Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to facilitate their exploration and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure etc. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.

Duchenne muscular dystrophy (DMD) is a classical monogenic disorder, a model disease for genomic studies and a priority candidate for regenerative medicine and gene therapy. Although the genetic cause of DMD is well known, the molecular pathogenesis of disease and the response to therapy are incompletely understood. Here,we describe analyses of protein, mRNA and microRNA expression in the tibialis anterior of the mdx mouse model of DMD. Notably, 3272 proteins were quantifiable and 525 identified as differentially expressed in mdx muscle (P < 0.01). Therapeutic restoration of dystrophin by exon skipping induced widespread shifts in protein and mRNA expression towards wild-type expression levels, whereas the miRNome was largely unaffected. Comparison analyses between datasets showed that protein and mRNA ratios were only weakly correlated (r = 0.405), and identified a multitude of differentially affected cellular pathways, upstream regulators and predicted miRNA–target interactions. This study provides fundamental new insights into gene expression and regulation in dystrophic muscle. 3 Wt, 4 mdx and 4 Pip6e-PMO treated mdx mice

Global omics lab services market size is expected to reach USD 245.69 billion by 2032 at a CAGR of 13.4%, according to a new study by Polaris market research.

Additional file 3: Table S2. Sequencing details per sample and condition.

Acetyl-CoA is a key metabolite in all organisms, implicated in transcriptional regulation, post-translational modification as well as fuelling the TCA-cycle and the synthesis and elongation of fatty acids (FAs). The obligate intracellular parasite Toxoplasma gondii possesses two enzymes which produce acetyl-CoA in the cytosol and nucleus: acetyl-CoA synthetase (ACS) and ATP-citrate lyase (ACL), while the branched-chain α-keto acid dehydrogenase-complex (BCKDH) generates acetyl-CoA in the mitochondrion. To obtain a global and integrative picture of the role of distinct sub-cellular acetyl-CoA pools, we measured the acetylome, transcriptome, proteome and metabolome of parasites lacking ACL/ACS or BCKDH. Loss of ACL/ACS results in the hypo-acetylation of nucleo-cytosolic and secretory proteins, alters gene expression broadly and is required for the synthesis of parasite-specific FAs. In contrast, loss of BCKDH causes few specific changes in the acetylome, transcriptome and proteome which allow these parasites to rewire their metabolism to adapt to the obstruction of the TCA-cycle.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Stony coral tissue loss disease (SCTLD) has devastated Florida’s Coral Reef since 2014, affecting many endangered coral species and particularly Orbicella faveolata. While there exists anecdotal evidence of disease resistance in O. faveolata populations, no study to date has quantitatively assessed the potential for certain genotypes to survive the SCTLD epidemic. Previous efforts have focused on field experiments, which cannot standardize disease exposure and often have covarying impacts of environmental variability and non-disease-associated mortality. With previous support from FDEP (CPR C2002; Muller et al., 2023), a collaborative team from Mote Marine Laboratory, University of Miami Rosenstiel School, and NOAA’s Atlantic Oceanographic and Meteorological Laboratory conducted the largest coral disease transmission study to date (170 putative genotypes, 345 total fragments, 38% with >2 replicates) using O. faveolata genotypes from Mote’s land-based nursery. This study also prioritized sampling of corals at multiple time points, including initial, pre-exposure, early exposure, initial lesion signs, and >10% tissue mortality to better understand disease responses and progression using multi-‘omic analyses. A total of 2,565 ‘omics samples were collected for population genomics, microbial genomics, transcriptomics, and histological analyses. This comprehensive sampling approach facilitated the greatest possible examination of molecular responses for any coral disease to date. In this current project, we analyzed these multi-’omic datasets, specifically to address the following goals: to 1) screen nursery-reared O. faveolata genotypes for SCTLD resistance profiles using updated genome and transcriptome assemblies, 2) evaluate the natural evolutionary adaptation of O. faveolata microbial communities to SCTLD resistance, and 3) develop a SCTLD susceptibility hierarchy of restoration genotypes combining transmission and genetic datasets.

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus infectious clone (WNV-NY99 clone 382) and mutant WNV-E218A (WNV-NY99 382 E218A 2 nt) virus infection. Sample data was obtained from mouse popliteal draining lymph nodes for proteomics, metabolomics, and lipidomics expression analysis. See Experiment WLN002 for corresponding transcriptome analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, and lipidomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

Pathophysiology of cancer-associated syndroms such as cachexia is poorly understood and no routine biomarkers have been established, yet. Using shotgun proteomics, known marker molecules including PMEL, CRP, SAA and CSPG4 were found deregulated in patients with metastatic melanoma. Targeted analysis of 58 selected proteins with multiple reaction monitoring was applied for independent data verification. In three patients, two of which suffered from cachexia, a tissue damage signature was determined, consisting on nine proteins, PLTP, CD14, TIMP1, S10A8, S10A9, GP1BA, PTPRJ, CD44 and CO4A, as well as increased levels of glycine and asparagine, and decreased levels of polyunsaturated phosphatidylcholine concentrations, as determined by targeted metabolomics. Remarkably, these molecules are known to be involved in key processes of cancer cachexia. Based on these results, we propose a model how metastatic melanoma may lead to reprogramming of organ functions via formation of platelet activating factors from long-chain polyunsaturated phosphatidylcholines under oxidative conditions and via systemic induction of intracellular calcium mobilization. Calcium mobilization in platelets was demonstrated to regulate several of these marker molecules. Additionally, platelets from melanoma patients proved to be in a rather exhausted state, and platelet-derived eicosanoids implicated in tumor growth were found massively increased in blood from three melanoma patients. Platelets were thus identified as important source of serum protein and lipid alterations in late stage melanoma patients. As a result, the proposed model describes the crosstalk between lipolysis of fat tissue and muscle wasting mediated by oxidative stress, resulting in the metabolic deregulations characteristic for cachexia.

Additional file 2: Table S1. Design overview of the panel of targeted regions.

Collection of over 60 comprehensive international databases on the structure of the global economy, and standardized metadata for each, covering both technical characteristics of the data and detailed access information. Areas represented in the collection include output and value added by industrial sector, labor force, social and demographic data, productivity, and measures of economic endowments.

Table S4. Dominant and combinatorial features of enrichment in 434 functional classes of TCGA breast cancer data. The table shows the full list of significantly enriched gene sets and their combinatorial relations for TCGA breast cancer data. The reduced functional category for each gene set is also reported. An NA entry denotes that the gene set and its reduced functional category are identical. (XLSX 28 kb)

This dataset contains all the inputs used and output produced from the modified GEOPHIRES for the economic analysis of base case hybrid GDHC system, improved hybrid GDHC system with heat pump and for hot water GDHC. Software required: Microsoft Notepad, Microsoft Excel and GEOPHIRES modified source code

Naive CD4+ T cells are the common precursors of multiple effector and memory T cell subsets and possess a high plasticity in terms of differentiation potential. This stem-cell like character is important for cell therapies aiming at regeneration of specific immunity. Cell surface proteins are crucial for recognition and response to signals mediated by other cells or environmental changes. Knowledge of cell surface proteins of human naive CD4+ T cells and their changes during the early phase of T cell activation is urgently needed for a guided differentiation of naive T cells and may support the selection of pluripotent cells for cell therapy.
Periodate oxidation and aniline-catalyzed oxime ligation (PAL) technology was applied with subsequent quantitative LC-MS/MS (PAL-qLC-MS/MS) to generate a dataset describing the surface proteome of human naive CD4+ T cells and to monitor dynamic changes during the early phase of activation. This led to the identification of 173 N-glycosylated surface proteins, of which 24 were previously not known to be expressed on human naive CD4+ T cells or have no defined role within T cell activation. To independently confirm the proteomic dataset and to analyse the cell surface by an alternative technique a systematic phenotypic expression analysis of surface antigens via flow cytometry was performed. This screening expanded the previous dataset, resulting in 229 surface proteins which are expressed on naive unstimulated and activated CD4+ T cells. Furthermore, we generated a surface expression atlas based on transcriptome data, experimental annotation and predicted subcellular localization, and correlated the proteomics result with this transcriptional dataset.
This extensive surface atlas provides an overall naive CD4+ T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments.

Narrowed-down metatranscriptome read hits of sample AT2. Gene-read hits assigned using the database depicted in ‘Additional file 13’ and the Trinotate pipeline for sample AT2. (XLSX 7618 kb)

In 2017, the County Department of Economic Development, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for Allegheny County. A similar MVA was completed with the Pittsburgh Urban Redevelopment Authority in 2016. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. The 2016 Allegheny County MVA does not include the City of Pittsburgh, which was characterized at the same time in the fourth update of the City of Pittsburgh’s MVA. All calculations herein therefore do not include the City of Pittsburgh. While the methodology between the City and County MVA's are very similar, the classification of communities will differ, and so the data between the two should not be used interchangeably. Allegheny County's MVA utilized data that helps to define the local real estate market. Most data used covers the 2013-2016 period, and data used in the analysis includes: •Residential Real Estate Sales; • Mortgage Foreclosures; • Residential Vacancy; • Parcel Year Built; • Parcel Condition; • Owner Occupancy; and • Subsidized Housing Units. The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. During the research process, staff from the County and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market. Please refer to the report (included here as a pdf) for more information about the data, methodology, and findings.

Additional file 5: Table S1: MSEA top pathways. Table S2: Scores of pathways after applying 12 topological algorithms. Table S3: Detailed information of key drivers in wKDA network. Table S4: The details of hub genes. Table S5: The details of TFs. Table S6: Detailed results of drug repositioning results.

The objective of this project was for the facility to conduct a techno-economic assessment (TEA) of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth). MADWEC is used for powering remote monitoring and Autonomous Underwater Vehicle (AUV) charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The 18 assessed scenarios include two end-uses: 1) AUV charging and 2) offshore remote monitoring); three power sources: 1) MADWEC), 2) photovoltaic (PV) solar buoy, 3) and traditional battery swapping); and three locations; 1) nearshore, 2) far-offshore, and 3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario. The techno-economic model, created in Microsoft Excel, estimates CapEx, OpEx, and the power performance of each power supply source. The model has a dynamic format that allows custom inputs to accommodate future changes to the systems being assessed. This ismore » a TEA for the MADWEC project, TEAMER RFTS 7 (request for technical support) program.« less

Enrichment analysis of genes associated with COVID-19 status.

Graph and download economic data for Government subsidies: Economic affairs (G170791A027NBEA) from 1959 to 2023 about economic affairs, subsidies, government, GDP, and USA.