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.

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).

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.

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.

This archive is a database generated using the novel Virus Pop pipeline, which simulates realistic protein sequences and adds new branches to a protein phylogenetic tree. An article describing the pipeline is currently under review.

The database contains simulations of 995 different proteins from 93 virus genera, providing a total of 24,138,277 sequences, both in amino acid and nucleotide.

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

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.

Methylation and Hydroxymethylation data.

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.

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.

This dataset supports the manuscript:

DNMT3A-R882 mutation intrinsically mimics maladaptive myelopoiesis from human haematopoietic stem cells

Giovanna Mantica1*, Aditi Vedi1,2*, Amos Tuval3§, Hector Huerga-Encabo4§, Daniel Hayler1§, Aleksandra Krzywon1,5, Emily Mitchell6, William Dunn1, Tamir Biezuner3, Kendig Sham1, Antonella Santoro1, Joe Lee6, Adi Danin3, Noa Chapal3, Yoni Moskovitz3,7, Andrea Arruda8, Edoardo Fiorillo9, Valeria Orru9, Michele Marongiu9, Eoin McKinney10, Francesco Cucca9,11, Matthew Collin12, Mark Minden8, Peter Campbell6, George S Vassiliou1, Margarete Fabre1, Jyoti Nangalia1,6, Dominique Bonnet4, Liran Shlush3,7,8, Elisa Laurenti1

• These authors contributed equally. § These authors contributed equally.

Affiliations: 1 Department of Haematology and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. 2 Department of Paediatric Oncology, Cambridge University Hospitals NHS Foundation Trust 3 Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel. 4 Haematopoietic Stem Cell Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK 5 Department of Biostatistics and Bioinformatics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland 6 Wellcome Sanger Institute, Hinxton, CB10 1SA, UK 7 Division of Haematology Rambam Healthcare Campus, Haifa 31096, Israel. 8 Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada. 9 Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Lanusei, Italy. 10 Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK 11 Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy 12 Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK

Chorismate mutase is a well-known model enzyme, catalyzing the Claisen rearrangement of chorismate to prephenate. Recent high-resolution crystal structures along the reaction coordinate of this enzyme enable computational analyses at unprecedented detail. Using quantum chemical simulations, we have investigated how the catalytic reaction mechanism is affected by electrostatic and hydrogen bond interactions. Our calculations showed that the transition state was mainly stabilized electrostatically, with Arg90 playing the leading role. The effect was augmented by selective hydrogen bond formation to the transition state in the wild-type enzyme, facilitated by a small-scale local induced fit. We further identified a previously underappreciated water molecule, which separates the negative charges during the reaction. The analysis includes the wild-type enzyme and a non-natural enzyme variant, where the catalytic arginine was replaced with an isosteric citrulline residue.

Spectral database of the subspecies of the Mycobacterium abscessus complex (MALDI-TOF Mass Spectrometry)

This data set originates from a collection of 41 clinical strains of Mycobacterium abscessus complex corresponding to 1001 mass spectra:

25 strains of Mycobacterium abscessus subsp. abscessus (633 mass spectra)

9 strains of Mycobacterium abscessus subsp. massiliense (204 mass spectra)

7 strains of Mycobacterium abscessus subsp. bolletii (164 mass spectra)

Each strain has been characterized using molecular method (DNA/DNA hydridation, using GenoType NTM-DR (Hain Lifescience, Nehren, Germany) according to the manufacturer's instructions for identification and analyzed by MALDI-TOF mass spectrometry according MycoEx protocol (Bruker®). The mass spectra spectra were obtained according to the following steps :

Each of the 41 strains was cultured in aerobic atmosphere at 37°C for 7 ± 2 days on blood agar (COH, bioMerieux®). Then, one colony was extracted according to the MycoEx protocol (Bruker®). For each of the extracts, 8 technical replicates were realized and analyzed by MALDITOF MS (Bruker®). Dried spots were overlaid with 1µL of MALDI matrix (α-HCCA).

Data acquisition was performed using a Microflex LT (Bruker® Daltonics) mass spectrometer equipped with a N2 laser (λ =377 nm). Instrument parameters used were as follows: a masse range between 200-20000 Da, ion source 1: 20 kV, ion source 2: 18.5 kV, Iens: 8.45 kV, pulsed ion extraction: 330 ns, laser frequency: 20.0 Hz. Spectra were obtained after 500 shots. Each spot was analyzed three times. In total 24 spectra were obtained for each extraction.

Spectra acquired for each isolate were visualized and analyzed using Flex Analysis software (Bruker® Daltonics), and spectra with low quality peaks were removed. A minimum of 15 spectra per extraction was necessary to validate the extraction.

This database is only intended for medical research. Please contact: medecine-drv@sorbonne-universite.fr for data access.

After access agreement, the three following files will be available :

The MABSC_spectra.zip file contains the MS peak list data in a Matlab compatible format.

The MABSC_metadata.pdf file contains the molecular identifications of strains.

The MABSC_notes.txt file contains informations concerning contains informations on the method of obtaining the data.

dataset referring to Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design Lorenzo Bertizzolo1, Patrick M Bossuyt2, Ignacio Atal1, 5, Philippe Ravaud1, 3-6, Agnès Dechartres7 1 INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France. 2 Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands. 3 Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France. 4 Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France. 5 Cochrane France, Paris, France 6 Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA 7 Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France

Data set from the article Caruso R, Rebora P, Luciani M, Di Mauro S, Ausili D. Sex-related differences in self-care behaviors of adults with type 2 diabetes mellitus. Endocrine. 2020 Feb;67(2):354-362. doi: 10.1007/s12020-020-02189-5. Epub 2020 Jan 11. PMID: 31927750.

Abstract

Purpose: To describe sex-related differences in self-care; to identify determinants of self-care according to sex, and to investigate how sex interacts with the effect of clinical and socio-demographic variables on self-care in adults with Type 2 Diabetes Mellitus (T2DM).

Methods: Cross-sectional multicentre study with a consecutive sampling recruitment strategy, enrolling 540 adults with T2DM at six outpatient diabetes services. Clinical and socio-demographic variables were collected by medical records. Self-care maintenance, monitoring, management, and confidence were measured by the self-care of diabetes inventory.

Results: Females reported higher disease prevention behaviors (P < 0.001), health-promoting behaviors (P < 0.001), body listening (P < 0.001), and symptom recognition (P = 0.010), but lower health-promoting exercise behaviors (P < 0.001). Determinants of self-care were different in male and female patients, where the role of task-specific self-care confidence predicted self-care monitoring (RR = 0.98; P < 0.001) and management (RR = 0.99; P < 0.001) among males, while persistence self-care confidence predicted self-care maintenance (RR = 0.97; P = 0.016) and management (RR = 0.99; P = 0.009) among females.

Conclusions: Males and females differently perform self-care. Self-care confidence plays a different role predicting self-care behaviors in males and females. Future research should longitudinally describe self-care and its determinants in males and females with T2DM. Sex-specific self-care confidence interventions should be developed to improve self-care in male and female patients with T2DM.

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.

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

This package contains synthetic datasets used to evaluate the the SNVPhyl (http://snvphyl.readthedocs.io/) pipeline. This is divided into two separate datasets. Additional details on how these datasets were constructed are available at https://github.com/apetkau/snvphyl-validations.1. e-coli-simulated-dataset: Simulated reads for evaluated SNVPhyl's SNV detection accuracy.Reads are based off of an E. Coli reference genome (NC_002695), plus two plasmids (NC_002128, NC_002127) which were concatenated into a single fasta file reference-genome/e_coli_sakai_w_plasmids.fasta. Random mutations were introduced to produce the variant genomes present in the genomes under variant-genomes/. Reads were simulated using ART Illumina (http://www.niehs.nih.gov/research/resources/software/biostatistics/art/) to generate the fastq files in this directory.2. salmonella-heidelberg-contamination: Simulated reads for evaluating SNVPhyl's performance in the presence of contamination from another genomic sample.Reads for the sample SH13-001 (BioSample: SAMN04334637) were downsampled and contaminated with SH12-001 (BioSample: SAMN04334627) at percentages of 5%, 10%, 20%, and 30%.