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      Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA

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          Abstract

          Background

          Transcriptome analysis is of great interest in clinical research, where significant differences between individuals can be translated into biomarkers of disease. Although next generation sequencing provides robust, comparable and highly informative expression profiling data, with several million of tags per blood sample, reticulocyte globin transcripts can constitute up to 76% of total mRNA compromising the detection of low abundant transcripts. We have removed globin transcripts from 6 human whole blood RNA samples with a human globin reduction kit and compared them with the same non-reduced samples using deep Serial Analysis of Gene Expression.

          Results

          Globin tags comprised 52-76% of total tags in our samples. Out of 21,633 genes only 87 genes were detected at significantly lower levels in the globin reduced samples. In contrast, 11,338 genes were detected at significantly higher levels in the globin reduced samples. Removing globin transcripts allowed us to also identify 2112 genes that could not be detected in the non-globin reduced samples, with roles in cell surface receptor signal transduction, G-protein coupled receptor protein signalling pathways and neurological processes.

          Conclusions

          The reduction of globin transcripts in whole blood samples constitutes a reproducible and reliable method that can enrich data obtained from next generation sequencing-based expression profiling.

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          Most cited references20

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          Small-sample estimation of negative binomial dispersion, with applications to SAGE data.

          We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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            Non-coding RNAs: regulators of disease.

            For 50 years the term 'gene' has been synonymous with regions of the genome encoding mRNAs that are translated into protein. However, recent genome-wide studies have shown that the human genome is pervasively transcribed and produces many thousands of regulatory non-protein-coding RNAs (ncRNAs), including microRNAs, small interfering RNAs, PIWI-interacting RNAs and various classes of long ncRNAs. It is now clear that these RNAs fulfil critical roles as transcriptional and post-transcriptional regulators and as guides of chromatin-modifying complexes. Here we review the biology of ncRNAs, focusing on the fundamental mechanisms by which ncRNAs facilitate normal development and physiology and, when dysfunctional, underpin disease. We also discuss evidence that intergenic regions associated with complex diseases express ncRNAs, as well as the potential use of ncRNAs as diagnostic markers and therapeutic targets. Taken together, these observations emphasize the need to move beyond the confines of protein-coding genes and highlight the fact that continued investigation of ncRNA biogenesis and function will be necessary for a comprehensive understanding of human disease.
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              Prognostically useful gene-expression profiles in acute myeloid leukemia.

              In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis. Copyright 2004 Massachusetts Medical Society
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2012
                18 January 2012
                : 13
                : 28
                Affiliations
                [1 ]Center for Human and Clinical Genetics, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, The Netherlands
                [2 ]Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, 2333ZC Leiden, The Netherlands
                Article
                1471-2164-13-28
                10.1186/1471-2164-13-28
                3275489
                22257641
                f65adcee-6f7c-459c-8433-68bf6b389c3d
                Copyright ©2012 Mastrokolias et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 July 2011
                : 18 January 2012
                Categories
                Research Article

                Genetics
                Genetics

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