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Outline

Title
Abstract
Research Questions
Resources and Databases
Computational Strategy 2.1 Introduction
Data Description and Preprocessing
Data Pre-Processing Workflow
Training & Testing Dataset Partitions
Additional Testing Datasets: Mirbase R18 and R19
Introduction
DMDS Performance Given Unseen Testing Data
Hypothesis Generation Using Mirnavisa
Results
Analysis and Discussion
Discussion
References

Species-independent MicroRNA Gene Discovery

Profile image of Timothy Kevin K. KamanuTimothy Kevin K. Kamanu

2012

https://doi.org/10.25781/KAUST-W6777
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225 pages

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Abstract

Species-independent MicroRNA Gene Discovery Timothy Kevin Kuria Kamanu microRNA (miRNA) are a class of small endogenous non-coding RNA that are mainly negative transcriptional and post-transcriptional regulators in both plants and animals. Recent studies have shown that miRNA are involved in different types of cancer and other incurable diseases such as autism and Alzheimer's. Functional miRNAs are excised from hairpin-like sequences that are known as miRNA genes. There are about 21, 000 known miRNA genes, most of which have been determined using experimental methods. miRNA genes are classified into different groups (miRNA families). This study reports about 19, 000 unknown miRNA genes in nine species whereby approximately 15, 300 predictions were computationally validated to contain at least one experimentally verified functional miRNA product. The predictions are based on a novel computational strategy which relies on miRNA family groupings and exploits the physics and geometry of miRNA genes to unveil the hidden palindromic signals and symmetries in miRNA gene sequences. Unlike conventional computational miRNA gene discovery methods, the algorithm developed here is speciesindependent: it allows prediction at higher accuracy and resolution from arbitrary RNA/DNA sequences in any species and thus enables examination of repeat-prone genomic regions which

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