Pseudo-Alignment Bioinformatics Tool

A Python-based tool for pseudoalignment and equivalence class analysis using k-mers. This tool processes transcriptome files and read files to create k-mer indexes, perform pseudoalignment, and visualize equivalence class statistics, inspired by Kallisto.

Below is an example of the Kallisto pseudoalignment graph:

Features

• Creates a k-mer index from a transcriptome FASTA file.
• Performs pseudoalignment of reads using k-mers.
• Outputs equivalence class counts and isoform associations.
• Provides visualization of equivalence class size distribution.

Installation

Prerequisites

Ensure you have Python 3.8+ installed and install the following dependencies:

pip install -r requirements.txt

Dependencies

• Biopython
• matplotlib
• tqdm
• pandas

Running the Tool

git clone https://github.com/williamhuang3/pseudoaligner.git
cd pseudoaligner

To run the pseudoalignment tool, execute the main.py script:

python main.py

By default, the script expects the following input files:

• chr11_transcriptome.fasta: A FASTA file containing the transcriptome sequences.
• reads.fasta.gz: A gzipped FASTA file with the reads to pseudoalign.

You can modify the file paths and parameters directly in the main.py file.

The results will be saved to equivalence_classes.tsv, and the top 15 equivalence classes will be printed to the console. A plot showing the equivalence class size distribution will also be displayed.

Example Output

Below is an example of the top 15 equivalence class counts output by the tool:

Counts	Number of Items in Equivalence Class	Isoforms in Equivalence Class
50420	2	(ENST00000329251, ENST00000496634)
15022	1	(ENST00000449131,)
14169	6	(ENST00000273550, ENST00000526640, ENST00000532601, ENST00000532829, ENST00000534180, ENST00000620041)
12500	1	(ENST00000536684,)
10517	1	(ENST00000393067,)
10039	7	(ENST00000227157, ENST00000379412, ENST00000396222, ENST00000422447, ENST00000430553, ENST00000540430, ENST00000545215)
8546	5	(ENST00000345732, ENST00000389939, ENST00000532073, ENST00000534668, ENST00000674194)
8168	3	(ENST00000321153, ENST00000530398, ENST00000530797)
8113	2	(ENST00000321153, ENST00000530797)
7585	2	(ENST00000228140, ENST00000525634)
7165	2	(ENST00000527673, ENST00000532567)
6416	4	(ENST00000273550, ENST00000530019, ENST00000532829, ENST00000620041)
6008	8	(ENST00000227378, ENST00000524552, ENST00000526110, ENST00000526686, ENST00000532636, ENST00000533540, ENST00000534319, ENST00000534624)
5989	1	(ENST00000260197,)
5890	3	(ENST00000527673, ENST00000527791, ENST00000532567)

The following plot shows the distribution of equivalence class sizes based on the number of isoforms:

File Descriptions

• indexer.py: Contains the function to create the k-mer index from the transcriptome.
• aligner.py: Contains the pseudoalignment logic that compares reads against the k-mer index.
• utils.py: Utility functions, including a function to calculate the reverse complement of a sequence.
• plotter.py: Functions for plotting the equivalence class size distribution.
• main.py: The main script that coordinates all the tasks and outputs results.
• requirements.txt: List of Python packages required to run the code.