Promoting CO2 Electroreduction to Multi-Carbon Products by Hydrophobicity-Induced Electro-Kinetic Retardation.

Advancing the performance of the Cu-catalyzed electrochemical CO2 reduction reaction (CO2 RR) is crucial for its practical applications. Still, the wettable pristine Cu surface often suffers from low exposure to CO2 , reducing the Faradaic efficiencies (FEs) and current densities for multi-carbon (C2+ ) products. Recent studies have proposed that increasing surface availability for CO2 by cation-exchange ionomers can enhance the C2+ product formation rates. However, due to the rapid formation and consumption of *CO, such promotion in reaction kinetics can shorten the residence of *CO whose adsorption determines C2+ selectivity, and thus the resulting C2+ FEs remain low. Herein, we discover that the electro-kinetic retardation caused by the strong hydrophobicity of quaternary ammonium group-functionalized polynorbornene ionomers can greatly prolong the *CO residence on Cu. This unconventional electro-kinetic effect is demonstrated by the increased Tafel slopes and the decreased sensitivity of *CO coverage change to potentials. As a result, the strongly hydrophobic Cu electrodes exhibit C2+ Faradaic efficiencies of ≈90 % at a partial current density of 223 mA cm-2 , more than twice of bare or hydrophilic Cu surfaces.

Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.

KEY MESSAGE: Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel. With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.

Distribution and evolution of cotton fiber development genes in the fibreless Gossypium raimondii genome.

Cotton fiber represents the largest single cell in plants and they serve as models to study cell development. This study investigated the distribution and evolution of fiber Unigenes anchored to recombination hotspots between tetraploid cotton (Gossypium hirsutum) At and Dt subgenomes, and within a parental diploid cotton (Gossypium raimondii) D genome. Comparative analysis of At vs D and Dt vs D showed that 1) the D genome provides many fiber genes after its merger with another parental diploid cotton (Gossypium arboreum) A genome although the D genome itself does not produce any spinnable fiber; 2) similarity of fiber genes is higher between At vs D than between Dt vs D genomic hotspots. This is the first report that fiber genes have higher similarity between At and D than between Dt and D. The finding provides new insights into cotton genomic regions that would facilitate genetic improvement of natural fiber properties.

The impact of population structure on genomic prediction in stratified populations.

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

Unravelling the carbonate issue through the regulation of mass transport and charge transfer in mild acid.

The electrochemical CO2 reduction reaction (CO2RR) triggered by renewable electricity provides a promising route to produce chemical feedstocks and fuels with low-carbon footprints. The intrinsic challenge for the current CO2RR electrolyzer is the carbonate issue arising from the reaction between hydroxide and CO2. Acid CO2RR electrolyzers, in principle, can effectively solve the carbonate formation, but it remains inevitable practically. In this work, we thoroughly investigated the electrode processes of the CO2RR on the benchmark Ag catalyst in mild acid. The root of the carbonate issue arises from the imbalanced supply-consumption rate of protons-the electron transfer vs. mass transport. Regulating the hydrodynamics substantially reduces the proton diffusion length by 80%, increasing the single-pass carbon utilization efficiency of CO2-to-CO to 44% at -100 mA cm-2. The fundamental difference between mass transport and electron transfer on the spatial and temporal scale still leads to unavoidable carbonate formation. Future work to design intrinsically active catalysts in strong acid or metal-cation-free media is critical to solving the carbonate issue.

Addressing the Carbonate Issue: Electrocatalysts for Acidic CO2 Reduction Reaction.

Electrochemical CO2 reduction reaction (CO2RR) powered by renewable energy provides a promising route to CO2 conversion and utilization. However, the widely used neutral/alkaline electrolyte consumes a large amount of CO2 to produce (bi)carbonate byproducts, leading to significant challenges at the device level, thereby impeding the further deployment of this reaction. Conducting CO2RR in acidic electrolytes offers a promising solution to address the "carbonate issue"; however, it presents inherent difficulties due to the competitive hydrogen evolution reaction, necessitating concerted efforts toward advanced catalyst and electrode designs to achieve high selectivity and activity. This review encompasses recent developments of acidic CO2RR, from mechanism elucidation to catalyst design and device engineering. This review begins by discussing the mechanistic understanding of the reaction pathway, laying the foundation for catalyst design in acidic CO2RR. Subsequently, an in-depth analysis of recent advancements in acidic CO2RR catalysts is provided, highlighting heterogeneous catalysts, surface immobilized molecular catalysts, and catalyst surface enhancement. Furthermore, the progress made in device-level applications is summarized, aiming to develop high-performance acidic CO2RR systems. Finally, the existing challenges and future directions in the design of acidic CO2RR catalysts are outlined, emphasizing the need for improved selectivity, activity, stability, and scalability.

Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18.

Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa (Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model's overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).

Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa.

Alfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability.

Analyzing Medicago spp. seed morphology using GWAS and machine learning.

Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 other Medicago spp., for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness & red-green-blue (RGB) intensities. The results revealed phenotypic diversity and correlations among the tested accessions. Based on the phenotypic data of M. sativa subsp. sativa, a genome-wide association study (GWAS) was conducted using single nucleotide polymorphisms (SNPs) called against the Medicago truncatula genome. Genes in proximity to associated markers were detected, including CPR1, MON1, a PPR protein, and Wun1(threshold of 1E-04). Machine learning models were utilized to validate GWAS, and identify additional marker-trait associations for potentially complex traits. Marker S7_33375673, upstream of Wun1, was the most important predictor variable for red color intensity and highly important for brightness. Fifty-two markers were identified in coding regions. Along with strong correlations observed between seed morphology traits, these genes will facilitate the process of understanding the genetic basis of seed morphology in Medicago spp.

Genetic Mapping of Tolerance to Bacterial Stem Blight Caused by Pseudomonas syringae pv. syringae in Alfalfa (Medicago sativa L.).

The bacterial stem blight of alfalfa (Medicago sativa L.), first reported in the United States in 1904, has emerged recently as a serious disease problem in the western states. The causal agent, Pseudomonas syringae pv. syringae, promotes frost damage and disease that can reduce first harvest yields by 50%. Resistant cultivars and an understanding of host-pathogen interactions are lacking in this pathosystem. With the goal of identifying DNA markers associated with disease resistance, we developed biparental F1 mapping populations using plants from the cultivar ZG9830. Leaflets of plants in the mapping populations were inoculated with a bacterial suspension using a needleless syringe and scored for disease symptoms. Bacterial populations were measured by culture plating and using a quantitative PCR assay. Surprisingly, leaflets with few to no symptoms had bacterial loads similar to leaflets with severe disease symptoms, indicating that plants without symptoms were tolerant to the bacterium. Genotyping-by-sequencing identified 11 significant SNP markers associated with the tolerance phenotype. This is the first study to identify DNA markers associated with tolerance to P. syringae. These results provide insight into host responses and provide markers that can be used in alfalfa breeding programs to develop improved cultivars to manage the bacterial stem blight of alfalfa.

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

Phenotyping seedlings for selection of root system architecture in alfalfa (Medicago sativa L.).

BACKGROUND: The root system architecture (RSA) of alfalfa (Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software. RESULTS: The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively. CONCLUSIONS: The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle.

Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom.

BACKGROUND: As a major component of plant cell wall, lignin plays important roles in mechanical support, water transport, and stress responses. As the main cause for the recalcitrance of plant cell wall, lignin modification has been a major task for bioenergy feedstock improvement. The study of the evolution and function of lignin biosynthesis genes thus has two-fold implications. First, the lignin biosynthesis pathway provides an excellent model to study the coordinative evolution of a biochemical pathway in plants. Second, understanding the function and evolution of lignin biosynthesis genes will guide us to develop better strategies for bioenergy feedstock improvement. RESULTS: We analyzed lignin biosynthesis genes from fourteen plant species and one symbiotic fungal species. Comprehensive comparative genome analysis was carried out to study the distribution, relatedness, and family expansion of the lignin biosynthesis genes across the plant kingdom. In addition, we also analyzed the comparative synteny map between rice and sorghum to study the evolution of lignin biosynthesis genes within the Poaceae family and the chromosome evolution between the two species. Comprehensive lignin biosynthesis gene expression analysis was performed in rice, poplar and Arabidopsis. The representative data from rice indicates that different fates of gene duplications exist for lignin biosynthesis genes. In addition, we also carried out the biomass composition analysis of nine Arabidopsis mutants with both MBMS analysis and traditional wet chemistry methods. The results were analyzed together with the genomics analysis. CONCLUSION: The research revealed that, among the species analyzed, the complete lignin biosynthesis pathway first appeared in moss; the pathway is absent in green algae. The expansion of lignin biosynthesis gene families correlates with substrate diversity. In addition, we found that the expansion of the gene families mostly occurred after the divergence of monocots and dicots, with the exception of the C4H gene family. Gene expression analysis revealed different fates of gene duplications, largely confirming plants are tolerant to gene dosage effects. The rapid expansion of lignin biosynthesis genes indicated that the translation of transgenic lignin modification strategies from model species to bioenergy feedstock might only be successful between the closely relevant species within the same family.

Gene-rich islands for fiber development in the cotton genome.

Cotton fiber is an economically important seed trichome and the world's leading natural fiber used in the manufacture of textiles. As a step toward elucidating the genomic organization and distribution of gene networks responsible for cotton fiber development, we investigated the distribution of fiber genes in the cotton genome. Results revealed the presence of gene-rich islands for fiber genes with a biased distribution in the tetraploid cotton (Gossypium hirsutum L.) genome that was also linked to discrete fiber developmental stages based on expression profiles. There were 3 fiber gene-rich islands associated with fiber initiation on chromosome 5, 3 islands for the early to middle elongation stage on chromosome 10, 3 islands for the middle to late elongation stage on chromosome 14, and 1 island on chromosome 15 for secondary cell wall deposition, for a total of 10 fiber gene-rich islands. Clustering of functionally related gene clusters in the cotton genome displaying similar transcriptional regulation indicates an organizational hierarchy with significant implications for the genetic enhancement of particular fiber quality traits. The relationship between gene-island distribution and functional expression profiling suggests for the first time the existence of functional coupling gene clusters in the cotton genome.

An integrated genetic and physical map of homoeologous chromosomes 12 and 26 in Upland cotton (G. hirsutum L.).

BACKGROUND: Upland cotton (G. hirsutum L.) is the leading fiber crop worldwide. Genetic improvement of fiber quality and yield is facilitated by a variety of genomics tools. An integrated genetic and physical map is needed to better characterize quantitative trait loci and to allow for the positional cloning of valuable genes. However, developing integrated genomic tools for complex allotetraploid genomes, like that of cotton, is highly experimental. In this report, we describe an effective approach for developing an integrated physical framework that allows for the distinguishing between subgenomes in cotton. RESULTS: A physical map has been developed with 220 and 115 BAC contigs for homeologous chromosomes 12 and 26, respectively, covering 73.49 Mb and 34.23 Mb in physical length. Approximately one half of the 220 contigs were anchored to the At subgenome only, while 48 of the 115 contigs were allocated to the Dt subgenome only. Between the two chromosomes, 67 contigs were shared with an estimated overall physical similarity between the two chromosomal homeologs at 40.0 %. A total of 401 fiber unigenes plus 214 non-fiber unigenes were located to chromosome 12 while 207 fiber unigenes plus 183 non-fiber unigenes were allocated to chromosome 26. Anchoring was done through an overgo hybridization approach and all anchored ESTs were functionally annotated via blast analysis. CONCLUSION: This integrated genomic map describes the first pair of homoeologous chromosomes of an allotetraploid genome in which BAC contigs were identified and partially separated through the use of chromosome-specific probes and locus-specific genetic markers. The approach used in this study should prove useful in the construction of genome-wide physical maps for polyploid plant genomes including Upland cotton. The identification of Gene-rich islands in the integrated map provides a platform for positional cloning of important genes and the targeted sequencing of specific genomic regions.

Genome physical mapping from large-insert clones by fingerprint analysis with capillary electrophoresis: a robust physical map of Penicillium chrysogenum.

Physical mapping with large-insert clones is becoming an active area of genomics research, and capillary electrophoresis (CE) promises to revolutionize the physical mapping technology. Here, we demonstrate the utility of the CE technology for genome physical mapping with large-insert clones by constructing a robust, binary bacterial artificial chromosome (BIBAC)-based physical map of Penicillium chrysogenum. We fingerprinted 23.1x coverage BIBAC clones with five restriction enzymes and the SNaPshot kit containing four fluorescent-ddNTPs using the CE technology, and explored various strategies to construct quality physical maps. It was shown that the fingerprints labeled with one or two colors, resulting in 40-70 bands per clone, were assembled into much better quality maps than those labeled with three or four colors. The selection of fingerprinting enzymes was crucial to quality map construction. From the dataset labeled with ddTTP-dROX, we assembled a physical map for P.chrysogenum, with 2-3 contigs per chromosome and anchored the map to its chromosomes. This map represents the first physical map constructed using the CE technology, thus providing not only a platform for genomic studies of the penicillin-producing species, but also strategies for efficient use of the CE technology for genome physical mapping of plants, animals and microbes.

An integrated BAC and genome sequence physical map of Phytophthora sojae.

Phytophthora spp. are serious pathogens that threaten numerous cultivated crops, trees, and natural vegetation worldwide. The soybean pathogen P. sojae has been developed as a model oomycete. Here, we report a bacterial artificial chromosome (BAC)-based, integrated physical map of the P. sojae genome. We constructed two BAC libraries, digested 8,681 BACs with seven restriction enzymes, end labeled the digested fragments with four dyes, and analyzed them with capillary electrophoresis. Fifteen data sets were constructed from the fingerprints, using individual dyes and all possible combinations, and were evaluated for contig assembly. In all, 257 contigs were assembled from the XhoI data set, collectively spanning approximately 132 Mb in physical length. The BAC contigs were integrated with the draft genome sequence of P. sojae by end sequencing a total of 1,440 BACs that formed a minimal tiling path. This enabled the 257 contigs of the BAC map to be merged with 207 sequence scaffolds to form an integrated map consisting of 79 superscaffolds. The map represents the first genome-wide physical map of a Phytophthora sp. and provides a valuable resource for genomics and molecular biology research in P. sojae and other Phytophthora spp. In one illustration of this value, we have placed the 350 members of a superfamily of putative pathogenicity effector genes onto the map, revealing extensive clustering of these genes.