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Paten, B.

Publications and source records attributed to Paten, B..

At least 73 records · Page 4Linked to original sources

A survey of rare epigenetic variation in 23,116 human genomes identifies disease-relevant epivariations and novel CGG expansions

There is growing recognition that epivariations, most often recognized as promoter hypermethylation events that lead to gene silencing, are associated with a number of human diseases. However, little information exists on the prevalence and distribution of rare epigenetic variation in the human population. In order to address this, we performed a survey of methylation profiles from 23,116 individuals using the Illumina 450k array. Using a robust outlier approach, we identified 4,452 unique autosomal epivariations, including potentially inactivating promoter methylation events at 384 genes linked to human disease. For example, we observed promoter hypermethylation of BRCA1 and LDLR at population frequencies of ~1 in 3,000 and ~1 in 6,000 respectively, suggesting that epivariations may underlie a fraction of human disease which would be missed by purely sequence-based approaches. Using expression data, we confirmed that many epivariations are associated with outlier gene expression. Analysis of SNV data and monozygous twin pairs suggests that approximately two thirds of epivariations segregate in the population secondary to underlying sequence mutations, while one third are likely sproradic events that occur post-zygotically. We identified 25 loci where rare hypermethylation coincided with the presence of an unstable CGG tandem repeat, and validated the presence of novel CGG expansions at several of these, identifying the molecular defect underlying most of the known folate-sensitive fragile sites in the genome. Our study provides a catalog of rare epigenetic changes in the human genome, gives insight into the underlying origins and consequences of epivariations, and identifies many novel hypermethylated CGG repeat expansions.

genomics↗

Distance Indexing and Seed Clustering in Sequence Graphs

Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but much more complicated in a graph context. In read mapping algorithms, distance calculations are commonly used in a clustering step to determine if seed alignments could belong to the same mapping. Clustering algorithms are a bottleneck for some mapping algorithms due to the cost of repeated distance calculations. We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for mapping algorithms.

bioinformatics↗

A fully phased accurate assembly of an individual human genome

The prevailing genome assembly paradigm is to produce consensus sequences that "collapse" parental haplotypes into a consensus sequence. Here, we leverage the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing (Strand-seq)1,2 and combine them with high-fidelity (HiFi) long sequencing reads3, in a novel reference-free workflow for diploid de novo genome assembly. Employing this strategy, we produce completely phased de novo genome assemblies separately for each haplotype of a single individual of Puerto Rican origin (HG00733) in the absence of parental data. The assemblies are accurate (QV > 40), highly contiguous (contig N50 > 25 Mbp) with low switch error rates (0.4%) providing fully phased single-nucleotide variants (SNVs), indels, and structural variants (SVs). A comparison of Oxford Nanopore and PacBio phased assemblies identifies 150 regions that are preferential sites of contig breaks irrespective of sequencing technology or phasing algorithms.

bioinformatics↗

Telomere-to-telomere assembly of a complete human X chromosome

After nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist 1,2. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Here we present a de novo human genome assembly that surpasses the continuity of GRCh38 2, along with the first gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome 3, we reconstructed the [~]2.8 megabase centromeric satellite DNA array and closed all 29 remaining gaps in the current reference, including new sequence from the human pseudoautosomal regions and cancer-testis ampliconic gene families (CT-X and GAGE). This complete chromosome X, combined with the ultra-long nanopore data, also allowed us to map methylation patterns across complex tandem repeats and satellite arrays for the first time. These results demonstrate that finishing the human genome is now within reach and will enable ongoing efforts to complete the remaining human chromosomes.

bioinformatics↗

Progressive alignment with Cactus: a multiple-genome aligner for the thousand-genome era

Cactus, a reference-free multiple genome alignment program, has been shown to be highly accurate, but the existing implementation scales poorly with increasing numbers of genomes, and struggles in regions of highly duplicated sequence. We describe progressive extensions to Cactus that enable reference-free alignment of tens to thousands of large vertebrate genomes while maintaining high alignment quality. We show that Cactus is capable of scaling to hundreds of genomes and beyond by describing results from an alignment of over 600 amniote genomes, which is to our knowledge the largest multiple vertebrate genome alignment yet created. Further, we show improvements in orthology resolution leading to downstream improvements in annotation.

genomics↗

Efficient de novo assembly of eleven human genomes using PromethION sequencing and a novel nanopore toolkit

Present workflows for producing human genome assemblies from long-read technologies have cost and production time bottlenecks that prohibit efficient scaling to large cohorts. We demonstrate an optimized PromethION nanopore sequencing method for eleven human genomes. The sequencing, performed on one machine in nine days, achieved an average 63x coverage, 42 Kb read N50, 90% median read identity and 6.5x coverage in 100 Kb+ reads using just three flow cells per sample. To assemble these data we introduce new computational tools: Shasta - a de novo long read assembler, and MarginPolish & HELEN - a suite of nanopore assembly polishing algorithms. On a single commercial compute node Shasta can produce a complete human genome assembly in under six hours, and MarginPolish & HELEN can polish the result in just over a day, achieving 99.9% identity (QV30) for haploid samples from nanopore reads alone. We evaluate assembly performance for diploid, haploid and trio-binned human samples in terms of accuracy, cost, and time and demonstrate improvements relative to current state-of-the-art methods in all areas. We further show that addition of proximity ligation (Hi-C) sequencing yields near chromosome-level scaffolds for all eleven genomes.

bioinformatics↗

ProTECT: Prediction of T-cell Epitopes for Cancer Therapy

Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies (ACTs) and Peptide Vaccines (PVs) to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patients HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC (pMHC) binding prediction, and ranking of final candidates. We demonstrate ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, and compare it with published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 minutes per sample when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect.

bioinformatics↗

A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

ObjectiveMany antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high inter-sample variance. Moreover, some cancer samples have misidentified tissues or origin, or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparing to a single patient sample.\n\nMaterials and MethodsWe propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and under-expression.\n\nResultsWe demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissues samples. Further, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.\n\nConclusionsThis exploratory method is suitable for identifying expression outliers from comparative RNA-seq analysis for individual samples and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing their pediatric cohort.

bioinformatics↗

Genotyping structural variants in pangenome graphs using the vg toolkit

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmarked vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

bioinformatics↗

Haplotype-aware graph indexes

MotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes. ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows-Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes. AvailabilityOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/gcsa2. Contactjouni.siren@iki.fi Supplementary informationSupplementary data are available.

bioinformatics↗

The genome of C57BL/6J \"Eve\", the mother of the laboratory mouse genome reference strain

Isogenic laboratory mouse strains are used to enhance reproducibility as individuals within a strain are essentially genetically identical. For the most widely used isogenic strain, C57BL/6, there is also a wealth of genetic, phenotypic, and genomic data, including one of the highest quality reference genomes (GRCm38.p6). However, laboratory mouse strains are living reagents and hence genetic drift occurs and is an unavoidable source of accumulating genetic variability that can have an impact on reproducibility over time. Nearly 20 years after the first release of the mouse reference genome, individuals from the strain it represents (C57BL/6J) are at least 26 inbreeding generations removed from the individuals used to generate the mouse reference genome. Moreover, C57BL/6J is now maintained through the periodic reintroduction of mice from cryopreserved embryo stocks that are derived from a single breeder pair, aptly named C57BL/6J Adam and Eve. To more accurately represent the genome of todays C57BL/6J mice, we have generated a de novo assembly of the C57BL/6J Eve genome (B6Eve) using high coverage, long-read sequencing, optical mapping, and short-read data. Using these data, we addressed recurring variants observed in previous mouse studies. We have also identified structural variations that impact coding sequences, closed gaps in the mouse reference assembly, some of which are in genes, and we have identified previously unannotated coding sequences through long read sequencing of cDNAs. This B6Eve assembly explains discrepant observations that have been associated with GRCm38-based analyses, and has provided data towards a reference genome that is more representative of the C57BL/6J mice that are in use today.

genomics↗

Nanopore native RNA sequencing of a human poly(A) transcriptome

High throughput cDNA sequencing technologies have dramatically advanced our understanding of transcriptome complexity and regulation. However, these methods lose information contained in biological RNA because the copied reads are often short and because modifications are not carried forward in cDNA. We address these limitations using a native poly(A) RNA sequencing strategy developed by Oxford Nanopore Technologies (ONT). Our study focused on poly(A) RNA from the human cell line GM12878, generating 9.9 million aligned sequence reads. These native RNA reads had an aligned N50 length of 1294 bases, and a maximum aligned length of over 21,000 bases. A total of 78,199 high-confidence isoforms were identified by combining long nanopore reads with short higher accuracy Illumina reads. We describe strategies for assessing 3' poly(A) tail length, base modifications and transcript haplotypes from nanopore RNA data. Together, these nanopore-based techniques are poised to deliver new insights into RNA biology.\n\nDISCLOSURESMA holds shares in Oxford Nanopore Technologies (ONT). MA is a paid consultant to ONT. REW, WT, TG, JRT, JQ, NJL, JTS, NS, AB, MA, HEO, MJ, and ML received reimbursement for travel, accommodation and conference fees to speak at events organised by ONT. NL has received an honorarium to speak at an ONT company meeting. WT has two patents (8,748,091 and 8,394,584) licensed to Oxford Nanopore. JTS, ML and MA received research funding from ONT.

genomics↗

KRAB Zinc Finger Proteins coordinate across evolutionary time scales to battle retroelements

KRAB Zinc Finger Proteins (KZNFs) are the largest and fastest evolving family of human transcription factors1,2. The evolution of this protein family is closely linked to the tempo of retrotransposable element (RTE) invasions, with specific KZNF family members demonstrated to transcriptionally repress specific families of RTEs3,4. The competing selective pressures between RTEs and the KZNFs results in evolutionary arms races whereby KZNFs evolve to recognize RTEs, while RTEs evolve to escape KZNF recognition5. Evolutionary analyses of the primate-specific RTE family L1PA and two of its KZNF binders, ZNF93 and ZNF649, reveal specific nucleotide and amino changes consistent with an arms race scenario. Our results suggest a model whereby ZNF649 and ZNF93 worked together to target independent motifs within the L1PA RTE lineage. L1PA elements eventually escaped the concerted action of this KZNF \"team\" over [~]30 million years through two distinct mechanisms: a slow accumulation of point mutations in the ZNF649 binding site and a rapid, massive deletion of the entire ZNF93 binding site.

evolutionary biology↗

An average-case sublinear exact Li and Stephens forward algorithm

Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithms as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its variants are linear in reference panel size unless heuristic approximations are used. However, sequencing projects numbering in the thousands to hundreds of thousands of individuals are underway, and others numbering in the millions are anticipated.\n\nTo make the Li and Stephens forward algorithm for these datasets computationally tractable, we have created a numerically exact version of the algorithm with observed average case [O](nk0.35) runtime, avoiding any tradeoff between runtime and model complexity. We demonstrate that our approach also provides a succinct data structure for general purpose haplotype data storage. We discuss generalizations of our algorithmic techniques to other hidden Markov models.\n\n2012 ACM Subject ClassificationTheory of computation {longrightarrow} Streaming, sublinear and near linear time algorithms; Applied computing {longrightarrow} Bioinformatics\n\nSupplement Materialhttps://github.com/yoheirosen/sublinear-Li-Stephens.\n\nFundingThis work was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number 5U54HG007990, the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number 1U01HL137183-01, and grants from the W.M. Keck foundation and the Simons Foundation.\n\nAcknowledgementsWe would like to thank Jordan Eizenga for his helpful discussions throughout the development of this work.

bioinformatics↗

Haplotype-aware genotyping from noisy long reads

MotivationCurrent genotyping approaches for single nucleotide variations (SNVs) rely on short, relatively accurate reads from second generation sequencing devices. Presently, third generation sequencing platforms able to generate much longer reads are becoming more widespread. These platforms come with the significant drawback of higher sequencing error rates, which makes them ill-suited to current genotyping algorithms. However, the longer reads make more of the genome unambiguously mappable and typically provide linkage information between neighboring variants.\n\nResultsIn this paper we introduce a novel approach for haplotype-aware genotyping from noisy long reads. We do this by considering bipartitions of the sequencing reads, corresponding to the two haplotypes. We formalize the computational problem in terms of a Hidden Markov Model and compute posterior genotype probabilities using the forward-backward algorithm. Genotype predictions can then be made by picking the most likely genotype at each site. Our experiments indicate that longer reads allow significantly more of the genome to potentially be accurately genotyped. Further, we are able to use both Oxford Nanopore and Pacific Biosciences sequencing data to independently validate millions of variants previously identified by short-read technologies in the reference NA12878 sample, including hundreds of thousands of variants that were not previously included in the high-confidence reference set.

bioinformatics↗

Sequence variation aware genome references and read mapping with the variation graph toolkit

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individuals genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.

genomics↗

Comparative Annotation Toolkit (CAT) - simultaneous clade and personal genome annotation

The recent introductions of low-cost, long-read, and read-cloud sequencing technologies coupled with intense efforts to develop efficient algorithms have made affordable, high-quality de novo sequence assembly a realistic proposition. The result is an explosion of new, ultra-contiguous genome assemblies. To compare these genomes we need robust methods for genome annotation. We describe the fully open source Comparative Annotation Toolkit (CAT), which provides a flexible way to simultaneously annotate entire clades and identify orthology relationships. We show that CAT can be used to improve annotations on the rat genome, annotate the great apes, annotate a diverse set of mammals, and annotate personal, diploid human genomes. We demonstrate the resulting discovery of novel genes, isoforms and structural variants, even in genomes as well studied as rat and the great apes, and how these annotations improve cross-species RNA expression experiments.

bioinformatics↗

Linear Assembly of a Human Y Centromere using Nanopore Long Reads

The human genome reference sequence remains incomplete due to the challenge of assembling long tracts of near-identical tandem repeats-in centromeric regions. To address this, we have implemented a nanopore sequencing strategy to generate high quality reads that span hundreds of kilobases of highly repetitive DNAs. Here, we use this advance to produce a sequence assembly and characterization of the centromeric region of a human Y chromosome.

genomics↗