Dataset Information

Evaluating nanopore sequencing data processing pipelines for structural variation identification.

ABSTRACT: BACKGROUND:Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated. RESULTS:Using four nanopore datasets, including both empirical and simulated reads, we evaluate four alignment tools and three SV detection tools. We also evaluate the impact of sequencing depth on SV detection. Finally, we develop a machine learning approach to integrate call sets from multiple pipelines. Overall SV callers' performance varies depending on the SV types. For an initial data assessment, we recommend using aligner minimap2 in combination with SV caller Sniffles because of their speed and relatively balanced performance. For detailed analysis, we recommend incorporating information from multiple call sets to improve the SV call performance. CONCLUSIONS:We present a workflow for evaluating aligners and SV callers for nanopore sequencing data and approaches for integrating multiple call sets. Our results indicate that additional optimizations are needed to improve SV detection accuracy and sensitivity, and an integrated call set can provide enhanced performance. The nanopore technology is improving, and the sequencing community is likely to grow accordingly. In turn, better benchmark call sets will be available to more accurately assess the performance of available tools and facilitate further tool development.

SUBMITTER: Zhou A

PROVIDER: S-EPMC6857234 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Evaluating nanopore sequencing data processing pipelines for structural variation identification.

Zhou Anbo A Lin Timothy T Xing Jinchuan J

Genome biology 20191114 1

<h4>Background</h4>Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated.<h4>Results</h4>Using four nanopore datasets, including both empirical and simul ...[more]

PMID: 31727126

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Project description:BackgroundPopulation genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its "Best Practices" bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification.ResultsWe surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK "Best Practices" are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called.ConclusionsWe conclude that applying the GATK "Best Practices" pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations.

Project description:BACKGROUND:The MinION Access Program (MAP, 2014-2016) allowed selected users to test the prospects of long nanopore reads for diverse organisms and applications through the rapid development of improving chemistries. In 2014, faced with a fragmented Illumina assembly for the chloroplast genome of the green algal holobiont Caulerpa ashmeadii, we applied to the MAP to test the prospects of nanopore reads to investigate such intricacies, as well as further explore the hologenome of this species with native and hybrid approaches. RESULTS:The chloroplast genome could only be resolved as a circular molecule in nanopore assemblies, which also revealed structural variants (i.e. chloroplast polymorphism or heteroplasmy). Signal and Illumina polishing of nanopore-assembled organelle genomes (chloroplast and mitochondrion) reflected the importance of coverage on final quality and current limitations. In hybrid assembly, our modest nanopore data sets showed encouraging results to improve assembly length, contiguity, repeat content, and binning of the larger nuclear and bacterial genomes. Profiling of the holobiont with nanopore or Illumina data unveiled a dominant Rhodospirillaceae (Alphaproteobacteria) species among six putative endosymbionts. While very fragmented, the cumulative hybrid assembly length of C. ashmeadii's nuclear genome reached 24.4 Mbp, including 2.1 Mbp in repeat, ranging closely with GenomeScope's estimate (> 26.3 Mbp, including 4.8 Mbp in repeat). CONCLUSION:Our findings relying on a very modest number of nanopore R9 reads as compared to current output with newer chemistries demonstrate the promising prospects of the technology for the assembly and profiling of an algal hologenome and resolution of structural variation. The discovery of polymorphic 'chlorotypes' in C. ashmeadii, most likely mediated by homing endonucleases and/or retrohoming by reverse transcriptases, represents the first report of chloroplast heteroplasmy in the siphonous green algae. Improving contiguity of C. ashmeadii's nuclear and bacterial genomes will require deeper nanopore sequencing to greatly increase the coverage of these larger genomic compartments.

Project description:BackgroundExtensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary.MethodsWe designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays.ResultsWe set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays.DiscussionByapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.

Project description:BackgroundStructural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars.ResultsWe used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE.ConclusionsWe show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

Dataset Information

Evaluating nanopore sequencing data processing pipelines for structural variation identification.

Publications

Evaluating nanopore sequencing data processing pipelines for structural variation identification.

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