TensorFlow I/O

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Voir sur TensorFlow.org

Exécuter dans Google Colab

Voir la source sur GitHub

Télécharger le cahier

Aperçu

Ce tutoriel montre le tfio.genome package qui fournit la génomique couramment utilisés fonctionnalité IO - à savoir la lecture de plusieurs formats de fichiers en génomique et en fournissant également des opérations communes pour la préparation des données (par exemple - un codage à chaud ou de l' analyse qualité Phred en probabilités).

Ce package utilise le Nucleus Google bibliothèque pour fournir certaines des fonctionnalités de base.

Installer

try:
  %tensorflow_version 2.x
except Exception:
  pass
!pip install -q tensorflow-io

import tensorflow_io as tfio
import tensorflow as tf

Données FASTQ

FASTQ est un format de fichier génomique courant qui stocke à la fois les informations de séquence en plus des informations de qualité de base.

Tout d' abord, nous allons télécharger un échantillon fastq fichier.

# Download some sample data:
curl -OL https://raw.githubusercontent.com/tensorflow/io/master/tests/test_genome/test.fastq

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   407  100   407    0     0   2035      0 --:--:-- --:--:-- --:--:--  2035

Lire les données FASTQ

Maintenant, l' utilisation let tfio.genome.read_fastq lire ce fichier (notez une tf.data API à venir).

fastq_data = tfio.genome.read_fastq(filename="test.fastq")
print(fastq_data.sequences)
print(fastq_data.raw_quality)

tf.Tensor(
[b'GATTACA'
 b'CGTTAGCGCAGGGGGCATCTTCACACTGGTGACAGGTAACCGCCGTAGTAAAGGTTCCGCCTTTCACT'
 b'CGGCTGGTCAGGCTGACATCGCCGCCGGCCTGCAGCGAGCCGCTGC' b'CGG'], shape=(4,), dtype=string)
tf.Tensor(
[b'BB>B@FA'
 b'AAAAABF@BBBDGGGG?FFGFGHBFBFBFABBBHGGGFHHCEFGGGGG?FGFFHEDG3EFGGGHEGHG'
 b'FAFAF;F/9;.:/;999B/9A.DFFF;-->.AAB/FC;9-@-=;=.' b'FAD'], shape=(4,), dtype=string)

Comme vous le voyez, le retour fastq_data a fastq_data.sequences qui est un tenseur de chaîne de toutes les séquences dans le fichier fastq (qui peut être chacun d' une taille différente) ainsi fastq_data.raw_quality qui comprend des informations de qualité codées Phred sur la qualité de chaque lecture de base dans la séquence.

Qualité

Vous pouvez utiliser une opération d'assistance pour convertir ces informations de qualité en probabilités si vous êtes intéressé.

quality = tfio.genome.phred_sequences_to_probability(fastq_data.raw_quality)
print(quality.shape)
print(quality.row_lengths().numpy())
print(quality)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
(4, None, 1)
[ 7 68 46  3]
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Un encodage à chaud

Vous pouvez également pour coder les données de séquence de génome (qui se compose de A T C G bases) en utilisant un seul codeur à chaud. Il y a une opération intégrée qui peut aider avec cela.

one_hot = tfio.genome.sequences_to_onehot(fastq_data.sequences)
print(one_hot)
print(one_hot.shape)

<tf.RaggedTensor [[[0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]]>
(4, None, 4)

print(tfio.genome.sequences_to_onehot.__doc__)

Convert DNA sequences into a one hot nucleotide encoding.

    Each nucleotide in each sequence is mapped as follows:
    A -> [1, 0, 0, 0]
    C -> [0, 1, 0, 0]
    G -> [0 ,0 ,1, 0]
    T -> [0, 0, 0, 1]

    If for some reason a non (A, T, C, G) character exists in the string, it is
    currently mapped to a error one hot encoding [1, 1, 1, 1].

    Args:
        sequences: A tf.string tensor where each string represents a DNA sequence

    Returns:
        tf.RaggedTensor: The output sequences with nucleotides one hot encoded.