Ensembl 2019

Cunningham, Fiona; Achuthan, Premanand; Akanni, Wasiu; Allen, James; Amode, M Ridwan; Armean, Irina M.; Bennett, Ruth; Bhai, Jyothish; Billis, Konstantinos; Boddu, Sanjay; Cummins, Carla; Davidson, Claire; Dodiya, Kamalkumar; Gall, Astrid; Girón, Carlos García; Gil, Laurent; Grego, Tiago; Haggerty, Leanne; Haskell, Erin; Hourlier, Thibaut; Izuogu, Osagie; Janacek, Sophie H; Juettemann, Thomas; Kay, Mike; Laird, Matthew R.; Lavidas, Ilias; Liu, Zhicheng; Loveland, Jane; Marugán, José Carlos; Maurel, Thomas; McMahon, Aoife; Moore, Benjamin; Morales, Joannella; Mudge, Jonathan M.; Nuhn, Michael; Ogeh, Denye; Parker, Anne; Parton, Andrew; Patrício, Mateus; Salam, Ahamed Imran Abdul; Schmitt, Bianca M; Schuilenburg, Helen; Sheppard, Dan; Sparrow, Helen; Stapleton, Eloise; Szuba, M.; Taylor, Kieron; Threadgold, Glen; Thormann, Anja; Vullo, Alessandro; Walts, Brandon; Winterbottom, Andrea; Zadissa, Amonida; Chakiachvili, Marc; Frankish, Adam; Hunt, Sarah; Kostadima, Myrto; Langridge, Nicholas; Martin, Fergal J.; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Staines, Daniel M.; Trevanion, Stephen J.; Aken, Bronwen; Yates, Andy; Zerbino, Daniel R.; Flicek, Paul

doi:10.1093/nar/gky1113

Cited by 900 publications

(803 citation statements)

References 43 publications

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“…The techniques described for processing the ChIP-seq and ATAC-seq reads are based on ENCODE/Roadmap guidelines with a few modifications (Gjoneska et al, 2015;Landt et al, 2012;Roadmap Epigenomics et al, 2015). The reads were aligned to the mm10 (GRCm38) genome (Cunningham et al, 2019) using Bowtie 2 (Langmead and Salzberg, 2012). The genome fasta files were first indexed and then aligned using the command: bowtie2 -x /directory/with/reference/genome/rootfilename --fast -U /directoryTree/fastq/SAMPLE.fastq -S /directoryTree/fastq/SAMPLE.sam, where SAMPLE was replaced with a unique sample identifier.…”

Section: Aligning Readsmentioning

confidence: 99%

“…Equal amounts of D. Melanogaster DNA were spiked in to ChIP-seq samples. In addition to aligning to the mouse genome, we aligned reads to the D. Melanogaster dm6 genome (Cunningham et al, 2019). To provide a sense of total ChIP-seq signal strength, the proportion of reads aligning to the dm6 genome were compared to the proportion aligning to the mouse genome (Orlando et al, 2014).…”

Section: Spike-in Controlsmentioning

confidence: 99%

“…Peaks are annotated based on their mapping to the nearest transcription start site, which was performed using BEDTools closestBed command (Quinlan and Hall, 2010) based on ENSEMBL gene annotations, GRCm38 version 79 (Cunningham et al, 2019).…”

Section: Peak Annotationmentioning

confidence: 99%

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Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals

Yang

et al. 2019

Preprint

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Short-title: Epigenetic noise and cell identity loss during agingOne Sentence Summary: The act of repairing DNA breaks induces chromatin reorganization and a loss of cell identity that may contribute to mammalian aging 1 SUMMARY All living things experience entropy, manifested as a loss of inherited genetic and epigenetic information over time. As budding yeast cells age, epigenetic changes result in a loss of cell identity and sterility, both hallmarks of yeast aging. In mammals, epigenetic information is also lost over time, but what causes it to be lost and whether it is a cause or a consequence of aging is not known. Here we show that the transient induction of genomic instability, in the form of a low number of non-mutagenic DNA breaks, accelerates many of the chromatin and tissue changes seen during aging, including the erosion of the epigenetic landscape, a loss of cellular identity, advancement of the DNA methylation clock and cellular senescence. These data support a model in which a loss of epigenetic information is a cause of aging in mammals.2

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Section: Aligning Readsmentioning

confidence: 99%

Section: Spike-in Controlsmentioning

confidence: 99%

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Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals

Yang

et al. 2019

Preprint

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“…Genomic data including transcript and open reading frame (ORF) boundaries were obtained from Ensembl ( https://www.ensembl.org/index.html ) 72 , with the exceptions of organisms Saccharomyces cerevisiae C288, for which data were obtained from the Saccharomyces Genome Database ( https://www.yeastgenome.org/ ) 59,73 and additional published transcript and ORF boundaries were used 74,75 , and Escherichia coli K-12 MG1655, where all data were obtained from the RegulonDB database ( http://regulondb.ccg.unam.mx/ ) 76 (Table S1-1). Processed raw RNA sequencing Star counts were obtained from the Digital Expression Explorer V2 database ( http://dee2.io/index.html ) 31 and filtered for experiments that passed quality control.…”

Section: Datamentioning

confidence: 99%

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec

Buric

Muhammad

et al. 2019

Preprint

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Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets to learn the genetic regulatory code controlling mRNA expression in 7 model organisms ranging from bacteria to human.We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we propose a holistic system that spans all regions of the gene structure and is required to analyse, understand, and design any future gene expression systems.

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“…The names of functions and orthologous proteins are linked to the description pages of the Gene Ontology (GO) database (The Gene Ontology Consortium, 2019) and the Ensembl database (Cunningham et al, 2019), respectively. Gene symbol, UniProtKB-AC ID, Ensembl protein and gene ID, and Entrez Gene ID are also shown with a link to the description page of a related database.…”

Section: Of 18mentioning

confidence: 99%

Using INTERSPIA to Explore the Dynamics of Protein‐Protein Interactions Among Multiple Species

Kwon

Lee

Kim

et al. 2019

CP in Bioinformatics

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INTER‐Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein‐protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server‐side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy‐to‐use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Running INTERSPIA using a list of input proteins

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Ensembl 2019

Cited by 900 publications

References 43 publications

Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals

Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Using INTERSPIA to Explore the Dynamics of Protein‐Protein Interactions Among Multiple Species

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