Bioinformatics Computational Biology Sequence Analysis

Computational Approaches to Nanopore Sequencing

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Nanopore sequencing is a rapidly maturing technology delivering long reads in real time on a portable instrument at low cost. Not surprisingly, the community has rapidly taken up this new way of sequencing and has used it successfully for a variety of research applications. A major limitation of nanopore sequencing is its high error rate, which despite recent improvements to the nanopore chemistry and computational tools still ranges between 5% and 15%. 

Nanopore sequencing offers an exciting opportunity to the field of genomics and bioinformatics to address advanced biological and computational problems. 

The nanopore sequencing concept was first proposed in the 1980s and has been developed and refined over the past three decades. Rather than the commonly used sequencing-by-synthesis approach, nanopores directly sense DNA or RNA bases by means of pores that are embedded in a membrane separating two compartments. An electric potential is applied over the membrane, resulting in an ion current and flow of DNA through the pore. Nucleotides in the pore change the ion flow, causing distinct current signals that can be used to infer the DNA sequence.

In recent years, a third generation of sequencing technologies has been developed and has been applied in parallel and complementarity to the former sequencing strategies. In particular, Oxford Nanopore Technologies (ONT) introduced nanopore sequencing which has become very popular among molecular ecologists. Nanopore technology offers a low price, portability and fast sequencing throughput. This powerful technology has been recently tested for 16S rRNA analyses show- ing promising results. However, compared with previous technologies, there is a scarcity of bioinformatic tools and protocols designed specifically for the analysis of Nanopore 16S sequences. Due to its notable characteristics, researchers have recently started performing assessments regarding the suitability MinION on 16S rRNA sequencing studies, and have obtained remarkable results.

There are several factors in play during sequencing that may contribute to a low signal-to-noise ratio: 

  • the structural similarity of the nucleotides
  • the simultaneous influence of multiple nucleotides on the signal 
  • the nonuniform speed at which nucleotides pass through the pore 
  • the fact that the signal does not change within homopolymers 

Assessment of bacterial diversity through sequencing of 16S ribosomal RNA (16S rRNA) genes has been an approach widely used in environmental microbiology, particularly since the advent of high- throughput sequencing technologies. An additional innovation introduced by these technologies was the need of developing new strategies to manage and investigate the massive amount of sequencing data generated. This situation stimulated the rapid expansion of the field of bioinformatics with the release of new tools to be used for the downstream analysis and interpretation of sequencing data mainly generated using Illumina technology. 

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