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A model system for assessing and comparing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells

Overview of attention for article published in BMC Genomics, November 2012
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Title
A model system for assessing and comparing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells
Published in
BMC Genomics, November 2012
DOI 10.1186/1471-2164-13-596
Pubmed ID
Authors

Maria Bro Kloster, Anders Ellern Bilgrau, Maria Rodrigo-Domingo, Kim Steve Bergkvist, Alexander Schmitz, Mads Sønderkær, Julie Støve Bødker, Steffen Falgreen, Mette Nyegaard, Hans Erik Johnsen, Kåre Lehmann Nielsen, Karen Dybkaer, Martin Bøgsted

Abstract

Malignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Recently, gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. However, the varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. This study provides a quantitative assessment and comparison of the ability of microarrays and tag-seq to detect mRNA transcripts from malignant B-cells. A model system was established by eight serial dilutions of the malignant B-cell lymphoma cell line, OCI-Ly8, into the embryonic kidney cell line, HEK293, prior to parallel analysis by exon microarrays and tag-seq.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 7%
United Arab Emirates 1 3%
Canada 1 3%
Australia 1 3%
Unknown 24 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 24%
Student > Ph. D. Student 6 21%
Student > Master 4 14%
Other 3 10%
Student > Bachelor 2 7%
Other 7 24%
Readers by discipline Count As %
Medicine and Dentistry 9 31%
Agricultural and Biological Sciences 8 28%
Biochemistry, Genetics and Molecular Biology 5 17%
Mathematics 2 7%
Engineering 2 7%
Other 2 7%
Unknown 1 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2012.
All research outputs
#15,256,044
of 22,685,926 outputs
Outputs from BMC Genomics
#6,660
of 10,616 outputs
Outputs of similar age
#115,512
of 183,395 outputs
Outputs of similar age from BMC Genomics
#93
of 142 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 183,395 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.