Nanopanel2 calls phased low-frequency variants in Nanopore panel sequencing data

Clinical decision making is increasingly guided by accurate and recurrent determination of presence and frequency of (somatic) variants and their haplotype through panel sequencing of disease-relevant genomic regions. Haplotype calling (phasing), however, is difficult and error prone unless variants are located on the same read which limits the ability of short-read sequencing to detect, e.g., co-occurrence of drug-resistance variants. Long-read panel sequencing enables direct phasing of amplicon variants besides having multiple other benefits, however, high error rates of current technologies prevented their applicability in the past. We have developed nanopanel2 (np2), a variant caller for Nanopore panel sequencing data. Np2 works directly on base-called FAST5 files and uses allele probability distributions and several other filters to robustly separate true from false positive calls. It effectively calls SNVs and INDELs with variant allele frequencies (VAF) as low as 1% and 5% respectively and produces only few low-frequency false-positive calls. Haplotype compositions are then determined by direct phasing. Np2 is the first somatic variant caller for Nanopore data, enabling accurate, fast (turnaround <48h) and cheap (sequencing costs ~10$/sample) diagnostic workflows.

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Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

10.1101/2022.01.11.475979 ◽

2022 ◽

Author(s):

Kumeren Nadaraj Govender ◽

David W Eyre

Keyword(s):

Clinical Decision Making ◽

Sequence Data ◽

Clinical Decision ◽

Classification Performance ◽

Error Rates ◽

Species Level ◽

Sequencing Error ◽

Sequencing Data ◽

Real World Data ◽

Median Percentage

Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of ten common bloodstream pathogens and six frequent blood culture contaminants (n=1600). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken, and Centrifuge, utilising mini (8GB) and standard (30-50GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 98.46% (IQR 93.0:99.3) [range 57.1:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 79.3% (IQR 39.1:88.8) [range 11.2:100]. Classification performance varied by species, with E. coli being more challenging to classify correctly (59.4% to 96.4% reads with correct species, varying by tool used). By filtering out shorter Nanopore reads (<3500bp) we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. This “bioinformatic contamination” should be accounted for in metagenomic pipelines to ensure accurate results that can support clinical decision making.

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Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads

Nature Biotechnology ◽

10.1038/s41587-020-0719-5 ◽

2020 ◽

Author(s):

David Porubsky ◽

◽

Peter Ebert ◽

Peter A. Audano ◽

Mitchell R. Vollger ◽

...

Keyword(s):

Single Cell ◽

Genome Assembly ◽

De Novo ◽

Error Rates ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

De Novo Genome Assembly ◽

Parental Data ◽

Human Genome Assembly ◽

Long Read

AbstractHuman genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.

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LRSDAY: Long-read Sequencing Data Analysis for Yeasts

10.1101/184572 ◽

2017 ◽

Author(s):

Jia-Xing Yue ◽

Gianni Liti

Keyword(s):

Genome Assembly ◽

Model Organism ◽

Sequencing Data ◽

Protein Coding ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read ◽

Downstream Analysis ◽

Eukaryotic Organisms ◽

Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

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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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SGPNet: A Three-Dimensional Multitask Residual Framework for Segmentation and IDH Genotype Prediction of Gliomas

Computational Intelligence and Neuroscience ◽

10.1155/2021/5520281 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yao Wang ◽

Yan Wang ◽

Chunjie Guo ◽

Shuangquan Zhang ◽

Lili Yang

Keyword(s):

Brain Tumor ◽

Clinical Decision Making ◽

Three Dimensional ◽

Clinical Decision ◽

Error Rates ◽

The Cancer Genome Atlas ◽

Classification Error ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

The Status

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.

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Minimizer-space de Bruijn graphs

10.1101/2021.06.09.447586 ◽

2021 ◽

Author(s):

Barış Ekim ◽

Bonnie Berger ◽

Rayan Chikhi

Keyword(s):

Human Genome ◽

Dna Sequences ◽

Graphical Representation ◽

Error Rates ◽

Sequencing Error ◽

Sequencing Data ◽

De Bruijn Graphs ◽

Human Genome Assembly ◽

Long Read ◽

Metagenome Assembly

DNA sequencing data continues to progress towards longer reads with increasingly lower sequencing error rates. We focus on the problem of assembling such reads into genomes, which poses challenges in terms of accuracy and computational resources when using cutting-edge assembly approaches, e.g. those based on overlapping reads using minimizer sketches. Here, we introduce the concept of minimizer-space sequencing data analysis, where the minimizers rather than DNA nucleotides are the atomic tokens of the alphabet. By projecting DNA sequences into ordered lists of minimizers, our key idea is to enumerate what we call k-min-mers, that are k-mers over a larger alphabet consisting of minimizer tokens. Our approach, mdBG or minimizer-dBG, achieves orders-of magnitude improvement in both speed and memory usage over existing methods without much loss of accuracy. We demonstrate three uses cases of mdBG: human genome assembly, metagenome assembly, and the representation of large pangenomes. For assembly, we implemented mdBG in software we call rust-mdbg, resulting in ultra-fast, low memory and highly-contiguous assembly of PacBio HiFi reads. A human genome is assembled in under 10 minutes using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4 minutes using 1 GB RAM. For pangenome graphs, we newly allow a graphical representation of a collection of 661,405 bacterial genomes as an mdBG and successfully search it (in minimizer-space) for anti-microbial resistance (AMR) genes. We expect our advances to be essential to sequence analysis, given the rise of long-read sequencing in genomics, metagenomics and pangenomics.

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FaStore – a space-saving solution for raw sequencing data

10.1101/168096 ◽

2017 ◽

Cited By ~ 1

Author(s):

Łukasz Roguski ◽

Idoia Ochoa ◽

Mikel Hernaez ◽

Sebastian Deorowicz

Keyword(s):

Decision Making ◽

Compression Ratio ◽

Clinical Decision Making ◽

Variant Calling ◽

Clinical Decision ◽

Genomic Information ◽

Sequencing Data ◽

Space Saving ◽

Reference Sequences ◽

Significant Compression

AbstractThe affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed, and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. The proposed algorithm does not use any reference sequences for compression, and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs. We demonstrate through extensive simulations that FaStore achieves a significant improvement in compression ratio with respect to previously proposed algorithms for this task. In addition, we perform an analysis on the effect that the different lossy modes have on variant calling, the most widely used application for clinical decision making, especially important in the era of precision medicine. We show that lossy compression can offer significant compression gains, while preserving the essential genomic information and without affecting the variant calling performance.

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Noise-cancelling repeat finder: uncovering tandem repeats in error-prone long-read sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btz484 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4809-4811 ◽

Cited By ~ 8

Author(s):

Robert S Harris ◽

Monika Cechova ◽

Kateryna D Makova

Keyword(s):

Tandem Repeats ◽

Error Rates ◽

Superior Performance ◽

Supplementary Information ◽

Whole Genome Sequencing Data ◽

Dna Repeats ◽

Sequencing Data ◽

Heat Shock Stress ◽

Noise Cancelling ◽

Long Read

Abstract Summary Tandem DNA repeats can be sequenced with long-read technologies, but cannot be accurately deciphered due to the lack of computational tools taking high error rates of these technologies into account. Here we introduce Noise-Cancelling Repeat Finder (NCRF) to uncover putative tandem repeats of specified motifs in noisy long reads produced by Pacific Biosciences and Oxford Nanopore sequencers. Using simulations, we validated the use of NCRF to locate tandem repeats with motifs of various lengths and demonstrated its superior performance as compared to two alternative tools. Using real human whole-genome sequencing data, NCRF identified long arrays of the (AATGG)n repeat involved in heat shock stress response. Availability and implementation NCRF is implemented in C, supported by several python scripts, and is available in bioconda and at https://github.com/makovalab-psu/NoiseCancellingRepeatFinder. Supplementary information Supplementary data are available at Bioinformatics online.

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Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbz058 ◽

2019 ◽

Vol 21 (4) ◽

pp. 1164-1181 ◽

Cited By ~ 9

Author(s):

Leandro Lima ◽

Camille Marchet ◽

Ségolène Caboche ◽

Corinne Da Silva ◽

Benjamin Istace ◽

...

Keyword(s):

Error Correction ◽

Rna Sequencing ◽

Gene Families ◽

Error Rates ◽

Open Reading Frames ◽

Sequencing Data ◽

Isoform Diversity ◽

Long Reads ◽

Long Read ◽

Read Error Correction

Abstract Motivation Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software https://gitlab.com/leoisl/LR_EC_analyser

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Translating Genomics to the Clinic: Implications of Cancer Heterogeneity

Clinical Chemistry ◽

10.1373/clinchem.2012.184580 ◽

2013 ◽

Vol 59 (1) ◽

pp. 127-137 ◽

Cited By ~ 13

Author(s):

Nardin Samuel ◽

Thomas J Hudson

Keyword(s):

Genome Sequencing ◽

Large Scale ◽

Clinical Decision Making ◽

Cancer Genomics ◽

Clinical Decision ◽

Sequencing Analysis ◽

Breast Cancers ◽

Sequencing Data ◽

Cancer Heterogeneity ◽

Cancer Genomes

BACKGROUND Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics. CONTENT The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients. SUMMARY The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

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