共查询到20条相似文献，搜索用时 15 毫秒
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Hicham Benzekri Paula Armesto Xavier Cousin Mireia Rovira Diego Crespo Manuel Alejandro Merlo David Mazurais Rocío Bautista Darío Guerrero-Fernández Noe Fernandez-Pozo Marian Ponce Carlos Infante Jose Luis Zambonino Sabine Nidelet Marta Gut Laureana Rebordinos Josep V Planas Marie-Laure Bégout M Gonzalo Claros Manuel Manchado 《BMC genomics》2014,15(1)
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Background
The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision.Results
The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units.Conclusions
NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-015-0520-5) contains supplementary material, which is available to authorized users. 相似文献13.
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Background
Human leukocyte antigen (HLA) genes are critical genes involved in important biomedical aspects, including organ transplantation, autoimmune diseases and infectious diseases. The gene family contains the most polymorphic genes in humans and the difference between two alleles is only a single base pair substitution in many cases. The next generation sequencing (NGS) technologies could be used for high throughput HLA typing but in silico methods are still needed to correctly assign the alleles of a sample. Computer scientists have developed such methods for various NGS platforms, such as Illumina, Roche 454 and Ion Torrent, based on the characteristics of the reads they generate. However, the method for PacBio reads was less addressed, probably owing to its high error rates. The PacBio system has the longest read length among available NGS platforms, and therefore is the only platform capable of having exon 2 and exon 3 of HLA genes on the same read to unequivocally solve the ambiguity problem caused by the “phasing” issue.Results
We proposed a new method BayesTyping1 to assign HLA alleles for PacBio circular consensus sequencing reads using Bayes’ theorem. The method was applied to simulated data of the three loci HLA-A, HLA-B and HLA-DRB1. The experimental results showed its capability to tolerate the disturbance of sequencing errors and external noise reads.Conclusions
The BayesTyping1 method could overcome the problems of HLA typing using PacBio reads, which mostly arise from sequencing errors of PacBio reads and the divergence of HLA genes, to some extent.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-296) contains supplementary material, which is available to authorized users. 相似文献15.
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Stefanie Hartmann Natascha Hasenkamp Jens Mayer Johan Michaux Serge Morand Camila J. Mazzoni Alfred L. Roca Alex D. Greenwood 《BMC genomics》2015,16(1)