Sentence examples for use of microarrays and from inspiring English sources

Sven Koglin (RWTH Aachen, Germany) provided information on the use of microarrays and RNA sequencing to investigate how the hepatic transcriptional profile of a non-model fish species, roach (Rutilus rutilus), responds to a variety of dioxin-like compounds.

The results described here provide a blueprint for the eventual use of microarrays and sequencing as routine diagnostic tools for pathogens in clinical and public health laboratories.

There are additional approaches that combine the use of microarrays and restriction digests such as diversity array technology (DArT) [5] and restriction site associated DNA (RAD) tags [6] to assay up to several thousand markers.

We expect that similar use of microarrays and advanced bioinformatics will greatly enhance our understanding of pathophysiological mechanisms in various erythroid diseases involving abnormal genetic regulation of erythropoiesis (i.e., aplastic anemia or polycythemia vera), as well as erythrocyte-related dysfunction or hemoglobinopathies (i.e., hemolytic anemia, sickle cell disease).

It will be of considerable use to anyone considering employing microarray experiments as well as those who need to troubleshoot previous use of microarrays and accompanying statistical evaluations.

Jaksik et al. have written a review article on the use of microarrays and the cautions and complications of using the results of them to evaluate research data.

Despite these advances, this study is the first to profile miRNAs in the porcine central nervous system (CNS) by use of microarray and quantitative PCR.

However, the use of microarray and fold-change has the disadvantage that it needs cut-off values.

We would like to thank our colleague Patrizia Torrigiani for providing fruit peach material of cv Springcrest and the "MicroCribi" http://microcribi.cribi.unipd.it team headed by Prof. G. Lanfranchi for the valuable help and advice in both the use of microarray and the analyses of data.

One version focused on the broader biological concepts and uses of microarrays and how statistics are used to analyze and interpret microarray data.

Scanning of microarrays is typically labor intensive and a significant source of system noise that complicates the use of microarrays significantly; and has hampered the transition of these technologies into high throughput clinical scenarios by adding undue instrumentation cost and complexity.