50,510 research outputs found
Jabba: hybrid error correction for long sequencing reads using maximal exact matches
Third generation sequencing platforms produce longer reads with higher error rates than second generation sequencing technologies. While the improved read length can provide useful information for downstream analysis, underlying algorithms are challenged by the high error rate. Error correction methods in which accurate short reads are used to correct noisy long reads appear to be attractive to generate high-quality long reads. Methods that align short reads to long reads do not optimally use the information contained in the second generation data, and suffer from large runtimes. Recently, a new hybrid error correcting method has been proposed, where the second generation data is first assembled into a de Bruijn graph, on which the long reads are then aligned. In this context we present Jabba, a hybrid method to correct long third generation reads by mapping them on a corrected de Bruijn graph that was constructed from second generation data. Unique to our method is that this mapping is constructed with a seed and extend methodology, using maximal exact matches as seeds. In addition to benchmark results, certain theoretical results concerning the possibilities and limitations of the use of maximal exact matches in the context of third generation reads are presented
Nanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions
Nanopore sequencing technology has the potential to render other sequencing
technologies obsolete with its ability to generate long reads and provide
portability. However, high error rates of the technology pose a challenge while
generating accurate genome assemblies. The tools used for nanopore sequence
analysis are of critical importance as they should overcome the high error
rates of the technology. Our goal in this work is to comprehensively analyze
current publicly available tools for nanopore sequence analysis to understand
their advantages, disadvantages, and performance bottlenecks. It is important
to understand where the current tools do not perform well to develop better
tools. To this end, we 1) analyze the multiple steps and the associated tools
in the genome assembly pipeline using nanopore sequence data, and 2) provide
guidelines for determining the appropriate tools for each step. We analyze
various combinations of different tools and expose the tradeoffs between
accuracy, performance, memory usage and scalability. We conclude that our
observations can guide researchers and practitioners in making conscious and
effective choices for each step of the genome assembly pipeline using nanopore
sequence data. Also, with the help of bottlenecks we have found, developers can
improve the current tools or build new ones that are both accurate and fast, in
order to overcome the high error rates of the nanopore sequencing technology.Comment: To appear in Briefings in Bioinformatics (BIB), 201
DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing
We consider the correction of errors from nucleotide sequences produced by
next-generation targeted amplicon sequencing. The next-generation sequencing
(NGS) platforms can provide a great deal of sequencing data thanks to their
high throughput, but the associated error rates often tend to be high.
Denoising in high-throughput sequencing has thus become a crucial process for
boosting the reliability of downstream analyses. Our methodology, named
DUDE-Seq, is derived from a general setting of reconstructing finite-valued
source data corrupted by a discrete memoryless channel and effectively corrects
substitution and homopolymer indel errors, the two major types of sequencing
errors in most high-throughput targeted amplicon sequencing platforms. Our
experimental studies with real and simulated datasets suggest that the proposed
DUDE-Seq not only outperforms existing alternatives in terms of
error-correction capability and time efficiency, but also boosts the
reliability of downstream analyses. Further, the flexibility of DUDE-Seq
enables its robust application to different sequencing platforms and analysis
pipelines by simple updates of the noise model. DUDE-Seq is available at
http://data.snu.ac.kr/pub/dude-seq
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