A complete reference genome improves analysis of human genetic variation

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 Mbp of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome to clinical and functional study. Here we demonstrate how the new reference universally improves read mapping and variant calling for 3,202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of novel variants per sample - a new frontier for evolutionary and biomedical discovery. Simultaneously, the new reference eliminates tens of thousands of spurious variants per sample, including up to a 12-fold reduction of false positives in 269 medically relevant genes. The vast improvement in variant discovery coupled with population and functional genomic resources position T2T-CHM13 to replace GRCh38 as the prevailing reference for human genetics.

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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks

10.1101/2019.12.29.890418 ◽

2019 ◽

Cited By ~ 1

Author(s):

Umair Ahsan ◽

Qian Liu ◽

Li Fang ◽

Kai Wang

Keyword(s):

Deep Neural Network ◽

Deep Neural Networks ◽

Variant Calling ◽

Sequencing Data ◽

Long Reads ◽

Novel Variants ◽

Long Read ◽

Variant Detection ◽

Genomic Regions ◽

Haplotype Information

AbstractVariant (SNPs/indels) detection from high-throughput sequencing data remains an important yet unresolved problem. Long-read sequencing enables variant detection in difficult-to-map genomic regions that short-read sequencing cannot reliably examine (for example, only ~80% of genomic regions are marked as “high-confidence region” to have SNP/indel calls in the Genome In A Bottle project); however, the high per-base error rate poses unique challenges in variant detection. Existing methods on long-read data typically rely on analyzing pileup information from neighboring bases surrounding a candidate variant, similar to short-read variant callers, yet the benefits of much longer read length are not fully exploited. Here we present a deep neural network called NanoCaller, which detects SNPs by examining pileup information solely from other nonadjacent candidate SNPs that share the same long reads using long-range haplotype information. With called SNPs by NanoCaller, NanoCaller phases long reads and performs local realignment on two sets of phased reads to call indels by another deep neural network. Extensive evaluation on 5 human genomes (sequenced by Nanopore and PacBio long-read techniques) demonstrated that NanoCaller greatly improved performance in difficult-to-map regions, compared to other long-read variant callers. We experimentally validated 41 novel variants in difficult-to-map regions in a widely-used benchmarking genome, which cannot be reliably detected previously. We extensively evaluated the run-time characteristics and the sensitivity of parameter settings of NanoCaller to different characteristics of sequencing data. Finally, we achieved the best performance in Nanopore-based variant calling from MHC regions in the PrecisionFDA Variant Calling Challenge on Difficult-to-Map Regions by ensemble calling. In summary, by incorporating haplotype information in deep neural networks, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing data.

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A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings

International Journal of Molecular Sciences ◽

10.3390/ijms21239177 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9177

Author(s):

Simone Maestri ◽

Maria Giovanna Maturo ◽

Emanuela Cosentino ◽

Luca Marcolungo ◽

Barbara Iadarola ◽

...

Keyword(s):

Diagnostic Testing ◽

Variant Calling ◽

Clinical Settings ◽

Sequencing Data ◽

Sequencing Platform ◽

Variant Discovery ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Second Generation Sequencing

The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.

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Genome Graphs

10.1101/101378 ◽

2017 ◽

Cited By ~ 34

Author(s):

Adam M. Novak ◽

Glenn Hickey ◽

Erik Garrison ◽

Sean Blum ◽

Abram Connelly ◽

...

Keyword(s):

Reference Genome ◽

Human Genetics ◽

De Novo ◽

Variant Calling ◽

Reference Structure ◽

Read Mapping ◽

Human Reference Genome ◽

A Genome ◽

Universal Reference ◽

Genome Graph

AbstractThere is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

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Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

10.1101/2021.05.29.446291 ◽

2021 ◽

Author(s):

Yilei Fu ◽

Medhat Mahmoud ◽

Viginesh Vaibhav Muraliraman ◽

Fritz J Sedlazeck ◽

Todd J Treangen

Keyword(s):

Human Genome ◽

Variant Calling ◽

Dual Mode ◽

Read Mapping ◽

Structural Variant ◽

Long Reads ◽

Oxford Nanopore ◽

Mutational Hotspots ◽

Long Read ◽

High Level

Background: Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely-used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection. Findings: We tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone. Conclusions: Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan

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Merfin: improved variant filtering and polishing via k-mer validation

10.1101/2021.07.16.452324 ◽

2021 ◽

Author(s):

Giulio Formenti ◽

Arang Rhie ◽

Brian P Walenz ◽

Francoise Thibaud-Nissen ◽

Kishwar Shafin ◽

...

Keyword(s):

Human Genome ◽

Variant Calling ◽

Read Mapping ◽

Mapping Algorithm ◽

Copy Numbers ◽

Long Reads ◽

Variant Filtering ◽

Long Read ◽

Finishing Tool

Read mapping and variant calling approaches have been widely used for accurate genotyping and improving consensus quality assembled from noisy long reads. Variant calling accuracy relies heavily on the read quality, the precision of the read mapping algorithm and variant caller, and the criteria adopted to filter the calls. However, it is impossible to define a single set of optimal parameters, as they vary depending on the quality of the read set, the variant caller of choice, and the quality of the unpolished assembly. To overcome this issue, we have devised a new tool called Merfin (k-mer based finishing tool), a k-mer based variant filtering algorithm for improved genotyping and polishing. Merfin evaluates the accuracy of a call based on expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller internal score. Moreover, we introduce novel assembly quality and completeness metrics that account for the expected genomic copy numbers. Merfin significantly increased the precision of a variant call and reduced frameshift errors when applied to PacBio HiFi, PacBio CLR, or Nanopore long read based assemblies. We demonstrate the utility while polishing the first complete human genome, a fully phased human genome, and non-human high-quality genomes.

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Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

GigaScience ◽

10.1093/gigascience/giab063 ◽

2021 ◽

Vol 10 (9) ◽

Author(s):

Yilei Fu ◽

Medhat Mahmoud ◽

Viginesh Vaibhav Muraliraman ◽

Fritz J Sedlazeck ◽

Todd J Treangen

Keyword(s):

Human Genome ◽

Variant Calling ◽

Dual Mode ◽

Read Mapping ◽

Structural Variant ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

High Level ◽

Improved Accuracy

Abstract Background Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. Findings We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. Conclusions Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.

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Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

10.1101/269316 ◽

2018 ◽

Cited By ~ 1

Author(s):

Allison A. Regier ◽

Yossi Farjoun ◽

David Larson ◽

Olga Krasheninina ◽

Hyun Min Kang ◽

...

Keyword(s):

Data Processing ◽

Genome Sequencing ◽

Statistical Power ◽

Human Genetics ◽

Variant Calling ◽

Joint Analysis ◽

Sequencing Analysis ◽

Batch Effects ◽

Many Sources ◽

Genomic Regions

AbstractHundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes. Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce “functionally equivalent” (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results – including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) – and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide “big-data” human genetics studies.

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Chromosome-level de novo assembly of Coprinopsis cinerea A43mut B43mut pab1-1 #326 and genetic variant identification of mutants using Nanopore MinION sequencing

10.1101/2020.11.09.367581 ◽

2020 ◽

Author(s):

Yichun Xie ◽

Yiyi Zhong ◽

Jinhui Chang ◽

Hoi Shan Kwan

Keyword(s):

Genome Assembly ◽

Dna Isolation ◽

Variant Calling ◽

Genomic Analysis ◽

Single Nucleotide ◽

Sequencing Platform ◽

Genomic Dna Isolation ◽

Long Reads ◽

Dna Isolation Protocol ◽

Chromosome Level

AbstractThe homokaryotic Coprinopsis cinerea strain A43mut B43mut pab1-1 #326 is a widely used experimental model for developmental studies in mushroom-forming fungi. It can grow on defined artificial media and complete the whole lifecycle within two weeks. The mutations in mating type factors A and B result in the special feature of clamp formation and fruiting without mating. This feature allows investigations and manipulations with a homokaryotic genetic background. Current genome assembly of strain #326 was based on short-read sequencing data and was highly fragmented, leading to the bias in gene annotation and downstream analyses. Here, we report a chromosome-level genome assembly of strain #326. Oxford Nanopore Technology (ONT) MinION sequencing was used to get long reads. Illumina short reads was used to polish the sequences. A combined assembly yield 13 chromosomes and a mitochondrial genome as individual scaffolds. The assembly has 15,250 annotated genes with a high synteny with the C. cinerea strain Okayama-7 #130. This assembly has great improvement on contiguity and annotations. It is a suitable reference for further genomic studies, especially for the genetic, genomic and transcriptomic analyses in ONT long reads. Single nucleotide variants and structural variants in six mutagenized and cisplatin-screened mutants could be identified and validated. A 66 bp deletion in Ras GTPase-activating protein (RasGAP) was found in all mutants. To make a better use of ONT sequencing platform, we modified a high-molecular-weight genomic DNA isolation protocol based on magnetic beads for filamentous fungi. This study showed the use of MinION to construct a fungal reference genome and to perform downstream studies in an individual laboratory. An experimental workflow was proposed, from DNA isolation and whole genome sequencing, to genome assembly and variant calling. Our results provided solutions and parameters for fungal genomic analysis on MinION sequencing platform.HighlightA chromosome-level genome assembly of C. cinerea #326A fast and efficient high-molecular-weight fungal genomic DNA isolation protocolStructural variant and single nucleotide variant calling using Nanopore readsA series of solutions and reference parameters for fungal genomic analysis on MinION

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Fast and sensitive mapping of error-prone nanopore sequencing reads with GraphMap

10.1101/020719 ◽

2015 ◽

Cited By ~ 1

Author(s):

Ivan Sovic ◽

Mile Sikic ◽

Andreas Wilm ◽

Shannon Nicole Fenlon ◽

Swaine Chen ◽

...

Keyword(s):

Human Genome ◽

Variant Calling ◽

Error Rates ◽

Nanopore Sequencing ◽

Structural Variants ◽

Specific Identification ◽

Long Reads ◽

Long Read ◽

Specific Error ◽

Very High

Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast graph traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWA-MEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.

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Geographic patterns of human allele frequency variation: a variant-centric perspective

10.1101/2020.07.01.182311 ◽

2020 ◽

Author(s):

Arjun Biddanda ◽

Daniel P. Rice ◽

John Novembre

Keyword(s):

Genetic Variation ◽

Gene Flow ◽

Genetic Structure ◽

Allele Frequency ◽

Human Genetics ◽

Geographic Region ◽

Frequency Variation ◽

Human Genetic Variation ◽

Alternative Representation ◽

Geographic Patterns

AbstractA key challenge in human genetics is to describe and understand the distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, in each case drawing inferences over a large number of variants. Here, we present an alternative representation of human genetic variation that reveals the relative abundance of different allele frequency patterns across populations. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are common globally, regardless of whether the individuals are from the same region or different regions. To guide interpretation of the results, we also apply the visualization to contrasting theoretical scenarios with varying levels of divergence and gene flow. Our variant-centric visualization clarifies the major geographic patterns of human variation and can be used to help correct potential misconceptions about the extent and nature of genetic differentiation among populations.

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