Hardcastle, T. J. & Kelly, K. A. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.
SS, SW and RY conceived the idea to investigate repeat expression in sequencing data.
The basic units of expression in sequencing, such as transcripts per million reads (TPM) or fragments per kilobase per million reads (FPKM), are still undergoing revision [ 5, 6].
A dynamic descriptor facilitates robust recognition of facial expressions in video sequences.
However, automatically analyzing facial expressions in video sequences is a challenging task due to the fact that current techniques for the detection and tracking of facial expressions are sensitive to head pose, occlusion, pose, and variations in lighting conditions [2].
They can be used as effective regular expressions in protein sequences, even among distantly-related proteins, whose classification based on other methods would be quite challenging.
The conditional expressions are evaluated in sequence until one is found to be true.
To verify the quantitative results of the RNA sequencing experiments, six genes were selected for RT-qPCR analysis on the basis of their expression levels in sequencing data and importance in the regulation of betalain biosynthesis.
Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation.
Antisense oligonucleotides are efficient tools for the inhibition of gene expression in a sequence specific way.
MicroRNAs (miRNAs) are a class of small non-coding RNAs that down-regulate gene expression in a sequence specific manner to control plant growth and development.
