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Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains. 

We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.

GWASpro is freely available at https://bioinfo.noble.org/GWASPRO.

Supplementary data are available at Bioinformatics online.

 

From a single transgenic line harboring fiveTnt1transposon insertions, we generated a near‐saturated insertion population inMedicago truncatula. Using thermal asymmetric interlaced‐polymerase chain reaction followed by sequencing, we recovered 388 888 flanking sequence tags (FSTs) from 21 741 insertion lines in this population.FSTrecovery from 14Tnt1lines using the whole‐genome sequencing (WGS) and/orTnt1‐capture sequencing approaches suggests an average of 80 insertions per line, which is more than the previous estimation of 25 insertions. Analysis of the distribution pattern and preference ofTnt1insertions showed thatTnt1is overall randomly distributed throughout theM. truncatulagenome. At the chromosomal level,Tnt1insertions occurred on both arms of all chromosomes, with insertion frequency negatively correlated with theGCcontent. Based on 174 546 filteredFSTs that show exact insertion locations in theM. truncatulagenome version 4.0 (Mt4.0), 0.44Tnt1insertions occurred per kb, and 19 583 genes containedTnt1with an average of 3.43 insertions per gene. Pathway and gene ontology analyses revealed thatTnt1‐inserted genes are significantly enriched in processes associated with ‘stress’, ‘transport’, ‘signaling’ and ‘stimulus response’. Surprisingly, gene groups with higher methylation frequency were more frequently targeted for insertion. Analysis of 19 583Tnt1‐inserted genes revealed that 59% (1265) of 2144 transcription factors, 63% (765) of 1216 receptor kinases and 56% (343) of 616 nucleotide‐binding site‐leucine‐rich repeat genes harbored at least oneTnt1insertion, compared with the overall 38% ofTnt1‐inserted genes out of 50 894 annotated genes in the genome.

 

Legumes, comprising one of the largest, most diverse, and most economically important plant families, are the subject of vibrant research and development worldwide. Continued improvement of legume crops will benefit from the recent proliferation of genetic (including genomic) resources; but the diversity, scale, and complexity of these resources presents challenges to those managing and using them. A workshop held in March of 2019 addressed questions of data resources and priorities for the legumes. The workshop identified various needs and recommendations: (a) Develop strategies to effectively store, integrate, and relate genetic resources collected in different projects. (b) Leverage information collected across many legume species by standardizing data formats and ontologies, improving the state of metadata about datasets, and increasing use of the FAIR data principles. (c) Advocate for the critical role that curators exercise in integrating complex datasets into databases and adding high value metadata that enable downstream analytics and facilitate practical applications. (d) Implement standardized software and database development practices to best leverage limited developer time and expertise gained from the various legume (and other) species. (e) Develop tools and databases that can manage genetic information for the world's plant genetic resources, enabling efficient incorporation of important traits into breeding programs. (f) Centralize information on databases, tools, and training materials and establish funding streams to support training and outreach.