Biology subjects

Grytten, I.

Publications and source records attributed to Grytten, I..

9 recordsLinked to original sources

bioRxiv2023

KAGE 2: Fast and accurate genotyping of structural variation using pangenomes

Structural variation is known to play an important and often overlooked role in regulating disease and traits, but accurately detecting structural variants from sequencing data has traditionally been difficult. However, recent improvements in high-quality genome assembly along with methodological advancements in pangenome creation have opened up the landscape for methods that use such pangenomes for structural variant calling and genotyping. We here present KAGE2, which accurately and efficiently genotypes structural variation by exploiting the availability of pangenomes that represent known variation in a population. Through comprehensive benchmarking, we highlight limitations of existing methodology and show that KAGE2 is more accurate and considerably faster than existing methods.

bioinformatics↗

bioRxiv2023

Ultra-fast genotyping of SNPs and short indels using GPU acceleration

As decreasing DNA sequencing costs leads to a steadily increasing rate of generated data, the development of efficient algorithms for processing of the sequence data is increasingly important to reduce costs and energy consumption. Recent work have shown that genotyping can be done efficiently and accurately using alignment-free methods that are based on analyzing kmers from sequenced reads. In particular, we have recently presented the KAGE genotyper, which uses an efficient pangenome representation of known individuals in a population to further increase accuracy and efficiency. While existing genotypers like KAGE use the Central Processing Unit (CPU) to count and analyze kmers, the Graphical Processing Unit (GPU) has shown promising for reducing runtime for similar problems. We here present GKAGE, a new and improved version of KAGE that utilizes the GPU to further increase the computational efficiency. This is done by counting and analyzing large amounts of kmers in the many parallel cores of a GPU. We show that GKAGE is, on hardware of comparable cost, able to genotype an individual up to an order of magnitude faster than KAGE while producing the same output, which makes it by far the fastest genotyper available today. GKAGE can run on consumer-grade GPUs, and enables genotyping of a human sample in only a matter of minutes without the need for expensive high-performance computers. GKAGE is open source and available at https://github.com/kage-genotyper/kage.

bioinformatics↗

bioRxiv2022

BioNumPy: Fast and easy analysis of biological data with Python

Python is a popular and widespread programming language for scientific computing, in large part due to the powerful array programming library NumPy, which makes it easy to write clean, vectorized and efficient code for handling large datasets. A challenge with using array programming for biological data is that the data is often non-numeric and variable-length (such as DNA sequences), inhibiting out-of-the-box use of standard array programming techniques. Thus, a tradition in bioinformatics has been to use low-level languages like C and C++ to write efficient code. This makes the tools less transparent to the average computational biologist - making them harder to understand, modify and contribute to. We here present a new Python package BioNumPy, which adds a layer on top of NumPy in order to enable intuitive array programming on biological datasets. BioNumPy is able to efficiently load biological datasets (e.g. FASTQ-files, BED-files and BAM-files) into NumPy-like data structures, so that NumPy operations like indexing, vectorized functions and reductions can be applied to the data. We show that BioNumPy is considerably faster than vanilla Python and other Python packages for common bioinformatics tasks, and in many cases as fast as tools written in C/C++. BioNumPy thus bridges a long-lasting gap in bioinformatics, allowing the same programming language (Python) to be used across the full spectrum from quick and simple scripts to computationally efficient processing of large-scale data.

bioinformatics↗

bioRxiv2021

KAGE: Fast alignment-free graph-based genotyping of SNPs and short indels

Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.

bioinformatics↗

bioRxiv2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

bioinformatics↗

bioRxiv2019

Open the LID: LXRα regulates ChREBPα transactivity in a target gene-specific manner through an agonist-modulated LBD-LID interaction

The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in liver. We have previously shown that LXR regulates ChREBP transcription and activity; however, the underlying mechanisms are unclear. In the current study, we demonstrate that LXR and ChREBP interact physically, and show a high co-occupancy at regulatory regions in the mouse genome. LXR co-activates ChREBP, and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP, but not for LXR, indicating that ChREBP recruits LXR to chromatin in trans. The two factors interact via their key activation domains; ChREBPs low glucose inhibitory domain (LID) and the ligand-binding domain (LBD) of LXR. While unliganded LXR co-activates ChREBP, ligand-bound LXR surprisingly represses ChREBP activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXR:ChREBP interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This ligand-driven molecular switch highlights an unappreciated role of LXR that was overlooked due to LXR lipogenesis-promoting function.

molecular biology↗

bioRxiv2019

Assessing graph-based read mappers against a novel baseline approach highlights strengths and weaknesses of the current generation of methods

Graph-based reference genomes have become popular as they allow read mapping and follow-up analyses in settings where the exact haplotypes underlying a high-throughput sequencing experiment are not precisely known. Two recent papers show that mapping to graph-based reference genomes can improve accuracy as compared to methods using linear references. Both of these methods index the sequences for most paths up to a certain length in the graph in order to enable direct mapping of reads containing common variants. However, the combinatorial explosion of possible paths through nearby variants also leads to a huge search space and an increased chance of false positive alignments to highly variable regions. We here assess two prominent graph-based read mappers against a novel hybrid baseline approach that combines an initial path determination with a tuned linear read mapping method. We show, using a previously proposed benchmark, that this simple approach is able to improve accuracy of read-mapping to graph-based reference genomes. Our method is implemented in a tool, Two-step Graph Mapper, which is available at https://github.com/uio-bmi/two_step_graph_mapper along with data and scripts for reproducing the experiments.

bioinformatics↗

bioRxiv2018

Beware the Jaccard: the choice of metric is important and non-trivial in genomic colocalisation analysis.

BackgroundThe generation and systematic collection of genome-wide data is ever-increasing. This vast amount of data has enabled researchers to study relations between a variety of genomic and epigenomic features, including genetic variation, gene regulation, and phenotypic traits. Such relations are typically investigated by comparatively assessing genomic co-occurrence. Technically, this corresponds to assessing the similarity of pairs of genome-wide binary vectors. A variety of metrics have been proposed for this problem in other fields like ecology. However, while several of these metrics have been employed for assessing genomic co-occurrence, their appropriateness for the genomic setting has never been investigated.\n\nResultsWe show that the choice of metric may strongly influence results and propose two alternative modelling assumptions that can be used to guide this choice. On both simulated and real genomic data, the Jaccard index is strongly affected by dataset size and should be used with caution. The Forbes coefficient (fold change) and tetrachoric correlation are less affected by dataset size, but one should be aware of increased variance for small datasets.\n\nAvailabilityAll results on simulated and real data can be inspected and reproduced at: https://hyperbrowser.uio.no/sim-measure

bioinformatics↗

bioRxiv2018

Graph Peak Caller: calling ChIP-Seq Peaks on Graph-based Reference Genomes

Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.

bioinformatics↗