A
Anna S E Cuomo
Researcher at European Bioinformatics Institute
Publications - 12
Citations - 821
Anna S E Cuomo is an academic researcher from European Bioinformatics Institute. The author has contributed to research in topics: Expression quantitative trait loci & Population. The author has an hindex of 7, co-authored 11 publications receiving 270 citations. Previous affiliations of Anna S E Cuomo include Wellcome Trust Sanger Institute.
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Journal ArticleDOI
Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.
Christoph Muus,Malte D Luecken,Gökcen Eraslan,Lisa Sikkema,Avinash Waghray,Graham Heimberg,Yoshihiko Kobayashi,Eeshit Dhaval Vaishnav,Eeshit Dhaval Vaishnav,Ayshwarya Subramanian,Christopher Smillie,Karthik A. Jagadeesh,Elizabeth Thu Duong,Evgenij Fiskin,Elena Torlai Triglia,Meshal Ansari,Peiwen Cai,Brian M. Lin,Justin Buchanan,Sijia Chen,Jian Shu,Adam L. Haber,Adam L. Haber,Hattie Chung,Daniel T. Montoro,Taylor Adams,Hananeh Aliee,Samuel J. Allon,Samuel J. Allon,Samuel J. Allon,Zaneta Andrusivova,Ilias Angelidis,Orr Ashenberg,Kevin Bassler,Christophe Bécavin,Inbal Benhar,Joseph Bergenstråhle,Ludvig Bergenstråhle,Liam Bolt,Emelie Braun,Linh T. Bui,Steven Callori,Mark Chaffin,Evgeny Chichelnitskiy,Joshua Chiou,Thomas M. Conlon,Michael S. Cuoco,Anna S E Cuomo,Marie Deprez,Grant Duclos,Denise Fine,David S. Fischer,Shila Ghazanfar,Astrid Gillich +53 more
TL;DR: In this paper, cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues was assessed.
Journal ArticleDOI
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression
Anna S E Cuomo,Daniel D Seaton,Davis J. McCarthy,Davis J. McCarthy,Iker Martinez,Marc Jan Bonder,Marc Jan Bonder,Jose Garcia-Bernardo,Shradha Amatya,Pedro Madrigal,Abigail Isaacson,Florian Buettner,Andrew J Knights,Kedar Nath Natarajan,Kedar Nath Natarajan,Ludovic Vallier,Ludovic Vallier,John C. Marioni,John C. Marioni,John C. Marioni,Mariya Chhatriwala,Oliver Stegle,Oliver Stegle +22 more
TL;DR: Induced pluripotent stem cells from 125 donors are exploited to track gene expression changes and expression quantitative trait loci at single cell resolution during in vitro endoderm differentiation to identify molecular markers that are predictive of differentiation efficiency of individual lines.
Journal ArticleDOI
Computational principles and challenges in single-cell data integration.
Ricard Argelaguet,Ricard Argelaguet,Anna S E Cuomo,Anna S E Cuomo,Oliver Stegle,Oliver Stegle,John C. Marioni,John C. Marioni,John C. Marioni +8 more
TL;DR: In this article, a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription are reviewed, as the number of single-cell experiments with multiple data modalities increases.
Journal ArticleDOI
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Julie Jerber,Daniel D Seaton,Anna S E Cuomo,Natsuhiko Kumasaka,James Haldane,Juliette Steer,Minal Patel,Daniel Pearce,Malin Andersson,Marc Jan Bonder,Ed Mountjoy,Maya Ghoussaini,Madeline A. Lancaster,John C. Marioni,John C. Marioni,John C. Marioni,Florian T. Merkle,Daniel J. Gaffney,Oliver Stegle +18 more
TL;DR: In this article, the authors used an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points.
Posted ContentDOI
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Julie Jerber,Daniel D Seaton,Anna S E Cuomo,Natsuhiko Kumasaka,James Haldane,Juliette Steer,Minal Patel,Daniel Pearce,Malin Andersson,Marc Jan Bonder,Ed Mountjoy,Maya Ghoussaini,Madeline A. Lancaster,John C. Marioni,John C. Marioni,John C. Marioni,Florian T. Merkle,Oliver Stegle,Daniel J. Gaffney +18 more
TL;DR: This study uses an efficient pooling strategy to differentiate 215 iPS cell lines towards a midbrain neural fate, and profiles over 1 million cells sampled across three differentiation timepoints using single cell RNA sequencing.