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1.

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   
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Next-generation sequencing (NGS) technologies allow for the generation of whole exome or whole genome sequencing data, which can be used to identify novel genetic alterations associated with defined phenotypes or to expedite discovery of functional variants for improved patient care. Because this robust technology has the ability to identify all mutations within a genome, incidental findings (IF)- genetic alterations associated with conditions or diseases unrelated to the patient’s present condition for which current tests are being performed- may have important clinical ramifications. The current debate among genetic scientists and clinicians focuses on the following questions: 1) should any IF be disclosed to patients, and 2) which IF should be disclosed – actionable mutations, variants of unknown significance, or all IF? Policies for disclosure of IF are being developed for when and how to convey these findings and whether adults, minors, or individuals unable to provide consent have the right to refuse receipt of IF. In this review, we detail current NGS technology platforms, discuss pressing issues regarding disclosure of IF, and how IF are currently being handled in prenatal, pediatric, and adult patients.  相似文献   
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人体肠道共生着数以万亿计的微生物,肠道微生物在维持宿主正常生理功能中发挥重要作用,其成分和功能变化可导致严重的肠道和全身性疾病。以新一代测序技术和生物信息学分析为基础的元基因组学研究不仅极大地推动了对人类肠道微生物的整体认识,还加深了对肠道微生物代谢产物促进人类健康机理的理解,为肠道炎症、代谢性疾病和癌症等人类疾病的诊断与治疗提供了新思路。就肠道微生物元基因组学与肠道相关疾病的研究进展作一综述。  相似文献   
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High‐throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost‐effectively, enabling single investigator laboratories to address a variety of ‘omics’ questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS‐generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard ‘health check‐up’ QC procedures recommended for HTS data sets and establishing what ‘healthy’ HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of ‘pathologies’, discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful ‘treatment’ protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies – and consequently their pathologies – change, we argue that careful QC of HTS data is an important – yet often neglected – aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC ‘best practices’ guide.  相似文献   
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