Biology subjects

Kainer, D.

Publications and source records attributed to Kainer, D..

16 recordsLinked to original sources

bioRxiv2026

FLASH-P: Turning decades of biology into accurate causal networks with AI agents

Mechanistic networks that encode causal regulatory logic can predict the effects of genetic and environmental perturbations but constructing them is a bottleneck in systems biology because the relevant knowledge lies scattered across thousands of resources, untapped for both building and validating such networks. Here we present FLASH-P, a multi-agent framework that autonomously curates this literature into perturbable, signed-directed network models for any trait-species combination in under an hour without much computational power. Twelve FLASH-P networks across seven species predicted the directional outcome of 1,088 published perturbations with a mean accuracy of 90%. This accuracy was driven by the regulatory topology FLASH-P constructs, which is why it outperformed knowledge-graph derived networks. Its merging agent combined six networks into one that preserved single-trait accuracy and recovered pleiotropic effects, and consolidated independent runs of one trait into a comprehensive, high-accuracy network. FLASH-P networks enable applications that require trait models.

Plant Biology↗

bioRxiv2025

Going Against the Grain: Investigating the C4 Wheat Hypothesis with Spatial Transcriptomics

The possibility of a C4 photosynthetic pathway present in the developing grain of wheat, a C3 plant, has been the source of scientific debate. Wheat is critical to food security and may benefit greatly from the biological advantages conferred by C4 photosynthesis under heat and drought stress. Therefore, significant research has gone towards engineering wheat to use C4 biochemistry, resulting in the discovery of a unique photosynthetic pathway in the grain that has been suggested to be C4 specific. Here, we employed a spatial transcriptomics analysis of the developing wheat grain to further investigate the spatial expression patterns of C4 specific genes. Our results showed that most of the genes related to C4 photosynthesis were expressed in the grain in the theorised tissue locations, including phosphoenolpyruvate carboxylase (ppc) and pyruvate orthophosphate dikinase (ppdk). The photosynthetic pericarp cells were the site of ppc synthesis while the endosperm was the site of ppc carboxylation activity. Notably, isoforms of aspartate aminotransferase, alanine aminotransferase and malate dehydrogenase exhibited spatially distinct expression patterns, with tissue specificity, possibly linked to the unique functions of individual isoforms. As wheat performance under stress has been associated with the levels of expression of these C4 genes, confirmation of an active C4 pathway in the grain would have significant agronomic implications. Our results provide novel gene expression data for key genes related to photosynthesis, which could contribute to future development of highly productive, climate change resilient wheat varieties.

genetics↗

bioRxiv2025

Telomere-to-telomere assemblies reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by helitrons

BackgroundThe model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases (KCS), which has been difficult to confirm based on short-read assemblies. New long-read sequencing provides opportunities to develop telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed in traditional analyses. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. ResultsOur analysis of 78 telomere-to-telomere long-read haplotypes identified more than twice as many KCS genes as previously reported, along with numerous intragenic non-synonymous substitutions. Random forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a helitron transposon. A phylogenetic analysis indicates they are the evolutionary mechanism for generating KCS tandem arrays. ConclusionsLong-read sequencing and telomere-to-telomere assembles revealed large-effect loci critical to genetic studies that are unattainable from short-reads. These approaches also have the potential to reveal novel insights into genome structure and function, such as the helitrons identified here. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

genomics↗

bioRxiv2025

Spatial transcriptomics of developing wheat seed reveals radial expression patterns in endosperm and subgenome biased expression of key genes

Gene expression of developing seeds drives essential processes such as nutrient storage, stress tolerance and germination. However, the spatial organisation of gene expression within the complex structure of the seed remains largely unexplored. Here we report the use of the STOmics spatial transcriptomics platform to visualise spatial expression patterns in the wheat (Triticum aestivum) seed at the critical period of grain filling in mid seed development. We analysed >4,000,000 spatially resolved transcripts, achieving subcellular resolution of transcript localization across multiple tissue domains, and identified gene expression clusters linked to eight functional cellular groups. Notably, our analysis characterised four distinct clusters within the endosperm, which exhibited radial expression patterns from the inner to outer regions of the grain, and identified novel marker gene candidates for the clusters found. We further investigated known tissue-specific genes and identified subgenome biased expression between paralogs of puroindoline-B, metallothionein protein, and -amylase/subtilisin inhibitor. These findings provide new detail about gene expression across and within different functional cellular groups of the developing seed and demonstrate that spatial transcriptomics could further our understanding of subgenome differences in polyploid plants.

plant biology↗

bioRxiv2024

The effectiveness of Large Language Models with RAG for auto-annotating phenotype descriptions

Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPTs capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations. These workflows make use of an LLM to intelligently parse phenotypes into short concepts, followed by finding appropriate ontology terms via embedding vector similarity or via Retrieval-Augmented Generation (RAG). The RAG model is a state-of-the-art approach that augments conversational prompts to the LLM with context-specific data to empower it beyond its pre-trained parameter space. We show that the RAG produces the most accurate automated annotations that are often highly similar or identical to expert-curated annotations. Short descriptionLarge Language Models excel at tasks that require semantic understanding of text. Here we use that capability to auto-annotate plant phenotypes with ontological terms and compare to expert annotation.

bioinformatics↗

bioRxiv2024

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Leveraging the use of multiplex multi-omic networks, key insights into genetic and epigenetic mechanisms supporting biofuel production have been uncovered. Here, we introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelisation on high performance cluster systems, which enables high throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

bioinformatics↗

bioRxiv2024

Centromeres are Hotspots for Chromosomal Inversions and Breeding Traits in Mango

Chromosomal inversions can preserve combinations of favorable alleles by suppressing recombination. Simultaneously, they reduce the effectiveness of purifying selection enabling deleterious alleles to accumulate. This study explores how areas of low recombination, including centromeric regions and chromosomal inversions, contribute to the accumulation of deleterious and favorable loci in 225 Mangifera indica genomes from the Australian Mango Breeding Program. Here, we identify 17 chromosomal inversions that cover 7.7% (29.7Mb) of the M. indica genome: eight pericentric (inversion includes the centromere) and nine paracentric (inversion is on one arm of the chromosome). Our results show that these large pericentric inversions are accumulating deleterious loci, while the paracentric inversions show deleterious levels above and below the genome wide average. We find that despite their deleterious load, chromosomal inversions contain small effect loci linked to variation in crucial breeding traits, indicating that chromosomal inversions have likely facilitated their selection. The results from this study have important implications for selective breeding of favorable combinations of alleles in regions of low recombination. Significance StatementChromosomal inversions and other low recombination regions of the genome can drive trait evolution. Fewer recombination events can assist in maintaining favorable combinations of alleles, but it can also make disentangling favorable and deleterious alleles difficult. Understanding whether these low recombination regions contain favorable or deleterious loci could drive our decision to increase or decrease the frequency of these regions in target breeding populations. Breeding for large segments of the genome based on presence or absence of an inversion can rapidly drive large trait differences within few generations. Harnessing the impact of large low recombination regions of the genome could have major implications for future genetic improvement in breeding.

genomics↗

bioRxiv2023

Clustering the Planet: An Exascale Approach to Determining Global Climatype Zones

We present an exascale approach for producing global scale, high resolution, longitudinally based geoclimate classifications. Using a GPU implementation of the DUO Similarity Metric on the Summit supercomputer, we calculated the pairwise environmental similarity of 156,384,190 vectors of 414,640 encoded elements derived from 71 environmental variables over a 50-year time span at 1km2 resolution. GPU matrix-matrix (GEMM) kernels were optimized for the GPU architecture and their outputs were managed through aggressive concurrent MPI rank CPU communication, calculations, and transfers. Using vector transformation and highly optimized operations of generalized distributed dense linear algebra, calculation of all-vector-pairs similarity resulted in 5.07 x 1021 element comparisons and reached a peak performance of 2.31 exaflops. We demonstrated this method using existing and synthesized climate layers to show how geography can be parsed using high-performance computing. Geoclimate zones are important tools for understanding how environmental variables impact natural systems, particularly for agriculture and conservation with relevance to climate change. Historically, classification systems have been low resolution, based on limited variables, or subjective. To identify climate classes, we clustered DUO outputs at varying stringency, producing 69, 133, 340, and 717 global geoclimate zones. Our approach produced global scale, high resolution, longitudinally informed climate classifications that can be used in precision agriculture, cultivar breeding efforts, and conservation programs.

bioinformatics↗

bioRxiv2022

A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

AO_SCPLOWBSTRACTC_SCPLOWFungal specialized metabolites include many bioactive compounds with potential applications as pharmaceuticals, agrochemical agents, and industrial chemicals. Exploring and discovering novel fungal metabolites is critical to combat antimicrobial resistance in various fields, including medicine and agriculture. Yet, identifying the conditions or treatments that will trigger the production of specialized metabolites in fungi can be cumbersome since most of these metabolites are not produced under standard culture conditions. Here, we introduce a data-driven algorithm comprising various network analysis routes to characterize the production of known and putative specialized metabolites and unknown analytes triggered by different exogenous compounds. We use bipartite networks to quantify the relationship between the metabolites and the treatments stimulating their production through two routes. The first, called the direct route, determines the production of known and putative specialized metabolites induced by a treatment. The second, called the auxiliary route, is specific for unknown analytes. We demonstrated the two routes by applying chitooligosaccharides and lipids at two different temperatures to the opportunistic human fungal pathogen Aspergillus fumigatus. We used various network centrality measures to rank the treatments based on their ability to trigger a broad range of specialized metabolites. The specialized metabolites were ranked based on their receptivity to various treatments. Altogether, our data-driven techniques can track the influence of any exogenous treatment or abiotic factor on the metabolomic output for targeted metabolite research. This approach can be applied to complement existing LC/MS analyses to overcome bottlenecks in drug discovery and development from fungi. NoticeThis manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Author summaryTriggering silent biosynthetic gene clusters in fungi to produce specialized metabolites is a tedious process that requires assessing various environmental conditions, applications of epigenetic modulating agents, or co-cultures with other microbes. We provide a data-driven solution using network analysis, called "direct route", to characterize the production of known and putative specialized metabolites triggered by various exogenous compounds. We also provide a "auxiliary route" to distinguish unique unknown analytes amongst the abundantly produced analytes in response to these treatments. The developed techniques can assist researchers to identify treatments or applications that could positively influence the production of a targeted metabolite or recognize unique unknown analytes that can be further fractionated, characterized, and screened for their biological activities and hence, discover new metabolites.

systems biology↗

bioRxiv2022

Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering

CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been established for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and new public resource, produced with quantum chemical tensors, for interpreting and predicting sgRNA efficiency. Feature engineering for sgRNA efficiency is performed using an explainable-artificial intelligence model; iterative Random Forest (iRF). By encoding quantitative attributes of position-specific sequences for E. coli sgRNAs, we identify important traits for sgRNA design in bacterial species. Additionally, we show that expanding positional encoding to quantum descriptors of base-pair, dimer, trimer and tetramer sequences captures intricate interactions in local and neighboring nucleotides of the target DNA. These features highlight variation in CRISPR-Cas9 sgRNA dynamics between E. coli and H. sapien genomes. These novel encodings of sgRNAs greatly enhance our understanding of the elaborate quantum biological processes involved in CRISPR-Cas9 machinery.

systems biology↗

bioRxiv2021

The draft genome sequence of Eucalyptus polybractea based on hybrid assembly with short- and long-reads reads

Eucalyptus polybractea is a small, multi-stemmed tree, which is widely cultivated in Australia for the production of Eucalyptus oil. We report the hybrid assembly of the E. polybractea genome utilizing both short- and long-read technology. We generated 44 Gb of Illumina HiSeq short reads and 8 Gb of Nanopore long reads, representing approximately 83x and 15x genome coverage, respectively. The hybrid-assembled genome, after polishing, contained 24,864 scaffolds with an accumulated length of 523 Mb (N50 = 40.3 kb; BUSCO-calculated genome completeness of 94.3%). The genome contained 35,385 predicted protein-coding genes detected by combining homology-based and de novo approaches. We have provided the first assembled genome based on hybrid sequences from the highly diverse Eucalyptus subgenus Symphyomyrtus, and revealed the value of including long-reads from Nanopore technology for enhancing the contiguity of the assembled genome, as well as for improving its completeness. We anticipate that the E. polybractea genome will be an invaluable resource supporting a range of studies in genetics, population genomics and evolution of related species in Eucalyptus.

genomics↗

bioRxiv2020

Confronting the COVID-19 Pandemic with Systems Biology

Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. One Sentence SummaryA Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

systems biology↗

bioRxiv2019

A phylogenomic approach reveals a low somatic mutation rate in a long-lived plant

Somatic mutations can have important effects on the life history, ecology, and evolution of plants, but the rate at which they accumulate is poorly understood, and has been very difficult to measure directly. Here, we demonstrate a novel method to measure somatic mutations in individual plants and use this approach to estimate the somatic mutation rate in a large, long-lived, phenotypically mosaic Eucalyptus melliodora tree. Despite being 100 times larger than Arabidopsis, this tree has a per-generation mutation rate only ten times greater, which suggests that this species may have evolved mechanisms to reduce the mutation rate per unit of growth. This adds to a growing body of evidence that illuminates the correlated evolutionary shifts in mutation rate and life history in plants.

genomics↗

bioRxiv2019

The draft nuclear genome assembly of Eucalyptus pauciflora: new approaches to comparing de novo assemblies

BackgroundSelecting the best genome assembly from a collection of draft assemblies for the same species remains a difficult task. Here, we combine new and existing approaches to help to address this, using the non-model plant Eucalyptus pauciflora (snow gum) as a test case. Eucalyptus pauciflora is a long-lived tree with high economic and ecological importance. Currently, little genomic information for Eucalyptus pauciflora is available.\n\nFindingsWe generated high coverage of long-(Nanopore, 174x) and short-(Illumina, 228x) read data from a single Eucalyptus pauciflora individual and compared assemblies from four assemblers with a variety of settings: Canu, Flye, Marvel, and MaSuRCA. A key component of our approach is to keep a randomly selected collection of ~10% of both long- and short-reads separate from the assemblies to use as a validation set with which to assess the assemblies. Using this validation set along with a range of existing tools, we compared the assemblies in eight ways: contig N50, BUSCO scores, LAI scores, assembly ploidy, base-level error rate, computing genome assembly likelihoods, structural variation and genome sequence similarity. Our result showed that MaSuRCA generated the best assembly, which is 594.87 Mb in size, with a contig N50 of 3.23 Mb, and an estimated error rate of ~0.006 errors per base.\n\nConclusionsWe report a draft genome of Eucalyptus pauciflora, which will be a valuable resource for further genomic studies of eucalypts. These approaches for assessing and comparing genomes should help in assessing and choosing among many potential genome assemblies for a single species.

genomics↗

bioRxiv2018

Assembly of chloroplast genomes with long- and short-read data: a comparison of approaches using Eucalyptus pauciflora as a test case

BackgroundChloroplasts are organelles that conduct photosynthesis in plant and algal cells. Chloroplast genomes code for around 130 genes, and the information they contain is widely used in agriculture and studies of evolution and ecology. Correctly assembling complete chloroplast genomes can be challenging because the chloroplast genome contains a pair of long inverted repeats (10-30 kb). The advent of long-read sequencing technologies should alleviate this problem by providing sufficient information to completely span the inverted repeat regions. Yet, long-reads tend to have higher error rates than short-reads, and relatively little is known about the best way to combine long- and short-reads to obtain the most accurate chloroplast genome assemblies. Using Eucalyptus pauciflora, the snow gum, as a test case, we evaluated the effect of multiple parameters, such as different coverage of long (Oxford nanopore) and short (Illumina) reads, different long-read lengths, different assembly pipelines, and different genome polishing steps, with a view to determining the most accurate and efficient approach to chloroplast genome assembly.\n\nResultsHybrid assemblies combining at least 20x coverage of both long-reads and short-reads generated a single contig spanning the entire chloroplast genome with few or no detectable errors. Short-read-only assemblies generated three contigs representing the long single copy, short single copy and inverted repeat regions of the chloroplast genome. These contigs contained few single-base errors but tended to exclude several bases at the beginning or end of each contig. Long-read-only assemblies tended to create multiple contigs with a much higher single-base error rate, even after polishing. The chloroplast genome of Eucalyptus pauciflora is 159,942 bp, contains 131 genes of known function, and confirms the phylogenetic position of Eucalyptus pauciflora as a close relative of Eucalyptus regnans.\n\nConclusionsOur results suggest that very accurate assemblies of chloroplast genomes can be achieved using a combination of at least 20x coverage of long- and short-reads respectively, provided that the long-reads contain at least ~5x coverage of reads longer than the inverted repeat region. We show that further increases in coverage give little or no improvement in accuracy, and that hybrid assemblies are more accurate than long-read-only or short-read-only assemblies.

genomics↗

bioRxiv2018

A comprehensive toolkit to enable MinION long-read sequencing in any laboratory

Long-read sequencing technologies are transforming our ability to assemble highly complex genomes. Realising their full potential relies crucially on extracting high quality, high molecular weight (HMW) DNA from the organisms of interest. This is especially the case for the portable MinION sequencer which potentiates all laboratories to undertake their own genome sequencing projects, due to its low entry cost and minimal spatial footprint. One challenge of the MinION is that each group has to independently establish effective protocols for using the instrument, which can be time consuming and costly. Here we present a workflow and protocols that enabled us to establish MinION sequencing in our own laboratories, based on optimising DNA extractions from a challenging plant tissue as a case study. Following the workflow illustrated we were able to reliably and repeatedly obtain > 8.5 Gb of long read sequencing data with a mean read length of 13 kb and an N50 of 26 kb. Our protocols are open-source and can be performed in any laboratory without special equipment. We also illustrate some more elaborate workflows which can increase mean and average read lengths if this is desired. We envision that our workflow for establishing MinION sequencing, including the illustration of potential pitfalls, will be useful to others who plan to establish long-read sequencing in their own laboratories.

genomics↗