scholarly journals Re-Annotator: Annotation Pipeline for Microarrays

2015 ◽  
Author(s):  
Janine Arloth ◽  
Daniel Magnus Bader ◽  
Simone Röh ◽  
Andre Altmann
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
High Throughput ◽  
Gene Expression Array ◽  
Expression Array ◽  
Annotation Pipeline ◽  
Gene Expression Microarrays ◽  
Array Probe ◽  
Expression Microarrays ◽  
High Throughput Gene Expression

Background: Microarray technologies are established approaches for high throughput gene expression, methylation and genotyping analysis. An accurate mapping of the array probes is essential to generate reliable biological findings. Manufacturers typically provide incomplete and outdated annotation tables, which often rely on older genome and transcriptome versions differing substantially from up-to-date sequence databases. Results: Here, we present the Re-Annotator, a re-annotation pipeline for microarrays. It is primarily designed for gene expression microarrays but can be adapted to other types of microarrays. The Re-Annotator is based on a custom-built mRNA reference, used to identify the positions of gene expression array probe sequences. A comparison of our re-annotation of the Human-HT12-v4 microarray to the manufacturer's annotation led to over 25% differently interpreted probes. Conclusions: A thorough re-annotation of probe information is crucial to any microarray analysis. The Re-Annotator pipeline consists of Perl and Shell scripts, freely available at (http://sourceforge.net/projects/reannotator). Re-annotation files for Illumina microarrays Human HT-12 v3/v4 and MouseRef-8 v2 are available as well.

An Introduction to Text Analysis in Genomics

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Computational Methods ◽  
High Throughput ◽  
Data Interpretation ◽  
Genome Project ◽  
Biological Data ◽  
Data Sets ◽  
Gene Expression Microarrays ◽  
Small Collection ◽  
Expression Microarrays

The February 16th, 2001 issue of Science magazine announced the completion of the human genome project—making the entire nucleotide sequence of the genome available (Venter, Adams et al. 2001). For the first time a comprehensive data set was available with nucleotide sequences for every gene. This marked the beginning of a new era, the ‘‘genomics’’ era, where molecular biological science began a shift from the investigation of single genes towards the investigation of all genes in an organism simultaneously. Alongside the completion of the genome project came the introduction of new high throughput experimental approaches such as gene expression microarrays, rapid single nucleotide polymorphism detection, and proteomics methods such as yeast two hybrid screens (Brown and Botstein 1999; Kwok and Chen 2003; Sharff and Jhoti 2003; Zhu, Bilgin et al. 2003). These methods permitted the investigation of hundreds if not thousands of genes simultaneously. With these high throughput methods, the limiting step in the study of biology began shifting from data collection to data interpretation. To interpret traditional experimental results that addressed the function of only a single or handful of genes, investigators needed to understand only those few genes addressed in the study in detail and perhaps a handful of other related genes. These investigators needed to be familiar with a comparatively small collection of peer-reviewed publications and prior results. Today, new genomics experimental assays, such as gene expression microarrays, are generating data for thousands of genes simultaneously. The increasing complexity and sophistication of these methods makes them extremely unwieldy for manual analysis since the number and diversity of genes involved exceed the expertise of any single investigator. The only practical solution to analyzing these types of data sets is using computational methods that are unhindered by the volume of modern data. Bioinformatics is a new field that emphasizes computational methods to analyze such data sets (Lesk 2002). Bioinformatics combines the algorithms and approaches employed in computer science and statistics to analyze, understand, and hypothesize about the large repositories of collected biological data and knowledge.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals Integrated analysis using methylation and gene expression microarrays reveals PDE4C as a prognostic biomarker in human glioma

2014 ◽  
Vol 32 (1) ◽  
pp. 250-260 ◽  
Author(s):  
ZHAOSHI BAO ◽  
YING FENG ◽  
HONGJUN WANG ◽  
CHUANBAO ZHANG ◽  
LIHUA SUN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prognostic Biomarker ◽  
Integrated Analysis ◽  
Human Glioma ◽  
Gene Expression Microarrays ◽  
Expression Microarrays

Sa1165 - A Gene Expression Array as a Diagnostic Test in Pediatric Eosinophilic Esophagitis

2018 ◽  
Vol 154 (6) ◽  
pp. S-265-S-266
Author(s):  
Christopher Foster ◽  
Sydney Chatfield ◽  
Todd Jensen ◽  
Zhu Wang ◽  
Christine Finck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diagnostic Test ◽  
Eosinophilic Esophagitis ◽  
Gene Expression Array ◽  
Expression Array

scholarly journals Solar simulated ultraviolet radiation inactivates HCoV-NL63 and SARS-CoV-2 coronaviruses at environmentally relevant doses

2021 ◽  
Author(s):  
Georg T. Wondrak ◽  
Jana Jandova ◽  
Spencer J. Williams ◽  
Dominik Schenten
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Uv Light ◽  
Gene Expression Array ◽  
Array Analysis ◽  
Expression Array ◽  
Response Gene ◽  
Full Spectrum ◽  
Solar Uv ◽  
Stress Response Gene

The germicidal properties of short wavelength ultraviolet C (UVC) light are well established and used to inactivate many viruses and other microbes. However, much less is known about germicidal effects of terrestrial solar UV light, confined exclusively to wavelengths in the UVA and UVB regions. Here, we have explored the sensitivity of the human coronaviruses HCoV-NL63 and SARS-CoV-2 to solar-simulated full spectrum ultraviolet light (sUV) delivered at environmentally relevant doses. First, HCoV-NL63 coronavirus inactivation by sUV-exposure was confirmed employing (i) viral plaque assays, (ii) RT-qPCR detection of viral genome replication, and (iii) infection-induced stress response gene expression array analysis. Next, a detailed dose-response relationship of SARS-CoV-2 coronavirus inactivation by sUV was elucidated, suggesting a half maximal suppression of viral infectivity at low sUV doses. Likewise, extended sUV exposure of SARS-CoV-2 blocked cellular infection as revealed by plaque assay and stress response gene expression array analysis. Moreover, comparative (HCoV-NL63 versus SARS-CoV-2) single gene expression analysis by RT-qPCR confirmed that sUV exposure blocks coronavirus-induced redox, inflammatory, and proteotoxic stress responses. Based on our findings, we estimate that solar ground level full spectrum UV light impairs coronavirus infectivity at environmentally relevant doses. Given the urgency and global scale of the unfolding SARS-CoV-2 pandemic, these prototype data suggest feasibility of solar UV-induced viral inactivation, an observation deserving further molecular exploration in more relevant exposure models.

scholarly journals Detection ofCEBPADouble Mutants in Acute Myeloid Leukemia Using a Custom Gene Expression Array

2013 ◽  
Vol 17 (5) ◽  
pp. 395-400 ◽  
Author(s):  
Martin H. van Vliet ◽  
Pia Burgmer ◽  
Linda de Quartel ◽  
Jaap P.L. Brand ◽  
Leonie C.M. de Best ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Gene Expression Array ◽  
Expression Array ◽  
Acute Myeloid
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