Unified biological sequence manipulation in Python


Keywords
biological-sequences, biopython, machine-learning, sequence
License
Apache-2.0
Install
pip install seqlike==1.5.4

Documentation

SeqLike - flexible biological sequence objects in Python

PyPI - Supported Python Version PyPI - Package Version Conda - Platform Conda (channel only) Docs - GitHub.io

Introduction

A single object API that makes working with biological sequences in Python more ergonomic. It'll handle anything like a sequence.

Built around the Biopython SeqRecord class, SeqLikes abstract over the semantics of molecular biology (DNA -> RNA -> AA) and data structures (strings, Seqs, SeqRecords, numerical encodings) to allow manipulation of a biological sequence at the level which is most computationally convenient.

Code samples and examples

Build data-type agnostic functions

def f(seq: SeqLikeType, *args):
	seq = SeqLike(seq, seq_type="nt").to_seqrecord()
	# ...

Streamline conversion to/from ML friendly representations

prediction = model(aaSeqLike('MSKGEELFTG').to_onehot())
new_seq = ntSeqLike(generative_model.sample(), alphabet="-ACGTUN")

Interconvert between AA and NT forms of a sequence

Back-translation is conveniently built-in!

s_nt = ntSeqLike("ATGTCTAAAGGTGAA")
s_nt[0:3] # ATG
s_nt.aa()[0:3] # MSK, nt->aa is well defined
s_nt.aa()[0:3].nt() # ATGTCTAAA, works because SeqLike now has both reps
s_nt[:-1].aa() # TypeError, len(s_nt) not a multiple of 3

s_aa = aaSeqLike("MSKGE")
s_aa.nt() # AttributeError, aa->nt is undefined w/o codon map
s_aa = aaSeqLike(s_aa, codon_map=random_codon_map)
s_aa.nt() # now works, backtranslated to e.g. ATGTCTAAAGGTGAA
s_aa[:1].nt() # ATG, codon_map is maintained

Easily plot multiple sequence alignments

seqs = [s for s in SeqIO.parse("file.fasta", "fasta")]
df = pd.DataFrame(
    {
        "names": [s.name for s in seqs],
        "seqs": [aaSeqLike(s) for s in seqs],
    }
)
df["aligned"] = df["seqs"].seq.align()
df["aligned"].seq.plot()

Flexibly build and parse numerical sequence representations

# Assume you have a dataframe with a column of 10 SeqLikes of length 90
df["seqs"].seq.to_onehot().shape # (10, 90, 23), padded if needed

To see more in action, please check out the docs!

Getting Started

Install the library with pip or conda.

With pip

pip install seqlike

With conda

conda install -c conda-forge seqlike

Authors

Support

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Nasos Dousis
Nasos Dousis

💻
andrew giessel
andrew giessel

💻
Max Wall
Max Wall

💻 📖
Eric Ma
Eric Ma

💻 📖
Mihir Metkar
Mihir Metkar

🤔 💻
Marcus Caron
Marcus Caron

📖
pagpires
pagpires

📖
Sugato Ray
Sugato Ray

🚇 🚧
Damien Farrell
Damien Farrell

💻
Farbod Mahmoudinobar
Farbod Mahmoudinobar

💻
Jacob Hayes
Jacob Hayes

🚇

This project follows the all-contributors specification. Contributions of any kind welcome!