Computational Non-coding RNA Biology

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.

PART 1 - BACKGROUND 1. Introductions

PART 2 - SMALL NCRNAS 2. Identification of microRNAs 3. Identification of TAS and PHAS 4. Identification of editing and mutation sites in miRNAs

PART 3 - MIRNA TARGETS 5. Identifying animal miRNA targets 6. Identifying plant miRNA targets

PART 4 - LONG NCRNAS  7. Identification of long non-coding RNAs 8. Identification of lariat RNAs 9. Identification of circular RNAs

A. A usage guide of web-based ncRNA resources B. Abbreviations and acronyms

Biologists, computational biologists, researchers in proteins and proteomics, botanists; researchers and graduate researchers working on non-coding RNA data.

• 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