Biology subjects

Jensen, S. E.

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

6 recordsLinked to original sources

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

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↗

bioRxiv2021

Predicting protein domain temperature adaptation across the prokaryote-eukaryote divide

Protein thermostability is important for fitness but difficult to measure across the proteome. Fortunately, protein thermostability is correlated with prokaryote optimal growth temperatures (OGTs), which can be predicted from genome features. Models that can predict temperature sensitivity across the prokaryote-eukaryote divide would help inform how eukaryotes adapt to elevated temperatures, such as those predicted by climate change models. In this study we test whether prediction models can cross the prokaryote-eukaryote divide to predict protein stability in both prokaryotes and eukaryotes. We compare models built using a) the whole proteome, b) Pfam domains, and c) individual amino acid residues. Proteome-wide models accurately predict prokaryote optimal growth temperatures (r2 up to 0.93), while site-specific models demonstrate that nearly half of the proteome is associated with optimal growth temperature in both Archaea and Bacteria. Comparisons with the small number of eukaryotes with temperature sensitivity data suggest that site-specific models are the most transferable across the prokaryote-eukaryote divide. Using the site-specific models, we evaluated temperature sensitivity for 323,850 amino acid residues in 2,088 Pfam domain clusters in Archaea and Bacteria species separately. 59.0% of tested residues are significantly associated with OGT in Archaea and 75.2% of tested residues are significantly associated with OGT in Bacteria species at a 5% false discovery rate. These models make it possible to identify which Pfam domains and amino acid residues are involved in temperature adaptation and facilitate future research questions about how species will fare in the face of increasing environmental temperatures.

molecular biology↗

bioRxiv2021

Teosinte introgression modulates phosphatidylcholine levels and induces early maize flowering time

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp.parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability, and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern US, Canada and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time.

plant biology↗

bioRxiv2020

Building a tRNA thermometer to access the world's biochemical diversity

ABSTRACTBecause ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA, and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a model using tRNA sequence to predict OGT. We used tRNA sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum r2 of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps, and widening the scope of valid downstream analyses.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗