CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models
This paper discusses a new method called CRANE that improves the accuracy of nanopore sequencing, a technology that reads long DNA sequences. CRANE uses a statistical model to fix errors in the raw data generated during sequencing.
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- 1 To investigate the discrepancy in HMM size, we perform two more sets of parameter searches, both of which are guided by RawHash2 with HPC off.
- 2 To decide if a sequencing process should stop early, tools must analyze raw nanopore signals as these signals are generated in realtime.
- 3 To effectively model event sequences, CRANE uses probabilistic graph structures, Hidden Markov Models (HMMs), which are trained and utilized in three key steps.
- 4 An HMM generates an observed output, called an emission, according to its current state.
Introduction
Nanopore sequencing technology has enabled the highthroughput sequencing of very long nucleic acid molecules (e.g., DNA), called long reads, often exceeding thousands of bases in length . These long reads are particularly useful for many applications in genomics such as identifying complex and repetitive regions of genomes , and constructing gapless assemblies .
To sequence these long reads, nanopore sequencing produces series of noisy electrical signals based on the ionic current disruptions that nucleic acid molecules generate as each nucleotide passes through a nanometer-scale pore, called a nanopore.
First, nanopore sequencing enables stopping the sequencing process of a read (i.e., Read Until ) or the entire sequencing run (i.e., Run Until ) without fully sequencing it, a technique known as adaptive sampling.
We hope and believe that CRANE enables future work.
Research Question
To investigate the discrepancy in HMM size, we perform two more sets of parameter searches, both of which are guided by RawHash2 with HPC off.
Methodology
With capabilities including ultra long reads, adaptive sampling and portable sequencing, many analysis pipelines use nanopore sequencing for various applications such as telomere-to-telomere gapless genome assembly , metagenomics , complex structural variant detection , and in-the-field analyses such as continuous outbreak tracing . These raw signal analysis approaches provide substantial benefits in terms of the computational resources they require compared to a pipeline that uses computationally costly basecalling.
Study Design
Although existing raw signal analysis approaches provide substantial benefits in terms of lower computational overhead, they generally exhibit lower accuracy than the analysis pipelines that use basecalling.
This is mainly because 1) basecalling techniques are heavily optimized for very accurate translation from noisy electrical signals and 2) the signal processing algorithms used in raw signal analysis are prone to making errors, which usually propagates to the later steps in signal analysis and reduces the overall accuracy of these approaches.
Although HPC significantly improves the overall raw signal analysis by reducing some of the oversegmentation errors, it can remove error-free and informative events, and it is limited to correcting only oversegmentation errors, leaving other types of errors such as noise.
We discuss the benefits of CRANE for raw signal analysis, its current limitations, and promising directions for future work.
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Results & Findings
Apart from the capabilities to produce ultra long reads up to a few million bases, nanopore sequencing provides two unique benefits. This can significantly reduce sequencing time and cost by better utilizing sequencing consumables, called flow cells.
- Apart from the capabilities to produce ultra long reads up to a few million bases, nanopore sequencing provides two unique benefits.
- This can significantly reduce sequencing time and cost by better utilizing sequencing consumables, called flow cells.
- To decide if a sequencing process should stop early, tools must analyze raw nanopore signals as these signals are generated in realtime.
- Second, to avoid the significant computational demand of basecalling, several approaches [26, directly analyze raw nanopore signals without basecalling them.
- A common initial processing step in raw signal analyses aims to identify segments in electrical signals, also called events, before further processing the signals.
We identify promising directions for future work.
To decide if a sequencing process should stop early, tools must analyze raw nanopore signals as these signals are generated in realtime.
Practical Applications
Such situations might require minimal computational latency for effective adaptive sampling. An HMM is defined by a set of possible states and transition probabilities between each state.
A k-mer in a nanopore is dependent on the k-mers which come immediately before and after it, just how an HMM state may transition to and from a limited number of states.
The DNA sequence used to generate the synthetic events is a de Bruijn sequence which contains every possible n-mer exactly once, where n is a tunable parameter.
A k-mer in a nanopore is dependent on the k-mers which come immediately before and after it, just how an HMM state may transition to and from a limited number of states.
HMM Training
CRANE employs HMMs to model the relationship between nucleotide sequences and electrical signals. It uses Gaussian distributions for emissions and initializes parameters based on expected signal values to improve training behavior.
Parameter Search
After training, CRANE adapts the model to real-world data by searching for optimal correction parameters based on the observed error profile. This involves adjusting transition probabilities to model oversegmentation and undersegmentation errors.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1: Overview of CRANE’s error correction process.. Illustrates the three key steps in CRANE’s methodology for correcting raw nanopore events.
Frequently Asked Questions
To enable many of these applications, raw nanopore electrical signals are mainly analyzed in two ways. To investigate the discrepancy in HMM size, we perform two more sets of parameter searches, both of which are guided by RawHash2 with HPC off.
The primary benefit of CRANE is its ability to improve the accuracy of raw signal analysis tools that rely on segmentation algorithms. The motivation behind raw signal analysis is to perform analyses faster and with fewer computational resources than basecalling, enabling real-time.
To decide if a sequencing process should stop early, tools must analyze raw nanopore signals as these signals are generated in realtime. To effectively model event sequences, CRANE uses probabilistic graph structures, Hidden Markov Models (HMMs), which are trained and utilized in.
A k-mer in a nanopore is dependent on the k-mers which come immediately before and after it, just how an HMM state may transition to and from a limited number of states. The DNA sequence used to generate the synthetic events is.
Although HPC significantly improves the overall raw signal analysis by reducing some of the oversegmentation errors, it can remove error-free and informative events, and it is limited to correcting only oversegmentation errors, leaving other types of errors such as noise unhandled. A.
This paper discusses a new method called CRANE that improves the accuracy of nanopore sequencing, a technology that reads long DNA sequences. CRANE uses a statistical model to fix errors in the raw data generated during sequencing.