Motivation: Reproducibility, a cornerstone of research, requires defined data formats, which include the setup and output of experiments. The real-time PCR data markup language (RDML) is a recommended standard of the minimum information for publication of quantitative real-time PCR experiments guidelines. Despite the popularity of the RDML format for analysis of quantitative PCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares.
Results: This study describes the open-source RDML package for the statistical computing language R. RDML is compatible with RDML versions ≤ 1.2 and provides functionality to (i) import RDML data; (ii) extract sample information (e.g. targets and concentration); (iii) transform data to various formats of the R environment; (iv) generate human-readable run summaries; and (v) to create RDML files from user data. In addition, RDML offers a graphical user interface to read, edit and create RDML files.
Availability And Implementation: https://cran.r-project.org/package=RDML. rdmlEdit server http://shtest.evrogen.net/rdmlEdit/. Documentation: http://kablag.github.io/RDML/.
Contact: k.blag@yandex.ru.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx528 | DOI Listing |
BMC Bioinformatics
November 2024
Department of Biostatistics, Key Laboratory of Public Health Safety of Ministry of Education, Key Laboratory for Health Technology Assessment, National Commission of Health, School of Public Health, Fudan University, Shanghai, China.
Background: Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in estimating causal effects within high-dimensional complex data. However, the DML model is sensitive to the presence of outliers and heavy-tailed noise in the outcome variable.
View Article and Find Full Text PDFMol Oncol
May 2023
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Accuracy and transparency of scientific data are becoming more and more relevant with the increasing concern regarding the evaluation of data reproducibility in many research areas. This concern is also true for quantifying coding and noncoding RNAs, with the remarkable increase in publications reporting RNA profiling and sequencing studies. To address the problem, we propose the following recommendations: (a) accurate documentation of experimental procedures in Materials and methods (and not only in the supplementary information, as many journals have a strict mandate for making Materials and methods as visible as possible in the main text); (b) submission of RT-qPCR raw data for all experiments reported; and (c) adoption of a unified, simple format for submitted RT-qPCR raw data.
View Article and Find Full Text PDFCancer Treat Res Commun
May 2023
Oncology Center, Hospital Sírio-Libanês, Street Dona Adma Jafet, number 115, zip-code 01308-050, São Paulo, SP, Brazil.
Background: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have been recently developed and introduced into clinical practice.
Methods: We retrospectively analyzed data from patients with confirmed HR+/HER2 metastatic breast cancer treated with hormonal therapy in combination with ribociclib (R), palbociclib (P), or abemaciclib (A).
Outcomes: median progression-free survival (mPFS), time to treatment discontinuation (mTTD), and objective response rate (ORR).
BMC Bioinformatics
August 2021
Department of Medical Biology, Amsterdam University Medical Centres, Academic Medical Center, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
Background: The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results.
Implementation: The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and C per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product.
Front Bioeng Biotechnol
April 2020
Laboratory for Transplantation Immunology, National Research Center for Hematology, Moscow, Russia.
Minor histocompatibility antigens (MiHA) are critical elements for the immune response after allogeneic hematopoietic stem cell transplantation. They may cause both beneficial and detrimental effects in forms of graft-versus-tumor and graft-versus-host accordingly. MiHAs originate from donor-recipient discrepancies in single nucleotide polymorphisms, insertions, and deletions.
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