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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
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% |