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Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery.Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association.We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes.The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene.The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

Heat waves occurring during droughts can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security. Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, sepal stomatal aperture, hormone concentrations and transcriptomic response of closed soybean flowers developing on plants subjected to a combination of drought and heat stress. Here, we report that, during a combination of drought and heat stress, soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed ‘differential transpiration’, lowers flower inner temperature by about 2–3°C, protecting reproductive processes at the expense of vegetative tissues. Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture.

Key message An epistatic interaction between SCN resistance loci rhg1-a and rhg2 in PI 90763 imparts resistance against virulent SCN populations which can be employed to diversify SCN resistance in soybean cultivars. Abstract With more than 95% of the $46.1B soybean market dominated by a single type of genetic resistance, breeding for soybean cyst nematode (SCN)-resistant soybean that can effectively combat the widespread increase in virulent SCN populations presents a significant challenge. Rhg genes (for Resistance to Heterodera glycines) play a key role in resistance to SCN; however, their deployment beyond the use of the rhg1-b allele has been limited. In this study, quantitative trait loci (QTL) were mapped using PI 90763 through two biparental F3:4 recombinant inbred line (RIL) populations segregating for rhg1-a and rhg1-b alleles against a SCN HG type 1.2.5.7 (Race 2) population. QTL located on chromosome 18 (rhg1-a) and chromosome 11 (rhg2) were determined to confer SCN resistance in PI 90763. The rhg2 gene was fine-mapped to a 169-Kbp region pinpointing GmSNAP11 as the strongest candidate gene. We demonstrated a unique epistatic interaction between rhg1-a and rhg2 loci that not only confers resistance to multiple virulent SCN populations. Further, we showed that pyramiding rhg2 with the conventional mode of resistance, rhg1-b, is ineffective against these virulent SCN populations. This highlights the importance of pyramiding rhg1-a and rhg2 to maximize the impact of gene pyramiding strategies toward management of SCN populations virulent on rhg1-b sources of resistance. Our results lay the foundation for the next generation of soybean resistance breeding to combat the number one pathogen of soybean.

The monocytic/macrophage system is essential for skeletal muscle homeostasis, but its dysregulation contributes to the pathogenesis of muscle degenerative disorders. Despite our increasing knowledge of the role of macrophages in degenerative disease, it still remains unclear how macrophages contribute to muscle fibrosis. Here, we used single-cell transcriptomics to determine the molecular attributes of dystrophic and healthy muscle macrophages. We identified six novel clusters. Unexpectedly, none corresponded to traditional definitions of M1 or M2 macrophage activation. Rather, the predominant macrophage signature in dystrophic muscle was characterized by high expression of fibrotic factors, galectin-3 and spp1. Spatial transcriptomics and computational inferences of intercellular communication indicated that spp1 regulates stromal progenitor and macrophage interactions during muscular dystrophy. Galectin-3+macrophages were chronically activated in dystrophic muscle and adoptive transfer assays showed that the galectin-3+phenotype was the dominant molecular program induced within the dystrophic milieu. Histological examination of human muscle biopsies revealed that galectin-3+macrophages were also elevated in multiple myopathies. These studies advance our understanding of macrophages in muscular dystrophy by defining the transcriptional programs induced in muscle macrophages, and reveal spp1 as a major regulator of macrophage and stromal progenitor interactions.

Waterlogging stress (WLS) negatively impacts the growth and yield of crops resulting in heavy losses to agricultural production. Previous studies revealed that WLS induces a systemic response in shoots that is partially dependent on the plant hormones ethylene and abscisic acid. However, the role of rapid cell-to-cell signaling pathways, such as the reactive oxygen species (ROS) and calcium waves, in systemic responses of plants to WLS is unknown at present. Here we reveal that an abrupt WLS treatment ofArabidopsis thalianaplants growing in peat moss triggers systemic ROS and calcium wave responses, and that the WLS-triggered ROS wave response of Arabidopsis is dependent on the ROS generating RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD), calcium-permeable channels GLUTAMATE-LIKE RECEPTOR 3.3 and 3.6 (GLR3.3 and GLR3.6), and aquaporin PLASMA MEMBRANE INTRINSIC PROTEIN 2;1 (PIP2;1) proteins. We further show that WLS is accompanied by a rapid systemic transcriptomic response that is evident as early as 10 min following waterlogging initiation, includes many hypoxia-response transcripts, and is partially dependent on RBOHD. Interestingly, the abrupt WLS of Arabidopsis resulted in the triggering of a rapid hydraulic wave response and the transient opening of stomata on leaves. Taken together, our findings reveal that the initiation of WLS in plants is accompanied by rapid systemic physiological and transcriptomic responses that involve the ROS, calcium, and hydraulic waves. These findings reveal that systemic plant responses to WLS are rapid and at least partially dependent on cell-to-cell signaling mechanisms.

Background The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. Results The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. Conclusions The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website (https://soykb.org/SoybeanAlleleCatalogTool/), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website (https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

Certain cultivars of maize show increased tolerance to water deficit conditions by maintenance of root growth. To better understand the molecular mechanisms related to this adaptation, nodal root growth zone samples were collected from the reference inbred line B73 and inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water deficits. Plants were grown under various water stress levels in both field and controlled environment settings. FR697-specific RNA-Seq datasets were generated and used for a de novo transcriptome assembly to characterize any genotype-specific genetic features. The assembly was aided by an Iso-Seq library of transcripts generated from various FR697 plant tissue samples. The Necklace pipeline was used to combine a Trinity de novo assembly along with a reference guided assembly and the Viridiplantae proteome to generate an annotated consensus “SuperTranscriptome” assembly of 47,915 transcripts with a N50 of 3152 bp in length. The results were compared by Blastn to maize reference genes, a Benchmarking Universal Single-Copy Orthologs (BUSCO) genome completeness report and compared with three maize reference genomes. The resultant ‘SuperTranscriptome’ was demonstrated to be of high-quality and will serve as an important reference for analysis of the maize nodal root transcriptomic response to environmental perturbations.

Nuclear DNA viruses simultaneously access cellular factors that aid their life cycle while evading inhibitory factors by localizing to distinct nuclear sites. Adeno-associated viruses (AAVs), which are Dependoviruses in the family Parvovirinae, are non-enveloped icosahedral viruses, which have been developed as recombinant AAV vectors to express transgenes. AAV2 expression and replication occur in nuclear viral replication centers (VRCs), which relies on cellular replication machinery as well as coinfection by helper viruses such as adenoviruses or herpesviruses, or exogenous DNA damage to host cells. AAV2 infection induces a complex cellular DNA damage response (DDR), in response to either viral DNA or viral proteins expressed in the host nucleus during infection, where VRCs co-localized with DDR proteins. We have previously developed a modified iteration of a viral chromosome conformation capture (V3C-seq) assay to show that the autonomous parvovirus minute virus of mice localizes to cellular sites of DNA damage to establish and amplify its replication. Similar V3C-seq assays to map AAV2 show that the AAV2 genome co-localized with cellular sites of DNA damage under both non-replicating and replicating conditions. The AAV2 non-structural protein Rep 68/78, also localized to cellular DDR sites during both non-replicating and replicating infections, and also when ectopically expressed. Ectopically expressed Rep could be efficiently re-localized to DDR sites induced by micro-irradiation. Recombinant AAV2 gene therapy vector genomes derived from AAV2 localized to sites of cellular DNA damage to a lesser degree, suggesting that the inverted terminal repeat origins of replication were insufficient for targeting.

The complexity of environmental factors affecting plants is gradually increasing due to global warming, an increase in the number and intensity of climate change-driven weather events, such as droughts, heat waves, and floods, and the accumulation of different pollutants. The impact of multiple stress conditions on plants was recently termed ‘multifactorial stress combination’ (MFSC) and defined as the occurrence of three or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different stressors, the growth and survival ofArabidopsis thalianaseedlings declines; even if the level of each individual stress is low enough to have no significant effect on plants. This finding is alarming since it reveals that MFSCs of different low-level stressors could impact crops and cause a dramatic reduction in overall growth. However, whether MFSC would impact commercial crop cultivars has not been studied. Here, we reveal that a MFSC of 5 different low level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. We further report on the first proteomics analysis of MFSC in plants that identified over 300 proteins common to all 4- and 5-MFSCs. Taken together our findings reveal that the impacts of MFSC on two different crop species are severe, and that MFSC may significantly affect agricultural productivity.

Climate change is causing an increase in the frequency and intensity of droughts, heat waves, and their combinations, diminishing agricultural productivity and destabilizing societies worldwide. We recently reported that during a combination of water deficit (WD) and heat stress (HS) stomata on leaves of soybean plants are closed, while stomata on flowers are open. This unique stomatal response was accompanied by differential transpiration (higher in flowers, while lower in leaves) that cooled flowers during a combination of WD+HS. Here we reveal that developing pods of soybean plants subjected to a combination of WD+HS use a similar acclimation strategy of differential transpiration to reduce internal pod temperature by about 4°C. We further show that enhanced expression of transcripts involved in abscisic acid degradation accompanies this response, and that preventing pod transpiration by sealing stomata causes a significant increase in internal pod temperature. Using an RNA-Seq analysis of pods developing on plants subjected to WD+HS, we also show that the response of pods to WD, HS, or WD+HS is distinct from that of leaves or flowers. Interestingly, we report that although flower, pod and seed numbers per plant are decreased under conditions of WD+HS, seed mass of plants subjected to WD+HS is larger than that of plants subjected to HS, and number of seeds with suppressed/aborted development is lower in WD+HS compared to HS. Taken together our findings reveal that differential transpiration occurs in pods of soybean plants subjected to WD+HS and that this process limits heat-induced damage to seed production.One sentence summaryDifferential transpiration between pods and leaves of soybean plants subjected to a combination of water deficit and heat stress buffers internal pod temperature.

Sprouty4 (SPRY4) has been frequently reported as a tumor suppressor and is therefore downregulated in various cancers. For the first time, we report that SPRY4 is epigenetically upregulated in colorectal cancer (CRC). In this study, we explored DNA methylation and hydroxymethylation levels ofSPRY4 gene expression is increased in colorectal cancerThe

Opioid drugs are commonly prescribed analgesic to pregnant women. Direct exposure to such drugs may slow gut motility, alter gut permeability, and affect the gut microbiome. While such drugs affect gut microbiome in infants, no study to date has determined whether developmental exposure to such drugs results in longstanding effects on gut microbiota and correspondingly on host responses. We hypothesized developmental exposure to oxycodone (OXY) leads to enduring effects on gut microbiota and such changes are associated with adult neurobehavioral and metabolic changes. Female mice were treated daily with 5 mg OXY/kg or saline solution (control [CTL]) for 2 weeks prior to breeding and then throughout gestation. Male and female offspring pups were weaned, tested with a battery of behavioral and metabolic tests, and fecal boli were collected adulthood (120 days of age). In females, relative abundance of

Increased fluid-flow shear stress (FFSS) contributes to hyperfiltration-induced podocyte and glomerular injury resulting in progression of chronic kidney disease (CKD). We reported that increased FFSS in vitro and in vivo upregulates PGE2 receptor EP2 (but not EP4 expression), COX2-PGE

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line (‘Osage’) with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

Federated multi-cloud resource allocation for data-intensive application workflows is generally performed based on performance or quality of service (i.e., QSpecs ) considerations. At the same time, end-to-end security requirements of these workflows across multiple domains are considered as an afterthought due to lack of standardized formalization methods. Consequently, diverse/heterogenous domain resource and security policies cause inter-conflicts between application's security and performance requirements that lead to sub-optimal resource allocations. In this paper, we present a joint performance and security-driven federated resource allocation scheme for data-intensive scientific applications. In order to aid joint resource brokering among multi-cloud domains with diverse/heterogenous security postures, we first define and characterize a data-intensive application's security specifications (i.e., SSpecs ). Then we describe an alignment technique inspired by Portunes Algebra to homogenize the various domain resource policies (i.e., RSpecs ) along an application's workflow lifecycle stages. Using such formalization and alignment, we propose a near optimal cost-aware joint QSpecs - SSpecs -driven, RSpecs -compliant resource allocation algorithm for multi-cloud computing resource domain/location selection as well as network path selection. We implement our security formalization, alignment, and allocation scheme as a framework, viz., “OnTimeURB” and validate it in a multi-cloud environment with exemplar data-intensive application workflows involving distributed computing and remote instrumentation use cases with different performance and security requirements.

The ultrafiltrate flow over the major processes and cell body generates fluid flow shear stress (FFSS) on podocytes. Hyperfiltration-associated increase in FFSS can lead to podocyte injury and detachment. Previously, we showed that FFSS-induced upregulation of the cyclooxygenase 2 (COX2)-PGE2-prostaglandin E receptor 2 (EP2) axis in podocytes activates Akt-glycogen synthase kinase-3β-β-catenin and MAPK/ERK signaling in response to FFSS. Integrative MultiOmics Pathway Resolution (IMPRes) is a new bioinformatic tool that enables simultaneous time-series analysis of more than two groups to identify pathways and molecular connections. In the present study, we used previously characterized COX2 [prostaglandin-endoperoxide synthase 2 ( Ptgs2)], EP2 ( Ptger2), and β1-catenin ( Ctnnb1) as “seed genes” from an array data set of four groups analyzed over a time course. The 3 seed genes shared 7 pathways and 50 genes of 14 pathways and 89 genes identified by IMPRes. A composite of signaling pathways highlighted the temporal molecular connections during mechanotransduction signaling in FFSS-treated podocytes. We investigated the “proteoglycans in cancer” and “galactose metabolism” pathways predicted by IMPRes. A custom-designed PCR array validated 60.7% of the genes predicted by IMPRes analysis, including genes for the above-named pathways. Further validation using Western blot analysis showed increased expression of phosho-Erbb2, phospho-mammalian target of rapamycin (mTOR), CD44, and hexokinase II (Hk2); decreased total Erbb2, galactose mutarotase (Galm), and β-1,4-galactosyltransferase 1 (B4galt1); and unchanged total mTOR and AKT3. These findings corroborate our previously reported results. This study demonstrates the potential of the IMPRes method to identify novel pathways. Identifying the “proteoglycans in cancer” and “galactose metabolism” pathways has generated a lead to study the significance of FFSS-induced glycocalyx remodeling and possible detachment of podocytes from the glomerular matrix.

Modern soybean [

Adaptation of soybean cultivars to the photoperiod in which they are grown is critical for optimizing plant yield. However, despite its importance, only the major loci conferring variation in flowering time and maturity of US soybean have been isolated. By contrast, over 200 genes contributing to floral induction in the model organism Arabidopsis thaliana have been described. In this work, putative alleles of a library of soybean orthologs of these Arabidopsis flowering genes were tested for their latitudinal distribution among elite US soybean lines developed in the United States. Furthermore, variants comprising the alleles of genes with significant differences in latitudinal distribution were assessed for amino acid conservation across disparate genera to infer their impact on gene function. From these efforts, several candidate genes from various biological pathways were identified that are likely being exploited toward adaptation of US soybean to various maturity groups.

Adaptation of soybean cultivars to the photoperiod in which they are grown is critical for optimizing plant yield. However, despite its importance, only the major loci conferring variation in flowering time and maturity of US soybean have been isolated. By contrast, over 200 genes contributing to floral induction in the model organism

Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Heat waves, occurring during droughts, can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security. Previous studies revealed that during a combination of drought and heat stress stomata on leaves of many plants are closed, preventing cooling by transpiration. Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, and sepal stomatal aperture of closed soybean flowers, developing on plants subjected to a combination of drought and heat stress. Here, we report that during a combination of drought and heat stress soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed ‘differential transpiration’, lowers flower inner temperature by about 2-3°C, protecting reproductive processes at the expense of vegetative tissues. Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could therefore serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture.One sentence summaryDuring stress conditions that result in higher flower inner temperature plants use a differential transpiration strategy to protect reproductive processes at the expense of vegetative tissues.

Nowadays, large amounts of omics data have been generated and contributed to increasing knowledge about associated biological mechanisms. A new challenge coming along is how to identify the active pathways and extract useful insights from these data with huge background information and noise. Although biologically meaningful modules can often be detected by many existing informatics tools, it is still hard to interpret or make use of the results towards in silico hypothesis generation and testing. To address this gap, we previously developed the IMPRes (Integrative MultiOmics Pathway Resolution) v 1.0 algorithm, a new step-wise active pathway detection method using a dynamic programming approach. This approach enables the network detection one step at a time, making it easy for researchers to trace the pathways, and leading to more accurate drug design and more effective treatment strategies. In this paper, we present IMPRes-Pro, an enhancement to IMPRes v1.0 by integrating proteomics data along with transcriptomics data and constructing a heterogeneous background network. The evaluation experiment conducted on human primary breast cancer dataset has shown the advantage over the original IMPRes v1.0 method. Furthermore, a case study on human metastatic breast cancer dataset was performed and we have provided several insights regarding the selection of optimal therapy strategy. IMPRes-Pro algorithm and visualization tool is available as a web service at http://digbio.missouri.edu/impres.

Enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase that suppresses gene expression. Previously, we developed a conditional null model where EZH2 is knocked out in uterus. Deletion of uterine EZH2 increased proliferation of luminal and glandular epithelial cells. Herein, we used RNA-Seq in wild-type (WT) and EZH2 conditional knockout ( Ezh2cKO) uteri to obtain mechanistic insights into the gene expression changes that underpin the pathogenesis observed in these mice. Ovariectomized adult Ezh2cKO mice were treated with vehicle (V) or 17β-estradiol (E2; 1 ng/g). Uteri were collected at postnatal day (PND) 75 for RNA-Seq or immunostaining for epithelial proliferation. Weighted gene coexpression network analysis was used to link uterine gene expression patterns and epithelial proliferation. In V-treated mice, 88 transcripts were differentially expressed (DEG) in Ezh2cKO mice, and Bmp5, Crabp2, Lgr5, and Sprr2f were upregulated. E2 treatment resulted in 40 DEG with Krt5, Krt15, Olig3, Crabp1, and Serpinb7 upregulated in Ezh2cKO compared with control mice. Transcript analysis relative to proliferation rates revealed two module eigengenes correlated with epithelial proliferation in WT V vs. Ezh2cKO V and WT E2 vs. Ezh2cKO E2 mice, with a positive relationship in the former and inverse in the latter. Notably, the ESR1, Wnt, and Hippo signaling pathways were among those functionally enriched in Ezh2cKO females. Current results reveal unique gene expression patterns in Ezh2cKO uterus and provide insight into how loss of this critical epigenetic regulator assumingly contributes to uterine abnormalities.

Graphical abstract, Highlights • A novel graph classification formulation in inferring gene regulatory relationships. • Graph neural network is powerful to ensemble powers from heuristic skeletons. • Our results show GRGRNN outperforms previous methods inductively on benchmarks. • GRGNN can be interpreted following the biological network motif hypothesis., Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.

Ophthalmology researchers are becoming increasingly reliant on protected data sets to find new trends and enhance patient care. However, there is an inherent lack of trust in the current healthcare community ecosystem between the data custodians (i.e., health care organizations and hospitals) and data consumers (i.e., researchers and clinicians). This typically results in a manual governance approach that causes slow data accessibility for researchers due to concerns such as ensuring auditability for any authorization of data consumers, and assurance to ensure compliance with health data security standards. In this paper, we address this issue of long-drawn data accessibility by proposing a semi-automated “honest broker” framework that can be implemented in an online health application. The framework establishes trust between the data consumers and the custodians by: 1. improving the eiciency in compliance checking for data consumer requests using a risk assessment technique; 2. incorporating auditability for consumers to access protected data by including a custodian-in-the-loop only when essential; and 3. increasing the speed of large-volume data actions (such as view, copy, modify, and delete) using a popular common data model. Via an ophthalmology case study involving an age-related cataract research use case in a community cloud testbed, we demonstrate how our solution approach can be implemented in practice to improve timely data access and secure computation of protected data for ultimately achieving data-driven eye health insights.

Pancreatic cancer (PaC) is resistant to immune checkpoint therapy, but the underlying mechanisms are largely unknown. In this study, we have established four orthotopic PaC murine models with different PaC cell lines by intra-pancreatic inoculation. Therapeutic examinations demonstrate that only tumors induced with Panc02-H7 cells respond to αPD-1 antibody treatment, leading to significantly reduced tumor growth and increased survival in the recipient mice. Transcriptomic profiling at a single-cell resolution characterizes the molecular activity of different cells within tumors. Comparative analysis and validated experiments demonstrate that αPD-1-sensitive and -resistant tumors differently shape the immune landscape in the tumor microenvironment (TME) and markedly altering effector CD8+ T cells and tumor-associated macrophages (TAMs) in their number, frequency, and gene profile. More exhausted effector CD8+ T cells and increased M2-like TAMs with a reduced capacity of antigen presentation are detected in resistant Panc02-formed tumors versus responsive Panc02-H7-formed tumors. Together, our data highlight the correlation of tumor-induced imbalance of macrophages with the fate of tumor-resident effector CD8+ T cells and PaC response to αPD-1 immunotherapy. TAMs as a critical regulator of tumor immunity and immunotherapy contribute to PaC resistance to immune checkpoint blockade.

Multi-cloud resource brokering is becoming a critical requirement for applications that require high scale, diversity, and resilience. Applications demand timely selection of distributed data storage and computation platforms that span local private cloud resources as well as resources from multiple cloud service providers (CSPs). The distinct capabilities and policies, as well as performance/cost of the cloud services, are amongst the prime factors for CSP selection. However, application owners who need suitable cyber resources in community/public clouds, often have preliminary knowledge and preferences of certain CSPs. They also lack expert guidance to handle the problem of overwhelming resource choice from CSPs, and optimization to compensate for service dynamics. In this paper, we address this challenge of optimal resource selection while also leveraging limited user’s expertise and preferences towards CSPs through multi-level fuzzy logic modeling based on convoluted factors of performance, agility, cost, and security. We evaluate the efficiency of our fuzzy-engineered resource brokering in improving allocation of resources as well as user satisfiability by using case studies and independent validations of CSPs evaluation.

Systemic inflammation in pregnant obese women is associated with 1.5- to 2-fold increase in serum Interleukin-6 (IL-6) and newborns with lower kidney/body weight ratio but the role of IL-6 in increased susceptibility to chronic kidney (CKD) in adult progeny is not known. Since IL-6 crosses the placental barrier, we administered recombinant IL-6 (10 pg/g) to pregnant mice starting at mid-gestation yielded newborns with lower body (p

BackgroundKangaroo Mother Care (KMC) is a low-resource, evidence-based, high-impact intervention for low-birth weight (LBW) care. Quality improvement in KMC requires meso-level, macro-level and micro-level interventions. Our institution, a public teaching hospital, hosts a level-II/III neonatal intensive care unit (NICU). The average demand for beds typically exceeds available capacity, with 60% occupancy attributed to LBW patients. There was low uptake of KMC practice at our unit.Aim statementIn the initial phase, we aimed to improve the coverage of KMC in admitted eligible neonates from a baseline of 20%–80% within 15 days. After a period of complacency, we revised the aim statement with a target of improving the percentage of babies receiving 6-hour KMC from 30% to 80% in 12 weeks.MethodsWe report this quasi-experimental time-series study. With the Point of Care Quality Improvement methodology, we performed Plan-Do-Study-Act (PDSA) cycles to improve KMC practice. We involved all the healthcare workers, mothers and caregivers to customise various KMC tools (KMC book format, KMC bag, mother’s gown) and minimise interruptions. Feedback from all levels guided our PDSA cycles.ResultsThe percentage of babies receiving at least 1-hour KMC increased from 20% to 100% within 15 days of August 2017. In the improvement phase, baseline 6-hour KMC coverage of 30% increased to 80% within 12 weeks (October–December 2017). It sustained for more than 2 years (January 2018 till February-2020) at 76.5%±2.49%.ConclusionsQuality improvement methods helped increase the coverage and percentage of babies receiving 6-hour KMC per day in our NICU. The duration specified KMC coverage should be adopted as the quality indicator of KMC. The training of healthcare workers and KMC provider should include hands-on sessions involving the mother and the baby. Maintaining data and providing suitable KMC tools are necessary elements for improving KMC. Minimising interruption is possible with family support and appropriate scheduling of activities. Having a designated KMC block helps in peer motivation.

Single nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) are widely spread across all chromosomes of the genome and act as biological markers, which aid in identification of genes associated with traits or phenotypes. With the advances in next-generation sequencing (NGS) technology, large amounts of SNPs and Indels data have become available, making it difficult to effectively perform analysis across multiple samples and intuitively integrate, compare and/or visualize them simultaneously. Genome-wide association studies (GWAS) is a widely used method to find genetic variations associated with a trait, but it lacks an efficient way to investigate genomic variant functions. To tackle these issues, we have developed SNPViz v2.0, a web-based tool to visualize large-scale haplotype blocks with detailed SNPs and Indels grouped by their chromosomal coordinates, along with their overlapping gene models, phenotype to genotype accuracies, Gene Ontology (GO) annotations, protein families (Pfam) annotations, genomic variant annotations, and their functional effects. Moreover, SNPViz v2.0 integrates several large scale soybean SNPs and Indels datasets including G. Soja, GWAS, NAM41, USB-15x, USB-40x, MSMC and Zhou302, available from multiple studies. SNPViz v2.0 is deployed for all organisms and available in both SoyKB and KBCommons frameworks. For soybean data only, the SNPViz 2.0 is publicly available at http://soykb.org/SNPViz2/. For other organisms such as Arabidopsis thaliana, Mus musculus and Zea mays, SNPViz 2.0 is publicly available in their respective knowledge bases at https://kbcommons.org.

Big data computation tools are vital for researchers and educators from various domains such as plant science, animal science, biomedical science and others. With the growing computational complexity of biology big data, advanced analytic systems, known as prescriptive systems, are being built using machine learning models to intelligently predict optimum computation solutions for users for better data analysis. However, lack of user-friendly prescriptive systems poses a critical roadblock to facilitating informed decision-making by users. In this paper, we detail a formative usability study to address the complexities faced by users while using prescriptive systems. Our usability research approach considers bioinformatics workflows and community cloud resources in the KBCommons framework’s science gateway. The results show that recommendations from usability studies performed in iterations during the development of prescriptive systems can improve user experience, user satisfaction and help novice as well as expert users to make decisions in a well-informed manner.

Maize is one of the major food crops grown in the continental US, and as such, major interest is directed towards understanding its adaptability to drought stress. Certain cultivars of maize have shown increased resistance to water shortages, by continuing to maintain root growth even when under severe water stress. To better understand the molecular mechanisms which lead to such adaptation, we analyzed multiomics datasets generated from the growth zone of nodal roots from FR697, an inbred line that shows a superior capacity for root growth maintenance under drought stress. We used a research pipeline consisting of a discriminatory multiomics data integration approach, which uses a combination of sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square (PLS) analysis instead of traditional “filter funnel” approaches, to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. We then linked significant elements from these clusters to various observations associated with drought stress in the root tip samples, reinforced by their roles in biological pathways.

Due to their antimicrobial properties, silver nanoparticles (AgNPs) are used in a wide range of consumer products that includes topical wound dressings, coatings for biomedical devices, and food-packaging to extend the shelf-life. Despite their beneficial antimicrobial effects, developmental exposure to such AgNPs may lead to gut dysbiosis and long-term health consequences in exposed offspring. AgNPs can cross the placenta and blood–brain-barrier to translocate in the brain of offspring. The underlying hypothesis tested in the current study was that developmental exposure of male and female mice to AgNPs disrupts the microbiome–gut–brain axis. To examine for such effects, C57BL6 female mice were exposed orally to AgNPs at a dose of 3 mg/kg BW or vehicle control 2 weeks prior to breeding and throughout gestation. Male and female offspring were tested in various mazes that measure different behavioral domains, and the gut microbial profiles were surveyed from 30 through 120 days of age. Our study results suggest that developmental exposure results in increased likelihood of engaging in repetitive behaviors and reductions in resident microglial cells. Echo-MRI results indicate increased body fat in offspring exposed to AgNPs exhibit. Coprobacillus spp., Mucispirillum spp., and Bifidobacterium spp. were reduced, while Prevotella spp., Bacillus spp., Planococcaceae, Staphylococcus spp., Enterococcus spp., and Ruminococcus spp. were increased in those developmentally exposed to NPs. These bacterial changes were linked to behavioral and metabolic alterations. In conclusion, developmental exposure of AgNPs results in long term gut dysbiosis, body fat increase and neurobehavioral alterations in offspring.

Transporters, a class of membrane proteins that facilitate exchange of solutes including diverse molecules and ions across the cellular membrane, are vital component for the survival of all organisms. Understanding plant transporters is important to get insight of the basic cellular processes, physiology, and molecular mechanisms including nutrient uptake, signaling, response to external stress, and many more. In this regard, extensive analysis of transporters predicted in soybean and other plant species was performed. In addition, an integrated database for soybean transporter protein, SoyTD, was developed that will facilitate the identification, classification, and extensive characterization of transporter proteins by integrating expression, gene ontology, conserved domain and motifs, gene structure organization, and chromosomal distribution features. A comprehensive analysis was performed to identify highly confident transporters by integrating various prediction tools. Initially, 7541 transmembrane (TM) proteins were predicted in the soybean genome; out of these, 3306 non-redundant transporter genes carrying two or more transmembrane domains were selected for further analysis. The identified transporter genes were classified according to a standard transporter classification (TC) system. Comparative analysis of transporter genes among 47 plant genomes provided insights into expansion and duplication of transporter genes in land plants. The whole genome resequencing (WGRS) and tissue-specific transcriptome datasets of soybean were integrated to investigate the natural variants and expression profile associated with transporter(s) of interest. Overall, SoyTD provides a comprehensive interface to study genetic and molecular function of soybean transporters. SoyTD is publicly available at http://artemis.cyverse.org/soykb_dev/SoyTD/.

Campuses are increasingly adopting hybrid cloud architectures for supporting data-intensive science applications that require “on-demand” resources, which are not always available locally on-site. Policies at the campus edge for handling multiple such applications competing for remote resources can cause bottlenecks across applications. These bottlenecks can be proactively avoided with pertinent profiling, monitoring and control of application flows using software-defined networking and pertinent selection of local or remote compute resources. In this paper, we present an “application-driven overlay network-as-a-service” (ADON) that manages the hybrid cloud requirements of multiple applications in a scalable and extensible manner by allowing users to specify requirements of the application that are translated into the underlying network and compute provisioning requirements. Our solution involves scheduling transit selection, a cost optimized selection of site(s) for computation and traffic engineering at the campus-edge based upon real-time policy control that ensures prioritized application performance delivery for multi-tenant traffic profiles. We validate our ADON approach through an emulation study and through a wide-area overlay network testbed implementation across two campuses. Our workflow orchestration results show the ADON effectiveness in handling temporal behavior of multi-tenant traffic burst arrivals using profiles from a diverse set of actual data-intensive applications.

Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.

Hyperfiltration is an important underlying cause of glomerular dysfunction associated with several systemic and intrinsic glomerular conditions leading to chronic kidney disease (CKD). These include obesity, diabetes, hypertension, focal segmental glomerulosclerosis (FSGS), congenital abnormalities and reduced renal mass (low nephron number). Hyperfiltration-associated biomechanical forces directly impact the cell membrane, generating tensile and fluid flow shear stresses in multiple segments of the nephron. Ongoing research suggests these biomechanical forces as the initial mediators of hyperfiltration-induced deterioration of podocyte structure and function leading to their detachment and irreplaceable loss from the glomerular filtration barrier. Membrane lipid-derived polyunsaturated fatty acids (PUFA) and their metabolites are potent transducers of biomechanical stress from the cell surface to intracellular compartments. Omega-6 and ω-3 long-chain PUFA from membrane phospholipids generate many versatile and autacoid oxylipins that modulate pro-inflammatory as well as anti-inflammatory autocrine and paracrine signaling. We advance the idea that lipid signaling molecules, related enzymes, metabolites and receptors are not just mediators of cellular stress but also potential targets for developing novel interventions. With the growing emphasis on lifestyle changes for wellness, dietary fatty acids are potential adjunct-therapeutics to minimize/treat hyperfiltration-induced progressive glomerular damage and CKD.

Motivation Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. Results We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. Availability and implementation MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. Supplementary information Supplementary data are available at Bioinformatics online.

Some crops inhibit shoot growth but maintain root growth under water-deficit conditions. Unraveling the molecular mechanisms of root plasticity under water deficit conditions in plants remains a major challenge. We developed an efficient platform for identifying core transcription factors (TFs) that collectively regulate each other and/or themselves in response to water stress, and exploring their interconnected regulatory circuitry involved in root growth maintenance under water deficit in soybean. We performed multi-species phylogenetic footprinting combined with spatial-temporal transcriptome analysis of soybean (Glycine max) roots under water deficit to identify conserved motifs that function in the water-stress response. Using these functional conserved cis-motifs, we applied a new approach to trace back motifs-associated core TFs ingroup as signal mediators, which mediate signaling between abiotic and endogenous stimuli. We integrated a co-functional TF–TF network and conserved motif-centered TF–DNA networks to construct a core TF network defined by mutual cross-regulation among core TFs. We found that core TF ARG (Abscisic acid response element binding factor-like Root Growth regulator) represses BRG (Brassinosteroid enhanced expression-like Root Growth regulator) expression through binding to its promoter at a conserved binding site. ARG and BRG antagonistically regulate Phytochrome-interacting factor-like Root Growth regulator (PRG) and combinatorially regulate some other core TFs. These core TFs form complex regulatory circuits to integrate light and multiple hormone signaling pathways and maintain root growth in response to varying degrees of water stress. Our study provides valuable information to unravel the complicated mechanisms of molecular networks involved in the regulation of root growth under water deficit.

Data-intensive science applications often use federated multi-cloud infrastructures to support their compute-intensive processing needs. However, lack of knowledge about: a) individual domain's security policies, b) how that translates to application security assurance, and c) nature of performance and security trade-offs - can cause performance-security conflicts for applications and inefficient resource usage. In this paper, we propose a security-aware resource brokering middleware framework to allocate application resources by satisfying their performance and security requirements. The proposed middleware implements MCPS (Multi-Cloud Performance and Security) Broker that uses a common data model to represent applications' performance and security requirements. It performs a security-aware global scheduling to choose the optimal cloud domain, and a local scheduling to choose the optimal server within the chosen cloud domain. Using real SoyKB application workflows, we implement the proposed MCPS Broker in the GENI Cloud and demonstrate its utility through a NIST-guided risk assessment.

The underlying neurological events accompanying dog domestication remain elusive. To reconstruct the domestication process in an experimental setting, silver foxes (Vulpes vulpes) have been deliberately bred for tame vs aggressive behaviors for more than 50 generations at the Institute for Cytology and Genetics in Novosibirsk, Russia. The hypothalamus is an essential part of the hypothalamic-pituitary-adrenal axis and regulates the fight-or-flight response, and thus, we hypothesized that selective breeding for tameness/aggressiveness has shaped the hypothalamic transcriptomic profile. RNA-seq analysis identified 70 differentially expressed genes (DEGs). Seven of these genes, DKKL1, FBLN7, NPL, PRIMPOL, PTGRN, SHCBP1L and SKIV2L, showed the same direction expression differences in the hypothalamus, basal forebrain and prefrontal cortex. The genes differentially expressed across the three tissues are involved in cell division, differentiation, adhesion and carbohydrate processing, suggesting an association of these processes with selective breeding. Additionally, 159 transcripts from the hypothalamus demonstrated differences in the abundance of alternative spliced forms between the tame and aggressive foxes. Weighted gene coexpression network analyses also suggested that gene modules in hypothalamus were significantly associated with tame vs aggressive behavior. Pathways associated with these modules include signal transduction, interleukin signaling, cytokine-cytokine receptor interaction and peptide ligand-binding receptors (eg, G-protein coupled receptor [GPCR] ligand binding). Current studies show the selection for tameness vs aggressiveness in foxes is associated with unique hypothalamic gene profiles partly shared with other brain regions and highlight DEGs involved in biological processes such as development, differentiation and immunological responses. The role of these processes in fox and dog domestication remains to be determined.

Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.

Scientific workflows due to their data and memory intensive requirements are among the prime applications which benefit by leveraging cloud computing. However, Cloud service providers (CSPs) have distinct policies and service dynamics that present a problem of excess choice for users. Performance and cost of the cloud services are among the principal factors in CSP selection for scientific bioinformatics workflows. The workflows typically are based on private data, and require diverse cloud resources, thus often requiring synergistic services from multiple CSPs. In this paper, we address this challenge of multi-cloud resource selection using cloud template solutions based on user specifications. We propose an optimizer that incorporates a combinatorial optimization model built on performance, cost and CSPs interoperability factors. The optimizer is integrated within a novel resource broker (i.e., OnTimeURB) for prescriptive recommendations of template solutions with intuitive choices for users. We implement and evaluate the OnTimeURB recommendations framework with a catalog of bioinformatics workflow applications integrated within a KBCommons science gateway. The evaluation considered four CSP resources featuring more than 300 different machine configuration instances. Our evaluation results show that our OnTimeURB creates consistently more economical, performance optimized and practical cloud solutions compared to a k-nearest neighbors (k-NN) approach.

The precise mechanism behind treatment resistance in cancer is still not fully understood. Despite advances in precision oncology, there is a lack of tools that help to understand a mechanistic picture of treatment resistance in cancer patients. Existing enrichment methods heavily rely on quantitative data and limited to analysis of differentially expressed genes, ignoring crucial players that might be involved in this process. In order to tackle treatment resistance, the precise identification of deregulated flow of signal transduction is critical. Here, we introduce a bioinformatics framework that is capable of inferring deregulated flow of signal transduction given evidence-based knowledge about pathway topology and patient-specific mutations. While testing the proposed pipeline on a case study, our algorithm was able to confirm findings from biological experiment, where KRAS mutant cells developed treatment resistance to MEK inhibitor. Our model provides a framework for mechanistic understanding of acquired treatment resistance, thus, equipped clinicians with tool for searching more accurate diagnostic clues in patients with non-trivial disease representations.