Skip to main content

No project description provided

Project description

SeqLike

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

💻

andrew giessel

💻

Max Wall

💻 📖

Eric Ma

💻 📖

Mihir Metkar

🤔 💻

Marcus Caron

📖

pagpires

📖

Sugato Ray

🚇 🚧

Damien Farrell

💻

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

seqlike-1.3.2.tar.gz (369.3 kB view details)

Uploaded Source

Built Distribution

seqlike-1.3.2-py3-none-any.whl (372.1 kB view details)

Uploaded Python 3

File details

Details for the file seqlike-1.3.2.tar.gz.

File metadata

  • Download URL: seqlike-1.3.2.tar.gz
  • Upload date:
  • Size: 369.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for seqlike-1.3.2.tar.gz
Algorithm Hash digest
SHA256 08b487f565df80c046323b4258fd94fd5354f73903dc5cd66f698f7ca2435adf
MD5 79d9b17a5609928c561a83c06f3c7ff2
BLAKE2b-256 bc23f91f9c8d4b36379c2a4b836beb9948d8cb2a519742e6a39cce6b1933ed1d

See more details on using hashes here.

File details

Details for the file seqlike-1.3.2-py3-none-any.whl.

File metadata

  • Download URL: seqlike-1.3.2-py3-none-any.whl
  • Upload date:
  • Size: 372.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for seqlike-1.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 599a91b54b6e75d34e35d8ccf10873c824f7f50b101f1415bbf871b8317dbbaf
MD5 ca69ecb22c5ad6828792dcdda9c3f236
BLAKE2b-256 e1d57f3e908aef73b9b3ef2a03e8731e872fdf5852493adf74d1489df32258c1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page