Mapping Floral Resources for Bees using Drone Imagery

Poor nutrition among modern day honey bee colonies is contributing to their decline. Yet understanding how the diversity and abundance of flowering species around a colony affects its health remains difficult because of the manual labor required to analyze these large foraging landscapes. We describe a procedure for automatically mapping the species of flowering plants around a colony from overlapping drone images. We developed a pipeline for stitching the images together, identifying plants within them, and classifying each plant by its species. The resulting map of the flowering species surrounding a colony could be used in future experiments that aim to assess how a colony’s health and foraging behavior is influenced by the spatial distribution of the floral species in its vicinity

Haptools: a toolkit for admixture and haplotype analysis.

SummaryLeveraging local ancestry and haplotype information in genome-wide association studies and downstream analyses can improve the utility of genomics for individuals from diverse and recently admixed ancestries. However, most existing simulation, visualization and variant analysis frameworks are based on variant-level analysis and do not automatically handle these features. We present haptools, an open-source toolkit for performing local ancestry aware and haplotype-based analysis of complex traits. Haptools supports fast simulation of admixed genomes, visualization of admixture tracks, simulation of haplotype- and local ancestry-specific phenotype effects and a variety of file operations and statistics computed in a haplotype-aware manner.Availability and implementationHaptools is freely available at https://github.com/cast-genomics/haptools.DocumentationDetailed documentation is available at https://haptools.readthedocs.io.Supplementary informationSupplementary data are available at Bioinformatics online

Polymorphic short tandem repeats make widespread contributions to blood and serum traits

Short tandem repeats (STRs) are genomic regions consisting of repeated sequences of 1-6 bp in succession. Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) do not fully capture STR effects. To study these effects, we imputed 445,720 STRs into genotype arrays from 408,153 White British UK Biobank participants and tested for association with 44 blood phenotypes. Using two fine-mapping methods, we identify 119 candidate causal STR-trait associations and estimate that STRs account for 5.2%-7.6% of causal variants identifiable from GWASs for these traits. These are among the strongest associations for multiple phenotypes, including a coding CTG repeat associated with apolipoprotein B levels, a promoter CGG repeat with platelet traits, and an intronic poly(A) repeat with mean platelet volume. Our study suggests that STRs make widespread contributions to complex traits, provides stringently selected candidate causal STRs, and demonstrates the need to consider a more complete view of genetic variation in GWASs

Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost 'capture' method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels within ATAC-seq peak regions with at least 10 reads. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance

gymrek-lab/TRTools: v5.1.0

<p>New features:</p> <ul> <li>Added prancSTR for mosaicism detection</li> <li>Added simTR for simulating NGS reads with stutter errors at TRs</li> </ul&gt

gymrek-lab/TRTools: v5.1.1

<p>Bug fixes:</p> <ul> <li>Remove stray files from source distribution (#195)</li> </ul&gt