Nanopore Sequencing: Bioinformatics Analysis of ONT Data
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30 Nov 2023 07:08:08 am.
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Data Analysis Pipeline for ONT Sequencing
The bioinformatics analysis of ONT data typically involves a multi-step pipeline to transform raw electrical signals into meaningful genomic information. The pipeline includes base calling, error correction, alignment, variant calling, and additional steps for specialized analyses, such as detecting modifications and assessing transcriptome complexity.
Base Calling and Base Modification Detection
Base calling is a fundamental step in ONT data analysis, converting the raw ionic current signals into DNA base sequences. Early versions of base callers had relatively high error rates, hindering downstream analyses. However, with continuous improvements, modern base callers, such as Guppy and Chiron, have significantly enhanced accuracy and now offer real-time base calling capabilities.
Furthermore, ONT technology is uniquely suited to detect epigenetic modifications, such as DNA methylation. Specialized algorithms, including Tombo and DeepSignal, have been developed to identify base modifications by analyzing specific changes in the ionic current signal associated with modified bases. This epigenetic information is crucial for understanding gene regulation and other biological processes.
Detecting DNA and RNA Modifications in ONT Sequencing
One of the key advantages of Oxford Nanopore Technologies (ONT) sequencing is its ability to directly detect DNA and RNA modifications. By distinguishing the unique current shifts caused by modified bases from those of unmodified bases, ONT sequencing offers insights into epigenetic modifications and post-transcriptional RNA modifications. In this section, we explore the methodologies and tools developed for the detection of DNA and RNA modifications using ONT sequencing data.
CD Genomics offers Epigenetics and Methylation Analysis Using Long-Read Sequencing for both DNA and RNA modifications.
DNA Modification Detection
ONT sequencing enables the direct detection of certain DNA modifications, such as 5-methylcytosine (5mC), 6-methyladenine (6mA), and N4-methylcytosine (4mC), at different levels of resolution, ranging from bulk-level detection to the single-molecule level. Several tools have been developed to identify DNA modifications from ONT data:
• Nanoraw: Integrated into the Tombo software package, Nanoraw was the first tool to detect DNA modifications, including 5mC, 6mA, and 4mC, from ONT data.
• Nanopolish: This tool specializes in detecting 5mC modifications in DNA sequences and has been widely used for accurate modification calling.
• SignalAlign: SignalAlign detects 5mC, 5-hydroxymethylcytosine (5hmC), and 6mA modifications in DNA sequences, providing valuable information on the epigenetic landscape.
• mCaller, DeepMod, and DeepSignal: These tools have been developed to identify both 5mC and 6mA modifications, contributing to a comprehensive view of DNA modifications in ONT sequencing data.
• NanoMod: NanoMod is another tool designed to detect 5mC and 6mA modifications in DNA, further enhancing the accuracy and coverage of modification detection.
RNA Modification Detection
Detecting RNA modifications directly using ONT sequencing has also shown promise, although the resolution varies, and single-nucleotide resolution at the single-molecule level is yet to be demonstrated. In the past, PacBio sequencing was used to detect N6-methyladenosine (m6A) modifications in RNA molecules. More recently, ONT direct RNA sequencing has generated robust data of reasonable quality, paving the way for the detection of RNA modifications. Several pilot studies have successfully detected bulk-level RNA modifications using various methodologies:
• EpiNano and ELIGOS: These tools examine error distribution profiles or current signals to detect m6A and 5-methoxyuridine (5moU) modifications in RNA.
• Tombo extension and MINES: By analyzing current signals, these tools have been used to identify m6A modifications in RNA molecules.
Although these pilot studies have detected bulk-level RNA modifications, achieving single-nucleotide resolution at the single-molecule level remains a challenge.
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