Biology subjects

Nattestad, M.

Publications and source records attributed to Nattestad, M..

13 recordsLinked to original sources

bioRxiv2024

Highly accurate assembly polishing with DeepPolisher

Accurate genome assemblies are essential for biological research, but even the highest quality assemblies retain errors caused by the technologies used to construct them. Base-level errors are typically fixed with an additional polishing step that uses reads aligned to the draft assembly to identify necessary edits. However, current methods struggle to find a balance between over- and under-polishing. Here, we present an encoder-only transformer model for assembly polishing called DeepPolisher, which predicts corrections to the underlying sequence using Pacbio HiFi read alignments to a diploid assembly. Our pipeline introduces a method, PHARAOH (Phasing Reads in Areas Of Homozygosity), which uses ultra-long ONT data to ensure alignments are accurately phased and to correctly introduce heterozygous edits in falsely homozygous regions. We demonstrate that the DeepPolisher pipeline can reduce assembly errors by half, with a greater than 70% reduction in indel errors. We have applied our DeepPolisher-based pipeline to 180 assemblies from the next Human Pangenome Reference Consortium (HPRC) data release, producing an average predicted Quality Value (QV) improvement of 3.4 (54% error reduction) for the majority of the genome.

bioinformatics↗

bioRxiv2023

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

bioinformatics↗

bioRxiv2023

Accurate human genome analysis with Element Avidity sequencing

We investigate the new sequencing technology Avidity from Element Biosciences. We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20x-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Elements ability to generate paired end sequencing with longer insert sizes than typical short-read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving noticeably more accurate genome analyses at all coverages.

bioinformatics↗

bioRxiv2021

DeepConsensus: Gap-Aware Sequence Transformers for Sequence Correction

Pacific BioScience (PacBio) circular consensus sequencing (CCS) generates long (10-25 kb), accurate "HiFi" reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation uses a hidden Markov model (pbccs). Here, we introduce DeepConsensus, which uses a unique alignment-based loss to train a gap-aware transformer-encoder (GATE) for sequence correction. Compared to pbccs, DeepConsensus reduces read errors in the same dataset by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27%, and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9Mb to 17.2Mb), increase gene completeness (94% to 97%), reduce false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45), and also reduce variant calling errors by 24%.

bioinformatics↗

bioRxiv2021

DeepTrio: Variant Calling in Families Using Deep Learning

Every human inherits one copy of the genome from their mother and another from their father. Parental inheritance helps us understand the transmission of traits and genetic diseases, which often involve de novo variants and rare recessive alleles. Here we present DeepTrio, which learns to analyze child-mother-father trios from the joint sequence information, without explicit encoding of inheritance priors. DeepTrio learns how to weigh sequencing error, mapping error, and de novo rates and genome context directly from the sequence data. DeepTrio has higher accuracy on both Illumina and PacBio HiFi data when compared to DeepVariant. Improvements are especially pronounced at lower coverages (with 20x DeepTrio roughly equivalent to 30x DeepVariant). DeepTrio includes pre-trained models for Illumina WGS, Illumina exome, and PacBio HiFi.

bioinformatics↗

bioRxiv2021

Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read based phasing. Third-generation nanopore sequence data has demonstrated a long read length, but current interpretation methods for its novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline PEPPER-Margin-DeepVariant that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single nucleotide variant identification method at the whole genome-scale and produces high-quality single nucleotide variants in segmental duplications and low-mappability regions where short-read based genotyping fails. We show that our pipeline can provide highly-contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% to 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance than the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio-HiFi-polished).

bioinformatics↗

bioRxiv2020

An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development

Accurate standards and extensive development datasets are the foundation of technical progress. To facilitate benchmarking and development, we sequence 9 samples, covering the Genome in a Bottle truth sets on multiple instruments (NovaSeq, HiSeqX, HiSeq4000, PacBio Sequel II System) and sample preparations (PCR-Free, PCR-Positive) for both whole genome and multiple exome kits. We benchmark pipelines, quantifying strengths and limitations for sequencing and analysis methods. We identify variability within and between instruments, preparation methods, and analytical pipelines, across various sequencing depths. We discuss the relevance of this variability to downstream analyses, and strategies to reduce variability.

genomics↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗

bioRxiv2019

BigTop: A Three-Dimensional Virtual Reality Tool for GWAS Visualization

BackgroundGenome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset.\n\nResultsWe created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file.\n\nConclusionsUsing additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

bioinformatics↗

bioRxiv2017

Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences, and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available with nearly 20,000 variants present, most of which were missed by prior efforts. Surrounding the important HER2 locus, we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the transcriptome and sheds new light on the complexity of cancer progression.

genomics↗

bioRxiv2017

Accurate detection of complex structural variations using single molecule sequencing

Structural variations (SVs) are the largest source of genetic variation, but remain poorly understood because of limited genomics technology. Single molecule long read sequencing from Pacific Biosciences and Oxford Nanopore has the potential to dramatically advance the field, although their high error rates challenge existing methods. Addressing this need, we introduce open-source methods for long read alignment (NGMLR, https://github.com/philres/ngmlr) and SV identification (Sniffles, https://github.com/fritzsedlazeck/Sniffles) that enable unprecedented SV sensitivity and precision, including within repeat-rich regions and of complex nested events that can have significant impact on human disorders. Examining several datasets, including healthy and cancerous human genomes, we discover thousands of novel variants using long reads and categorize systematic errors in short-read approaches. NGMLR and Sniffles are further able to automatically filter false events and operate on low amounts of coverage to address the cost factor that has hindered the application of long reads in clinical and research settings.

bioinformatics↗

bioRxiv2016

SplitThreader: Exploration and analysis of rearrangements in cancer genomes

Genomic rearrangements and associated copy number changes are important drivers in cancer as they can alter the expression of oncogenes and tumor suppressors, create gene fusions, and misregulate gene expression. Here we present SplitThreader (http://splitthreader.com), an open-source interactive web application for analysis and visualization of genomic rearrangements and copy number variation in cancer genomes. SplitThreader constructs a sequence graph of genomic rearrangements in the sample and uses a priority queue breadth-first search algorithm on the graph to search for novel interactions. This is applied to detect gene fusions and other novel sequences, as well as to evaluate distances in the rearranged genome between any genomic regions of interest, especially the repositioning of regulatory elements and their target genes. SplitThreader also analyzes each variant to categorize it by its relation to other variants and by its copy number concordance. This identifies balanced translocations, identifies simple and complex variants, and suggests likely false positives when copy number is not concordant across a candidate breakpoint. It also provides explanations when multiple variants affect the copy number state and obscure the contribution of a single variant, such as a deletion within a region that is overall amplified. Together, these categories triage the variants into groups and provide a starting point for further systematic analysis and manual curation. To demonstrate its utility, we apply SplitThreader to three cancer cell lines, MCF-7 and A549 with Illumina paired-end sequencing, and SK-BR-3, with long-read PacBio sequencing. Using SplitThreader, we examine the genomic rearrangements responsible for previously observed gene fusions in SK-BR-3 and MCF-7, and discover many of the fusions involved a complex series of multiple genomic rearrangements. We also find notable differences in the types of variants between the three cell lines, in particular a much higher proportion of reciprocal variants in SK-BR-3 and a distinct clustering of interchromosomal variants in SK-BR-3 and MCF-7 that is absent in A549.

bioinformatics↗

bioRxiv2016

Ribbon: Visualizing complex genome alignments and structural variation

To the EditorVisualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships1-3. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes.

bioinformatics↗