Calculate Entropy for Amino Acid Sequences

Description

This function calculates the entropy of a set of amino acid sequences. It is used to evaluate the diversity of sequences in a given alignment.

Usage

entropy_RR(alignment)

Arguments

Details

The entropy is calculated based on the frequency of amino acids at each position in the alignment. Higher entropy indicates greater variability at that position.

Value

A numeric vector representing the entropy values for each position in the alignment.

Examples

# Example usage:
library(Biostrings)
sequences <- AAStringSet(c("ACDEFGHIK", "ACDEFGHIK", "ACDEFGHIK"))
entropy_values <- entropy_RR(sequences)
print(entropy_values)

Filter Sequences

Description

Filters sequences to remove non-standard amino acids.

Usage

filter_sequences(input_file)

Arguments

Value

A 'AAStringSet' object containing the filtered sequences.

Perform DBSCAN Clustering on a Phylogenetic Tree

Description

This function applies the DBSCAN clustering algorithm on a set of protein sequences to identify clusters and remove outliers based on a distance cutoff.

Usage

phyl_tree_cluster_dbscan(input_obj, cutoff, nmin)

Arguments

Details

The function uses the DBSCAN algorithm to cluster sequences based on their phylogenetic distances. Sequences identified as outliers are excluded from the final output.

Value

This function returns a 'AAStringSet' object containing protein sequences with outliers removed.

Examples

# Example usage:
library(Biostrings)

# Create an AAStringSet object with the sequences
seqs <- AAStringSet(c(
  seq1 = "MKTIIALSYIFCLVFADYKDDDDK",
  seq2 = "MKTIIALSYIFCLVFADYKDLLKDDDD",
  seq3 = "MKTIIALSYIFCLVFADEELYKDDDD",
  seq4 = "MKTIEIALSYIFCLVFADYKDDDD",
  seq5 = "MKTIIKLAAASYIFCLVFADYKDDDD",
  seq6 = "MKTIIALSKIPFCLVFADYKDDDD",
  seq7 = "MKTIIALSYIFiQEERTCLVFADYKDDDD"
))

# Perform DBSCAN clustering and remove outliers
no_outliers <- phyl_tree_cluster_dbscan(seqs, cutoff = 0.5, nmin = 5)

Calculate Phylogenetic Tree Distances

Description

This function calculates the pairwise distances between sequences in a phylogenetic tree and returns a numeric vector of 5th smallest distances for each leaf in the tree.

Usage

phyl_tree_distance_k(input_obj)

Arguments

Details

The function uses the 'ape' package to construct a phylogenetic tree and calculate pairwise distances between sequences. The results are returned as a numeric vector.

Value

A numeric vector containing the 5th smallest distances ('Distances_k') for each leaf in the phylogenetic tree.

Examples

# Input file
input_file <- system.file("extdata", "input.fasta", package = "VIProDesign")
seqs <- Biostrings::readAAStringSet(input_file)
distances_k <- phyl_tree_distance_k(seqs)

Run VIProDesign Workflow

Description

This function performs the VIProDesign workflow for clustering and analyzing protein sequences. It includes steps for filtering sequences, removing redundancy, identifying and removing outliers, and clustering sequences using PAM (Partitioning Around Medoids). This function requires the ‘cd-hit' executable to be installed and accessible in the system’s PATH if 'use_cd_hit = TRUE'. If 'cd-hit' is not available, the workflow will skip redundancy removal and proceed with the filtered sequences.

Usage

run_VIProDesign(
  file,
  output_prefix,
  max_cluster_number = NULL,
  predefined_cluster_number = NULL,
  use_cd_hit = TRUE,
  cd_hit_path,
  cutoff = 0.99,
  remove_outliers = TRUE,
  verbose = FALSE
)

Arguments

Details

To install 'cd-hit', you can use conda: “' conda install -c bioconda cd-hit “' Or download it from the official website: http://weizhong-lab.ucsd.edu/cd-hit/

The workflow includes the following steps: - Filtering sequences. - Removing redundancy using 'cd-hit'. - Identifying and removing outliers using DBSCAN clustering. - Performing PAM clustering to identify representative sequences. - Calculating entropy to evaluate clustering quality.

Value

A list containing the following elements:

• filtered_file: A file containing the filtered sequences (if redundancy removal was performed).

• non_redundant_file: A file containing the non-redundant sequences (if redundancy removal was performed).

• no_outlier_obj: A 'AAStringSet' object containing the sequences with outliers removed (if outlier removal was performed).

• clustering_info: Clustering information generated by PAM clustering.

• final_panel: The final representative sequences selected by the workflow.

Examples

# Example usage:
temp_dir <- tempdir()
temp_prefix <- file.path(temp_dir, "output")
input_file <- system.file("extdata", "input.fasta", package = "VIProDesign")
run_VIProDesign(
  file = input_file,
  output_prefix = temp_prefix,
  max_cluster_number = 5,
  use_cd_hit = TRUE,
  cd_hit_path = "/data/kiryst/conda/envs/VIProDesign/bin/cd-hit",
  cutoff = 0.99,
  remove_outliers = TRUE
)
# Clean up
unlink(list.files(temp_dir, full.names = TRUE))