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Background High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. Methods In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. Conclusion The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

Variation in the presence or absence of transposable elements (TEs) is a major source of genetic variation between individuals. Here, we identified 23,095 TE presence/absence variants between 216 Arabidopsis accessions. Most TE variants were rare, and we find these rare variants associated with local extremes of gene expression and DNA methylation levels within the population. Of the common alleles identified, two thirds were not in linkage disequilibrium with nearby SNPs, implicating these variants as a source of novel genetic diversity. Many common TE variants were associated with significantly altered expression of nearby genes, and a major fraction of inter-accession DNA methylation differences were associated with nearby TE insertions. Overall, this demonstrates that TE variants are a rich source of genetic diversity that likely plays an important role in facilitating epigenomic and transcriptional differences between individuals, and indicates a strong genetic basis for epigenetic variation.

Flowering time (FT) is the developmental transition coupling an internal genetic program with external local and seasonal climate cues. The genetic loci sensitive to predictable environmental signals underlie local adaptation. We dissected natural variation in FT across a new global diversity set of 473 unique accessions, with >12,000 plants across two seasonal plantings in each of two simulated local climates, Spain and Sweden. Genome-wide association mapping was carried out with 213,497 SNPs. A total of 12 FT candidate quantitative trait loci (QTL) were fine-mapped in two independent studies, including 4 located within ±10 kb of previously cloned FT alleles and 8 novel loci. All QTL show sensitivity to planting season and/or simulated location in a multi-QTL mixed model. Alleles at four QTL were significantly correlated with latitude of origin, implying past selection for faster flowering in southern locations. Finally, maximum seed yield was observed at an optimal FT unique to each season and location, with four FT QTL directly controlling yield. Our results suggest that these major, environmentally sensitive FT QTL play an important role in spatial and temporal adaptation.

The genetic model plant Arabidopsis thaliana, like many plant species, experiences a range of edaphic conditions across its natural habitat. Such heterogeneity may drive local adaptation, though the molecular genetic basis remains elusive. Here, we describe a study in which we used genome-wide association mapping, genetic complementation, and gene expression studies to identify cis-regulatory expression level polymorphisms at the AtHKT1;1 locus, encoding a known sodium (Na+) transporter, as being a major factor controlling natural variation in leaf Na+ accumulation capacity across the global A. thaliana population. A weak allele of AtHKT1;1 that drives elevated leaf Na+ in this population has been previously linked to elevated salinity tolerance. Inspection of the geographical distribution of this allele revealed its significant enrichment in populations associated with the coast and saline soils in Europe. The fixation of this weak AtHKT1;1 allele in these populations is genetic evidence supporting local adaptation to these potentially saline impacted environments.

The distribution of genomic variation across landscapes can provide insights into the complex interactions between the environment and the genome that influence the distribution of species, and mediate phenotypic adaptation to local conditions. High throughput sequencing technologies now offer unprecedented power to explore these interactions, allowing powerful inferences about historical processes of colonization, gene flow and divergence, as well as the identification of loci that mediate local adaptation. These ‘landscape genomic’ approaches have been validated in model species and are now being applied to nonmodel organisms, including foundation species that have substantial effects on ecosystem processes. Here we review the growing field of landscape genomics from a very broad perspective. In particular, we describe the inferential power that is gained by taking a genome-wide view of genetic variation, strategies for study design to best capture adaptive variation, and how to apply this information to practical challenges, such as restoration.

Gene expression is a complex trait determined by various genetic and nongenetic factors. Among the genetic factors, allelic difference may play a critical role in gene regulation. In this study we globally dissected cis (allelic) and trans sources of genetic variation in F1 hybrids between two Arabidopsis thaliana wild accessions, Columbia (Col) and Vancouver (Van), using a new high-density SNP-tiling array. This array tiles the whole genome with 35-bp resolution and interrogates 250,000 SNPs identified from resequencing of 20 diverse A. thaliana strains. Quantitative assessment of 12,311 genes identified 3811 genes differentially expressed between parents, 1665 genes with allele-specific expression, and 1688 genes controlled by composite trans-regulatory variation. Loci with cis- or trans-regulatory variation were mapped onto sequence polymorphisms, epigenetic modifications, and transcriptional specificity. Genes regulated in cis tend to be located in polymorphic chromosomal regions, are preferentially associated with repressive epigenetic marks, and exhibit high tissue expression specificity. Genes that vary due to trans regulation reside in relatively conserved chromosome regions, show activating epigenetic marks and generally constitutive gene expression. Our findings demonstrate a method of global functional characterization of allele-specific expression and highlight that chromatin structure is intertwined with evolution of cis- and trans-regulatory variation.

Joy Bergelson and colleagues characterize genome-wide patterns of genetic variation in a collection of 1,307 worldwide Arabidopsis thaliana accessions from the Regional Mapping (RegMap) panel, a publicly available genomic resource that includes large regional panels. They characterize signatures of selection and patterns of recombination and identify an enrichment of hotspots in intergenic regions and in repetitive DNA. Arabidopsis thaliana is native to Eurasia and is naturalized across the world. Its ability to be easily propagated and its high phenotypic variability make it an ideal model system for functional, ecological and evolutionary genetics. To date, analyses of the natural genetic variation of A . thaliana have involved small numbers of individual plants or genetic markers. Here we genotype 1,307 worldwide accessions, including several regional samples, using a 250K SNP chip. This allowed us to produce a high-resolution description of the global pattern of genetic variation. We applied three complementary selection tests and identified new targets of selection. Further, we characterized the pattern of historical recombination in A. thaliana and observed an enrichment of hotspots in its intergenic regions and repetitive DNA, which is consistent with the pattern that is observed for humans but which is strikingly different from that observed in other plant species. We have made the seeds we used to produce this Regional Mapping (RegMap) panel publicly available. This panel comprises one of the largest genomic mapping resources currently available for global natural isolates of a non-human species.

Background The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. Results We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME . Conclusion HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

The genetics of plant variety Large-scale genome-wide association (GWA) studies have become an important tool in human genomics, mostly focused on disease but also on adaptive variations such as skin colour. The technique is now shown to be similarly useful in plants. Atwell et al . report a GWA study of over a hundred phenotypes in naturally occurring inbred lines of Arabidopsis thaliana . The results range from significant associations, usually for single genes, to more difficult-to-interpret findings that indicate confounding by complex genetics and population structure. The accompanying paper by Todesco et al . demonstrates the ability of this technique to detect major-effect gene loci. Using forward genetics and GWA analyses, they show that variation at a single locus ( ACD6 ) in Arabidopsis underlies phenotypic variation in vegetative growth as well as resistance to infection. The strong enhancement of resistance mediated by one of the alleles at this locus explains its persistence in natural populations throughout the world, despite it drastically reducing new leaf production. Here, large-scale genome-wide association studies were carried out with the naturally occurring inbred lines of Arabidopsis thaliana , which can be genotyped once and phenotyped repeatedly. The results range from significant associations, usually corresponding to single genes, to findings that are more difficult to interpret, because confounding by complex genetics and population structure makes it hard to distinguish true associations from false. Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases 1 , 2 , genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana , a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits 3 . Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms.

Stress recovery may prove to be a promising approach to increase plant performance and, theoretically, mRNA instability may facilitate faster recovery. Transcriptome (RNA-seq, qPCR, sRNA-seq, and PARE) and methylome profiling during repeated excess-light stress and recovery was performed at intervals as short as 3 min. We demonstrate that 87% of the stress-upregulated mRNAs analyzed exhibit very rapid recovery. For instance, abundance declined 2-fold every 5.1 min. We term this phenomenon rapid recovery gene downregulation (RRGD), whereby mRNA abundance rapidly decreases promoting transcriptome resetting. Decay constants ( ) were modeled using two strategies, linear and nonlinear least squares regressions, with the latter accounting for both transcription and degradation. This revealed extremely short half-lives ranging from 2.7 to 60.0 min for 222 genes. Ribosome footprinting using degradome data demonstrated RRGD loci undergo cotranslational decay and identified changes in the ribosome stalling index during stress and recovery. However, small RNAs and 5'-3' RNA decay were not essential for recovery of the transcripts examined, nor were any of the six excess light-associated methylome changes. We observed recovery-specific gene expression networks upon return to favorable conditions and six transcriptional memory types. In summary, rapid transcriptome resetting is reported in the context of active recovery and cellular memory.