![]() In a typical RNA-Seq expression analysis, once sequence reads, which are generally 10 7–10 9 reads with a length of 50–300 bases, are accumulated, they are mapped to the reference sequence, namely, a genome sequence corresponding to the organism that the RNA is prepared from Refs. One of these objectives is counting the number of tags to analyze the intensity of gene expression, and the other is determining the transcript sequences for various purposes, such as annotating the genome of non-model organisms and analyzing splice variants. The purposes of using RNA-Seq are basically divided into two categories. This technical improvement greatly contributes to the application of RNA-Seq to various microorganisms. Multiplexing by so-called bar coding facilitates the flexible utilization of the high output capacity of sequencers for large numbers of samples without a significant increase in the overall sequencing cost. Due to recent extreme improvements in sequencing technology in terms of throughput and cost, large amounts of data have been accumulated, and the amount of data is increasing in an accelerating manner. RNA sequencing (RNA-Seq) is currently one of the most powerful methods for the comprehensive analysis of the transcriptional expression of the entire genes of a particular organism. We believe that at least a portion of our approach is useful and applicable to the analysis of any microorganism. Visualization of the mapping results greatly helps evaluate and improve the entire analysis in terms of both wet experiment and data processing. The accuracy of the expression analysis through the refinement of gene models was achieved by the results of mapped RNA-Seq reads in combination with ab initio gene finding tools using generalized hidden Markov models (GHMMs). The use of mapping software tools, such as HISAT and STAR, precisely aligned RNA-Seq reads to the genome of a filamentous fungus considering exon-intron boundaries. We have developed a highly accurate and cost-effective mapping strategy that includes the exclusion of unreliable base calls and correction of the reference sequence through provisional mapping of RNA sequencing reads. ![]() The huge amounts of data generated by recently developed high-throughput sequencers have required highly efficient data analysis algorithms using recently developed high-performance computers. Due to the wide variety of basic studies and applications derived from the huge number of species and the microorganism diversity, the targets to be sequenced are also expanding. The rapid evolvement of sequencing technology has generated huge amounts of DNA/RNA sequences, even with the continuous performance acceleration. ![]()
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