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Long-Read Cdna Sequencing Identifies Functional Pseudogenes in the Human Transcriptome Robin-Lee Troskie1, Yohaann Jafrani1, Tim R
Troskie et al. Genome Biology (2021) 22:146 https://doi.org/10.1186/s13059-021-02369-0 SHORT REPORT Open Access Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome Robin-Lee Troskie1, Yohaann Jafrani1, Tim R. Mercer2, Adam D. Ewing1*, Geoffrey J. Faulkner1,3* and Seth W. Cheetham1* * Correspondence: adam.ewing@ mater.uq.edu.au; faulknergj@gmail. Abstract com; [email protected]. au Pseudogenes are gene copies presumed to mainly be functionless relics of evolution 1Mater Research Institute-University due to acquired deleterious mutations or transcriptional silencing. Using deep full- of Queensland, TRI Building, QLD length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we 4102 Woolloongabba, Australia Full list of author information is identify here hundreds of novel transcribed pseudogenes expressed in tissue-specific available at the end of the article patterns. Some pseudogene transcripts have intact open reading frames and are translated in cultured cells, representing unannotated protein-coding genes. To assess the biological impact of noncoding pseudogenes, we CRISPR-Cas9 delete the nucleus-enriched pseudogene PDCL3P4 and observe hundreds of perturbed genes. This study highlights pseudogenes as a complex and dynamic component of the human transcriptional landscape. Keywords: Pseudogene, PacBio, Long-read, lncRNA, CRISPR Background Pseudogenes are gene copies which are thought to be defective due to frame- disrupting mutations or transcriptional silencing [1, 2]. Most human pseudogenes (72%) are derived from retrotransposition of processed mRNAs, mediated by proteins encoded by the LINE-1 retrotransposon [3, 4]. Due to the loss of parental cis-regula- tory elements, processed pseudogenes were initially presumed to be transcriptionally silent [1] and were excluded from genome-wide functional screens and most transcrip- tome analyses [2]. -
Linkage and Association Analysis of Obesity Traits Reveals Novel Loci and Interactions with Dietary N-3 Fatty Acids in an Alaska Native (Yup’Ik) Population
METABOLISM CLINICAL AND EXPERIMENTAL XX (2015) XXX– XXX Available online at www.sciencedirect.com Metabolism www.metabolismjournal.com Linkage and association analysis of obesity traits reveals novel loci and interactions with dietary n-3 fatty acids in an Alaska Native (Yup’ik) population Laura Kelly Vaughan a, Howard W. Wiener b, Stella Aslibekyan b, David B. Allison c, Peter J. Havel d, Kimber L. Stanhope d, Diane M. O’Brien e, Scarlett E. Hopkins e, Dominick J. Lemas f, Bert B. Boyer e,⁎, Hemant K. Tiwari c a Department of Biology, King University, 1350 King College Rd, Bristol, TN 37620, USA b Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35294, USA c Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35294, USA d Departments of Nutrition and Molecular Biosciences, University of California at Davis, 1 Shields Ave, Davis, CA 95616, USA e USACenter for Alaska Native Health Research, Institute of Arctic Biology, 311 Irving I Building, University of Alaska Fairbanks, Fairbanks, AK 99775, USA f Department of Pediatrics, Section of Neonatology, University of Colorado Anschutz Medical Campus, 13123 East 16th Ave, Aurora, CO 80045, USA ARTICLE INFO ABSTRACT Article history: Objective. To identify novel genetic markers of obesity-related traits and to identify gene- Received 16 June 2014 diet interactions with n-3 polyunsaturated fatty acid (n-3 PUFA) intake in Yup’ik people. Accepted 28 February 2015 Material and methods. We measured body composition, plasma adipokines and ghrelin in 982 participants enrolled in the Center for Alaska Native Health Research (CANHR) Study. -
Gene and Alternative Splicing Annotation with AIR
Downloaded from genome.cshlp.org on May 8, 2012 - Published by Cold Spring Harbor Laboratory Press Gene and alternative splicing annotation with AIR Liliana Florea, Valentina Di Francesco, Jason Miller, et al. Genome Res. 2005 15: 54-66 Access the most recent version at doi:10.1101/gr.2889405 Supplemental http://genome.cshlp.org/content/suppl/2004/12/08/15.1.54.DC1.html Material References This article cites 49 articles, 34 of which can be accessed free at: http://genome.cshlp.org/content/15/1/54.full.html#ref-list-1 Article cited in: http://genome.cshlp.org/content/15/1/54.full.html#related-urls Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press Commons for the first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/. Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions © 2005, Published by Cold Spring Harbor Laboratory Press Downloaded from genome.cshlp.org on May 8, 2012 - Published by Cold Spring Harbor Laboratory Press Methods Gene and alternative splicing annotation with AIR Liliana Florea,1,4,5 Valentina Di Francesco,2 Jason Miller,1 Russell Turner,1 Alison Yao,2 Michael Harris,2 Brian Walenz,1 Clark Mobarry,1 Gennady V. -
A Literature-Based Database for Cell Senescence Genes and Its Application to Identify Critical Cell Aging Pathways and Associated Diseases
Citation: Cell Death and Disease (2016) 7, e2053; doi:10.1038/cddis.2015.414 OPEN & 2016 Macmillan Publishers Limited All rights reserved 2041-4889/16 www.nature.com/cddis CSGene: a literature-based database for cell senescence genes and its application to identify critical cell aging pathways and associated diseases M Zhao1, L Chen2 and H Qu*,2 Cell senescence is a cellular process in which normal diploid cells cease to replicate and is a major driving force for human cancers and aging-associated diseases. Recent studies on cell senescence have identified many new genetic components and pathways that control cell aging. However, there is no comprehensive resource for cell senescence that integrates various genetic studies and relationships with cell senescence, and the risk associated with complex diseases such as cancer is still unexplored. We have developed the first literature-based gene resource for exploring cell senescence genes, CSGene. We complied 504 experimentally verified genes from public data resources and published literature. Pathway analyses highlighted the prominent roles of cell senescence genes in the control of rRNA gene transcription and unusual rDNA repeat that constitute a center for the stability of the whole genome. We also found a strong association of cell senescence with HIV-1 infection and viral carcinogenesis that are mainly related to promoter/enhancer binding and chromatin modification processes. Moreover, pan-cancer mutation and network analysis also identified common cell aging mechanisms in cancers and uncovered a highly modular network structure. These results highlight the utility of CSGene for elucidating the complex cellular events of cell senescence. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
Navigo: Interactive Tool for Visualization and Functional Similarity and Coherence Analysis with Gene Ontology Qing Wei1, Ishita K
Wei et al. BMC Bioinformatics (2017) 18:177 DOI 10.1186/s12859-017-1600-5 SOFTWARE Open Access NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology Qing Wei1, Ishita K. Khan1, Ziyun Ding2, Satwica Yerneni3 and Daisuke Kihara2,1* Abstract Background: The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. Results: NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. Conclusions: We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo. Keywords: Gene function, Gene ontology, GO, Ontology, GO directed acyclic graph, Function similarity, Gene function prediction, GO annotation, function enrichment analysis, GO parental terms, GO association score Background their parental relationship make it non-trivial to provide Functional elucidation of genes is one of the central an intuitive summary of GO annotations of genes. -
The Human Protein Coevolution Network
Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Methods The human protein coevolution network Elisabeth R.M. Tillier1 and Robert L. Charlebois Department of Medical Biophysics, University of Toronto, and Ontario Cancer Institute, University Health Network, Toronto, Ontario M5G 1L7, Canada Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein–protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein–protein interactions than are currently known from high-throughput and other experimental evidence. -
Genomes of the Mouse Collaborative Cross
| MULTIPARENTAL POPULATIONS Genomes of the Mouse Collaborative Cross Anuj Srivastava,*,1 Andrew P. Morgan,†,‡,§,1 Maya L. Najarian,**,1 Vishal Kumar Sarsani,* J. Sebastian Sigmon,** John R. Shorter,†,‡ Anwica Kashfeen,** Rachel C. McMullan,†,‡,†† Lucy H. Williams,† Paola Giusti-Rodríguez,† Martin T. Ferris,†,‡ Patrick Sullivan,† Pablo Hock,†,‡ Darla R. Miller,†,‡ Timothy A. Bell,†,‡ Leonard McMillan,** Gary A. Churchill,*,2 and Fernando Pardo-Manuel de Villena†,‡,2 *The Jackson Laboratory, Bar Harbor, Maine 04609, and †Department of Genetics, ‡Lineberger Comprehensive Cancer Center, §Curriculum of Bioinformatics and Computational Biology, **Department of Computer Science, and ††Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, North Carolina 27599 ORCID IDs: 0000-0003-1942-4543 (A.P.M.); 0000-0003-4732-5526 (J.R.S.); 0000-0003-1241-6268 (M.T.F.); 0000-0002-0781-7254 (D.R.M.); 0000-0002-5738-5795 (F.P.-M.d.V.) ABSTRACT The Collaborative Cross (CC) is a multiparent panel of recombinant inbred (RI) mouse strains derived from eight founder laboratory strains. RI panels are popular because of their long-term genetic stability, which enhances reproducibility and integration of data collected across time and conditions. Characterization of their genomes can be a community effort, reducing the burden on individual users. Here we present the genomes of the CC strains using two complementary approaches as a resource to improve power and interpretation of genetic experiments. Our study also provides a cautionary tale regarding the limitations imposed by such basic biological processes as mutation and selection. A distinct advantage of inbred panels is that genotyping only needs to be performed on the panel, not on each individual mouse. -
Analysis of DNA Methylation Sites Used for Forensic Age Prediction and Their Correlation with Human Aging
Silva DSBS, et al., J Forensic Leg Investig Sci 2021, 7: 054 DOI: 10.24966/FLIS-733X/100054 HSOA Journal of Forensic, Legal & Investigative Sciences Research Article Analysis of DNA Methylation Introduction DNA samples left behind in crime scenes are often used for Sites used for Forensic Age identification purposes by comparing them to reference samples or to a forensic database. In cases where there is no match, investigators may Prediction and their Correlation turn to alternative approaches using advanced technologies to gain valuable leads on phenotypic traits (externally visible characteristics) with Human Aging of the person who had left the DNA material. Previous studies have found that phenotypes can be predicted by using information obtained from different DNA markers, such as single nucleotide Silva DSBS1* and Karantenislis G2 polymorphisms (SNPs), insertion/deletion polymorphisms (InDel), and epigenetic markers [1-11]. It is well established that epigenetic 1Department of Chemistry, Hofstra University, Hempstead-NY, USA modifications are major regulators in the translation of genotype 2John F Kennedy High School, Bellmore-NY, USA to phenotype. Epigenetic regulation encompasses various levels of gene expression and involves modifications of DNA, histones, RNA and chromatin, with functional consequences for the human Abstract genome [12-14]. DNA methylation is the most well-characterized epigenetic modification. It is usually associated with transcriptional Phenotype-related features, such as age, are linked to repression and it involves the addition of a methyl group (– CH3) in genetic components through regulatory pathways and epigenetic the cytosine of CpG dinucleotides. Differences in DNA methylation modifications, such as DNA methylation, are major regulators in levels in specific regions of the genome can affect the expression of the translation of genotype to phenotype. -
Biopython Tutorial and Cookbook
Biopython Tutorial and Cookbook Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock Last Update – September 2008 Contents 1 Introduction 5 1.1 What is Biopython? ......................................... 5 1.1.1 What can I find in the Biopython package ......................... 5 1.2 Installing Biopython ......................................... 6 1.3 FAQ .................................................. 6 2 Quick Start – What can you do with Biopython? 8 2.1 General overview of what Biopython provides ........................... 8 2.2 Working with sequences ....................................... 8 2.3 A usage example ........................................... 9 2.4 Parsing sequence file formats .................................... 10 2.4.1 Simple FASTA parsing example ............................... 10 2.4.2 Simple GenBank parsing example ............................. 11 2.4.3 I love parsing – please don’t stop talking about it! .................... 11 2.5 Connecting with biological databases ................................ 11 2.6 What to do next ........................................... 12 3 Sequence objects 13 3.1 Sequences and Alphabets ...................................... 13 3.2 Sequences act like strings ...................................... 14 3.3 Slicing a sequence .......................................... 15 3.4 Turning Seq objects into strings ................................... 15 3.5 Concatenating or adding sequences ................................. 16 3.6 Nucleotide sequences and -
Linkage and Association Analysis of Circulating Vitamin D and Parathyroid Hormone Identifies Novel Loci in Alaska Native Yup’Ik People Stella Aslibekyan1, Laura K
Aslibekyan et al. Genes & Nutrition (2016) 11:23 DOI 10.1186/s12263-016-0538-y RESEARCH Open Access Linkage and association analysis of circulating vitamin D and parathyroid hormone identifies novel loci in Alaska Native Yup’ik people Stella Aslibekyan1, Laura K. Vaughan2,4, Howard W. Wiener1, Bertha A. Hidalgo1, Dominick J. Lemas3, Diane M. O’Brien5, Scarlett E. Hopkins5, Kimber L. Stanhope6, Peter J. Havel6, Kenneth E. Thummel7, Bert B. Boyer5 and Hemant K. Tiwari2* Abstract Background: Vitamin D deficiency is a well-documented public health issue with both genetic and environmental determinants. Populations living at far northern latitudes are vulnerable to vitamin D deficiency and its health sequelae, although consumption of traditional native dietary pattern rich in fish and marine mammals may buffer the effects of reduced sunlight exposure. To date, few studies have investigated the genetics of vitamin D metabolism in circumpolar populations or considered genediet interactions with fish and n-3 fatty acid intake. Methods: We searched for genomic regions exhibiting linkage and association with circulating levels of vitamin D and parathyroid hormone (PTH) in 982 Yup’ik individuals from the Center for Alaska Native Health Research Study. We also investigated potential interactions between genetic variants and a biomarker of traditional dietary intake, the δ15N value. Results: We identified several novel regions linked with circulating vitamin D and PTH as well as replicated a previous linkage finding on 2p16.2 for vitamin D. Bioinformatic analysis revealed multiple candidate genes for both PTH and vitamin D, including CUBN, MGAT3,andNFKBIA. Targeted association analysis identified NEBL as a candidate gene for vitamin D and FNDC3B for PTH. -
X-Ray Ptychography Imaging of Human Chromosomes After Low-Dose Irradiation
Chromosome Res https://doi.org/10.1007/s10577-021-09660-7 ORIGINAL ARTICLE X-ray Ptychography Imaging of Human Chromosomes After Low-dose Irradiation Archana Bhartiya & Darren Batey & Silvia Cipiccia & Xiaowen Shi & Christoph Rau & Stanley Botchway & Mohammed Yusuf & Ian K. Robinson Received: 18 December 2020 /Revised: 15 February 2021 /Accepted: 9 March 2021 # The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract Studies of the structural and functional role of chromosomes of metaphase human B and T cells using chromosomes in cytogenetics have spanned more than hard X-ray ptychography. We have produced ‘X-ray 10 decades. In this work, we take advantage of the karyotypes’ of both heavy metal–stained and unstained coherent X-rays available at the latest synchrotron spreads to determine the gain or loss of genetic material sources to extract the individual masses of all 46 upon low-level X-ray irradiation doses due to radiation damage. The experiments were performed at the I-13 Responsible Editors: Kiichi Fukui and Toshiyuki Wako beamline, Diamond Light Source, Didcot, UK, using the phase-sensitive X-ray ptychography method. A. Bhartiya : M. Yusuf : I. K. Robinson London Centre for Nanotechnology, University College, London, UK Keywords X-ray imaging . X-ray microscopy . Karyotype . Irradiation . Chromosome structure . Mass A. Bhartiya determination Department of Chemistry, University College, London, UK A. Bhartiya : S. Botchway : M. Yusuf : I. K. Robinson (*) Introduction Research Complex at Harwell, Harwell Campus, Didcot, UK e-mail: [email protected] Chromatin, composed of DNA-protein complexes pres- D. Batey : S. Cipiccia : X. Shi : C.