rna-seq-tools 0.7.2

simple functions for manipulating sequences and secondary structures in pandas dataframe format

seq_tools

A Python package for manipulating and analyzing nucleic acid sequences (DNA and RNA) in pandas DataFrames.

Features

• Batch operations: Work with sequences in pandas DataFrames for efficient processing
• Sequence manipulation: Convert between DNA/RNA, reverse complement, add sequences
• Structure prediction: Fold RNA sequences using ViennaRNA
• Analysis tools: Calculate molecular weights, extinction coefficients, edit distances
• CLI interface: Command-line tools for quick sequence operations
• Python API: Full programmatic access to all functionality

Installation

pip install rna_seq_tools

Quick Start

Command Line Interface

# Get help
seq-tools --help

# Convert RNA to DNA
seq-tools to-dna "AUCG"

# Fold RNA sequence
seq-tools fold "GGGGUUUUCCCC"

# Calculate molecular weight
seq-tools mw "ATCG"

Python API

import pandas as pd
from seq_tools import sequences_to_dataframe, fold, get_molecular_weight_df, to_rna_df

# Create a DataFrame from sequences
sequences = ["ATCG", "GCTA", "AAAA"]
df = sequences_to_dataframe(sequences)

# Convert to RNA
df = to_rna_df(df)

# Fold RNA sequences
df = fold(df)

# Calculate molecular weights
df = get_molecular_weight_df(df, "RNA", double_stranded=False)

print(df)

Single Sequence Functions

For single sequence operations, import from the sequence module:

from seq_tools.sequence import to_dna, to_rna, get_reverse_complement, get_molecular_weight

# Convert sequences
rna_seq = to_rna("ATCG")  # Returns "AUCG"
dna_seq = to_dna("AUCG")  # Returns "ATCG"

# Reverse complement
rc = get_reverse_complement("ATCG", "DNA")  # Returns "CGAT"

# Molecular weight
mw = get_molecular_weight("ATCG", "DNA")  # Returns 1307.80

CLI Commands

add

Add a sequence to the 5' and/or 3' end of sequences.

seq-tools add -p5 "AAAA" "GGGGUUUUCCCC"
seq-tools add -p5 "AAAA" -p3 "CCCC" input.csv

ec

Calculate the extinction coefficient for each sequence.

seq-tools ec "GGGGUUUUCCCC"
seq-tools ec input.csv -nt RNA -ds  # RNA, double-stranded

edit-distance

Calculate the average edit distance of a sequence library.

seq-tools edit-distance input.csv
seq-tools edit-distance input.csv --parallel --workers 4

fold

Fold RNA sequences using ViennaRNA.

seq-tools fold "GGGGUUUUCCCC"
seq-tools fold input.csv

mw

Calculate the molecular weight for each sequence.

seq-tools mw "ATCG"
seq-tools mw input.csv -nt DNA -ds  # DNA, double-stranded

rc

Calculate reverse complement for each sequence.

seq-tools rc "ATCG"
seq-tools rc input.csv -nt DNA

to-dna

Convert RNA sequences to DNA (replace U with T).

seq-tools to-dna "AUCG"
seq-tools to-dna input.csv -o output.csv

to-dna-template

Convert RNA sequences to DNA template with T7 promoter.

seq-tools to-dna-template "AUCG"
seq-tools to-dna-template input.csv

to-rna

Convert DNA sequences to RNA (replace T with U).

seq-tools to-rna "ATCG"
seq-tools to-rna input.csv

transcribe

Transcribe DNA template sequences to RNA (removes T7 promoter).

seq-tools transcribe input.csv

trim

Trim 5'/3' ends of sequences.

seq-tools trim input.csv --start 5 --end 3

to-fasta

Generate FASTA file from CSV.

seq-tools to-fasta input.csv output.fasta

to-opool

Generate oligo pool file (Excel) from CSV.

seq-tools to-opool input.csv "pool_name" output.xlsx

DataFrame Functions

The package provides comprehensive DataFrame operations:

• Conversion: to_dna_df(), to_rna_df(), to_dna_template_df()
• Analysis: get_molecular_weight_df(), get_extinction_coeff(), get_length()
• Structure: fold() - predict RNA secondary structures
• Manipulation: add(), trim(), get_reverse_complement_df()
• Generation: generate_random_sequences(), generate_mutated_sequences()
• Validation: has_t7_promoter(), has_5p_sequence(), has_3p_sequence()
• File I/O: to_fasta(), to_opool()

See the notebooks directory for detailed examples.

Requirements

• Python 3.9+
• pandas
• numpy
• ViennaRNA (for structure prediction)
• editdistance
• click
• tabulate

Tutorial Notebooks

Interactive Jupyter notebooks are available in the notebooks/ directory:

• 01_introduction.ipynb: Package overview and quick start
• 02_sequence_operations.ipynb: Working with individual sequences
• 03_structure_analysis.ipynb: RNA folding and structure analysis
• 04_dataframe_operations.ipynb: Batch processing with DataFrames
• 05_advanced_features.ipynb: Advanced features and workflows

See the notebooks README for more details.

Development

# Clone the repository
git clone https://github.com/jyesselm/seq_tools.git
cd seq_tools

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in editable mode
pip install -e .

# Run tests
pytest test/ -v

License

This project is licensed under a Non-Commercial License. Commercial use is prohibited. See LICENSE file for details.

For commercial licensing inquiries, please contact [e-mail].

Author

Joe Yesselman - [e-mail]

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.