scholarly journals Assessing Differential Expression Measurements by Highly Parallel Pyrosequencing and DNA Microarrays: A Comparative Study

2013 ◽  
Vol 17 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Joaquín Ariño ◽  
Antonio Casamayor ◽  
Julián Perez Pérez ◽  
Laia Pedrola ◽  
Miguel Álvarez-Tejado ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Dna Microarrays ◽  
Parallel Pyrosequencing

scholarly journals A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e103207 ◽  
Author(s):  
Zong Hong Zhang ◽  
Dhanisha J. Jhaveri ◽  
Vikki M. Marshall ◽  
Denis C. Bauer ◽  
Janette Edson ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Rna Seq

scholarly journals Effect of Anaerobiosis and Nitrate on Gene Expression in Pseudomonas aeruginosa

2005 ◽  
Vol 73 (6) ◽  
pp. 3764-3772 ◽  
Author(s):  
M. J. Filiatrault ◽  
V. E. Wagner ◽  
D. Bushnell ◽  
C. G. Haidaris ◽  
B. H. Iglewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pseudomonas Aeruginosa ◽  
Differential Expression ◽  
Dna Microarrays ◽  
Transcriptional Response ◽  
Anaerobic Growth ◽  
Differentially Expressed

ABSTRACT DNA microarrays were used to examine the transcriptional response of Pseudomonas aeruginosa to anaerobiosis and nitrate. In response to anaerobic growth, 691 transcripts were differentially expressed. Comparisons of P. aeruginosa grown aerobically in the presence or the absence of nitrate showed differential expression of greater than 900 transcripts.

Silanization of silica and glass slides for DNA microarrays by impregnation and gas phase protocols: A comparative study

2011 ◽  
Vol 31 (2) ◽  
pp. 384-390 ◽  
Author(s):  
Magali Phaner-Goutorbe ◽  
Vincent Dugas ◽  
Yann Chevolot ◽  
Eliane Souteyrand
Keyword(s):  
Comparative Study ◽  
Gas Phase ◽  
Dna Microarrays ◽  
Glass Slides

scholarly journals A comparative study of techniques for differential expression analysis on RNA-Seq data

2014 ◽  
Author(s):  
Zong Hong Zhang ◽  
Dhanisha J. Jhaveri ◽  
Vikki M. Marshall ◽  
Denis C. Bauer ◽  
Janette Edson ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
False Positives ◽  
Sequencing Depth ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Next Generation Sequencing Technology

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

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