scholarly journals RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

2008 ◽  
Vol 18 (9) ◽  
pp. 1509-1517 ◽  
Author(s):  
J. C. Marioni ◽  
C. E. Mason ◽  
S. M. Mane ◽  
M. Stephens ◽  
Y. Gilad
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Technical Reproducibility

scholarly journals Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

2015 ◽  
Vol 23 (3) ◽  
pp. 617-626 ◽  
Author(s):  
Nophar Geifman ◽  
Sanchita Bhattacharya ◽  
Atul J Butte
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biomedical Literature ◽  
Cytokine Gene Expression ◽  
Future Research ◽  
Cytokine Gene ◽  
Medical Subject Headings ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Subject Headings

Abstract Objective Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. Results We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. Discussion We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Conclusion Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

scholarly journals Analysis of the Early Immune Response to Infection by Infectious Bursal Disease Virus in Chickens Differing in Their Resistance to the Disease

2014 ◽  
Vol 89 (5) ◽  
pp. 2469-2482 ◽  
Author(s):  
Jacqueline Smith ◽  
Jean-Remy Sadeyen ◽  
Colin Butter ◽  
Pete Kaiser ◽  
David W. Burt
Keyword(s):  
Gene Expression ◽  
Disease Virus ◽  
Infectious Bursal Disease Virus ◽  
Host Response ◽  
Infectious Bursal Disease ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Antiviral Genes ◽  
Bursal Disease ◽  
Comprehensive Study

ABSTRACTChicken whole-genome gene expression arrays were used to analyze the host response to infection by infectious bursal disease virus (IBDV). Spleen and bursal tissue were examined from control and infected birds at 2, 3, and 4 days postinfection from two lines that differ in their resistance to IBDV infection. The host response was evaluated over this period, and differences between susceptible and resistant chicken lines were examined. Antiviral genes, includingIFNA,IFNG,MX1,IFITM1,IFITM3, andIFITM5, were upregulated in response to infection. Evaluation of this gene expression data allowed us to predict several genes as candidates for involvement in resistance to IBDV.IMPORTANCEInfectious bursal disease (IBD) is of economic importance to the poultry industry and thus is also important for food security. Vaccines are available, but field strains of the virus are of increasing virulence. There is thus an urgent need to explore new control solutions, one of which would be to breed birds with greater resistance to IBD. This goal is perhaps uniquely achievable with poultry, of all farm animal species, since the genetics of 85% of the 60 billion chickens produced worldwide each year is under the control of essentially two breeding companies. In a comprehensive study, we attempt here to identify global transcriptomic differences in the target organ of the virus between chicken lines that differ in resistance and to predict candidate resistance genes.

Cytogenetic Analysis of Multiple Myeloma Identifies Cytogenetic Alterations Implicated in Disease Complexity and Progression

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3360-3360
Author(s):  
Erik Wendlandt ◽  
Guido J. Tricot ◽  
Benjamin Darbro ◽  
Fenghuang Zhan
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cathepsin B ◽  
Copy Number ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Copy Number Variations ◽  
Expression Arrays ◽  
Gene Expression Arrays

Abstract Background: Multiple myeloma is the second most common blood borne neoplasia, accounting for nearly 10% of all diagnosed hematologic malignancies and has a disproportionately high incidence in elderly populations. Here we explored copy number variations using the high fidelity CytoScan HD arrays to develop a detailed map of copy number variations and identify novel mediators of disease progression. The results from CytoScan HD microarrays provide a detailed view of the entire genome with a resolution up to 25kb. Furthermore, 750,000 single-nucleotide polymorphisms are included and the array provides information about loss of heterozygosity and uniparental disomy. Materials and methods: CytoScan HD arrays were performed on 97 myeloma patient samples to identify cytogenetic regions important to the development and progression of the disease. Gene expression profiles from 351 patients were analyzed to identify genes with a change in gene expression of 1.5 fold or more. Data from CytoScan and gene expression arrays was combined to perform chromosomal positional enrichment analysis to identify cytogenetic driver lesions, or lesions that provide a small, but significant growth and survival advantage to the cell. Furthermore, Kaplan-Meier, log-rank test and Hazard ratio analyses were performed to identify gene within the driver lesions that have a significant impact on survival when dysregulated. Results: The results from the CytoScan HD analysis closely mirrored what has been shown by FISH and SNP arrays, with gains to the odd numbered chromosomes, specifically 3, 5, 7, 9, 11, 15 and 17 as well as losses to chromosomes 1p and 13. Interestingly, we identified gains to a small region within chromosome 8p, contrary to published reports demonstrating a large scale loss of this region. We identified numerous genes within this region that are important for survival and their overexpression resulted in a decreased progression free survival. For example, Cathepsin B (CTSB) is encoded for in chromosome 8p22-p21 with an increased gene expression of at least 1.5 fold over normal controls, among others. Furthermore, Cathepsin B, a cysteine protease, has been linked to cancer of the ileum, suggesting that a similar role may be present within myeloma. We then integrated the 97 copy number profiles results with 351 myeloma gene expression profiles to identify cytogenetic driver lesions in myeloma important for disease development, progression and poor clinical outcome. Chromosomal positional enrichment analysis was employed to identify global myeloma cytogenetic driver aneuploidies as well as develop unique cytogenetic copy number profiles. Our results identified portions of chromosomes 1q, 3, 8p, 9, 13q and 16q, among others, as important driver lesions with changes to these regions providing growth advantages to the cell. Furthermore, our analysis identified five unique cytogenetic classifications based on common cytogenetic lesions. We continue to explore these driver regions to identify lesions important for the oncogenic properties of the larger regions. Conclusion: The data presented here represents a novel and highly sensitive approach for the identification of novel copy number variations and driver lesions. Furthermore, correlations between copy number variations and gene expression arrays identified novel targets important for disease progression and patient survival. CytoScan HD arrays in conjunction with gene expression analysis provided a high resolution image of important cytogenetic lesions in myeloma and identified potentially important therapeutic targets for drug development. Further work is needed to validate our findings and determine the therapeutic efficacy of the identified targets. Disclosures No relevant conflicts of interest to declare.

scholarly journals Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Michael Kenn ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Michael Cibena ◽  
Heinz Kölbl ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Breast Cancer Subtype ◽  
Good Deal ◽  
Cancer Biomarkers ◽  
Breast Cancer Patients ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Normalization Methods ◽  
Golden Standard

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

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