Expression profiling
Gene expression profiling is a measurement of the regulation of a transcriptome from the whole genome in the field of molecular biology. A conventional method to measure the relative activity of target genes is DNA microarray technology, which estimates expressed genes with the signals of hybridization of target genes (cDNA from mRNA) on the synthesized oligonucleotides [27]. The technology is still used for functional genomics in the wide era, including medicine, clinic, plant, and agricultural biotechnology [28-30]. In addition, microarray technology is also used in the comparative study of proteomics and expression, measuring the level of extracellular matrix protein [30]. Since NGS technology was developed in 2005, the transcriptome of novel whole genomes could be identified with massive parallel mRNA sequencing using Roche/454 and Illumina/Solexa [31-36]. The Roche/454 system is more useful for gaining novel gene discovery of novel species' genomes for long read sequencing [37, 38]. Otherwise, Illumina/Solexa is being used to profile the expression of known genes with mapping short read sequences to the known reference genes [39, 40]. In that case, rare expressed genes and novel genes could be identified with high-throughput expressed sequence tag sequences using Illumina/Solexa. Also, it is useful to find significant tissue-specific expression biases with comparison of transcript data [22]. Now, the hybrid mRNA sequence from Rohce/454 and Illumina/Solexa is more powerful for finding novel genes through de novo assembly in any whole-genome species.
The hybrid sequence data of 20× and 50× coverage of the estimated transcriptome sequence from Roche/454 and Illumina/Solexa, respectively, is effective in creating novel expressed reference sequences, while short-read Illumina/Solexa data are cost-efficient on expression quantification information for comparing exposed samples and natural phenotype samples through mapping to the reference genes (Fig. 4). Only and average 30× coverage of transcriptome depth of short-read sequences of Illumina/Solexa is enough to check expression quantification, compared to reference expressed sequence tag sequences. The expressed information could be different, depending on the software using CAP3, MIRA, Newbler, SeqMan, and CLC. Therefore, the results should be compared according to variable program options to define robust expression profiling [41]. To date, a powerful tool of ChIP-on-chip is used for understanding gene transcription regulation. Thus, two-channel microarray technology of a combination of chromatin immunoprecipitation could be used for genomewide mapping of binding sites of DNA-interacting proteins [29]. In any NGS application, the transcriptome expression information would be more useful than complete genome information research with the lowest sequencing budget for biologists to better understand gene regulation of related genetic phenotypes with the in silico method. Of in silico methods, conserved miRNA and novel miRNA discovery is available on the massive miRNAnome data in any species. Specially, the target genes of miRNA discovered could be robust information to approach genome biology studies. Transcriptome assembly is smaller than genome assembly and thus should be more computationally tractable but is often harder, as individual contigs can often have highly variable read coverages. Comparing single assemblers, Newbler 2.5 performed the best on our trial dataset, but other assemblers were closely comparable. Combining different optimal assemblies from different programs, however, gives a more credible final product, and this strategy is recommended [41].