GENCODE: reference annotation for the human and mouse genomes in 2023

Frankish, Adam; Carbonell-Sala, Sílvia; Diekhans, Mark; Jungreis, Irwin; Loveland, Jane; Mudge, Jonathan M.; Sisu, Cristina; Wright, James C.; Arnan, Carme; Barnes, If; Banerjee, Abhimanyu; Bennett, Ruth; Berry, Andrew; Bignell, Alexandra; Boix, Carles; Calvet, Ferriol; Cerdán-Vélez, Daniel; Cunningham, Fiona; Davidson, Claire; Donaldson, Sarah; Dursun, Cagatay; Fatima, Reham; Giorgetti, Stefano; Girón, Carlos García; González, José M.; Hardy, Matthew P.; Harrison, Peter W.; Hourlier, Thibaut; Hollis, Zoe; Hunt, Toby; James, Benjamin; Jiang, Yunzhe; Johnson, Rory; Kay, Mike; Lagarde, Julien; Martin, Fergal J.; Gómez, Laura Martínez; Nair, Surag; Ni, Pengyu; Pozo, Fernando Campo del; Ramalingam, Vivek; Ruffier, Magali; Schmitt, Bianca M; Schreiber, Jacob; Steed, Emily; Suner, Marie-Marthe; Sumathipala, Dulika; Sycheva, Irina; Uszczyńska-Ratajczak, Barbara; Wass, Elizabeth; Yang, Yucheng; Yates, Andy; Zafrulla, Zahoor; Choudhary, Jyoti; Gerstein, Mark; Guigó, Roderic; Hubbard, Tim; Kellis, M; Kundaje, Anshul; Paten, Benedict; Tress, Michael L.; Flicek, Paul

doi:10.1093/nar/gkac1071

Cited by 173 publications

(76 citation statements)

References 56 publications

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“…To find a larger statistical test, we used gnomAD’s LOF-intolerant genes. We overlapped the genes listed as for LOF-intolerance with the Gencode CDS regions [ 41 ] of these genes. We intersected the 1000Genomes callsets with these regions and generated statistics on the number of frameshift indels observed.…”

Section: Resultsmentioning

confidence: 99%

Improving variant calling using population data and deep learning

et al. 2023

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Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

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Section: Resultsmentioning

confidence: 99%

Improving variant calling using population data and deep learning

et al. 2023

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“…Raw fastq pairs were preprocessed and removed adapter and low-quality sequences using the cutadapt program (v2.7) 71 . Preprocessed reads were aligned to the mouse genome available at Gencode M18 72 using Bowtie2 (v2.3.5) 73 with the settings for Cut&Tag 74 . Final reads were retained after removing non-uniquely mapped reads using samtools (v1.9) with “-q 20” 75 and duplicated reads using picard (v2.21.4).…”

Section: Methods Detailsmentioning

confidence: 99%

An enhancer RNA recruits MLL1 to regulate transcription ofMyb

Kim,

Diaz,

Miller

et al. 2023

Preprint

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TheMybproto-oncogene encodes the transcription factor c-MYB, which is critical for the proliferation and differentiation of hematopoietic stem and progenitor cells. Distant enhancers ofMybexpression form a hub of interactions with theMybpromoter but the regulation ofMybduring hematopoiesis is still incompletely understood. Here we identified a novel nuclearMyblong non-coding enhancer RNA (Myrlin) that originates from the -81 kb murineMybenhancer within theMyb ─ Hbs1lintergenic region.MyrlinandMybare coordinately regulated in a developmental stage-specific fashion during maturation of erythroid progenitors and upon differentiation of MEL cells. CRISPR/Cas9 genome editing of theMyrlintranscription start site at the -81kb enhancer reduced bothMyrlinandMybexpression. Deletion of theMyrlinTSS reduced occupancy by the looping protein LDB1, compromising long-range hub contacts between theMybpromoter and its enhancer and reducing RNA Pol II occupancy across theMyblocus. In contrast, silencing ofMyrlinusing CRISPRi, while similarly reducing bothMyrlinandMybexpression, left theMybenhancer hub undisturbed, revealing that chromatin looping and transcription activation ofMybcan be decoupled. Investigating a role forMyrlin,per se, we found thatMyrlininteracts with MLL1 complex, a transcriptional coactivator that plays an essential role in regulating gene expression during hematopoiesis.MyrlinCRISPRi compromised MLL1 occupancy in theMyblocus and decreased CDK9 and RNA Pol II binding.MyrlinCRISPRi further resulted in pausing of RNA Pol II in theMybfirst exon/intron. These data document that Myrlin directly participates in activatingMybtranscription by recruiting MLL1.

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“…Studies of the human genome account for a large proportion of transcriptomic data being generated today, and several annotation databases are available for these studies. For our evaluation of ORFanage on the human genome, we used both the RefSeq (release 110) and GENCODE (release 41) annotations 1, 2 .…”

Section: Datasetsmentioning

confidence: 99%

“…Approximately 20,000 protein-coding genes have been annotated for the human genome [1][2][3][4][5] . While a single isoform is often the source of the dominant protein [6][7][8] , many human gene loci express isoforms that encode different protein sequences, some of which may be tissue-specific 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage

Varabyou

Erdogdu

Salzberg

et al. 2023

Preprint

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ORFanage is a system designed to assign open reading frames (ORFs) to both known and novel gene transcripts while maximizing similarity to annotated proteins. The primary intended use of ORFanage is the identification of ORFs in the assembled results of RNA sequencing (RNA-seq) experiments, a capability that most transcriptome assembly methods do not have. Our experiments demonstrate how ORFanage can be used to find novel protein variants in RNA-seq datasets, and to improve the annotations of ORFs in tens of thousands of transcript models in the RefSeq and GENCODE human annotation databases. Through its implementation of a highly accurate and efficient pseudo-alignment algorithm, ORFanage is substantially faster than other ORF annotation methods, enabling its application to very large datasets. When used to analyze transcriptome assemblies, ORFanage can aid in the separation of signal from transcriptional noise and the identification of likely functional transcript variants, ultimately advancing our understanding of biology and medicine.

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GENCODE: reference annotation for the human and mouse genomes in 2023

Cited by 173 publications

References 56 publications

Improving variant calling using population data and deep learning

Improving variant calling using population data and deep learning

An enhancer RNA recruits MLL1 to regulate transcription ofMyb

Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage

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