Biology subjects

Baker, D. N.

Publications and source records attributed to Baker, D. N..

10 recordsLinked to original sources

bioRxiv2025

Virtual World Coupling with Photosynthesis Evaluation for Synthetic Data Production

In this work, we couple the functional-structural plant model CPlantBox to the Unreal Engine by exploiting the implemented raytracing pipeline to evaluate light influx on the plant surface. There are many approaches for photosynthesis computation and light evaluation, though they typically are limited by versatility, compute speed, or operate on much coarser resolutions. This work specifically addresses the concern that data generation pipelines tend to be unresponsive and do not include model-based knowledge as part of the generation pipeline. Using established photosynthesis solvers, we model the interaction between the Unreal Engine and the FSPM to measure physical properties in the virtual world. This is successful if we are able to reproduce experimental results using an in silico model. As part of the pipeline, we generate a surface geometry and utilize material shaders that are designed to establish a baseline surface model for light interception and transmission, based on simple parameter sets that can be calibrated. Using a Selhausen field experiment as baseline, we reproduce the photosynthesis effectiveness of the plants in the 2016 winter wheat experiments. Our pipeline is deeply intertwined with data generation and has been proven to perform well at scale. In this work, we build on our previous work by showcasing both a simulation study of a light evaluation as well as quantifying how well our system performs on high-performance computing systems.

bioinformatics↗

bioRxiv2024

Ancient sedimentary DNA shows more than 5000 years of continuous beaver occupancy in Grand Teton National Park

Beaver-based restoration is emerging as a cost-effective conservation and climate adaptation strategy, but efforts are constrained by limited knowledge of pre-colonial beaver distribution and their long-term ecosystem impacts. Here, we apply sedimentary ancient DNA (sedaDNA) techniques to investigate the history of beaver occupancy at three lakes in Grand Teton National Park, Wyoming over the last [~]10 ka, as well as interactions with the local plant community. We documented a dynamic history of beaver presence in two sub-alpine lakes (Taggart and Jenny Lakes) and demonstrate no history of beaver occupancy at the higher-elevation alpine lake (Lake Solitude). Beavers were first detected at Jenny Lake around 7.2 ka and intermittently thereafter. At nearby Taggart Lake, beavers were first detected at [~]5.9 ka and continuously from 5.2 ka onwards. Vegetation metabarcoding revealed a shift in plant community coinciding with beaver establishment in these two sub-alpine lakes, as well as an increase in taxonomic diversity. These changes coincide with regional trends towards wetter conditions. Notably, beavers persist at Taggart Lake during inferred droughts, indicating a potential role in maintaining wetlands through extended periods of climatic stress. Our results demonstrate sedaDNA as a powerful, novel technique for reconstructing past beaver occupancy dynamics.

ecology↗

bioRxiv2024

Sawfish: Improving long-read structural variant discovery and genotyping with local haplotype modeling

MotivationStructural variants (SVs) play an important role in evolutionary and functional genomics but are challenging to characterize. High-accuracy, long-read sequencing can substantially improve SV characterization when coupled with effective calling methods. While state-of the-art long-read SV callers are highly accurate, further improvements are achievable by systematically modeling local haplotypes during SV discovery and genotyping. ResultsWe describe sawfish, an SV caller for mapped high-quality long reads incorporating systematic SV haplotype modeling to improve accuracy and resolution. Assessment against the draft Genome in a Bottle (GIAB) SV benchmark from the T2T-HG002-Q100 diploid assembly shows that sawfish has the highest accuracy among state-of-the-art long-read SV callers across every tested SV size group. Additionally, sawfish maintains the highest accuracy at every tested depth level from 10 to 32-fold coverage, such that other callers required at least 30-fold coverage to match sawfish accuracy at 15-fold coverage. Sawfish also shows the highest accuracy in the GIAB challenging medically relevant genes benchmark, demonstrating improvements in both comprehensive and medically relevant contexts. When joint-genotyping 10 samples from CEPH-1463, sawfish has over 9000 more pedigree-concordant calls than other state-of-the-art SV callers, with the highest proportion of concordant SVs (78%) as well. Sawfishs quality model can be used to select for an even higher proportion of concordant SVs (86%), while still calling over 5000 more pedigree-concordant SVs than other callers. These results demonstrate that sawfish improves on the state-of-the-art for long-read SV calling accuracy across both individual and joint-sample analyses. AvailabilitySawfish is released as a pre-compiled Linux binary and user guide on GitHub: https://github.com/PacificBiosciences/sawfish.

bioinformatics↗

bioRxiv2024

VRoot: An XR-Based Application for Manual Root System Architecture Reconstruction

This article describes an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns. We have conducted a laboratory user study to assess the performance of new users with our software in comparison to classical and established desktop software. We utilize a functional-structural plant model to derive a synthetic root architecture that serves as objective quantification for the root system architecture reconstruction. Additionally, we have collected quantitative feedback on our software in the form of standardized questionnaires. This work provides an overview of the extended reality software and the advantage of using immersive techniques for 3D data extraction in plant science. Through our formal study, we further provide a quantification of manual root system reconstruction accuracy. We observe an increase in root system architecture reconstruction accuracy (F1) compared to state-of-the-art desktop software and a more robust extraction quality.

bioinformatics↗

bioRxiv2024

Root System Architecture Reorganization Under Decreasing Soil Phosphorus Lowers Root System Conductance of Zea mays

The global supply of phosphorus is decreasing. At the same time, climate change reduces the water availability in most regions of the world. Insights on how decreasing phosphorus availability influences plant architecture is crucial to understand its influence on plant functional properties, such as the root systems water uptake capacity. In this study we investigated the structural and functional responses of Zea mays to varying phosphorus fertilization levels focusing especially on the root systems conductance. A rhizotron experiment with soils ranging from severe phosphorus deficiency to sufficiency was conducted. We measured architectural parameters of the whole plant and combined them with root hydraulic properties to simulate time-dependent root system conductance of growing plants under different phosphorus levels. We observed changes of the root system architecture, characterized by decreasing crown root elongation and reduced axial root radii with declining phosphorus availability. Modeling revealed that only plants with optimal phosphorus availability sustained a high root system conductance, while all other phosphorus levels led to a significantly lower root system conductance, both under light and severe phosphorus deficiency. We postulate that phosphorus deficiency initially enhances root system function for drought mitigation but eventually reduce biomass and impairs root development and water uptake in prolonged or severe cases of drought. Our results also highlight the fact that root system organization, rather than its total size, is critical to estimate important root functions.

plant biology↗

bioRxiv2022

Dashing 2: genomic sketching with multiplicities and locality-sensitive hashing

A genomic sketch is a small, probabilistic representation of the set of k-mers in a sequencing dataset. Sketches are building blocks for large-scale analyses that consider similarities between many pairs of sequences or sequence collections. While existing tools can easily compare 10,000s of genomes, relevant datasets can reach millions of sequences and beyond. Popular tools also fail to consider k-mer multiplicities, making them less applicable in quantitative settings. We describe a method called Dashing 2 that builds on the SetSketch data structure. SetSketch is related to HyperLogLog, but discards use of leading zero count in favor of a truncated logarithm of adjustable base. Unlike HLL, SetSketch can perform multiplicity-aware sketching when combined with the ProbMinHash method. Dashing 2 integrates locality-sensitive hashing to scale all-pairs comparisons to millions of sequences. Dashing 2 is free, open source software available at https://github.com/dnbaker/dashing2

bioinformatics↗

bioRxiv2022

A scalable and unbiased discordance metric with H+

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or consistency of the cluster, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the scale-agnostic G+ discordance metric, however this internal metric is slow to calculate for large data. Furthermore, we show that G+ varies as a function of the proportion of observations in the predicted cluster labels (group balance), which is an undesirable property. To address this problem, we propose a modification of G+, referred to as H+, and demonstrate that H+ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate H+, which are available in the fasthplus R package.

bioinformatics↗

bioRxiv2021

minicore: Fast scRNA-seq clustering with various distance measures

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicores novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattacharyya distance, which can be directly applied to count data and probability distributions. Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (<2-fold) speed differences among all distances. We show that a minicore pipeline consisting of k-means++, localsearch++ and minibatch k-means can cluster a 4-million cell dataset in minutes, using less than 10GiB of RAM. This memory-efficiency enables atlas-scale clustering on laptops and other commodity hardware. Finally, we report findings on which distance measures give clusterings that are most consistent with known cell type labels. AvailabilityThe open source library is at https://github.com/dnbaker/minicore. Code used for experiments is at https://github.com/dnbaker/minicore-experiments.

bioinformatics↗

bioRxiv2020

Megadepth: efficient coverage quantification for BigWigs and BAMs

MotivationA common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. ResultsMegadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. Availabilityhttps://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. Contactchris.wilks@jhu.edu

bioinformatics↗

bioRxiv2018

Dashing: Fast and Accurate Genomic Distances with HyperLogLog

Dashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets. It uses the HyperLogLog sketch together with cardinality estimation methods that are specialized for set unions and intersections. Dashing summarizes genomes more rapidly than previous MinHash-based methods while providing greater accuracy across a wide range of input sizes and sketch sizes. It can sketch and calculate pairwise distances for over 87K genomes in 6 minutes. Dashing is open source and available at https://github.com/dnbaker/dashing.

bioinformatics↗