Please use this identifier to cite or link to this item:
|Title:||Analysis of new long-read sequencing data||Authors:||Phoa, Yohanes Alfredo||Keywords:||DRNTU::Science::Mathematics::Applied mathematics::Simulation and modeling
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
|Issue Date:||2019||Abstract:||The rapid development of powerful high throughput sequencing technologies has enabled us to gain valuable insights into the complexities of a human transcriptome. In recent years, Oxford Nanopore has developed a new technology that can take RNA directly as the sequencing input and generates long reads. In this thesis, we are using nanopore reading results from synthetic RNA samples and employ machine learning based approaches to identify patterns that distinguish signals from modified RNA readings from the unmodified counterpart. Firstly, we performed explorations of our dataset using a statistical test. We then proposed a simple baseline algorithm that learns the distinguishing features between unmodified strands and unmodified strands. Finally, we proposed a novel method on detecting anomalies by sequence labeling using deep learning.||URI:||http://hdl.handle.net/10356/77170||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SPMS Student Reports (FYP/IA/PA/PI)|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.