Biology subjects

Nettelblad, C.

Publications and source records attributed to Nettelblad, C..

8 recordsLinked to original sources

bioRxiv2024

Dimensionality Reduction of Genetic Data using Contrastive Learning

We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create PCA-like population visualizations. Contrastive learning is a self-supervised deep learning method that uses similarities between samples to train the neural network to discriminate between samples. Many of the advances in these types of models have been made for computer vision, but some common methodology does not translate well from image to genetic data. We define a loss function that outperforms loss functions commonly used in contrastive learning, and a data augmentation scheme tailored specifically towards SNP genotype datasets. We compare the performance of our method to PCA and contemporary non-linear methods with respect to how well they preserve local and global structure, and how well they generalize to new data. Our method displays good preservation of global structure and has improved generalization properties over t-SNE, UMAP, and popvae, while preserving relative distances between individuals to a high extent. A strength of the deep learning framework is the possibility of projecting new samples and fine-tuning to new datasets using a pre-trained model without access to the original training data, and the ability to incorporate more domain-specific information in the model. We show examples of population classification on two datasets of dog and human genotypes.

genetics↗

bioRxiv2023

Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost

Conducting genomic selection in plant breeding programs can substantially speed up the development of new varieties. Genomic selection provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative while they are delicate to capture with sufficient statistical power. Despite the release of new performing technologies, the cost of large-scale genotyping remains a major limitation to the implementation of genomic selection. We suggest to combine pooled genotyping with population-based imputation as a cost-effective computational strategy for genotyping SNPs. Pooling saves genotyping tests and has proven to accurately capture the rare variants that are usually missed by imputation. In this study, we investigate an extension to our joint model of pooling and imputation via iterative coupling. In each iteration, the imputed genotype probabilities serve as feedback input for rectifying the decoded data, before running a new imputation in these adjusted data. Such flexible set up indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take full advantage of the strengths in both pooling and imputation. The iterations improve greatly upon the initial genotype predictions, achieving very high genotype accuracy for both low and high frequency variants. We enhance the average concordance from 94.5% to 98.4% at a very limited computational cost and without requiring any additional genotype testing. We believe that these results could be of interest for plant breeders and crop scientists. Author summaryIn applications such as large-scale population surveys or plant breeding, the cost of genetic testing can limit the number of samples that are genotyped, or force the reduction to more cost-effective low-density marker panels. A reduction in the number of samples or the number of variants surveyed can reduce the power to detect important genetic correlations. We propose a scheme of pooled genotype testing, which would allow for using half the number of test assays for the same number of individuals surveyed. The data from overlapping pool tests is augmented with genotype imputation. We have previously shown that this approach was competitive, but with some drawbacks. Most strikingly, the error rate for common variants could be in the range of 10%. Now, we propose a new computational method for reconstructing SNP genotypes with pooling and imputation, adding an iterative coupled model connecting the two. This model allows us to exploit the advantages of both methods and achieves consistently high genotype reconstruction accuracy. We demonstrate the performance of our approach on a hypothetical plant breeding application based on a public genetic dataset from wheat samples. However, main aspects of the methodology would translate to many other settings.

bioinformatics↗

bioRxiv2022

Achieving improved accuracy for imputation of ancient DNA

Genotype imputation has the potential to increase the amount of information that can be gained from the often limited biological material available in ancient samples. As many widely used tools have been developed with modern data in mind, their design is not necessarily reflective of the requirements in studies of ancient DNA. Here, we investigate if an imputation method based on the full probabilistic Li and Stephens model of haplotype frequencies might be beneficial for the particular challenges posed by ancient data. We present an implementation called prophaser, and compare imputation performance to two alternative pipelines that have been used in the ancient DNA community based on the Beagle software. Considering empirical ancient data downsampled to lower coverages as well as present-day samples with artificially thinned genotypes, we show that the proposed method is advantageous at lower coverages, where it yields improved accuracy and ability to capture rare variation. The software prophaser is optimized for running in a massively parallel manner and achieved reasonable runtimes on the experiments performed when executed on a GPU.

genomics↗

bioRxiv2022

Observation of a single protein by ultrafast X-ray diffraction

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory1, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes2. This was first demonstrated on biological samples a decade ago on the giant mimivirus3. Since then a large collaboration4 has been pushing the limit of the smallest sample that can be imaged5,6. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter7 is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.

biophysics↗

bioRxiv2021

An empirical evaluation of genotype imputation of ancient DNA

With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle 4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference and study sample size. Making use of five ancient samples with high-coverage data available, we evaluated imputed data with respect to accuracy, reference bias and genetic affinities as captured by PCA. We obtained genotype concordance levels of over 99% for data with 1x coverage, and similar levels of accuracy and reference bias at levels as low as 0.75x. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1x. We also show that a large and varied phased reference set as well as the inclusion of low- to moderate-coverage ancient samples can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for post-processing and validation prior to downstream analysis.

genomics↗

bioRxiv2020

A deep learning framework for characterization of genotype data

Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. Among frequently used methods are PCA, which is a linear transform that often misses more fine-scale structures, and neighbor-graph based methods which focus on local relationships rather than large-scale patterns. Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this paper, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data. Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to PCA, while preserving global geometry to a higher extent than t-SNE and UMAP. We also discuss the use of the methodology for more general characterization of genotype data, showing that models of a similar architecture can be used as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

bioinformatics↗

bioRxiv2018

The presence and impact of reference bias on population genomic studies of prehistoric human populations

High quality reference genomes are an important resource in genomic research projects. A consequence is that DNA fragments carrying the reference allele will be more likely to map suc-cessfully, or receive higher quality scores. This reference bias can have effects on downstream population genomic analysis when heterozygous sites are falsely considered homozygous for the reference allele.\n\nIn palaeogenomic studies of human populations, mapping against the human reference genome is used to identify endogenous human sequences. Ancient DNA studies usually operate with low sequencing coverages and fragmentation of DNA molecules causes a large proportion of the sequenced fragments to be shorter than 50 bp - reducing the amount of accepted mismatches, and increasing the probability of multiple matching sites in the genome. These ancient DNA specific properties are potentially exacerbating the impact of reference bias on downstream analyses, especially since most studies of ancient human populations use pseudohaploid data, i.e. they randomly sample only one sequencing read per site.\n\nWe show that reference bias is pervasive in published ancient DNA sequence data of pre-historic humans with some differences between individual genomic regions. We illustrate that the strength of reference bias is negatively correlated with fragment length. Reference bias can cause differences in the results of downstream analyses such as population affinities, heterozygosity estimates and estimates of archaic ancestry. These spurious results highlight how important it is to be aware of these technical artifacts and that we need strategies to mitigate the effect. Therefore, we suggest some post-mapping filtering strategies to resolve reference bias which help to reduce its impact substantially.

genomics↗

bioRxiv2018

Electrospray sample injection for single-particle imaging with X-ray lasers

The possibility of imaging single proteins constitutes an exciting challenge for X-ray lasers. Despite encouraging results on large particles, imaging small particles has proven to be difficult for two reasons: not quite high enough pulse intensity from currently available X-ray lasers and, as we demonstrate here, contamination of the aerosolised molecules by non-volatile contaminants in the solution. The amount of contamination on the sample depends on the initial droplet-size during aerosolisation. Here we show that with our electrospray injector we can decrease the size of aerosol droplets and demonstrate virtually contaminant-free sample delivery of organelles, small virions, and proteins. The results presented here, together with the increased performance of next generation X-ray lasers, constitute an important stepping stone towards the ultimate goal of protein structure determination from imaging at room temperature and high temporal resolution.

biophysics↗