Ensembl Genomes 2020—enabling non-vertebrate genomic research

Howe, Kevin; Contreras‐Moreira, Bruno; Silva, Nishadi De; Maslen, G.; Akanni, Wasiu; Allen, James; Álvarez-Jarreta, Jorge; Barba, Matthieu; Bolser, Dan; Cambell, Lahcen; Carbajo, Manuel; Chakiachvili, Marc; Christensen, Mikkel; Cummins, Carla; Cuzick, Alayne; Davis, Paul A.; Fexová, Silvie; Gall, Astrid; George, Nancy; Gil, Laurent; Gupta, Parul; Hammond‐Kosack, K. E.; Haskell, Erin; Hunt, Sarah; Jaiswal, Pankaj; Janacek, Sophie H; Kersey, Paul J.; Langridge, Nicholas; Maheswari, Uma; Maurel, Thomas; McDowall, Mark D.; Moore, Benjamin; Muffato, Matthieu; Naamati, Guy; Naithani, Sushma; Olson, Andrew; Papatheodorou, Irene; Patrício, Mateus; Paulini, Michael; Pedro, Helder; Perry, Emily; Preece, Justin; Rosello, Marc; Russell, Matthew; Sitnik, Vasily; Staines, Daniel M.; Stein, Joshua C.; Tello-Ruiz, Marcela K.; Trevanion, Stephen J.; Urban, Martin; Wei, Sharon; Ware, Doreen; Williams, Gary W.; Yates, Andy; Flicek, Paul

doi:10.1093/nar/gkz890

Cited by 415 publications

(363 citation statements)

References 28 publications

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“…The first is a linear graph without variation based on the E. coli reference genome (strain: K-12 substr. MG1655, ASM584v2 [13]). The other two are variation graphs taken from the PaSGAL evaluations [15]: they are based on the Leukocyte Receptor Complex (LRC, with 1 099 856 nodes and 1 144 498 edges), and the Major Histocompatibility Complex (MHC1, with 5 138 362 nodes and 5 318 019 edges).…”

Section: Settingmentioning

confidence: 99%

AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov

Bichsel

Mustafa

et al. 2020

Preprint

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We present an algorithm for the optimal alignment of sequences to genome graphs. It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A paradigm with a novel domain-specific heuristic function that accounts for the upcoming subsequence in the query to be aligned, resulting in a provably optimal alignment algorithm called AStarix. Experimental evaluation of AStarix shows that it is 1-2 orders of magnitude faster than state-of-the-art optimal algorithms on the task of aligning Illumina reads to reference genome graphs. Implementations and evaluations are available at https://github.com/eth-sri/astarix.

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Section: Settingmentioning

confidence: 99%

AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov

Bichsel

Mustafa

et al. 2020

Preprint

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“…Many of the features which make expression so interesting also complicate its bioinformatics analysis, especially for multicellular organisms such as animals. While databases of reference genomes, providing standardized annotations and comparative frameworks within and between species, are well established [1][2][3] , this is not the case for expression data. There is not any concept of a "reference expression" for a species, and comparisons are complicated by the need to define comparable samples and conditions.…”

Section: Introductionmentioning

confidence: 99%

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bastian

Roux

Niknejad³

et al. 2020

Preprint

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Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced from multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as "healthy" or not). Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Several tools are available, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

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“…Reads were mapped to GRCm38.p5 (Ensemble, GCA_000001635.7) (Howe et al 2020) using STAR (Dobin et al 2013), with the GRCm38.91 annotation file (Ensembl). Default parameters were used, except for the following: multi-sample 2-pass, with default settings on first pass and sjdbFileChrStartEnd (for novel splice junctions), ScoreMinOverLread=0.4, MatchNminOverLread=0.4, MismatchNmax=5 on second pass (these parameters gave the most consistent unmapped reads % across all 24 TRAPseq samples).…”

Section: Methodsmentioning

confidence: 99%

Analysis of FGF20-regulated genes in organ of Corti progenitors by translating ribosome affinity purification

Yang¹,

Stout²,

Rauchman³

et al. 2020

Preprint

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BackgroundUnderstanding the mechanisms that regulate hair cell (HC) differentiation in the organ of Corti (OC) is essential to designing genetic therapies for hearing loss due to HC loss or damage. We have previously identified Fibroblast Growth Factor 20 (FGF20) as having a key role in HC and supporting cell differentiation in the mouse OC. To investigate the genetic landscape regulated by FGF20 signaling in OC progenitors, we employ Translating Ribosome Affinity Purification combined with Next Generation mRNA Sequencing (TRAPseq) in the Fgf20 lineage.ResultsWe show that TRAPseq targeting OC progenitors effectively enriched for mRNA within this rare cell population. TRAPseq identified differentially expressed genes downstream of FGF20, including Etv4, Etv5, Etv1, Dusp6, Hey1, Hey2, Heyl, Tectb, Fat3, Cpxm2, Sall1, Sall3, and cell cycle regulators such as Cdc20. Analysis of Cdc20 conditional-null mice identified decreased cochlea length, while analysis of Sall1-ΔZn2-10 mice, which harbor a mutation that causes Townes-Brocks syndrome, identified a decrease in outer hair cell number.ConclusionsWe present two datasets: genes with enriched expression in OC progenitors, and genes regulated by FGF20 in the embryonic day 14.5 cochlea. We validate select differentially expressed genes via in situ hybridization and in vivo functional studies in mice.Key findingsTranslating Ribosome Affinity Purification (TRAP) with Fgf20-Cre enriches for prosensory cell mRNATRAP combined with RNAseq identifies genes downstream of FGF20 during prosensory cell differentiationFGF20 regulates Sall1, gene implicated in human sensorineural hearing lossGrant Sponsor and NumberNational Institute on Deafness and Other Communication Disorders – DC017042 (DMO) Washington University Institute of Clinical and Translational Sciences and National Center for Advancing Translational Sciences – CTSA grant UL1TR002345 (JIT471 to DMO) March of Dimes – 6-FY13-127 (MR)

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Ensembl Genomes 2020—enabling non-vertebrate genomic research

Cited by 415 publications

References 28 publications

AStarix: Fast and Optimal Sequence-to-Graph Alignment

AStarix: Fast and Optimal Sequence-to-Graph Alignment

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Analysis of FGF20-regulated genes in organ of Corti progenitors by translating ribosome affinity purification

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