tidysq - Tidy Processing and Analysis of Biological Sequences

A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

Last updated 2 months ago

bioconductorbioinformaticsbiological-sequencesfastas3sequencestibbletidytidyversevctrscpp

7.56 score 40 stars 38 scripts 350 downloads

countfitteR - Comprehensive Automatized Evaluation of Distribution Models for Count Data

A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.

Last updated 2 years ago

cancercancer-imaging-researchcount-datacount-distributionfoci

5.33 score 4 stars 27 scripts 196 downloads

CancerGram - Prediction of Anticancer Peptides

Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/BioGenies/CancerGramModel>. For more information see: Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.

Last updated 4 years ago

anticancer-peptidesbioinformaticsk-mern-grampeptide-identificationrandom-forests

3.90 score 4 stars 3 scripts 207 downloads