In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis.
Previous DNA methylation data analysis methods have been assessed using the DNA methylaton differences between experimental groups verified using an independent method [ 5].
In the current study, all 18 known taxa of Tricyrtis have been included in DNA sequence data analysis using the rps16 intron (non-coding plastid DNA) region.
In order to correctly interpret the quality of the DNA sequencing data, however, proper quality control should be conducted at all stages of DNA sequencing data analysis: raw data, alignment, and variant detection.
Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth.
For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis.
Biclustering algorithms are extensively used in DNA microarray data analysis.
Statistical methods related to differential DNA methylation data analysis cover a number of different approaches.
MH designed the project, performed DNA barcode data analysis, and wrote the manuscript.
The two xenograft samples were not analyzed for new variants since off-target amplification of mouse DNA hampered data analysis.
