Package: HPiP 1.13.0

Matineh Rahmatbakhsh

HPiP: Host-Pathogen Interaction Prediction

HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.

Authors:Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led]

HPiP_1.13.0.tar.gz
HPiP_1.13.0.zip(r-4.5)HPiP_1.13.0.zip(r-4.4)HPiP_1.13.0.zip(r-4.3)
HPiP_1.13.0.tgz(r-4.4-any)HPiP_1.13.0.tgz(r-4.3-any)
HPiP_1.13.0.tar.gz(r-4.5-noble)HPiP_1.13.0.tar.gz(r-4.4-noble)
HPiP_1.13.0.tgz(r-4.4-emscripten)HPiP_1.13.0.tgz(r-4.3-emscripten)
HPiP.pdf |HPiP.html✨
HPiP/json (API)
NEWS

# Install 'HPiP' in R:

install.packages('HPiP', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mrbakhsh/hpip/issues

Datasets:

Gold_ReferenceSet - Gold-standard Reference Set of Inter-Species PPIs
UP000464024_df - Data.frame Containing SARS-CoV-2 FASTA Sequences
enrich.df - Enrichment Result
example_data - Input Data for Prediction Algorithm
host_se - Host SummarizedExperiment object
predicted_PPIs - Predicted HP-PPIs
unlabel_data - HP-PPIs with Unknown Class Labels
viral_se - Viral SummarizedExperiment object

On BioConductor:HPiP-1.11.0(bioc 3.20)HPiP-1.10.0(bioc 3.19)

proteomics systemsbiology networkinference structuralprediction geneprediction network

4.95 score 3 stars 6 scripts 130 downloads 30 exports 97 dependencies

Last updated 23 days agofrom:65e718fcde. Checks:OK: 1 NOTE: 1 WARNING: 5. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 30 2024
R-4.5-win	WARNING	Oct 30 2024
R-4.5-linux	NOTE	Oct 30 2024
R-4.4-win	WARNING	Oct 30 2024
R-4.4-mac	WARNING	Oct 30 2024
R-4.3-win	WARNING	Oct 30 2024
R-4.3-mac	WARNING	Oct 30 2024

Exports:calculateAAC calculateAutocor calculateBE calculateCTDC calculateCTDD calculateCTDT calculateCTriad calculateDC calculateF calculateKSAAP calculateQD_Sm calculateTC calculateTC_Sm corr_plot enrichfind_cpx enrichfind_hp enrichfindP enrichplot filter_missing_values FreqInteractors FSmethod get_negativePPI get_positivePPI getFASTA getHPI impute_missing_data plotPPI pred_ensembel run_clustering var_imp

Dependencies:askpass bit bit64 caret class cli clipr clock codetools colorspace corrplot cpp11 crayon curl data.table diagram digest dplyr e1071 expm fansi farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat hms httr igraph ipred isoband iterators jsonlite KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix MCL mgcv mime ModelMetrics munsell nlme nnet numDeriv openssl parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr protr proxy PRROC purrr R6 RColorBrewer Rcpp readr recipes reshape2 rlang rpart scales shape SQUAREM stringi stringr survival sys tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite vroom withr

Introduction to HPiP

Matineh Rahmatbakhsh

Rendered fromHPiP_tutorial.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2023-03-27
Started: 2021-09-10

Help page	Topics
Calculate Amino Acid Composition (AAC) Descriptor	calculateAAC
Calculate Autocorrelation Descriptors	calculateAutocor
Tranform a Seqeuence into Binary Encoding (BE)	calculateBE
Calculate CTD Descriptors - Composition (C)	calculateCTDC
Calculate CTD Descriptors - Distribution (D)	calculateCTDD
Calculate CTD Descriptors - Transition (T)	calculateCTDT
Calculate Conjoint Triad Descriptor	calculateCTriad
Calculate Dipeptide Composition (DC) Descriptor	calculateDC
Calculate F1 or F2 Descriptors	calculateF
Calculate k-spaced Amino Acid Pairs (KSAAP) Descriptor	calculateKSAAP
Calculate Quadruples Composition (QC) Descriptor from Biochemical Similarity Classes	calculateQD_Sm
Calculate Tripeptide Composition (TC) Descriptor	calculateTC
Calculate Tripeptide Composition (TC) Descriptor from Biochemical Similarity Classes	calculateTC_Sm
Plot Correlation Matrix between Input Features	corr_plot
Enrichment Result	enrich.df
Functional Enrichment Analysis for Predicted Modules	enrichfind_cpx
Functional Enrichment Analysis of all Host Proteins	enrichfind_hp
Functional Enrichment Analysis for Pathogen Interactors in the High-Confidence Network.	enrichfindP
Plot the Enrichment Reuslt	enrichplot
Input Data for Prediction Algorithm	example_data
Drop the Missing Values Above a Certain Threshold	filter_missing_values
Plot the Pathogen Proteins' frequency of Interactions with Host Proteins	FreqInteractors
Feature Selection via Matrix Correlation and Recursive Feature Elimination (RFE)	FSmethod
Construct Negative Reference Host-Pathogen Protein-Protein Interactions (HP-PPIs)	get_negativePPI
Fetch Positive Reference Host-Pathogen Protein-Protein Interactions (HP-PPIs) from the BioGRID Database	get_positivePPI
Fetch FASTA Sequence from the UniProt Database	getFASTA
Generating Host-Pathogen Protein-Protein Interaction (HP-PPI) Descriptors	getHPI
Gold-standard Reference Set of Inter-Species PPIs	Gold_ReferenceSet
Host SummarizedExperiment object	host_se
Impute missing Values per Features (i.e., Columns)	impute_missing_data
Plot the Predicted PPI	plotPPI
Predict Interactions via Ensemble Learning Method	pred_ensembel
Predicted HP-PPIs	predicted_PPIs
Module Detection	run_clustering
HP-PPIs with Unknown Class Labels	unlabel_data
Data.frame Containing SARS-CoV-2 FASTA Sequences	UP000464024_df
Variable Importance Plot	var_imp
Viral SummarizedExperiment object	viral_se

Package: HPiP 1.13.0

HPiP: Host-Pathogen Interaction Prediction

Introduction to HPiP

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)