2015 SMRT Informatics Developers Conference Presentation Slides: David Tse of Stanford University presented on a method his team is developing for de novo transcriptome error correction by convexification.
2015 SMRT Informatics Developers Conference Presentation Slides: Shinichi Morishita of the University of Tokyo presented on how his team has been using SMRT Sequencing to better understand methylomes, metagenomes and structural variation of various eukaryotic genomes.
2015 SMRT Informatics Developers Conference Presentation Slides: Kevin Corcoran of PacBio provided a brief review of community involvement in the development of analysis tools and showed a preview of upcoming sample preparation, chemistry and informatics improvements.
The Genome in a Bottle Consortium is developing the reference materials, reference methods , and reference data n
In addition to the genome and transcriptome, epigenetic information is essential to understand biological processes and their regulation, and their misregulation underlying disease. Traditionally, epigenetic DNA modifications are detected using upfront sample preparation steps such as bisulfite conversion, followed by sequencing. Bisulfite sequencing has provided a wealth of knowledge about human epigenetics, however it does not access the entire genome due to limitations in read length and GC- bias of the sequencing technologies used. In contrast, Single Molecule, Real-Time (SMRT) DNA Sequencing is unique in that it can detect DNA base modifications as part of the sequencing process. It can thereby leverage the long read lengths and lack of GC bias for more comprehensive views of the human epigenome. I will highlight several examples of this capability towards the generation of new biological insights, including the resolution of methylation states in repetitive and GC-rich regions of the genome, and large-scale changes in the methylation status across a cancer genome as a function of drug sensitivity.
Purpose: Clinical laboratories, research laboratories and technology developers all need DNA samples with reliably known genotypes in order to help validate and improve their methods. The Genome in a Bottle Consortium (genomeinabottle.org) has been developing Reference Materials with high-accuracy whole genome sequences to support these efforts.Methodology: Our pilot reference material is based on Coriell sample NA12878 and was released in May 2015 as NIST RM 8398 (tinyurl.com/giabpilot). To minimize bias and improve accuracy, 11 whole-genome and 3 exome data sets produced using 5 different technologies were integrated using a systematic arbitration method [1]. The Genome in a Bottle Analysis Group is adapting these methods and developing new methods to characterize 2 families, one Asian and one Ashkenazi Jewish from the Personal Genome Project, which are consented for public release of sequencing and phenotype data. We have generated a larger and even more diverse data set on these samples, including high-depth Illumina paired-end and mate-pair, Complete Genomics, and Ion Torrent short-read data, as well as Moleculo, 10X, Oxford Nanopore, PacBio, and BioNano Genomics long-read data. We are analyzing these data to provide an accurate assessment of not just small variants but also large structural variants (SVs) in both “easy” regions of the genome and in some “hard” repetitive regions. We have also made all of the input data sources publicly available for download, analysis, and publication.Results: Our arbitration method produced a reference data set of 2,787,291 single nucleotide variants (SNVs), 365,135 indels, 2744 SVs, and 2.2 billion homozygous reference calls for our pilot genome. We found that our call set is highly sensitive and specific in comparison to independent reference data sets. We have also generated preliminary assemblies and structural variant calls for the next 2 trios from long read data and are currently integrating and validating these.Discussion: We combined the strengths of each of our input datasets to develop a comprehensive and accurate benchmark call set. In the short time it has been available, over 20 published or submitted papers have used our data. Many challenges exist in comparing to our benchmark calls, and thus we have worked with the Global Alliance for Genomics and Health to develop standardized methods, performance metrics, and software to assist in its use.[1] Zook et al, Nat Biotech. 2014.
The human reference sequence has provided a foundation for studies of genome structure, human variation, evolutionary biology, and disease. At the time the reference was originally completed there were some loci recalcitrant to closure; however, the degree to which structural variation and diversity affected our ability to produce a representative genome sequence at these loci was still unknown. Many of these regions in the genome are associated with large, repetitive sequences and exhibit complex allelic diversity such producing a single, haploid representation is not possible. To overcome this challenge, we have sequenced DNA from two hydatidiform moles (CHM1 and CHM13), which are essentially haploid. CHM13 was sequenced with the latest PacBio technology (P6-C5) to 52X genome coverage and assembled using Daligner and Falcon v0.2 (GCA_000983455.1, CHM13_1.1). Compared to the first mole (CHM1) PacBio assembly (GCA_001007805.1, 54X) contig N50 of 4.5Mb, the contig N50 of CHM13_1.1 is almost 13Mb, and there is a 13-fold reduction in the number of contigs. This demonstrates the improved contiguity of sequence generated with the new chemistry. We annotated 50,188 RefSeq transcripts of which only 0.63% were split transcripts, and the repetitive and segmental duplication content was within the expected range. These data all indicate an extremely high quality assembly. Additionally, we sequenced CHM13 DNA using Illumina SBS technology to 60X coverage, aligned these reads to the GRCh37, GRCh38, and CHM13_1.1 assemblies and performed variant calling using the SpeedSeq pipeline. The number of single nucleotide variants (SNV) and indels was comparable between GRCh37 and GRCh38. Regions that showed increased SNV density in GRCh38 compared to GRCh37 could be attributed to the addition of centromeric alpha satellite sequence to the reference assembly. Alternatively, regions of decreased SNV density in GRCh38 were concentrated in regions that were improved from BAC based sequencing of CHM1 such as 1p12 and 1q21 containing the SRGAP2 gene family. The alignment of PacBio reads to GRCh37 and GRCh38 assemblies allowed us to resolve complex loci such as the MHC region where the best alignment was to the DBB (A2-B57-DR7) haplotype. Finally, we will discuss how combining the two high quality mole assemblies can be used for benchmarking and novel bioinformatics tool development.
Characterizing genomic structural variations (SV) is vital for understanding how genomes evolve. Furthermore, SVs are known for playing a role in a wide range of diseases including cancer, autism, and schizophrenia. Nevertheless, due to their complexity they remain harder to detect and less understood than single nucleotide variations. Recently, third-generation sequencing has proven to be an invaluable tool for detecting SVs. The markedly higher read length not only allows single reads to span a SV, it also enables reliable mapping to repetitive regions of the genome. These regions often contain SVs and are inaccessible to short-read mapping. However, current sequencing technologies like PacBio show a raw read error rate of 10% or more consisting mostly of insertions and deletions. Especially in repetitive regions the high error rate causes current mapping methods to fail finding exact borders for SVs, to split up large deletions and insertions into several small ones, or in some cases, like inversions, to fail reporting them at all. Furthermore, for complex SVs it is not possible to find one end-to-end alignment for a given read. The decision of when to split a read into two or more separate alignments without knowledge of the underlying SV poses an even bigger challenge to current read mappers. Here we present NextGenMap-LR for long single molecule PacBio reads which addresses these issues. NextGenMap-LR uses a fast k-mer search to quickly find anchor regions between parts of a read and the reference and evaluates them using a vectorized implementation of the Smith-Waterman (SW) algorithm. The resulting high-quality anchors are then used to determine whether a read spans an SV and has to be split or can be aligned contiguously. Finally, NextGenMap-LR uses a banded SW algorithm to compute the final alignment(s). In this last step, to account for both the sequencing error and real genomic variations, we employ a non-affine gap model that penalizes gap extensions for longer gaps less than for shorter ones. Based on simulated as well as verified human breast cancer SV data we show how our approach significantly improves mapping of long reads around SVs. The non-affine gap model is especially effective at more precisely identifying the position of the breakpoint, and the enhanced scoring scheme enables subsequent variation callers to identify SVs that would have been missed otherwise.
Structural Variants (SVs), which include deletions, insertions, duplications, inversions and chromosomal rearrangements, have been shown to effect organism phenotypes, including changing gene expression, increasing disease risk, and playing an important role in cancer development. Still it remains challenging to detect all types of SVs from high throughput sequencing data and it is even harder to detect more complex SVs such as a duplication nested within an inversion. To overcome these challenges we developed algorithms for SV analysis using longer third generation sequencing reads. The increased read lengths allow us to span more complex SVs and accurately assess SVs in repetitive regions, two of the major limitations when using short Illumina data. Our enhanced open-source analysis method Sniffles accurately detects structural variants based on split read mapping and assessment of the alignments. Sniffles uses a self-balancing interval tree in combination with a plane sweep algorithm to manage and assess the identified SVs. Central to its high accuracy is its advanced scoring model that can distinguish erroneous alignments from true breakpoints flanking SVs. In experiments with simulated and real genomes (e.g human breast cancer), we find that Sniffles outperforms all other SV analysis approaches in both the sensitivity of finding events as well as the specificity of those events. Sniffles is available at: https://github.com/fritzsedlazeck/Sniffles
There are many sequencing-based approaches to understanding complex metagenomic communities, spanning targeted amplification to whole-sample shotgun sequencing. While targeted approaches provide valuable data at low sequencing depth, they are limited by primer design and PCR amplification. Whole-sample shotgun experiments require a high depth of coverage. As such, rare community members may not be represented in the resulting assembly. Circular-consensus, Single Molecule, Real-Time (SMRT) Sequencing reads in the 1-2 kb range, with >99% consensus accuracy, can be efficiently generated for low amounts of input DNA, e.g. as little as 10 ng of input DNA sequenced in 4 SMRT Cells can generate >100,000 such reads. While throughput is low compared to second-generation sequencing, the reads are a true random sampling of the underlying community. Long read lengths translate to a high number of the reads harboring full genes or even full operons for downstream analysis. Here we present the results of circular-consensus sequencing on a mock metagenomic community with an abundance range of multiple orders of magnitude, and compare the results with both 16S and shotgun assembly methods. We show that even with relatively low sequencing depth, the long-read, assembly-free, random sampling allows to elucidate meaningful information from the very low-abundance community members. For example, given the above low-input sequencing approach, a community member at 1/1,000 relative abundance would generate 100 1-2 kb sequence fragments having 99% consensus accuracy, with a high probability of containing a gene fragment useful for taxonomic classification or functional insight.
For highly complex and large genomes, a well-annotated genome may be computationally challenging and costly, yet the study of alternative splicing events and gene annotations usually rely on the existence of a genome. Long-read sequencing technology provides new opportunities to sequence full-length cDNAs, avoiding computational challenges that short read transcript assembly brings. The use of single molecule, real-time sequencing from Pacific Biosciences to sequence transcriptomes (the Iso-SeqTM method), which produces de novo, high-quality, full-length transcripts, has revealed an astonishing amount of alternative splicing in eukaryotic species. With the Iso-Seq method, it is now possible to reconstruct the transcribed regions of the genome using just the transcripts themselves. We present Cogent, a tool for finding gene families and reconstructing the coding genome in the absence of a reference genome. Cogent uses k-mer similarities to first partition the transcripts into different gene families. Then, for each gene family, the transcripts are used to build a splice graph. Cogent identifies bubbles resulting from sequencing errors, minor variants, and exon skipping events, and attempts to resolve each splice graph down to the minimal set of reconstructed contigs. We apply Cogent to a Cuttlefish Iso-Seq dataset, for which there is a highly fragmented, Illumina-based draft genome assembly and little annotation. We show that Cogent successfully discovers gene families and can reconstruct the coding region of gene loci. The reconstructed contigs can then be used to visualize alternative splicing events, identify minor variants, and even be used to improve genome assemblies.
The assembly, annotation, and characterization of the sugar pine (Pinus lambertiana Dougl.) transcriptome represents an opportunity to study the genetic mechanisms underlying resistance to the invasive white pine blister rust (Cronartium ribicola) as well as responses to other abiotic stresses. The assembled transcripts also provide a resource to improve the genome assembly. We selected a diverse set of tissues allowing the first comprehensive evaluation of the sugar pine gene space. We have combined short read sequencing technologies (Illumina MiSeq and HiSeq) with the relatively new Pacific Biosciences Iso-Seq approach. From the 2.5 billion and 1.6 million Illumina and PacBio (46 SMRT cells) reads, 33,720 unigenes were de novo assembled. Comparison of sequencing technologies revealed improved coverage with Illumina HiSeq reads and better splice variant detection with PacBio Iso-Seq reads. The genes identified as unique to each library ranges from 199 transcripts (basket seedling) to 3,482 transcripts (female cones). In total, 10,026 transcripts were shared by all libraries. Genes differentially expressed in response to these provided insight on abiotic and biotic stress responses. To analyze orthologous sequences, we compared the translated sequences against 19 plant species, identifying 7,229 transcripts that clustered uniquely among the conifers. We have generated here a high quality transcriptome from one WPBR susceptible and one WPBR resistant sugar pine individual. Through the comprehensive tissue sampling and the depth of the sequencing achieved, detailed information on disease resistance can be further examined.
Outside of the simplest cases (haploid, bacteria, or inbreds), genomic information is not carried in a single reference per individual, but rather has higher ploidy (n=>2) for almost all organisms. The existence of two or more highly related sequences within an individual makes it extremely difficult to build high quality, highly contiguous genome assemblies from short DNA fragments. Based on the earlier work on a polyploidy aware assembler, FALCON ( https://github.com/PacificBiosciences/FALCON) , we developed new algorithms and software (“FALCON-unzip”) for de novo haplotype reconstructions from SMRT Sequencing data. We generate two datasets for developing the algorithms and the prototype software: (1) whole genome sequencing data from a highly repetitive diploid fungal (Clavicorona pyxidata) and (2) whole genome sequencing data from an F1 hybrid from two inbred Arabidopsis strains: Cvi-0 and Col-0. For the fungal genome, we achieved an N50 of 1.53 Mb (of the 1n assembly contigs) of the ~42 Mb 1n genome and an N50 of the haplotigs (haplotype specific contigs) of 872 kb from a 95X read length N50 ~16 kb dataset. We found that ~ 45% of the genome was highly heterozygous and ~55% of the genome was highly homozygous. We developed methods to assess the base-level accuracy and local haplotype phasing accuracy of the assembly with short-read data from the Illumina® platform. For the ArabidopsisF1 hybrid genome, we found that 80% of the genome could be separated into haplotigs. The long range accuracy of phasing haplotigs was evaluated by comparing them to the assemblies from the two inbred parental lines. We show that a more complete view of all haplotypes could provide useful biological insights through improved annotation, characterization of heterozygous variants of all sizes, and resolution of differential allele expression. The current Falcon-Unzip method will lead to understand how to solve more difficult polyploid genome assembly problems and improve the computational efficiency for large genome assemblies. Based on this work, we can develop a pipeline enabling routinely assemble diploid or polyploid genomes as haplotigs, representing a comprehensive view of the genomes that can be studied with the information at hand.
Outside of the simplest cases (haploid, bacteria, or inbreds), genomic information is not carried in a single reference per individual, but rather has higher ploidy (n=>2) for almost all organisms. The existence of two or more highly related sequences within an individual makes it extremely difficult to build high quality, highly contiguous genome assemblies from short DNA fragments. Based on the earlier work on a polyploidy aware assembler, FALCON (https://github.com/PacificBiosciences/FALCON), we developed new algorithms and software (FALCON-unzip) for de novo haplotype reconstructions from SMRT Sequencing data. We apply the algorithms and the prototype software for (1) a highly repetitive diploid fungal genome (Clavicorona pyxidata) and (2) an F1 hybrid from two inbred Arabidopsis strains: CVI-0 and COL-0. For the fungal genome, we achieved an N50 of 1.53 Mb (of the 1n assembly contigs) of the ~42 Mb 1n genome and an N50 of the haplotigs of 872 kb from a 95X read length N50 ~16 kb dataset. We found that ~ 45% of the genome was highly heterozygous and ~55% of the genome was highly homozygous. We developed methods to assess the base-level accuracy and local haplotype phasing accuracy of the assembly with short-read data from the Illumina platform. For the Arabidopsis F1 hybrid genome, we found that 80% of the genome could be separated into haplotigs. The long range accuracy of phasing haplotigs was evaluated by comparing them to the assemblies from the two inbred parental lines. We show that a more complete view of all haplotypes could provide useful biological insights through improved annotation, characterization of heterozygous variants of all sizes, and resolution of differential allele expression. Finally, we applied this method to WGS human data sets to demonstrate the potential for resolving complicated, medically-relevant genomic regions.
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