rdml: A Mathematica package for parsing and importing Real-Time qPCR data

Objective The purpose and objective of the research presented is to provide a package for easy importing of Real-Time PCR data markup language (RDML) data to Mathematica. Results Real-Time qPCR is the most widely used experimental method for the accurate quantification of gene expression. To enable the straightforward archiving and sharing of qPCR data and its associated experimental information, an XML-based data standard was developed—the Real-Time PCR data markup language (RDML)—devised by the RDML consortium. Here, we present rdml, a package to parse and import RDML data into Mathematica, allowing the quick loading and extraction of relevant data, thus promoting the re-analysis, meta-analysis or experimental re-validation of gene expression data deposited in RDML format.info:eu-repo/semantics/publishedVersio

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Gwasrapidd: an R package to query, download and wrangle GWAS catalog data

The National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies (GWAS) Catalog has collected, curated and made available data from over 7100 studies. The recently developed GWAS Catalog representational state transfer (REST) application programming interface (API) is the only method allowing programmatic access to this resource.FCT—Fundação para a Ciência e a Tecnologia and CRESC ALGARVE 2020: POCI-01-0145-FEDER-022184 ‘GenomePT’and ALG-01-0145-FEDER-31477 ‘DEvoCancer’.info:eu-repo/semantics/publishedVersio

Quincunx: um pacote R para consultar, baixar e wrangle dados do catálogo PGS

For two decades, GWAS identified individual variants associated with risk for complex diseases. These associations can be combined into polygenic scores (PGS) aiming at quantifying an individual’s risk to disease, inform on prognosis and even treatment response (Lambert et al., 2019). Broadly, PGS use summary statistics produced by GWAS to calculate a weighted sum of trait-associated alleles carried by each individual, in which the weights correspond to the per-allele size effects. Initially used to validate associations with disease and uncover interactions between variants, PGS have been more challenging to implement in the clinic. In 2020, over 1400 publications on PGS appeared in PubMed, raising the need for a standardized distribution of studies’ key data, assuring their wide evaluation and accurate use. The Polygenic Score (PGS) Catalog, created in 2019, is a publicly available, manually curated database of PGS and relevant metadata, that responds to this need (Lambert et al., 2020). Its current release [date 2021-02-03] includes data from 133 publications and 721 PGS associated with 194 traits. Currently, data is accessed via three ways: (i) the web graphical user interface (GUI); (ii) by downloading database dumps; and (iii) the recent PGS Catalog representational state transfer (REST) application programming interface (API), the preferred method for batch analyses.info:eu-repo/semantics/publishedVersio

Data from: Evolution and diversification of the organellar release factor family

Translation termination is accomplished by proteins of the Class I release factor family (RF) that recognize stop codons and catalyze the ribosomal release of the newly synthesized peptide. Bacteria have two canonical RFs: RF1 recognizes UAA and UAG, RF2 recognizes UAA and UGA. Despite that these 2 release factor proteins are sufficient for de facto translation termination, the eukaryotic organellar RF protein family, which has evolved from bacterial release factors, has expanded considerably, comprising multiple subfamilies, most of which have not been functionally characterized or formally classified. Here we integrate multiple sources of information to analyze the remarkable differentiation of the RF family among organelles. We document the origin, phylogenetic distribution and sequence structure features of the mitochondrial and plastidial release factors: mtRF1a, mtRF1, mtRF2a, mtRF2b, mtRF2c, ICT1, C12orf65, pRF1 and pRF2, and review published relevant experimental data. The canonical release factors (mtRF1a, mtRF2a, pRF1 and pRF2) and ICT1 are derived from bacterial ancestors, while the others have resulted from gene duplications of another release factor. These new RF family members have all lost one or more specific motifs relevant for bona fide release factor function but are mostly targeted to the same organelle as their ancestor. We also characterize the subset of canonical release factor proteins that bear non-classical PxT/SPF tripeptide motifs, and provide a molecular-model-based rationale for their retained ability to recognize stop codons. Finally we analyze the co-evolution of canonical RFs with the organellar genetic code. Although the RF presence in an organelle and its stop codon usage tend to co-evolve, we find three taxa that encode an RF2 without using UGA stop codons, and one reverse scenario, where mamiellales green algae use UGA stop codons in their mitochondria without having a mitochondrial type RF2. For the latter we put forward a “stop-codon re-invention” hypothesis that involves the retargeting of the plastid release factor to the mitochondrion

RF2 subfamily alignment (mtrf2a, mtrf2b, mtrf2c, prf2)

Fasta multiple sequence alignment of Release Factor 2 proteins. Sequences were retrieved from GenBank nr database using PSI-BLAST. The alignment was obtained with ClustalW (v2.0.10), and 60% of the gaps was removed with BMGE (v1.0). The alignment was visually inspected and manually adjusted to accuratly align all functionally characterized domains and sequence motifs. Each sequence header contains the species name, the identity of each protein and, for bacterial species, the bacterial group they belong to. The numbers present in the header are not meaningful

Recent advances in understanding vertebrate segmentation [version 1; referees: 3 approved]

Segmentation is the partitioning of the body axis into a series of repeating units or segments. This widespread body plan is found in annelids, arthropods, and chordates, showing it to be a successful developmental strategy for growing and generating diverse morphology and anatomy. Segmentation has been extensively studied over the years. Forty years ago, Cooke and Zeeman published the Clock and Wavefront model, creating a theoretical framework of how developing cells could acquire and keep temporal and spatial information in order to generate a segmented pattern. Twenty years later, in 1997, Palmeirim and co-workers found the first clock gene whose oscillatory expression pattern fitted within Cooke and Zeeman’s model. Currently, in 2017, new experimental techniques, such as new ex vivo experimental models, real-time imaging of gene expression, live single cell tracking, and simplified transgenics approaches, are revealing some of the fine details of the molecular processes underlying the inner workings of the segmentation mechanisms, bringing new insights into this fundamental process. Here we review and discuss new emerging views that further our understanding of the vertebrate segmentation clock, with a particular emphasis on recent publications that challenge and/or complement the currently accepted Clock and Wavefront model

RF1 subfamily alignment (mtRF1a, mtRF1 and pRF1)

Fasta multiple sequence alignment of Release Factor 1 proteins. Sequences were retrieved from GenBank nr database using PSI-BLAST. The alignment was obtained with ClustalW (v2.0.10), and 60% of the gaps was removed with BMGE (v1.0). The alignment was visually inspected and manually adjusted to accuratly align all functionally characterized domains and sequence motifs. Fasta multiple sequence alignment of Release Factor 2 proteins. Sequences were retrieved from GenBank nr database using PSI-BLAST. The alignment was obtained with ClustalW (v2.0.10), and 60% of the gaps was removed with BMGE (v1.0). The alignment was visually inspected and manually adjusted to accuratly align all functionally characterized domains and sequence motifs. Each sequence header contains the species name, the identity of each protein and, for bacterial species, the bacterial group they belong to. The numbers present in the header are not meaningful

Use of difunctional initiator in reverse iodine transfer polymerization (RITP)

International audienc

2,5-Di-(2-ethylhexanoylperoxy)-2,5-dimethylhexane as difunctional radical initiator in reverse iodine transfer polymerization (RITP) of styrene, methyl methacrylate and butyl acrylate

International audienc