TAILIEUCHUNG - Báo cáo y học: "miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data. | Hendrix et al. Genome Biology 2010 11 R39 http 2010 11 4 R39 w Genome Biology METHOD _ Open Access miRTRAP a computational method for the systematic identification of miRNAs from high throughput sequencing data David Hendrix Michael Levine and Weiyang Shi Abstract MicroRNAs miRs have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic whole-genome identification of miRs from high throughput sequencing information. This method miRTRAP incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci. Background microRNAs miRNAs miRs are small regulatory RNAs present throughout the Eukarya 1-3 . They modulate diverse biological processes including embryonic development tissue differentiation and tumorigenesis. miRs inhibit translation and promote mRNA degradation via sequence-specific binding to the 3 UTR regions of mRNAs 2 . They are produced from hairpin precursors pri-miRNAs that are sequentially processed by Drosha DGCR8 and Dicer to generate one or more 19- to 23-nucleotide RNAs. The most abundant product is referred to as miR while the less abundant sequence produced from the opposite arm of the hairpin is called miR . In addition it has been observed that some miRNA loci can produce up to two additional products immediately adjacent to the miR and miR sequences which are called miRNA offset RNAs moRs 4 5 . The comprehensive identification of the complete set of miRs is complicated by their small size which limits simple cross-species comparisons based on sequence homology. Moreover de novo computational miRNA prediction methods rely heavily on known miRNAs and are not always effective for characterizing novel genomes. Recent advances in high .

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