Biology subjects

Buckler, E. S.

Publications and source records attributed to Buckler, E. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Contrasting Rhizosphere Nitrogen Dynamics in Andropogoneae Grasses: Implications for Sustainable Agriculture

Background: Nitrogen (N) fertilization in crop production significantly impacts ecosystems, often disrupting natural plant-microbe-soil interactions and causing environmental pollution. Our research tested the hypothesis that phylogenetically related perennial grasses might preserve rhizosphere management strategies conducive to a sustainable N economy for crops. Method: We analyzed the N cycle in the rhizospheres of 36 Andropogoneae grass species related to maize and sorghum, investigating their impacts on N availability and losses. This assay is supplemented with the collection and comparison of native habitat environment data for ecological inference as well as cross-species genomic and transcriptomic association analyses for candidate gene discovery. Result: Contrary to our hypothesis, all examined annual species, including sorghum and maize, functioned as N "Conservationists," reducing soil nitrification potential and conserving N. In contrast, some perennial species enhanced nitrification and leaching ("Leachers"). Yet a few other species exhibited similar nitrification stimulation effects but limited NO3- losses ("Nitrate Keepers"). We identified significant soil characteristics as influential factors in the eco- evolutionary dynamics of plant rhizospheres, and highlighted the crucial roles of a few transporter genes in soil N management and utilization. Conclusion: These findings serve as valuable guidelines for future breeding efforts for global sustainability.

plant biology↗

bioRxiv2024

Current genomic deep learning architectures generalize across grass species but not alleles

Non-coding regions of the genome are just as important as coding regions for understanding the mapping from genotype to phenotype. Interpreting deep learning models trained on RNA-seq is an emerging method to highlight functional sites within non-coding regions. Most of the work on RNA abundance models has been done within humans and mice, with little attention paid to plants. Here, we benchmark four genomic deep learning model architectures with genomes and RNA-seq data from 18 species closely related to maize and sorghum within the Andropogoneae. The Andropogoneae are a tribe of C4 grasses that have adapted to a wide range of environments worldwide since diverging 18 million years ago. Hundreds of millions of years of evolution across these species has produced a large, diverse pool of training alleles across species sharing a common physiology. As model input, we extracted 1,026 base pairs upstream of each genes translation start site. We held out maize as our test set and two closely related species as our validation set, training each architecture on the remaining Andropogoneae genomes. Within a panel of 26 maize lines, all architectures predict expression across genes moderately well but poorly across alleles. DanQ consistently ranked highest or second highest among all architectures yet performance was generally very similar across architectures despite orders of magnitude differences in size. This suggests that state-of-the-art supervised genomic deep learning models are able to generalize moderately well across related species but not sensitively separate alleles within species, the latter of which agrees with recent work within humans. We are releasing the preprocessed data and code for this work as a community benchmark to evaluate new architectures on our across-species and across-allele tasks.

genomics↗

bioRxiv2024

Muller's Ratchet in Action: The Erosion of Sexual Reproduction Genes in Domesticated Cassava (Manihot esculenta)

Centuries of clonal propagation in cassava (Manihot esculenta) have engaged Mullers Ratchet, leading to the accumulation of deleterious mutations due to the absence of sexual recombination. This has resulted in both inbreeding depression affecting yield and a significant decrease in reproductive performance, creating hurdles for contemporary breeding programs. Cassava is a member of the Euphorbiaceae family, including notable species such as rubber tree (Hevea brasiliensis) and poinsettia (Euphorbia pulcherrima). Expanding upon preliminary draft genomes, we annotated 7 long-read genome assemblies and aligned a total of 52 genomes, to analyze selection across the genome and the phylogeny. Through this comparative genomic approach, we identified 48 genes under relaxed selection in cassava. Notably, we discovered an overrepresentation of floral expressed genes, especially focused at six pollen-related genes. Our results indicate that domestication and a transition to clonal propagation has reduced selection pressures on sexually reproductive functions in cassava leading to an accumulation of mutations in pollen-related genes. This relaxed selection and the genome-wide deleterious mutations responsible for inbreeding depression are potential targets for improving cassava breeding, where the generation of new varieties relies on recombining favorable alleles through sexual reproduction.

evolutionary biology↗

bioRxiv2023

Fishing for a reelGene: evaluating gene models with evolution and machine learning

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million gene models in maize, reelGene found that 28% were incorrectly annotated or nonfunctional. By leveraging a large cohort of related species and through learning the conserved grammar of proteins, reelGene provides a tool for both evaluating gene model accuracy and genome biology.

genomics↗

bioRxiv2023

Transposable element abundance subtly contributes to lower fitness in maize

Transposable elements (TEs) have long been shown to have deleterious effects on the survival and reproduction of their host organism. As TEs are mobile DNA that jump to new positions, this deleterious cost can occur directly, by inserting into genes and regulatory sequences. Classical population genetic theory suggests copy-number dependent selection against TEs is necessary to prevent TEs from expanding so much they take over a genome. Such models have been difficult to interpret when applied to large genomes like maize, where there are hundreds of thousands of TE insertions that collectively make up 85% of the genome. Here, we use nearly 5000 inbred lines from maize mapping populations and a pan-genomic imputation approach to measure TE content. Segregating TE content gives rise to 100 Mb differences between individuals, and populations often show transgressive segregation in TE content. We use replicated phenotypes measured in hybrids across numerous years and environments to empirically measure the fitness costs of TEs. For an annual plant like maize, grain yield is not only a key agronomic phenotype, but also a direct measure of reproductive output. We find weak negative effects of TE accumulation on grain yield, nearing the limit of the efficacy of natural selection in maize. This results in a loss of one kernel ({approx}0.1% of average per-plant yield) for every additional 14 Mb of TE content. This deleterious load is enriched in TEs within 1 kilobase of genes and young TE insertions. Together, we provide rare empirical measurements of the fitness costs of TEs, and suggest that the TEs we see today in the genome have been filtered by selection against their deleterious consequences on maize fitness.

evolutionary biology↗

bioRxiv2023

Features that Govern Terminator Strength in Plants

The 3 end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.

plant biology↗

bioRxiv2022

Spatio-temporal modeling of high-throughput multi-spectral aerial images improves agronomic trait genomic prediction in hybrid maize

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) was measured by a multi-spectral MicaSense camera and ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multi-trait model, a two-stage approach that quantified NDVI local environmental effects (NLEE) was proposed. Firstly, NLEE were separated from additive genetic effects over the growing season using two-dimensional spline (2DSpl), separable autoregressive (AR1) models, or random regression models (RR). Secondly, the NLEE were leveraged within agronomic trait genomic best linear unbiased prediction (GBLUP) either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of NLEE across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to all baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2DSpl NLEE were most correlated to the soil parameters and grain yield 2DSpl effects. Simulation of field effects demonstrated improved specificity for RR models. In summary, NLEE increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

genetics↗

bioRxiv2022

Cross-species predictive modeling reveals conserved drought responses between maize and sorghum

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water deficit responses. Differential gene expression identified few overlapping drought associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals. Significance StatementDrought is a complex and variable stress that is difficult to quantify and link to underlying mechanisms both within and across species. Here, we developed a predictive model to classify drought stress responses in sorghum and identify important features that are responsive to water deficit. Our model has high predictive accuracy across development, genotype, and stress severity, and the top features are enriched in genes related to classical stress responses and have functional and evolutionary conservation. We applied this sorghum trained model to maize, and observed similar predictive accuracy of drought responses, supporting transfer learning across plant species. Our findings suggest there are deeply conserved drought responses across C4 grasses that are unrelated to tolerance.

plant biology↗

bioRxiv2022

Utilizing Evolutionary Conservation to Detect Deleterious Mutations and Improve Genomic Prediction in Cassava

Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication [~]10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression. To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava. Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by <100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction. We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.

plant biology↗

bioRxiv2022

Melon pan-genome and multi-parental framework for high-resolution trait dissection

Linking between genotype and phenotype is a fundamental goal in biology and requires robust data for both layers. The prominent increase in plant genome sequencing and comparisons of multiple related individuals, exposed the abundance of structural genomic variation and suggest that a single reference genome cannot represent the complete sequence diversity of a crop species, leading to the expansion of the pan-genome concept. For high-resolution forward genetics, this unprecedented access to genomic variation should be paralleled by availability and phenotypic characterization of genetic diversity, and effective integration between these layers. Here, we describe a multi-parental framework for trait dissection in melon, leveraging a novel pan-genome constructed for this crop. Melon (Cucumis melo L.) is an important crop from the Cucurbitaceae family, which display extensive phenotypic variation available for breeding. A diverse core set of 25 founder lines (MelonCore25) was sequenced using a combination of short and long-read technologies and their genomes were assembled de novo. The construction of a melon pan-genome exposed substantial variation in genome size and structure, including detection of ~300,000 structural variants and ~9 million SNPs. A half-diallel derived set of 300 F2 populations representing all possible MelonCore25 parental combinations was constructed as framework for trait dissection through integration with the pan-genome. We demonstrate the potential of this unified framework for genetic analysis of various melon traits, including rind color and mottling pattern, fruit sugar content and resistance to fungal diseases. We anticipate that utilization of this integrated resource will enhance genetic dissection of important traits and accelerate melon breeding. Significance statementPan-genomes aim to address the abundance of genome structural variation within species for improved genomic analyses. New pan-genome, constructed from de novo genome assemblies of 25 diverse melon (Cucumis melo L.) accessions is integrated with a half-diallel derived set of 300 F2 populations representing all possible parental combinations. The potential of this unified multi-parental trait dissection framework for melon genetics and breeding is presented.

genetics↗

bioRxiv2022

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy - when a single gene controls two or more seemingly unrelated traits - has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of true biological pleiotropy across all maize phenotypes is largely unknown. Here we investigate the extent to which biological pleiotropy impacts phenotypes within maize through GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assessed the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide- and large-effect variants, leaving the prevalence of detectable pleiotropy relatively low. Author SummaryThe genetic basis of complex traits has been thought to exhibit pleiotropy, which is the notion that a single locus can control two or more unrelated traits. Widespread reports in the human disease literature show genomic signatures of pleiotropic loci across many traits. However, little is known about the prevalence and behavior of pleiotropy in maize across a large number of phenotypes. Using association mapping of common alleles in over one hundred thousand traits, we determine how pleiotropic each region was and use these quantitative scores to functionally characterize each region of the genome. Our results show little evidence that pleiotropy is a common phenomenon in maize. We observed that maize does not exhibit the same pleiotropic characteristics as human diseases in terms of prevalence, gene expression, chromatin accessibility, or sequence conservation. Rather than pervasive pleiotropy, we hypothesize that strong selection on large and wide effect loci and the need for trait independence at the gene level keep the prevalence of pleiotropy low, thus, allowing for the adaptation of maize varieties to novel environments and conditions.

plant biology↗

bioRxiv2022

A happy accident: a novel turfgrass reference genome

Poa pratensis, commonly known as Kentucky bluegrass, is a popular cool-season grass species used as turf in lawns and recreation areas globally. Despite its substantial economic value, a reference genome had not previously been assembled due to the genomes relatively large size and biological complexity that includes apomixis, polyploidy, and interspecific hybridization. We report here a fortuitous de novo assembly and annotation of a P. pratensis genome. Instead of sequencing the genome of a C4 grass, we accidentally sampled and sequenced tissue from a weedy P. pratensis whose stolon was intertwined with that of the C4 grass. The draft assembly consists of 6.09 Gbp with an N50 scaffold length of 65.1 Mbp, and a total of 118 scaffolds, generated using PacBio long reads and Bionano optical map technology. We annotated 256K gene models and found 58% of the genome to be composed of transposable elements. To demonstrate the applicability of the reference genome, we evaluated population structure and estimated genetic diversity in P. pratensis collected from three North American prairies, two in Manitoba, Canada and one in Colorado, USA. Our results support previous studies that found high genetic diversity and population structure within the species. The reference genome and annotation will be an important resource for turfgrass breeding and study of bluegrasses.

plant biology↗

bioRxiv2021

Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks

Accessible chromatin regions are critical components of gene regulation but modeling them directly from sequence remains challenging, especially within plants, whose mechanisms of chromatin remodeling are less understood than in animals. We trained an existing deep learning architecture, DanQ, on leaf ATAC-seq data from 12 angiosperm species to predict the chromatin accessibility of sequence windows within and across species. We also trained DanQ on DNA methylation data from 10 angiosperms, because unmethylated regions have been shown to overlap significantly with accessible chromatin regions in some plants. The across-species models have comparable or even superior performance to a model trained within species, suggesting strong conservation of chromatin mechanisms across angiosperms. Testing a maize held out model on a multi-tissue scATAC panel revealed our models are best at predicting constitutively-accessible chromatin regions, with diminishing performance as cell-type specificity increases. Using a combination of interpretation methods, we ranked JASPAR motifs by their importance to each model and saw that the TCP and AP2/ ERF transcription factor families consistently ranked highly. We embedded the top three JASPAR motifs for each model at all possible positions on both strands in our sequence window and observed position- and strand-specific patterns in their importance to the model. With our cross-species "a2z" model it is now feasible to predict the chromatin accessibility and methylation landscape of any angiosperm genome.

bioinformatics↗

bioRxiv2021

Prediction of evolutionary constraint by genomic annotations improves prioritization of causal variants in maize

Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at single-site resolution. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we used genomic annotations to accurately predict nucleotide conservation across Angiosperms, as a proxy for fitness effect of mutations. Using only sequence analysis, we annotated non-synonymous mutations in 25,824 maize gene models, with information from bioinformatics (SIFT scores, GC content, transposon insertion, k-mer frequency) and deep learning (predicted effects of polymorphisms on protein representations by UniRep). Our predictions were validated by experimental information: within-species conservation, chromatin accessibility, gene expression and gene ontology enrichment. Importantly, they also improved genomic prediction for fitness-related traits (grain yield) in elite maize panels (+5% and +38% prediction accuracy within and across panels, respectively), by stringent prioritization of [≤] 1% of single-site variants (e.g., 104 sites and approximately 15deleterious alleles per haploid genome). Our results suggest that predicting nucleotide conservation across Angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach - Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC) - could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing.

genomics↗

bioRxiv2021

Domestication Reshaped the Genetic Basis of Inbreeding Depression in a Maize Landrace Compared to its Wild Relative, Teosinte

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to rare large-effect variation versus potentially more common variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load predicted from sequence data in the maize population. For many traits - and more commonly in maize - genetic variation among selffertilized families was less than expected based on additive and dominance variance estimated in outcrossed families, suggesting that a negative covariance between additive and homozygous dominance effects limits the variation available to selection under partial inbreeding. We identified quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, smalleffect recessive variation underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following to the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against large effect variants, but polygenic load is harder to purge and segregating mutational burden increased in maize compared to teosinte.

evolutionary biology↗

bioRxiv2021

The Practical Haplotype Graph, a platform for storing and using pangenomes for imputation

MotivationPangenomes provide novel insights for population and quantitative genetics, genomics, and breeding not available from studying a single reference genome. Instead, a species is better represented by a pangenome or collection of genomes. Unfortunately, managing and using pangenomes for genomically diverse species is computationally and practically challenging. We developed a trellis graph representation anchored to the reference genome that represents most pangenomes well and can be used to impute complete genomes from low density sequence or variant data. ResultsThe Practical Haplotype Graph (PHG) is a pangenome pipeline, database (PostGRES & SQLite), data model (Java, Kotlin, or R), and Breeding API (BrAPI) web service. The PHG has already been able to accurately represent diversity in four major crops including maize, one of the most genomically diverse species, with up to 1000-fold data compression. Using simulated data, we show that, at even 0.1X coverage, with appropriate reads and sequence alignment, imputation results in extremely accurate haplotype reconstruction. The PHG is a platform and environment for the understanding and application of genomic diversity. AvailabilityAll resources listed here are freely available. The PHG Docker used to generate the simulation results is https://hub.docker.com/ as maizegenetics/phg:0.0.27. PHG source code is at https://bitbucket.org/bucklerlab/practicalhaplotypegraph/src/master/. The code used for the analysis of simulated data is at https://bitbucket.org/bucklerlab/phg-manuscript/src/master/. The PHG database of NAM parent haplotypes is in the CyVerse data store (https://de.cyverse.org/de/) and named /iplant/home/shared/panzea/panGenome/PHG_db_maize/phg_v5Assemblies_20200608.db. Contactpjb39@cornell.edu

bioinformatics↗

bioRxiv2021

AnchorWave: sensitive alignment of genomes with high diversity, structural polymorphism and whole-genome duplication variation

Millions of species are currently being sequenced and their genomes are being compared. Many of them have more complex genomes than model systems and raised novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication or polyploidy levels. Here we introduce AnchorWave, which performs whole-genome duplication informed collinear anchor identification between genomes and performs base-pair resolution global alignments for collinear blocks using the wavefront algorithm and a 2-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multi-kilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs between two maize lines. By contrast, other genome alignment tools showed almost zero power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome than the closest competitive approach, when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor binding sites (TFBSs) at a rate of 1.05-74.85 fold higher than other tools, while with significantly lower false positive alignments. AnchorWave shows obvious improvement when applied to genomes with dispersed repeats, active transposable elements, high sequence diversity and whole-genome duplication variation. Significance statementOne fundamental analysis needed to interpret genome assemblies is genome alignment. Yet, accurately aligning regulatory and transposon regions outside of genes remains challenging. We introduce AnchorWave, which implements a genome duplication informed longest path algorithm to identify collinear regions and performs base-pair resolved, end-to-end alignment for collinear blocks using an efficient 2-piece affine gap cost strategy. AnchorWave improves alignment of partially synthetic and real genomes under a number of scenarios: genomes with high similarity, large genomes with high TE activity, genomes with many inversions, and alignments between species with deeper evolutionary divergence and different whole-genome duplication histories. Potential use cases for the method include genome comparison for evolutionary analysis of non-genic sequences and population genetics of taxa with complex genomes.

genomics↗

bioRxiv2021

Pfam domain adaptation profiles reflect plant species' evolutionary history

The increase in global temperatures predicted by climate change models presents a serious problem for agriculture because high temperatures reduce crop yields. Protein biochemistry is at the core of plant heat stress response, and understanding the interactions between protein biochemistry and temperature will be key to developing heat-tolerant crop varieties. Current experimental studies of proteome-wide plant thermostability are limited by the complexity of plant proteomes: evaluating function for thousands of proteins across a variety of temperatures is simply not feasible with existing technologies. In this paper, we use homologous prokaryote sequences to predict plant Pfam temperature adaptation and gain insights into how thermostability varies across the proteome for three species: maize, Arabidopsis, and poplar. We find that patterns of Pfam domain adaptation across organelles are consistent and highly significant between species, with cytosolic proteins having the largest range of predicted Pfam stabilities and a long tail of highly-stable ribosomal proteins. Pfam adaptation in leaf and root organs varies between species, and maize root proteins have more low-temperature Pfam domains than do Arabidopsis or poplar root proteins. Both poplar and maize populations have an excess of low-temperature mutations in Pfam domains, but only the mutations identified in poplar accessions have a negative effect on Pfam temperature adaptation overall. These Pfam domain adaptation profiles provide insight into how different plant structures adapt to their surrounding environment and can help inform breeding or protein editing strategies to produce heat-tolerant crops.

plant biology↗