Discover the benefits of HiFi reads and learn how highly accurate long-read sequencing provides a single technology solution across a range of applications.
PacBio highly accurate long reads – HiFi reads – offer a single-platform solution for rare and inherited disease research, elucidating suspected genetic causes of disease in up to ~50% of cases that have not previously been explained using short-read exome or whole genome sequencing. PacBio offers an efficient workflow, developed in collaboration with Children’s Mercy Kansas City, which provides a scalable solution for sequencing 100s to 1000s of whole human genomes per year on the Sequel II and Sequel IIe Systems.
PacBio HiFi reads provide both long read lengths (up to 25 kb) and high accuracy (>99.9%) to quickly and affordably generate contiguous, complete, and correct de novo genome assemblies of even the most complex genomes.
This landmark study by members of the Telomere-to-Telomer Consortium is the first fully complete assembly to be produced 20 years after the initial drafts of the human genome.
Learn how PacBio highly accurate long reads enable an improved approach to whole genome sequencing to understand the genetic origins of rare diseases.
In this video Shawn Levy, Discovery Life Sciences’ Chief Scientific Officer, along with Cheryl Heiner, PacBio Principal Scientist, discuss the advantages of HudsonAlpha Discovery’s specialized sequencing services for PacBio HiFi…
In this talk, speakers provide an understanding HiFi sequencing methods for resolving viral diversity in complex systems, examples of how HiFi sequencing can phase entire viral genes or genomes, revealing…
In this talk, speakers provide an overview of PacBio-recommended tools for metagenome sequencing analysis, where to download example test data, the typical performance for HiFi metagenome sequencing of fecal samples,…
In this SMRT Science Journal Club talk, Phillip Tai from the University of Massachusetts Medical School discusses his investigation in the design compatibility of CRISPR components in AAV vectors.
In this SMRT Science Journal Club talk, Anoushka Joglekar from Weill Cornell Medicine discusses how she and her colleagues are developing tools to produce an isoform view of the brain…
Many neurological diseases result from expansion of unstable variable nucleotide tandem repeats (VNTRs) that influence gene transcription of neighboring genes. In this talk, Dr. Henne Holstege presents research that investigated…
Spinocerebellar ataxia type 10 (SCA10) is a rare autosomal-dominant disorder caused by an expanded intronic pentanucleotide repeat in the ATXN10 gene. This repeat expansion when fully penetrant can be typically…
Through Pharmacogenomics (PGx), we can explore how a person’s genome affects their response to drugs to enable the development of safe and effective medications tailored to their genetic makeup. In…
Recent work comparing metagenomic sequencing methods indicates that a comprehensive picture of the taxonomic and functional diversity of complex communities will be difficult to achieve with one sequencing technology alone. While the lower cost of short reads has enabled greater sequencing depth, the greater contiguity of long-read assemblies and lack of GC bias in SMRT Sequencing has enabled better gene finding. However, since long-read assembly typically requires high coverage for error correction, these benefits have in the past been lost for low-abundance species. The introduction of the Sequel II System has enabled a new, higher throughput, assembly-optional data type that addresses these challenges: HiFi reads. HiFi reads combine QV20 accuracy with long read lengths, eliminating the need for assembly for most metagenome applications, including gene discovery and metabolic pathway reconstruction. In fact, the read lengths and accuracy of HiFi data match or outperform the quality metrics of most metagenome assemblies, enabling cost-effective recovery of intact genes and operons while omitting the resource intensive and data-inefficient assembly step. Here we present the application of HiFi sequencing to both mock and human fecal samples using full-length 16S and shotgun methods. This proof-of-concept work demonstrates the unique strengths of the HiFi method. First, the high correspondence between the expected community composition,16S and shotgun profiling data reflects low context bias. In addition, every HiFi read yields ~5-8 predicted genes, without assembly, using standard tools. If assembly is desired, excellent results can be achieved with Canu and contig binning tools. In summary, HiFi sequencing is a new, cost-effective option for high-resolution functional profiling of metagenomes which complements existing short read workflows.
Long-read sequencing of diverse humans has revealed more than 20,000 insertion, deletion, and inversion structural variants spanning more than 12 Mb in a healthy human genome. Most of these variants are too large to detect with short reads and too small for array comparative genome hybridization (aCGH). While the standard approaches to calling structural variants with long reads thrive in the 50 bp to 10 kb size range, they tend to miss exactly the large (>50 kb) copy-number variants that are called more readily with aCGH. Standard algorithms rely on reference-based mapping of reads that fully span a variant or on de novo assembly; and copy-number variants are often too large to be spanned by a single read and frequently involve segmentally duplicated sequence that is not yet included in most de novo assemblies. To comprehensively detect large variants in human genomes, we extended pbsv – a structural variant caller for long reads – to call copy-number variants (CNVs) from read-clipping and read-depth signatures. In human germline benchmark samples, we detect more than 300 CNVs spanning around 10 Mb, and we call hundreds of additional events in re-arranged cancer samples. Together with insertion, deletion, inversion, duplication, and translocation calling from spanning reads, this allows pbsv to comprehensively detect large variants from a single data type.
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