QuickNGS elevates Next-Generation Sequencing data analysis to a new level of automation

Background

Next-Generation Sequencing (NGS) has emerged as a widely used tool in molecular biology. While time and cost for the sequencing itself are decreasing, the analysis of the massive amounts of data remains challenging. Since multiple algorithmic approaches for the basic data analysis have been developed, there is now an increasing need to efficiently use these tools to obtain results in reasonable time.

Results

We have developed QuickNGS, a new workflow system for laboratories with the need to analyze data from multiple NGS projects at a time. QuickNGS takes advantage of parallel computing resources, a comprehensive back-end database, and a careful selection of previously published algorithmic approaches to build fully automated data analysis workflows. We demonstrate the efficiency of our new software by a comprehensive analysis of 10 RNA-Seq samples which we can finish in only a few minutes of hands-on time. The approach we have taken is suitable to process even much larger numbers of samples and multiple projects at a time.

Conclusion

Our approach considerably reduces the barriers that still limit the usability of the powerful NGS technology and finally decreases the time to be spent before proceeding to further downstream analysis and interpretation of the data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1695-x) contains supplementary material, which is available to authorized users.     

Efficient Genome-Wide Detection and Cataloging of EMS-Induced Mutations Using Exome Capture and Next-Generation Sequencing

Chemical mutagenesis efficiently generates phenotypic variation in otherwise homogeneous genetic backgrounds, enabling functional analysis of genes. Advances in mutation detection have brought the utility of induced mutant populations on par with those produced by insertional mutagenesis, but systematic cataloguing of mutations would further increase their utility. We examined the suitability of multiplexed global exome capture and sequencing coupled with custom-developed bioinformatics tools to identify mutations in well-characterized mutant populations of rice (Oryza sativa) and wheat (Triticum aestivum). In rice, we identified ∼18,000 induced mutations from 72 independent M2 individuals. Functional evaluation indicated the recovery of potentially deleterious mutations for >2600 genes. We further observed that specific sequence and cytosine methylation patterns surrounding the targeted guanine residues strongly affect their probability to be alkylated by ethyl methanesulfonate. Application of these methods to six independent M2 lines of tetraploid wheat demonstrated that our bioinformatics pipeline is applicable to polyploids. In conclusion, we provide a method for developing large-scale induced mutation resources with relatively small investments that is applicable to resource-poor organisms. Furthermore, our results demonstrate that large libraries of sequenced mutations can be readily generated, providing enhanced opportunities to study gene function and assess the effect of sequence and chromatin context on mutations.     

Next-Generation Sequencing in the Mycology Lab

New state-of-the-art techniques in sequencing offer valuable tools in both detection of mycobiota and in understanding of the molecular mechanisms of resistance against antifungal compounds and virulence. Introduction of new sequencing platform with enhanced capacity and a reduction in costs for sequence analysis provides a potential powerful tool in mycological diagnosis and research. In this review, we summarize the applications of next-generation sequencing techniques in mycology.     

Targeted Next-Generation Sequencing in Uyghur Families with Non-Syndromic Sensorineural Hearing Loss

The mutation spectrum of deafness genes may vary in different ethnical groups. In this study, we investigated the genetic etiology of nonsyndromic deafness in four consanguineous and two multiplex Uyghur families in which mutations in common deafness genes GJB2, SLC26A4 and MT-RNR1 were excluded. Targeted next-generation sequencing of 97 deafness genes was performed in the probands of each family. Novel pathogenic mutations were identified in four probands including the p.L416R/p.A438T compound heterozygous mutations in TMC1, the homozygous p.V1880E mutation in MYO7A, c.1238delT frameshifting deletion in PCDH15 and c.9690+1G>A splice site mutation in MYO15A. Co-segregation of the mutations and the deafness were confirmed within each family by Sanger sequencing. No pathogenic mutations were identified in one multiplex family and one consanguineous family. Our study provided a useful piece of information for the genetic etiology of deafness in Uyghurs.     

基于高通量测序技术的微生物检测数据分析方法

高通量测序技术的发展正在逐渐改变诸多生物学领域的研究方法.为应对突发疫情以及新发未知微生物威胁的需求,微生物鉴定技术逐渐从传统的物理化学方法及核酸杂交等分子水平方法进一步走向利用无需培养的测序数据进行快速分析检测.随之而来的是对高通量数据分析在精度及速度的要求.基于高通量测序数据的微生物检测数据分析方法在近些年得到了快速的发展.本文分析了目前基于高通量测序数据的微生物检测数据分析方法,对其数据分析的处理流程和计算方法进行了研究,比较了各个微生物检测数据分析方法的特点及适用场景.最后结合本实验室工作总结微生物检测数据分析方法在实际应用中可能遇到的问题,希望对该应用领域的研究有一定的参考意义.     

Screening for SNPs with Allele-Specific Methylation Based on Next-Generation Sequencing Data

Allele-specific methylation (ASM) has long been studied but mainly documented in the context of genomic imprinting and X chromosome inactivation. Taking advantage of the next-generation sequencing technology, we conduct a high-throughput sequencing experiment with four prostate cell lines to survey the whole genome and identify single-nucleotide polymorphisms (SNPs) with ASM. A Bayesian approach is proposed to model the counts of short reads for each SNP conditional on its genotypes of multiple subjects, leading to a posterior probability of ASM. We flag SNPs with high posterior probabilities of ASM by accounting for multiple comparisons based on posterior false discovery rates. Applying the Bayesian approach to the in-house prostate cell line data, we identify 269 SNPs as candidates of ASM. A simulation study is carried out to demonstrate the quantitative performance of the proposed approach.     

Next-Generation Sequencing of Connective Tissue Genes in Patients with Classical Ehlers-Danlos Syndrome

Background: Ehlers-Danlos syndrome (EDS) is a common non-inflammatory, congenital connective tissue disorder. Classical type (cEDS) EDS is one of the more common forms, typically caused by mutations in the COL5A1 and COL5A2 genes, though causative mutations in the COL1A1 gene have also been described. Material and methods: The study group included 59 patients of Polish origin, diagnosed with cEDS. The analysis was performed on genomic DNA (gDNA) with NGS technology, using an Illumina sequencer. Thirty-five genes related to connective tissue were investigated. The pathogenicity of the detected variants was assessed by VarSome. Results: The NGS of 35 genes revealed variants within the COL5A1, COL5A2, COL1A1, and COL1A2 genes for 30 of the 59 patients investigated. Our panel detected no sequence variations for the remaining 29 patients. Discussion: Next-generation sequencing, with an appropriate multigene panel, showed great potential to assist in the diagnosis of EDS and other connective tissue disorders. Our data also show that not all causative genes giving rise to cEDS have been elucidated yet.     

NeSSM: A Next-Generation Sequencing Simulator for Metagenomics

Background

Metagenomics can reveal the vast majority of microbes that have been missed by traditional cultivation-based methods. Due to its extremely wide range of application areas, fast metagenome sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of metagenomics analysis tools.

Results

We present here a customizable metagenome simulation system: NeSSM (Next-generation Sequencing Simulator for Metagenomics). Combining complete genomes currently available, a community composition table, and sequencing parameters, it can simulate metagenome sequencing better than existing systems. Sequencing error models based on the explicit distribution of errors at each base and sequencing coverage bias are incorporated in the simulation. In order to improve the fidelity of simulation, tools are provided by NeSSM to estimate the sequencing error models, sequencing coverage bias and the community composition directly from existing metagenome sequencing data. Currently, NeSSM supports single-end and pair-end sequencing for both 454 and Illumina platforms. In addition, a GPU (graphics processing units) version of NeSSM is also developed to accelerate the simulation. By comparing the simulated sequencing data from NeSSM with experimental metagenome sequencing data, we have demonstrated that NeSSM performs better in many aspects than existing popular metagenome simulators, such as MetaSim, GemSIM and Grinder. The GPU version of NeSSM is more than one-order of magnitude faster than MetaSim.

Conclusions

NeSSM is a fast simulation system for high-throughput metagenome sequencing. It can be helpful to develop tools and evaluate strategies for metagenomics analysis and it’s freely available for academic users at http://cbb.sjtu.edu.cn/~ccwei/pub/software/NeSSM.php.     

Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data

Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.     

Extended RAS and BRAF Mutation Analysis Using Next-Generation Sequencing

Somatic mutations in KRAS, NRAS, and BRAF genes are related to resistance to anti-EGFR antibodies in colorectal cancer. We have established an extended RAS and BRAF mutation assay using a next-generation sequencer to analyze these mutations. Multiplexed deep sequencing was performed to detect somatic mutations within KRAS, NRAS, and BRAF, including minor mutated components. We first validated the technical performance of the multiplexed deep sequencing using 10 normal DNA and 20 formalin-fixed, paraffin-embedded (FFPE) tumor samples. To demonstrate the potential clinical utility of our assay, we profiled 100 FFPE tumor samples and 15 plasma samples obtained from colorectal cancer patients. We used a variant calling approach based on a Poisson distribution. The distribution of the mutation-positive population was hypothesized to follow a Poisson distribution, and a mutation-positive status was defined as a value greater than the significance level of the error rate (α = 2 x 10^-5). The cut-off value was determined to be the average error rate plus 7 standard deviations. Mutation analysis of 100 clinical FFPE tumor specimens was performed without any invalid cases. Mutations were detected at a frequency of 59% (59/100). KRAS mutation concordance between this assay and Scorpion-ARMS was 92% (92/100). DNA obtained from 15 plasma samples was also analyzed. KRAS and BRAF mutations were identified in both the plasma and tissue samples of 6 patients. The genetic screening assay using next-generation sequencer was validated for the detection of clinically relevant RAS and BRAF mutations using FFPE and liquid samples.     

A Microfluidic DNA Library Preparation Platform for Next-Generation Sequencing

Next-generation sequencing (NGS) is emerging as a powerful tool for elucidating genetic information for a wide range of applications. Unfortunately, the surging popularity of NGS has not yet been accompanied by an improvement in automated techniques for preparing formatted sequencing libraries. To address this challenge, we have developed a prototype microfluidic system for preparing sequencer-ready DNA libraries for analysis by Illumina sequencing. Our system combines droplet-based digital microfluidic (DMF) sample handling with peripheral modules to create a fully-integrated, sample-in library-out platform. In this report, we use our automated system to prepare NGS libraries from samples of human and bacterial genomic DNA. E. coli libraries prepared on-device from 5 ng of total DNA yielded excellent sequence coverage over the entire bacterial genome, with >99% alignment to the reference genome, even genome coverage, and good quality scores. Furthermore, we produced a de novo assembly on a previously unsequenced multi-drug resistant Klebsiella pneumoniae strain BAA-2146 (KpnNDM). The new method described here is fast, robust, scalable, and automated. Our device for library preparation will assist in the integration of NGS technology into a wide variety of laboratories, including small research laboratories and clinical laboratories.     

Next-Generation Sequencing of Aquatic Oligochaetes: Comparison of Experimental Communities

Aquatic oligochaetes are a common group of freshwater benthic invertebrates known to be very sensitive to environmental changes and currently used as bioindicators in some countries. However, more extensive application of oligochaetes for assessing the ecological quality of sediments in watercourses and lakes would require overcoming the difficulties related to morphology-based identification of oligochaetes species. This study tested the Next-Generation Sequencing (NGS) of a standard cytochrome c oxydase I (COI) barcode as a tool for the rapid assessment of oligochaete diversity in environmental samples, based on mixed specimen samples. To know the composition of each sample we Sanger sequenced every specimen present in these samples. Our study showed that a large majority of OTUs (Operational Taxonomic Unit) could be detected by NGS analyses. We also observed congruence between the NGS and specimen abundance data for several but not all OTUs. Because the differences in sequence abundance data were consistent across samples, we exploited these variations to empirically design correction factors. We showed that such factors increased the congruence between the values of oligochaetes-based indices inferred from the NGS and the Sanger-sequenced specimen data. The validation of these correction factors by further experimental studies will be needed for the adaptation and use of NGS technology in biomonitoring studies based on oligochaete communities.