Earlier implementations and research code
These repositories hold the code the TSR methods were first developed and published with, and tools written alongside particular papers. They are kept unchanged, for provenance and for work that depends on them.
Each repository's licence is shown as the repository states it on GitHub. BioTSR's Apache License 2.0 does not extend to any of them, and a repository with no licence stated gives no permission to reuse its code beyond reading it; ask its authors.
Earlier implementations of methods now in BioTSR
The first implementations of methods that BioTSR now runs. Where BioTSR's definition of a method differs from one of these programs, the method's page describes the difference.
TSR-Package — pooryakhajouie/TSR-Package on GitHub (opens in a new tab)
The earlier Python implementation of protein Cα TSR, written for the published methods: downloads PDB files and generates keys and triplets, with amino acid grouping, mirror images, size filtering, secondary structure elements and drug–target cross keys.
github.com/pooryakhajouie/TSR-Package
Licence MIT License
Documented on this site
TSR_AMINOACID_PACKAGE — KrishnaRauniyar/TSR_AMINOACID_PACKAGE on GitHub (opens in a new tab)
The earlier implementation of TSR for individual amino acids, rather than a whole protein chain: retrieves PDB files and generates key and triplet files per residue.
github.com/KrishnaRauniyar/TSR_AMINOACID_PACKAGE
Licence No licence stated
Documented on this site
TSR_NUCLEOTIDE_PACKAGE — KrishnaRauniyar/TSR_NUCLEOTIDE_PACKAGE on GitHub (opens in a new tab)
The earlier implementation of TSR for nucleotide residues: retrieves PDB files and generates key and triplet files.
github.com/KrishnaRauniyar/TSR_NUCLEOTIDE_PACKAGE
Licence No licence stated
Documented on this site
Nucleotide-Protein — KrishnaRauniyar/Nucleotide-Protein on GitHub (opens in a new tab)
Generates cross keys between a nucleotide and the protein it contacts. Its scripts and output columns are named for drugs, because the nucleotide occupies the drug position of a general drug–protein pipeline.
github.com/KrishnaRauniyar/Nucleotide-Protein
Licence No licence stated
Documented on this site
Separate research tools and workflows
Code written alongside particular papers, including analyses that are not part of BioTSR 1.0: the deep neural network, the per-chain and percentage common-key scripts, and the feature-selection options of the hsp70/actin comparison. The clustering workflow used in the papers is here too; BioTSR's own clustering runs from Python.
Nucleotide_Analysis — KrishnaRauniyar/Nucleotide_Analysis on GitHub (opens in a new tab)
Downstream analysis of generated keys: the common-key scripts under common_keys_analysis/, and the deep neural network under dnn/ that classifies residue types from key frequencies.
github.com/KrishnaRauniyar/Nucleotide_Analysis
Licence No licence stated
Documented on this site
Kinases-and-Phosphatases-Clustering — KrishnaRauniyar/Kinases-and-Phosphatases-Clustering on GitHub (opens in a new tab)
Key frequency generation and the hierarchical clustermap — the first and last steps of the clustering workflow.
github.com/KrishnaRauniyar/Kinases-and-Phosphatases-Clustering
Licence No licence stated
Documented on this site
hsp70_actin — dbxmcf/hsp70_actin on GitHub (opens in a new tab)
Jaccard similarity over key-frequency vectors — the middle step of the published clustering workflow — including the feature-selection options used in the hsp70/actin comparison, which BioTSR 1.0 does not provide. Maintained separately from the research group's own repositories.
github.com/dbxmcf/hsp70_actin
Licence GNU General Public License v3.0
Documented on this site