) propane sulfonate (DMMAPS), a quaternary ammonium salt (QUATS), and hexafluoroisopropanol (HFIP) as the coacervator for extraction, fractionation, and enrichment of yeast proteome in bottom-up proteomics. The coverage of the lower-abundance proteins (abundance below 2000 molecules/cell) improved by more than 100% using DMMAPS and DMMAPS + QUATS systems as compared to the conventional methods using urea or detergent solutions for protein solubilization. Additionally, these coacervate systems show increased coverage of integral membrane proteins and proteins with α-helices by up to 24 and 555%, respectively.
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Affiliation(s)
- Durga Devi Khanal
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Sajad Tasharofi
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Mohammadmehdi Azizi
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Morteza G Khaledi
- Department of Chemistry and Biochemistry, The University of Texas at Arlington, Arlington, Texas 76019, United States
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5
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Barbitoff YA, Matveenko AG, Matiiv AB, Maksiutenko EM, Moskalenko SE, Drozdova PB, Polev DE, Beliavskaia AY, Danilov LG, Predeus AV, Zhouravleva GA. Chromosome-level genome assembly and structural variant analysis of two laboratory yeast strains from the Peterhof Genetic Collection lineage. G3-GENES GENOMES GENETICS 2021; 11:6129118. [PMID: 33677552 PMCID: PMC8759820 DOI: 10.1093/g3journal/jkab029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/22/2021] [Indexed: 01/23/2023]
Abstract
Thousands of yeast genomes have been sequenced with both traditional and long-read technologies, and multiple observations about modes of genome evolution for both wild and laboratory strains have been drawn from these sequences. In our study, we applied Oxford Nanopore and Illumina technologies to assemble complete genomes of two widely used members of a distinct laboratory yeast lineage, the Peterhof Genetic Collection (PGC), and investigate the structural features of these genomes including transposable element content, copy number alterations, and structural rearrangements. We identified numerous notable structural differences between genomes of PGC strains and the reference S288C strain. We discovered a substantial enrichment of mid-length insertions and deletions within repetitive coding sequences, such as in the SCH9 gene or the NUP100 gene, with possible impact of these variants on protein amyloidogenicity. High contiguity of the final assemblies allowed us to trace back the history of reciprocal unbalanced translocations between chromosomes I, VIII, IX, XI, and XVI of the PGC strains. We show that formation of hybrid alleles of the FLO genes during such chromosomal rearrangements is likely responsible for the lack of invasive growth of yeast strains. Taken together, our results highlight important features of laboratory yeast strain evolution using the power of long-read sequencing.
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Affiliation(s)
- Yury A Barbitoff
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia.,Bioinformatics Institute, St. Petersburg 197342, Russia
| | - Andrew G Matveenko
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia.,Bioinformatics Institute, St. Petersburg 197342, Russia
| | - Anton B Matiiv
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia.,Bioinformatics Institute, St. Petersburg 197342, Russia
| | - Evgeniia M Maksiutenko
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia.,St. Petersburg Branch, Vavilov Institute of General Genetics of the Russian Academy of Sciences, St. Petersburg 199034, Russia
| | - Svetlana E Moskalenko
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia.,St. Petersburg Branch, Vavilov Institute of General Genetics of the Russian Academy of Sciences, St. Petersburg 199034, Russia
| | | | | | - Alexandra Y Beliavskaia
- Department of Invertebrate Zoology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Lavrentii G Danilov
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Alexander V Predeus
- Bioinformatics Institute, St. Petersburg 197342, Russia.,University of Liverpool, Liverpool, UK, L7 3EA
| | - Galina A Zhouravleva
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg 199034, Russia
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6
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Song G, Lee J, Kim J, Kang S, Lee H, Kwon D, Lee D, Lang GI, Cherry JM, Kim J. Integrative Meta-Assembly Pipeline (IMAP): Chromosome-level genome assembler combining multiple de novo assemblies. PLoS One 2019; 14:e0221858. [PMID: 31454399 PMCID: PMC6711525 DOI: 10.1371/journal.pone.0221858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 08/18/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Genomic data have become major resources to understand complex mechanisms at fine-scale temporal and spatial resolution in functional and evolutionary genetic studies, including human diseases, such as cancers. Recently, a large number of whole genomes of evolving populations of yeast (Saccharomyces cerevisiae W303 strain) were sequenced in a time-dependent manner to identify temporal evolutionary patterns. For this type of study, a chromosome-level sequence assembly of the strain or population at time zero is required to compare with the genomes derived later. However, there is no fully automated computational approach in experimental evolution studies to establish the chromosome-level genome assembly using unique features of sequencing data. METHODS AND RESULTS In this study, we developed a new software pipeline, the integrative meta-assembly pipeline (IMAP), to build chromosome-level genome sequence assemblies by generating and combining multiple initial assemblies using three de novo assemblers from short-read sequencing data. We significantly improved the continuity and accuracy of the genome assembly using a large collection of sequencing data and hybrid assembly approaches. We validated our pipeline by generating chromosome-level assemblies of yeast strains W303 and SK1, and compared our results with assemblies built using long-read sequencing and various assembly evaluation metrics. We also constructed chromosome-level sequence assemblies of S. cerevisiae strain Sigma1278b, and three commonly used fungal strains: Aspergillus nidulans A713, Neurospora crassa 73, and Thielavia terrestris CBS 492.74, for which long-read sequencing data are not yet available. Finally, we examined the effect of IMAP parameters, such as reference and resolution, on the quality of the final assembly of the yeast strains W303 and SK1. CONCLUSIONS We developed a cost-effective pipeline to generate chromosome-level sequence assemblies using only short-read sequencing data. Our pipeline combines the strengths of reference-guided and meta-assembly approaches. Our pipeline is available online at http://github.com/jkimlab/IMAP including a Docker image, as well as a Perl script, to help users install the IMAP package, including several prerequisite programs. Users can use IMAP to easily build the chromosome-level assembly for the genome of their interest.
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Affiliation(s)
- Giltae Song
- School of Computer Science and Engineering, Pusan National University, Busan, South Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, South Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, South Korea
| | - Seokwoo Kang
- School of Computer Science and Engineering, Pusan National University, Busan, South Korea
| | - Hoyong Lee
- School of Computer Science and Engineering, Pusan National University, Busan, South Korea
| | - Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, South Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, South Korea
| | - Gregory I. Lang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, United States of America
| | - J. Michael Cherry
- Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, South Korea
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7
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Liu X, Wei J, Ma Z, He Y. Rapamycin- and starvation-induced autophagy are associated with miRNA dysregulation in A549 cells. Acta Biochim Biophys Sin (Shanghai) 2019; 51:393-401. [PMID: 30908573 DOI: 10.1093/abbs/gmz022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 02/03/2019] [Indexed: 01/26/2023] Open
Abstract
MicroRNAs (miRNAs) are short (20-23 nt) non-coding RNAs that are involved in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the stability and translation of mRNAs. In recent years, deep sequencing of the transcription is being increasingly utilized with the promise of higher sensitivity for the identification of differential expression patterns as well as the opportunity to discover new transcripts, including new alternative isoforms and miRNAs. In this study, miRNAs from A549 cells treated with/without rapamycin or starvation were subject to genome-wide deep sequencing. A total of 1534 miRNAs were detected from the rapamycin- and starvation-treated A549 cells. Among them, 31 miRNAs were consistently upregulated and 131 miRNAs were downregulated in the treated cells when compared with the untreated cells. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the predicted target genes of the most significantly differentially expressed miRNAs revealed that the autophagy-related miRNAs are involved in cancer pathway. Taken together, our findings indicate that the underlying mechanism responsible for autophagy is associated with dysregulation of miRNAs in rapamycin- or starvation-induced A549 cells.
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Affiliation(s)
- Xiaomin Liu
- School of Environmental Science and Engineering, Shanghai University, Shanghai, China
- Lab for Non-coding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Jiali Wei
- Lab for Non-coding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Zhongliang Ma
- Lab for Non-coding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Yanyun He
- Lab for Non-coding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
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8
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Hellerstedt ST, Nash RS, Weng S, Paskov KM, Wong ED, Karra K, Engel SR, Cherry JM. Curated protein information in the Saccharomyces genome database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3066359. [PMID: 28365727 PMCID: PMC5467551 DOI: 10.1093/database/bax011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/27/2017] [Indexed: 12/21/2022]
Abstract
Due to recent advancements in the production of experimental proteomic data, the Saccharomyces genome database (SGD; www.yeastgenome.org) has been expanding our protein curation activities to make new data types available to our users. Because of broad interest in post-translational modifications (PTM) and their importance to protein function and regulation, we have recently started incorporating expertly curated PTM information on individual protein pages. Here we also present the inclusion of new abundance and protein half-life data obtained from high-throughput proteome studies. These new data types have been included with the aim to facilitate cellular biology research. Database URL: www.yeastgenome.org
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Affiliation(s)
| | - Robert S Nash
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shuai Weng
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Kelley M Paskov
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Edith D Wong
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Kalpana Karra
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Stacia R Engel
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - J Michael Cherry
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
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9
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Ramirez-Gaona M, Marcu A, Pon A, Guo AC, Sajed T, Wishart NA, Karu N, Djoumbou Feunang Y, Arndt D, Wishart DS. YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res 2016; 45:D440-D445. [PMID: 27899612 PMCID: PMC5210545 DOI: 10.1093/nar/gkw1058] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 10/18/2016] [Accepted: 10/31/2016] [Indexed: 12/31/2022] Open
Abstract
YMDB or the Yeast Metabolome Database (http://www.ymdb.ca/) is a comprehensive database containing extensive information on the genome and metabolome of Saccharomyces cerevisiae. Initially released in 2012, the YMDB has gone through a significant expansion and a number of improvements over the past 4 years. This manuscript describes the most recent version of YMDB (YMDB 2.0). More specifically, it provides an updated description of the database that was previously described in the 2012 NAR Database Issue and it details many of the additions and improvements made to the YMDB over that time. Some of the most important changes include a 7-fold increase in the number of compounds in the database (from 2007 to 16 042), a 430-fold increase in the number of metabolic and signaling pathway diagrams (from 66 to 28 734), a 16-fold increase in the number of compounds linked to pathways (from 742 to 12 733), a 17-fold increase in the numbers of compounds with nuclear magnetic resonance or MS spectra (from 783 to 13 173) and an increase in both the number of data fields and the number of links to external databases. In addition to these database expansions, a number of improvements to YMDB's web interface and its data visualization tools have been made. These additions and improvements should greatly improve the ease, the speed and the quantity of data that can be extracted, searched or viewed within YMDB. Overall, we believe these improvements should not only improve the understanding of the metabolism of S. cerevisiae, but also allow more in-depth exploration of its extensive metabolic networks, signaling pathways and biochemistry.
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Affiliation(s)
- Miguel Ramirez-Gaona
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Ana Marcu
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Allison Pon
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - An Chi Guo
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Tanvir Sajed
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Noah A Wishart
- Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Naama Karu
- Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | - David Arndt
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - David S Wishart
- Departments of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada .,Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB T6G 2M9, Canada
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10
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Engel SR, Weng S, Binkley G, Paskov K, Song G, Cherry JM. From one to many: expanding the Saccharomyces cerevisiae reference genome panel. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw020. [PMID: 26989152 PMCID: PMC4795930 DOI: 10.1093/database/baw020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 02/05/2016] [Indexed: 12/14/2022]
Abstract
In recent years, thousands of Saccharomyces cerevisiae genomes have been sequenced to varying degrees of completion. The Saccharomyces Genome Database (SGD) has long been the keeper of the original eukaryotic reference genome sequence, which was derived primarily from S. cerevisiae strain S288C. Because new technologies are pushing S. cerevisiae annotation past the limits of any system based exclusively on a single reference sequence, SGD is actively working to expand the original S. cerevisiae systematic reference sequence from a single genome to a multi-genome reference panel. We first commissioned the sequencing of additional genomes and their automated analysis using the AGAPE pipeline. Here we describe our curation strategy to produce manually reviewed high-quality genome annotations in order to elevate 11 of these additional genomes to Reference status. Database URL: http://www.yeastgenome.org/
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Affiliation(s)
- Stacia R Engel
- Department of Genetics, Stanford University, Stanford, CA, 94305
| | - Shuai Weng
- Department of Genetics, Stanford University, Stanford, CA, 94305
| | - Gail Binkley
- Department of Genetics, Stanford University, Stanford, CA, 94305
| | - Kelley Paskov
- Department of Genetics, Stanford University, Stanford, CA, 94305
| | - Giltae Song
- Department of Genetics, Stanford University, Stanford, CA, 94305
| | - J Michael Cherry
- Department of Genetics, Stanford University, Stanford, CA, 94305
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