Pacific Biosciences Data Research Articles

Benchmarking long-read genome sequence alignment tools for human genomics applications.

The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation. For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385. These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.

A chromosome-level genome assembly of the beet armyworm Spodoptera exigua

BackgroundThe beet armyworm Spodoptera exigua is a polyphagous caterpillar that causes serious damage to many species of crops and vegetables. To gain insight into how this polyphagous insect differs from less harmful oligophagous species, we generated a chromosome-level assembly and compared it to closely related species with the same or different feeding habits. ResultsBased on Illumina and Pacific Biosciences data and Hi-C technology, 425.6 Mb of genome sequences were anchored and oriented into 31 linkage groups, with an N50 length of 14.8 Mb. A total of 24,649 gene models were predicted, of which 97.4% were identified in the genome assembly. Chemosensory genes are vital for locating food: of the four main families, odorant-binding proteins, chemosensory proteins and olfactory receptors showed little difference, whereas gustatory receptors are greatly expanded in S. exigua. Examination of other polyphagous insects confirmed this difference from oligophagous congeners and further identified the bitter receptor subfamily as being particularly affected. ConclusionOur high-quality genome sequence for beet armyworm identified a key expansion of the bitter gustatory receptor subfamily in this and other pests that differs crucially from more benign relatives and offers insight into the biology and possible future means of control for these economically important insects.

Accurate isoform discovery with IsoQuant using long reads

Annotating newly sequenced genomes and determining alternative isoforms from long-read RNA data are complex and incompletely solved problems. Here we present IsoQuant—a computational tool using intron graphs that accurately reconstructs transcripts both with and without reference genome annotation. For novel transcript discovery, IsoQuant reduces the false-positive rate fivefold and 2.5-fold for Oxford Nanopore reference-based or reference-free mode, respectively. IsoQuant also improves performance for Pacific Biosciences data.

A chromosome-level genome assembly for the eastern fence lizard (Sceloporus undulatus), a reptile model for physiological and evolutionary ecology.

BackgroundHigh-quality genomic resources facilitate investigations into behavioral ecology, morphological and physiological adaptations, and the evolution of genomic architecture. Lizards in the genus Sceloporus have a long history as important ecological, evolutionary, and physiological models, making them a valuable target for the development of genomic resources.FindingsWe present a high-quality chromosome-level reference genome assembly, SceUnd1.0 (using 10X Genomics Chromium, HiC, and Pacific Biosciences data), and tissue/developmental stage transcriptomes for the eastern fence lizard, Sceloporus undulatus. We performed synteny analysis with other snake and lizard assemblies to identify broad patterns of chromosome evolution including the fusion of micro- and macrochromosomes. We also used this new assembly to provide improved reference-based genome assemblies for 34 additional Sceloporus species. Finally, we used RNAseq and whole-genome resequencing data to compare 3 assemblies, each representing an increased level of cost and effort: Supernova Assembly with data from 10X Genomics Chromium, HiRise Assembly that added data from HiC, and PBJelly Assembly that added data from Pacific Biosciences sequencing. We found that the Supernova Assembly contained the full genome and was a suitable reference for RNAseq and single-nucleotide polymorphism calling, but the chromosome-level scaffolds provided by the addition of HiC data allowed synteny and whole-genome association mapping analyses. The subsequent addition of PacBio data doubled the contig N50 but provided negligible gains in scaffold length.ConclusionsThese new genomic resources provide valuable tools for advanced molecular analysis of an organism that has become a model in physiology and evolutionary ecology.

Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing

Whole-genome sequencing using sequencing technologies such as Illumina enables the accurate detection of small-scale variants but provides limited information about haplotypes and variants in repetitive regions of the human genome. Single-molecule sequencing (SMS) technologies such as Pacific Biosciences and Oxford Nanopore generate long reads that can potentially address the limitations of short-read sequencing. However, the high error rate of SMS reads makes it challenging to detect small-scale variants in diploid genomes. We introduce a variant calling method, Longshot, which leverages the haplotype information present in SMS reads to accurately detect and phase single-nucleotide variants (SNVs) in diploid genomes. We demonstrate that Longshot achieves very high accuracy for SNV detection using whole-genome Pacific Biosciences data, outperforms existing variant calling methods, and enables variant detection in duplicated regions of the genome that cannot be mapped using short reads.

HLA*LA-HLA typing from linearly projected graph alignments.

SummaryHLA*LA implements a new graph alignment model for human leukocyte antigen (HLA) type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data) and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.Availability and implementationHLA*LA is implemented in C++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3).Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 3272: A graph remapping framework for in silico adjudication of SNVs, INDELs, and structural variants from genetic sequencing data

Abstract Several state-of-the-art, easy to use tools are available both for short-variant detection (e.g. GATK, FREEBAYES), and structural variant (SV) detection (e.g. LUMPY, MANTRA, DELLY), but these tools often produce divergent variant calls, especially INDELs, and it is very difficult to reconcile such variants into a single, accurate set. Furthermore, while it would be highly desirable to also detect larger, structural variants (SV), existing SV detector packages are typically difficult to integrate, highly resource-intensive to run, and result in call sets that require expert manual review to reduce false positive detection rate. Our algorithm, GRAPHITE (https://github.com/dillonl/graphite) requires as input a collection of variant calls, made by one or more short-variant or SV detection tools. Typically, this starting set is high sensitivity (i.e. inclusive), but low specificity (i.e. have a high false discovery rate). We then apply a novel “variant adjudication” procedure to discard false positives, while keeping true positive calls. This is accomplished by constructing a graph from these variants (the Variant Graph) representing allelic variants as graph branches, in addition to the branches formed by the current, linear genome reference sequence. Using a graph mapping algorithm (GSSW, a graph extension of the Smith-Waterman alignment algorithm) we developed earlier, we re-map all reads from each of the samples contributing to the candidate calls. We retain candidate variants confirmed by mappings to those branches in the graph that represent the corresponding variant allele, and discard those candidates that were not confirmed by such mappings. This procedure results in a highly specific callset that also maintains the high sensitivity of the inclusive starting callset constructed by multiple primary variant calling methods. Because the graph construction and mapping approach works for most types of SVs in addition to all short variants, variants of all different types can be integrated in a single step. Here we present the application of this method for cross-validating structural variants calls from Pacific Biosciences data by remapping deep Illumina WGS read sets to Variant Graphs constructed using the candidate Pacific Biosciences variants, as part of the Human Genome Structural Variation Consortium (HGSVC) data analysis project. We also present GRAPHITE's application to improving the accuracy of allele frequency measurement in tumor sequencing data, which is essential for the accurate reconstruction of subclonal evolution in longitudinal tumor samples. Citation Format: Dillon Lee, Yi Qiao, Gabor Marth. A graph remapping framework for in silico adjudication of SNVs, INDELs, and structural variants from genetic sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3272.

Comparison of genome sequencing technology and assembly methods for the analysis of a GC-rich bacterial genome.

Improvements in technology and decreases in price have made de novo bacterial genomic sequencing a reality for many researchers, but it has created a need to evaluate the methods for generating a complete and accurate genome assembly. We sequenced the GC-rich Caulobacter henricii genome using the Illumina MiSeq, Roche 454, and Pacific Biosciences RS II sequencing systems. To generate a complete genome sequence, we performed assemblies using eight readily available programs and found that builds using the Illumina MiSeq and the Roche 454 data produced accurate yet numerous contigs. SPAdes performed the best followed by PANDAseq. In contrast, the Celera assembler produced a single genomic contig using the Pacific Biosciences data after error correction with the Illumina MiSeq data. In addition, we duplicated this build using the Pacific Biosciences data with HGAP2.0. The accuracy of these builds was verified by pulsed-field gel electrophoresis of genomic DNA cut with restriction enzymes.

ALE: a generic assembly likelihood evaluation framework for assessing the accuracy of genome and metagenome assemblies

Researchers need general purpose methods for objectively evaluating the accuracy of single and metagenome assemblies and for automatically detecting any errors they may contain. Current methods do not fully meet this need because they require a reference, only consider one of the many aspects of assembly quality or lack statistical justification, and none are designed to evaluate metagenome assemblies. In this article, we present an Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process. ALE is released as open source software under the UoI/NCSA license at http://www.alescore.org. It is implemented in C and Python.

A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and illumina MiSeq sequencers

BackgroundNext generation sequencing (NGS) technology has revolutionized genomic and genetic research. The pace of change in this area is rapid with three major new sequencing platforms having been released in 2011: Ion Torrent’s PGM, Pacific Biosciences’ RS and the Illumina MiSeq. Here we compare the results obtained with those platforms to the performance of the Illumina HiSeq, the current market leader. In order to compare these platforms, and get sufficient coverage depth to allow meaningful analysis, we have sequenced a set of 4 microbial genomes with mean GC content ranging from 19.3 to 67.7%. Together, these represent a comprehensive range of genome content. Here we report our analysis of that sequence data in terms of coverage distribution, bias, GC distribution, variant detection and accuracy.ResultsSequence generated by Ion Torrent, MiSeq and Pacific Biosciences technologies displays near perfect coverage behaviour on GC-rich, neutral and moderately AT-rich genomes, but a profound bias was observed upon sequencing the extremely AT-rich genome of Plasmodium falciparum on the PGM, resulting in no coverage for approximately 30% of the genome. We analysed the ability to call variants from each platform and found that we could call slightly more variants from Ion Torrent data compared to MiSeq data, but at the expense of a higher false positive rate. Variant calling from Pacific Biosciences data was possible but higher coverage depth was required. Context specific errors were observed in both PGM and MiSeq data, but not in that from the Pacific Biosciences platform.ConclusionsAll three fast turnaround sequencers evaluated here were able to generate usable sequence. However there are key differences between the quality of that data and the applications it will support.

Pacific biosciences sequencing technology for genotyping and variation discovery in human data

BackgroundPacific Biosciences technology provides a fundamentally new data type that provides the potential to overcome some limitations of current next generation sequencing platforms by providing significantly longer reads, single molecule sequencing, low composition bias and an error profile that is orthogonal to other platforms. With these potential advantages in mind, we here evaluate the utility of the Pacific Biosciences RS platform for human medical amplicon resequencing projects.ResultsWe evaluated the Pacific Biosciences technology for SNP discovery in medical resequencing projects using the Genome Analysis Toolkit, observing high sensitivity and specificity for calling differences in amplicons containing known true or false SNPs. We assessed data quality: most errors were indels (~14%) with few apparent miscalls (~1%). In this work, we define a custom data processing pipeline for Pacific Biosciences data for human data analysis.ConclusionCritically, the error properties were largely free of the context-specific effects that affect other sequencing technologies. These data show excellent utility for follow-up validation and extension studies in human data and medical genetics projects, but can be extended to other organisms with a reference genome.