Description: Provides: 1) By Giving UniProtKB, the Protein Sequence will be returned from UniProt database. 2)By Giving UniProtKB, the Nucleotide Sequence will be returned from EBI database. 3) Amino acid Composition is calculated by four different methods: a) Twenty-two independent categories are considered, with one amino acid for each category. B) Five categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged) are considered according to the standard chemical structures of amino acids. c) Six categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged, Special cases) are considered which Cysteine, Selenocysteine, Glycine and Proline are placed in Special cases group. d)Eight categories are clustered via k-means algorithm on Physicochemical index of amino acids. 4) GC Content: Percentage of nucleotide g and c in sequence. 5) Codon usage: Frequency of occurrence of synonymous codons. 6) Stacking Energy: The NN model for nucleic acids assumes that the stability of a given base pair depends on the identity and orientation of neighboring base pairs. Stacking Energy = DeltaG(total) = Sigma (n(i)*DeltaG(i)) + DeltaG(init) + DeltaG(end) + DeltaG(sym), which DeltaG for i, init and end is obtained by Unified NN free energy parameter. Symmetry of self-complementary duplexes is also included by DeltaG(sym) equals to +0.43 (kcal/mol) if the duplex is self-complementary and zero if it is non-self-complementary. 7) Complement of desired nucleotide sequence. 8) Reverse of desired nucleotide sequence. 9) Reverse-Complement of desired nucleotide sequence. 10) Protein, Gene and Organism of desired UniProt ID. 11) Converting nucleotide sequence to protein sequence. 12) Getting Localization From Gene Ontology Cellular Component inside Uniprot. 13) All related codons of desired amino acid.

Title: Exact Bayesian Model Selection Methods for the Sparse Normal
person("Steven", "de Rooij", email = "steven.de.rooij@gmail.com", role = c("cre", "aut")),
person("Tim", "van Erven", email = "tim@timvanerven.nl", role = "aut"),
person("Botond", "Szabo", email = "b.t.szabo@math.leidenuniv.nl", role = "aut"))
Maintainer: Steven de Rooij <steven.de.rooij@gmail.com>
Description: Contains fast functions to calculate the exact Bayes posterior
for the Sparse Normal Sequence Model, which implement the algorithms described in
Van Erven and Szabo (2018) <arXiv:1810.10883>. For general hierarchical priors,
sample sizes up to 10,000 are feasible within half an hour on a standard laptop.
For beta-binomial spike-and-slab priors, a faster algorithm is provided, which
can handle sample sizes of 100,000 in half an hour. In the implementation,
special care has been taken to assure numerical stability of the methods even for
Imports: Rcpp (>= 0.12.18), RcppProgress (>= 0.4.1), selectiveInference