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The Oxford Nanopore and Pacbio SMRT sequencing technologies has revolutionized the Next-Generation Sequencing (NGS) environment by producing long reads that exceed 60 kbp and helped to the completion of many biological projects. But, long reads are characterized by a high error rate which increases the difficulty of biological problems like the genome assembly problem. Error correction of long reads...
A track to solve the problem of errors caused by the third generation of sequencing technology is to use the high coverage of the high quality of short reads generated by the second-generation sequencing technology. This paper presents a new approach for error correction and de novo assembly for long reads. We present MiRCA a hybrid approach based on the sequences alignments that detects and corrects...
In recent years, the Next Generation Sequencing (NGS) Technologies have evolved rapidly and has led to various developments in genome assembly field. Denovo assembly is a core problem in bioinformatics and remains a complex task with the high throughput data produced by NGS sequencers. In this study, we present a new denovo assembly approach based on clustering. Our denovo assembly approach uses long...
The new generation of long reads generated byOxford nanopore sequencing technology has revolutionized thenext generation sequencing environment with the appearanceof its new sequencer MinIon. This sequencer produces longreads with a low production costs and with high throughput. However, long reads generated by the MinIon sequencer havea high error rate which deteriorates the quality of resultsobtained...
The new MinIon sequencer provided by the Oxford Nanopore Technologies is characterized by his small size and is powered from the USB 3.0 port of a laptop computer. This sequencer produces long reads with a low production costs and with high throughput. However, long reads generated by the MinIon sequencer have a high error rate (about 25% [1]) which deteriorates the quality of results obtained by...
Feature extraction from biological data is a very important discipline in bioinformatics. The aim of this work is to classify protein sequences automatically. To do this, it seemed appropriate to use a data mining process: the process of Knowledge Discovery and Data mining (KDD) from biological data. We are interested in the first phase of the KDD, that consists in the preprocessing and we focus on...
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