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

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

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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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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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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BiSCoT: Improving large eukaryotic genome assemblies with optical maps

10.1101/674721 ◽

2019 ◽

Cited By ~ 1

Author(s):

Benjamin Istace ◽

Caroline Belser ◽

Jean-Marc Aury

Keyword(s):

Human Genome ◽

Fasta File ◽

Plant Genomes ◽

Link Type ◽

Long Reads ◽

Entire Chromosome ◽

Long Read ◽

Optical Maps ◽

Genome Assemblies

ABSTRACTMotivationLong read sequencing and Bionano Genomics optical maps are two techniques that, when used together, make it possible to reconstruct entire chromosome or chromosome arms structure. However, the existing tools are often too conservative and organization of contigs into scaffolds is not always optimal.ResultsWe developed BiSCoT (Bionano SCaffolding COrrection Tool), a tool that post-processes files generated during a Bionano scaffolding in order to produce an assembly of greater contiguity and quality. BiSCoT was tested on a human genome and four publicly available plant genomes sequenced with Nanopore long reads and improved significantly the contiguity and quality of the assemblies. BiSCoT generates a fasta file of the assembly as well as an AGP file which describes the new organization of the input assembly.AvailabilityBiSCoT and improved assemblies are freely available on Github at http://www.genoscope.cns.fr/biscot and Pypi at https://pypi.org/project/biscot/.

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BiSCoT: improving large eukaryotic genome assemblies with optical maps

PeerJ ◽

10.7717/peerj.10150 ◽

2020 ◽

Vol 8 ◽

pp. e10150

Author(s):

Benjamin Istace ◽

Caroline Belser ◽

Jean-Marc Aury

Keyword(s):

Human Genome ◽

Fasta File ◽

Plant Genomes ◽

Link Type ◽

Long Reads ◽

Entire Chromosome ◽

Long Read ◽

Optical Maps ◽

Genome Assemblies

Motivation Long read sequencing and Bionano Genomics optical maps are two techniques that, when used together, make it possible to reconstruct entire chromosome or chromosome arms structure. However, the existing tools are often too conservative and organization of contigs into scaffolds is not always optimal. Results We developed BiSCoT (Bionano SCaffolding COrrection Tool), a tool that post-processes files generated during a Bionano scaffolding in order to produce an assembly of greater contiguity and quality. BiSCoT was tested on a human genome and four publicly available plant genomes sequenced with Nanopore long reads and improved significantly the contiguity and quality of the assemblies. BiSCoT generates a fasta file of the assembly as well as an AGP file which describes the new organization of the input assembly. Availability BiSCoT and improved assemblies are freely available on GitHub at http://www.genoscope.cns.fr/biscot and Pypi at https://pypi.org/project/biscot/.

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Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly

Genome Biology ◽

10.1186/s13059-020-02244-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Guillaume Holley ◽

Doruk Beyter ◽

Helga Ingimundardottir ◽

Peter L. Møller ◽

Snædis Kristmundsdottir ◽

...

Keyword(s):

Error Correction ◽

Human Genome ◽

Error Rate ◽

Variant Calling ◽

High Error Rate ◽

Sequencing Data ◽

Short Read ◽

Long Reads ◽

Median Error ◽

Long Read

AbstractA major challenge to long read sequencing data is their high error rate of up to 15%. We present Ratatosk, a method to correct long reads with short read data. We demonstrate on 5 human genome trios that Ratatosk reduces the error rate of long reads 6-fold on average with a median error rate as low as 0.22 %. SNP calls in Ratatosk corrected reads are nearly 99 % accurate and indel calls accuracy is increased by up to 37 %. An assembly of Ratatosk corrected reads from an Ashkenazi individual yields a contig N50 of 45 Mbp and less misassemblies than a PacBio HiFi reads assembly.

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Expanding an expanded genome: long-read sequencing ofTrypanosoma cruzi

10.1101/279174 ◽

2018 ◽

Cited By ~ 3

Author(s):

Luisa Berná ◽

Matías Rodríguez ◽

María Laura Chiribao ◽

Adriana Parodi-Talice ◽

Sebastián Pita ◽

...

Keyword(s):

Repetitive Sequences ◽

Gc Content ◽

Gene Copy ◽

Accurate Estimation ◽

Sequencing Technologies ◽

Copy Numbers ◽

Long Reads ◽

Long Read ◽

Large Clusters ◽

Gene Copy Numbers

Although the genome ofTrypanosoma cruzi, the causative agent of Chagas disease, was first made available in 2005, with additional strains reported later, the intrinsic genome complexity of this parasite (abundance of repetitive sequences and genes organized in tandem) has traditionally hindered high-quality genome assembly and annotation. This also limits diverse types of analyses that require high degree of precision. Long reads generated by third-generation sequencing technologies are particularly suitable to address the challenges associated withT. cruzi´sgenome since they permit directly determining the full sequence of large clusters of repetitive sequences without collapsing them. This, in turn, allows not only accurate estimation of gene copy numbers but also circumvents assembly fragmentation. Here, we present the analysis of the genome sequences of twoT. cruziclones: the hybrid TCC (DTU TcVI) and the non-hybrid Dm28c (DTU TcI), determined by PacBio SMRT technology. The improved assemblies herein obtained permitted us to accurately estimate gene copy numbers, abundance and distribution of repetitive sequences (including satellites and retroelements). We found that the genome ofT. cruziis composed of a "core compartment" and a "disruptive compartment" which exhibit opposite gene and GC content composition. New tandem and disperse repetitive sequences were identified, including some located inside coding sequences. Additionally, homologous chromosomes were separately assembled, allowing us to retrieve haplotypes as separate contigs instead of a unique mosaic sequence. Finally, manual annotation of surface multigene families MUC and trans-sialidases allows now a better overview of these complex groups of genes.

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Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.761791 ◽

2021 ◽

Vol 12 ◽

Author(s):

Davide Bolognini ◽

Alberto Magi

Keyword(s):

Variant Calling ◽

Research Report ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Factors Affecting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Studies ◽

Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

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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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Reliable variant calling during runtime of Illumina sequencing

10.1101/387662 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tobias P. Loka ◽

Simon H. Tausch ◽

Bernhard Y. Renard

Keyword(s):

Real Time ◽

Disease Outbreaks ◽

Variant Calling ◽

Read Mapping ◽

Mapping Algorithm ◽

Infectious Disease Outbreaks ◽

Whole Exome ◽

Similar Accuracy ◽

Post Hoc

AbstractThe sequential paradigm of data acquisition and analysis in next-generation sequencing leads to high turnaround times for the generation of interpretable results. We combined a novel real-time read mapping algorithm with fast variant calling to obtain reliable variant calls still during the sequencing process. Thereby, our new algorithm allows for accurate read mapping results for intermediate cycles and supports large reference genomes such as the complete human reference. This enables the combination of real-time read mapping results with complex follow-up analysis. In this study, we showed the accuracy and scalability of our approach by applying real-time read mapping and variant calling to seven publicly available human whole exome sequencing datasets. Thereby, up to 89% of all detected SNPs were already identified after 40 sequencing cycles while showing similar precision as at the end of sequencing. Final results showed similar accuracy to those of conventional post-hoc analysis methods. When compared to standard routines, our live approach enables considerably faster interventions in clinical applications and infectious disease outbreaks. Besides variant calling, our approach can be adapted for a plethora of other mapping-based analyses.

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