HaemAtlas statistics

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Citations per year

Number of citations per year for the bioinformatics software tool HaemAtlas
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Tool usage distribution map

This map represents all the scientific publications referring to HaemAtlas per scientific context
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Protocols

HaemAtlas specifications

Information


Unique identifier OMICS_15071
Name HaemAtlas
Interface Web user interface
Restrictions to use None
Input data Names of the genes.
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Nicholas Watkins

Publication for HaemAtlas

HaemAtlas citations

 (18)
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Mediator Kinase Phosphorylation of STAT1 S727 Promotes Growth of Neoplasms With JAK STAT Activation

2017
PMCID: 5832629
PMID: 29239838
DOI: 10.1016/j.ebiom.2017.11.013
call_split See protocol

[…] Set Enrichment Analysis (GSEA (, ) version 1) was run with rank-ordered DE gene lists for CA or ruxolitinib treatment. The signatures used for GSEA included: TENEDINI_MEGAKARYOCYTE_MARKERS (MSigDB), HaemAtlas MK-specific gene set (extracted from ArrayExpress accession E-TABM-633) (), and CFU-MK and MK specific gene set (extracted from GEO accession GSE24759) () Gene lists were submitted to DAVID […]

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Genetic Evidence for Erythrocyte Receptor Glycophorin B Expression Levels Defining a Dominant Plasmodium falciparum Invasion Pathway into Human Erythrocytes

2017
Infect Immun
PMCID: 5607420
PMID: 28760933
DOI: 10.1128/IAI.00074-17
call_split See protocol

[…] A set of 314 host genes with known or potential roles in parasite invasion was defined by combining erythroblast-specific genes from HaemAtlas () with blood group genes from the International Society of Blood Transfusion (http://www.isbtweb.org/). Whole-blood raw transcriptomic data from 61 healthy Beninese children () were obtaine […]

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Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees

2016
BMC Proc
PMCID: 5133527
PMID: 27980617
DOI: 10.1186/s12919-016-0011-3
call_split See protocol

[…] D8+), helper (CD4+) T-, and B-lymphocytes and monocytes in peripheral blood mononuclear cells from each individual was achieved by identifying gene expression signatures for different cell types from HaemAtlas [] using 4879 probes that overlapped with the GAW19 data, and the quadratic programming algorithm of Gong et al. [] as implemented in the R package CellMix []. Examination of differences in […]

library_books

Disease specific classification using deconvoluted whole blood gene expression

2016
Sci Rep
PMCID: 5011717
PMID: 27596246
DOI: 10.1038/srep32976

[…] lies linear support vector regression, a machine learning approach highly robust with respect to noise, to deconvolve the mixture. We implemented the DSA algorithm in R and utilized cell markers from HaemAtlas project. The marker list represented by array probes were downloaded from R package CellMix, and mapped to gene symbols. The CIBERSORT R package and its associated leukocyte signature matrix […]

library_books

Large scale production of megakaryocytes from human pluripotent stem cells by chemically defined forward programming

2016
Nat Commun
PMCID: 4829662
PMID: 27052461
DOI: 10.1038/ncomms11208

[…] We used gsea2–2.0.13 with datasets for megakaryocytes (n=4) and other blood cells (n=46) from the Haematlas study (E-TABM-633; Illumina Human-6v2 array). […]

library_books

A draft network of ligand–receptor mediated multicellular signalling in human

2015
Nat Commun
PMCID: 4525178
PMID: 26198319
DOI: 10.1038/ncomms8866

[…] at 10 TPM, we compared the set of gene products not detected in the FANTOM5 B-cell transcriptome but present in the proteome data of Kim et al. to a high quality microarray data set collected for the Haematlas project. We found that only 4% of these gene products (8/192 with unique probes on the arrays) had detectable transcripts, in contrast to 78% of gene products detected by FANTOM5 at 10 TPM ( […]


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HaemAtlas institution(s)
Department of Haematology, University of Cambridge, National Health Service Blood and Transplant, Cambridge, UK; Medical Research Council Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge, UK; European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK; Structural Studies, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK; Wellcome Trust/Medical Research Council Building, Cambridge, UK; Division of Immunity and Infection, Medical Research Council Centre for Immune Regulation, University of Birmingham, Birmingham, UK; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK; Department of Haematology, Addenbrooke’s Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK; Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, UK; Department of Experimental Immunohaematology, Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands
HaemAtlas funding source(s)
This work was supported by the 6th Framework Programme of the European Union (LSHMCT-2004-503485), the National Institute for Health Research to National Health Service Blood and Transplant, the National Institute for Health Research Biomedical Research grant for Cambridge University Hospitals National Health Service Foundation Trust, and the Wellcome Trust.

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