Sentence examples for massively parallel experiments from inspiring English sources

Recent technological advances have enabled life scientists to conduct massively parallel experiments and access an abundance of data sets publicly available on the Internet [1].

With this aim, multiplex antibody-based techniques offer high sensitivity (typically 1 10 pg/ml) as well as high specificity, reproducibility across a broad range of concentrations, and the potential for massively parallel experiments.

Here, we present the results of a massively parallel experiment designed to measure the full spectrum of possible fates of new beneficial mutations in hundreds of experimental yeast populations, whether these mutations are ultimately successful or not.

We also addressed the validity of using duplicates in calculating coverage for BisSeq experiments because duplicate reads are traditionally discarded in massively parallel sequencing experiments.

Furthermore, quality control and standardisation of the massively parallel sequencing experiments and data reporting are important issues to consider.

In recent years, various next generation sequencing (NGS) platforms have become available which allow massively parallel resequencing experiments to be performed.

Because this is largely seen as inadequate to call methylation differences, different groups have included duplicates in their coverage calls, but duplicates are not normally used in coverage statistics from massively parallel sequencing experiments.

Expression status is based on GIS-PET evidence for Zhao et al. [ 51], while for Guenther et al. [ 49] expression is characterised in Additional file 1: Chromatin and expression states when data pooled from microarray experiments and massively parallel signature sequencing (MPSS) experiments were consistent.

For this purpose we assembled expression data from publicly available oligo-array [10] and MPSS (Massively Parallel Signature Sequencing) experiments [11].

We downloaded short-read (Illumina) sequences for 5 massively parallel mRNA sequencing experiments on S2 cells from two GEO datasets (GSM390063 and GSM390064), aligned these reads to the Drosophila reference genome (BDGP5) using TopHat [ 23], and calculated the Reads Per Kilobase of transcript per Million mapped reads (RPKM) for each gene reported in the BDGP5 reference annotation.

One massively parallel signature sequencing experiment identified 40,029 unique 21 bp transcript tags in this species, representing a lower-bound estimate of gene number (Erdner and Anderson 2006; Moustafa et al. 2010).