CDHit Module

The CDHit module in the Protein Data Handler project focuses on clustering protein sequences using the CD-HIT algorithm. It extends BioinfoOperatorBase for efficient database and configuration handling, catering to the needs of large-scale protein data processing.

Class Overview

The CDHit class is the centerpiece of this module, designed to handle the complexities of clustering protein sequences with CD-HIT. It manages loading sequences from the database, creating FASTA files, and executing the clustering algorithm.

class protein_metamorphisms_is.operations.cdhit.CDHit(conf)

Bases: OperatorBase

Class for processing protein data using the CD-HIT algorithm, an efficient algorithm for clustering and comparing protein or nucleotide sequences.

Extends the BioinfoOperatorBase to leverage its database and configuration handling capabilities, this class specifically focuses on clustering protein sequences. It facilitates the grouping of similar sequences, thereby reducing redundancy and improving the efficiency of subsequent analyses.

conf

Configuration dictionary containing necessary parameters for CD-HIT and other operations.

Type:: dict

start(): Initiates the process of sequence clustering using CD-HIT.

load_chains(): Loads protein chain data from the database for clustering.

create_fasta(): Creates a FASTA file from the protein chain data.

cluster(): Executes the CD-HIT algorithm and processes the output.

Usage:: The CDHit class can be utilized in bioinformatics pipelines for sequence analysis, especially where sequence redundancy reduction or efficient sequence comparison is required.

cluster()

Execute the CD-HIT algorithm for sequence clustering.

Runs the CD-HIT algorithm on the prepared FASTA file, then reads the output cluster file to store the clustering results in the database. Configuration parameters such as sequence identity threshold, alignment coverage, accurate mode and memory usage are used to control the CD-HIT execution.

create_fasta(chains)

Generate a FASTA file from a list of protein chains.

Writes the provided protein chains to a FASTA formatted file. The path for the FASTA file is specified in the configuration.

Parameters:: chains (list) – A list of PDBChains objects to be written to the FASTA file.

load_chains()

Retrieve protein chain data from the database.

Fetches all PDBChains records from the database. The method can be configured to include or exclude multiple chain models based on the ‘allow_multiple_chain_models’ (NMR samples) configuration.

Returns:: A list of PDBChains objects representing protein chains.
Return type:: list

start()

Start the protein sequence clustering process using CD-HIT.

Coordinates the steps for loading protein chains, creating a FASTA file, executing CD-HIT for clustering, and handling exceptions and key events. Logs the progress and any errors encountered during the process.

Key Features

Sequence Clustering: Efficiently clusters protein sequences using the CD-HIT algorithm.
Database Integration: Seamlessly integrates with SQL databases for loading and storing protein data.
FASTA File Handling: Capable of creating and managing FASTA files for the clustering process.
Multithreaded Operations: Supports concurrent processing for improved performance.
Comprehensive Logging: Implements robust logging for tracking the clustering process and troubleshooting.

Algorithm Integration

CDHit utilizes the CD-HIT algorithm for its core functionality:

Efficient Clustering: Employs the renowned CD-HIT algorithm for its efficiency in handling large sequence datasets.
Configurable Parameters: Offers flexibility in setting CD-HIT parameters like sequence identity threshold and memory usage.

Installation of CD-HIT

Before using the CDHit class, the CD-HIT algorithm must be installed on your system. CD-HIT can be easily installed on Debian-based systems using apt-get:

sudo apt-get update
sudo apt-get install cd-hit

This ensures that the CD-HIT executable is available in your system’s PATH, which is necessary for the CDHit class to function properly.

SQL/ORM Entities

The module interacts with several SQL/ORM entities for organizing and storing clustering results:

PDBChains: Manages Protein Data Bank chains information.
Cluster: Stores clustering results, including cluster identifiers and sequence information.

These entities facilitate the storage and retrieval of clustered protein sequence data in a relational database.

Configuration

CDHit requires specific configuration parameters for optimal operation. Here’s a configuration template:

# CDHit Configuration
max_workers: [Number of threads for CD-HIT]
fasta_path: [Path for FASTA file]
cdhit_out_path: [Path for CD-HIT output file]
sequence_identity_threshold: [Identity threshold for clustering]
memory_usage: [Maximum memory usage for CD-HIT]
alignment_coverage: [Minimum alignment coverage for clustering]
most_representative_search: [Boolean value to enable/disable most representative search]

Adjust these settings based on the specific requirements of your clustering tasks.

Usage

To use CDHit, initialize the class with your configuration and start the clustering process:

from protein_metamorphisms_is.operations.cdhit import CDHit

# Initialize with configuration
cdhit_instance = CDHit(conf)

# Start clustering
cdhit_instance.start()

This initiates the sequence clustering process, handling all aspects from data loading to clustering and result storage.