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The quality of the total reads can be assessed by running the FASTQC program fastqc351019.fq. Reads which fail any of the checks are not suitable for analysis and should either be re-processed or discarded.
Sequencing and alignment of a microRNA is similar to that of a gene. The key steps have been determined, including: quality filtering of the sequencing data; alignment of reads to the genome; and detection of microRNA candidates. The result of the analysis is a list of microRNAs, which with the genes of which they are a part, can be used to investigate the function of genes.
The mapping of an RNA-sequencing (RNA-seq) read to a genome is the first important process in studies of gene expression. Consequently, many of the tools to evaluate the accuracy of the mapping process specifically address the processing of RNA-seq data. In this review, we present a broad overview of some of these methods, which are effective in improving the quality of RNA-seq mapping. For example, a commonly used method to resolve mapping ambiguities is to remove all reads mapping to the same location in the genome without regard to which strand those reads map.
RNA-seq data, in combination with allele-specific expression analysis, can be used to distinguish between tissue-specific and germline-specific expression, as well as to identify coding and noncoding transcripts. As a result, it can predict the function of novel genes, predict active promoter or repressor regions and identify any tissue-specific promoters.
RNA-seq data have revolutionized our knowledge of the human transcriptome. Because of the large amount of data and limited knowledge of the transcriptome, we performed a systematic exploratory study of its complexity. The work included the creation of a high quality comprehensive annotation of human coding and noncoding RNAs, detection of alternative transcripts from isoforms, identification of novel RNAs and analysis of the landscape of ncRNAs. d2c66b5586
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