Computational Non-coding RNA Biology (eBook, ePUB)
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Computational Non-coding RNA Biology is a resource for the computation of non-coding RNAs. The book covers computational methods for the identification and quantification of non-coding RNAs, including miRNAs, tasiRNAs, phasiRNAs, lariat originated circRNAs and back-spliced circRNAs, the identification of miRNA/siRNA targets, and the identification of mutations and editing sites in miRNAs. The book introduces basic ideas of computational methods, along with their detailed computational steps, a critical component in the development of high throughput sequencing technologies for identifying diff...
Computational Non-coding RNA Biology is a resource for the computation of non-coding RNAs. The book covers computational methods for the identification and quantification of non-coding RNAs, including miRNAs, tasiRNAs, phasiRNAs, lariat originated circRNAs and back-spliced circRNAs, the identification of miRNA/siRNA targets, and the identification of mutations and editing sites in miRNAs. The book introduces basic ideas of computational methods, along with their detailed computational steps, a critical component in the development of high throughput sequencing technologies for identifying different classes of non-coding RNAs and predicting the possible functions of these molecules. Finding, quantifying, and visualizing non-coding RNAs from high throughput sequencing datasets at high volume is complex. Therefore, it is usually possible for biologists to complete all of the necessary steps for analysis. - Presents a comprehensive resource of computational methods for the identification and quantification of non-coding RNAs - Introduces 23 practical computational pipelines for various topics of non-coding RNAs - Provides a guide to assist biologists and other researchers dealing with complex datasets - Introduces basic computational methods and provides guidelines for their replication by researchers - Offers a solution to researchers approaching large and complex sequencing datasets
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