Contains most of the necessary files for running the analysis and plotting scripts.Please check analysis-plotting in https://github.com/josegcpa/wbs-prediction for more details.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for biostatistics, bioinformatics, and systems biology in the U.S.

The dataset was collected through whole-transcriptome RNA-Sequencing technologies. The processing method was described in the manuscript.

The dataset consists of 1763 observations, each representing a unique patient, and 12 different attributes associated with heart disease. This dataset is a critical resource for researchers focusing on predictive analytics in cardiovascular diseases.

Variables Overview: 1. Age: A continuous variable indicating the age of the patient. 2. Sex: A categorical variable with two levels ('Male', 'Female'), indicating the gender of the patient. 3. CP (Chest Pain type): A categorical variable describing the type of chest pain experienced by the patient, with categories such as 'Asymptomatic', 'Atypical Angina', 'Typical Angina', and 'Non-Angina'. 4. TRTBPS (Resting Blood Pressure): A continuous variable indicating the resting blood pressure (in mm Hg) on admission to the hospital. 5. Chol (Serum Cholesterol): A continuous variable measuring the serum cholesterol in mg/dl. 6. FBS (Fasting Blood Sugar): A binary variable where 1 represents fasting blood sugar > 120 mg/dl, and 0 otherwise. 7. Rest ECG (Resting Electrocardiographic Results): Categorizes the resting electrocardiographic results of the patient into 'Normal', 'ST Elevation', and other categories. 8. Thalachh (Maximum Heart Rate Achieved): A continuous variable indicating the maximum heart rate achieved by the patient. 9. Exng (Exercise Induced Angina): A binary variable where 1 indicates the presence of exercise-induced angina, and 0 otherwise. 10. Oldpeak (ST Depression Induced by Exercise Relative to Rest): A continuous variable indicating the ST depression induced by exercise relative to rest. 11. Slope (Slope of the Peak Exercise ST Segment): A categorical variable with levels such as 'Flat', 'Up Sloping', representing the slope of the peak exercise ST segment. 14. Target: A binary target variable indicating the presence (1) or absence (0) of heart disease.

Descriptive Statistics: The patients' age ranges from 29 to 77 years, with a mean age of approximately 54 years. The resting blood pressure spans from 94 to 200 mm Hg, and the average cholesterol level is about 246 mg/dl. The maximum heart rate achieved varies widely among patients, from 71 to 202 beats per minute.

Importance for Research: This dataset provides a comprehensive view of various factors that could potentially be linked to heart disease, making it an invaluable resource for developing predictive models. By analyzing relationships and patterns within these variables, researchers can identify key predictors of heart disease and enhance the accuracy of diagnostic tools. This could lead to better preventive measures and treatment strategies, ultimately improving patient outcomes in the realm of cardiovascular health

On this Zenodo link, we share the data that is required to reproduce all the analyses from our publication "satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications".

This repository includes input transcript-level expression matrices and metadata for all datasets, as well as intermediate results and final outputs of the respective DTU analyses. For a more elaborate description of the data, we refer to the companion GitHub for our publications; https://github.com/statOmics/satuRnPaper. Note that this is version 1.0.0 of the data (uploaded on 2021-01-14). If any changes were to be made to the datasets in the future, this will also be communicated on our companion GitHub page.

Multidimensional scaling (MDS) is a dimensionality reduction technique for microbial ecology data analysis that represents the multivariate structure while preserving pairwise distances between samples. While its improvements have enhanced the ability to reveal data patterns by sample groups, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination, “F-informed MDS,†which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using simulated compositional datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and ..., , # Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

• Dataset DOI: 10.5061/dryad.vmcvdnd3x
• Software: https://github.com/soob-kim/FinfoMDS
  (also in prep for Bioconductor submission)
• File or folder names are italicized. Package or variable names are monospaced.Â

File: Data.zip

Description:Â Raw data used in this study. Includes 3 folders and 1 file (see below).

1. Folder Simulated contains pairwise distances and ordination results from three simulated datasets. Includes 7 subfolders and 6 files.
   • Six files are the original dataset and its associated labels set. The names are formatted as "sim_<*x*>-<*type*>.*csv*" where <*x*> is the replicate number and <*type*> indicates whether the file is the design matrix ("data") or response vector ("Y").
   • Seven subfolders are grouped by the ordination method. Likewise, the file ...,

Portuguese National Registry on low weight newborns between 2013 and 2018, made available for research purposes. Dataset is composed of 3823 unique entries registering birthweight, biological sex of the infant (1-Male; 2-Female), CRIB score (0-21) and survival (0-Survival; 1-Death).

Cell refractive index (RI) was proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used Digital Holographic Microscopy (DHM) to compare RI and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the phase shifts distribution within the reconstructed quantitative phase images (QPIs) was analyzed by the method of bimodality coefficients. The observed correlation of RI and bimodality profile with the cells metastatic potential was validated by real time impedance based-assay and clonogenic tests. We suggest RI and QPIs histograms bimodality analysis to be developed as optical biomarkers useful in label-free detection and quantitative evaluation of cell metastatic potential.

Assisted reproductive technologies, including in vitro fertilization (IVF), are now frequently used, and increasing evidence indicates that IVF causes gene expression changes in children and adolescents that increase the risk of metabolic diseases. Although such gene expression changes are thought to be due to IVF-induced epigenetic changes, the mechanism remains elusive. We tested whether the transcription factor ATF7, – which mediates stress-induced changes in histone H3K9 tri- and di-methylation, typical marks of epigenetic silencing – is involved in the IVF-induced gene expression changes. IVF up- and down-regulated the expression of 688 and 204 genes, respectively, in the liver of 3-week-old wild-type (WT) mice, whereas 87% and 68% of these were not changed, respectively, by IVF in ATF7-deficient (Atf7—/—) mice. The genes, which are involved in metabolism, such as pyrimidine and purine metabolism, were up-regulated in WT mice but not in Atf7—/— mice. Of the genes whose expression was up-regulated by IVF in WT mice, 37% were also up-regulated by a loss of ATF7. These results indicate that ATF7 is a key factor in establishing the memory of IVF effects on metabolic pathways.

The conserved RNA-binding protein Hfq has multiple regulatory roles within the prokaryotic cell, including promoting stable duplex formation between small RNAs and mRNAs, and thus hfq deletion mutants have pleiotropic phenotypes. Previous proteome and transcriptome studies of Neisseria meningitidis have generated limited insight into differential gene expression due to Hfq loss. In this study, reversed-phase liquid chromatography combined with data-independent alternate scanning mass spectrometry (LC-MSE) was utilized for rapid high-resolution quantitative proteomic analysis to further elucidate the differentially expressed proteome of a meningococcal hfq deletion mutant. Whole cell lysates of N. meningitidis serogroup B H44/76 wild type (wt) and H44/76Δhfq (Δhfq) grown in liquid growth medium were subjected to tryptic digestion. The resulting peptide mixtures were separated by LC prior to analysis by MSE. Differential expression was analyzed by Student’s t-Test with control for false discovery rate (FDR). Reliable quantification of relative expression comparing wt and Δhfq was achieved with 506 proteins (20%). Upon FDR control at q ≤ 0.05, 48 up- and 59 downregulated proteins were identified. From these, 81 were identified as novel Hfq-regulated candidates, while 15 proteins were previously found by SDS-PAGE/MS and 24 with microarray analyses. Thus, using LC-MSE we have expanded the repertoire of Hfq regulated proteins. In conjunction with previous studies, a comprehensive network of Hfq regulated proteins was constructed and differentially expressed proteins were found to be involved in a large variety of cellular processes. The results and comparisons with other Gram-negative model systems, suggest still unidentified sRNA analogues in N. meningitidis.

Nephronectin (NPNT) is an extracellular matrix (ECM) protein involved in kidney development. We recently reported intracellular NPNT as a potential prognostic marker in breast cancer and that NPNT promotes metastasis in an integrin-dependent manner. Here we used Reverse Phase Protein Array (RPPA) to analyze NPNT-triggered intracellular signaling in the 66cl4 mouse breast cancer cell line. The results showed that the integrin binding enhancer motif is important for the cellular effects upon NPNT interaction with its receptors, including phosphorylation of p38 mitogen activated protein kinase (MAPK). Furthermore, analysis using prediction tools suggests involvement of NPNT in promoting cell viability. In conclusion, our results indicate that NPNT, via its integrin binding motifs, promote cell viability through phosphorylation of p38 MAPK.

Reference Viral Databases (RVDB-prot and RVDB-prot-HMM) were developed by Thomas Bigot in Marc Eloit’s Pathogen Discovery group in collaboration with Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI) at Institut Pasteur, for enhancing virus detection using next-generation sequencing (NGS) technologies. They are based on the reference Viral DataBase, courtesy of Arifa Khan’s group at CBER, FDA:https://hive.biochemistry.gwu.edu/rvdb/.They are updated after each new release of the nucleotidic database. The version number of the protein databases follows the one of the original nucleic database.

Signaling changes induced by haploinsufficient loss or over-expression of CEP350 by global phospho-serine/threonine profiling melanoma cells expressing oncogenic BRAF-V600E. Raw data produced by the Proteomics and Metabolomics Core Facility and data analysis performed by the Biostatistics and Bioinformatics Shared Resource at the H. Lee Moffitt Cancer Center & Research Institute.Supplementary datasets and other information accompanying manuscript: Tumor Suppressive Functions of CEP350 in Cutaneous Melanoma Cells by Aziz Aiderus, Bin Fang, John M. Koomen and Michael B. Mann.Abstract: We previously identified Cep350 as a novel melanoma haploinsufficient melanoma tumor suppressor gene using SB transposon-mediated mutagenesis to drive melanoma progression in Braf(V600E) mutant (SB|Braf) mice functionally demonstrated that the human CEP350 ortholog is a new melanoma tumor-suppressor gene in human cancer cell lines (Mann et al., Nature Genetics, 2015). Further dissection of the latent tumor suppressive functions of CEP350 in cutaneous melanoma cells is essential for understanding its role in melanoma imitation and progression. In this work, we investigated the role of the novel tumor suppressive functions of CEP350 in cutaneous melanoma cells using comparative informatics, molecular oncology, and proteomics approaches to demonstrate that CEP350 acts via altered cytoskeletal dynamics to contribute to BRAF-V600E driven melanoma.

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

Complete list of accession numbers of Rotavirus A VP4 proteins (Human) used in the statistical analysis of Pessia and Corander (2017). Statistical analysis done with Kpax3 (https://github.com/albertopessia/Kpax3.jl).Data can be downloaded from NCBI's Virus Variation Resource database (https://www.ncbi.nlm.nih.gov/genome/viruses/variation/) and aligned with MUSCLE (http://www.drive5.com/muscle/).Reference:Pessia, A. and Corander, J. (2017). "Bayesian cluster analysis of categorical data with supervised feature selection". Submitted.Brister, J. R., Bao, Y., Zhdanov, S. A., Ostapchuck, Y., Chetvernin, V., Kiryutin, B., Zaslavsky, L., Kimelman, M., and Tatusova, T. A. (2013). "Virus Variation Resource - Recent Updates and Future Directions". Nucleic Acids Research, 42(D1): D660-D665.Edgar, R. C. (2004). "MUSCLE: multiple sequence alignment with high accuracy and high throughput". Nucleic Acids Research, 32(5): 1792-1797.