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Overview
This tutorial demonstrates the tfio.genome package that provides commonly used genomics IO functionality--namely reading several genomics file formats and also providing some common operations for preparing the data (for example--one hot encoding or parsing Phred quality into probabilities).
This package uses the Google Nucleus library to provide some of the core functionality.
Setup
try:
%tensorflow_version 2.x
except Exception:
pass
!pip install -q tensorflow-io
import tensorflow_io as tfio
import tensorflow as tf
FASTQ Data
FASTQ is a common genomics file format that stores both sequence information in addition to base quality information.
First, let's download a sample fastq file.
# 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
Read FASTQ Data
Now, let's use tfio.genome.read_fastq to read this file (note a tf.data API coming soon).
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)
As you see, the returned fastq_data has fastq_data.sequences which is a string tensor of all sequences in the fastq file (which can each be a different size) along with fastq_data.raw_quality which includes Phred encoded quality information about the quality of each base read in the sequence.
Quality
You can use a helper op to convert this quality information into probabilities if you are interested.
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]
<tf.RaggedTensor [[[0.0005011872854083776], [0.0005011872854083776], [0.0012589251855388284], [0.0005011872854083776], [0.0007943279924802482], [0.00019952621369156986], [0.0006309572490863502]], [[0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.00019952621369156986], [0.0007943279924802482], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.0003162277571391314], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.0001584893325343728], [0.00012589251855388284], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0006309572490863502], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.00012589251855388284], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00019952621369156986], [0.00012589251855388284], [0.00012589251855388284], [0.0003981070767622441], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.00019952621369156986], [0.00012589251855388284], [0.0002511885541025549], [0.0003162277571391314], [0.0001584893325343728], [0.015848929062485695], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00012589251855388284], [0.0002511885541025549], [0.0001584893325343728], [0.00012589251855388284], [0.0001584893325343728]], [[0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.002511885715648532], [0.00019952621369156986], [0.03981072083115578], [0.003981071058660746], [0.002511885715648532], [0.050118714570999146], [0.003162277629598975], [0.03981072083115578], [0.002511885715648532], [0.003981071058660746], [0.003981071058660746], [0.003981071058660746], [0.0005011872854083776], [0.03981072083115578], [0.003981071058660746], [0.0006309572490863502], [0.050118714570999146], [0.0003162277571391314], [0.00019952621369156986], [0.00019952621369156986], [0.00019952621369156986], [0.002511885715648532], [0.06309572607278824], [0.06309572607278824], [0.0012589251855388284], [0.050118714570999146], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.03981072083115578], [0.00019952621369156986], [0.0003981070767622441], [0.002511885715648532], [0.003981071058660746], [0.06309572607278824], [0.0007943279924802482], [0.06309572607278824], [0.001584893325343728], [0.002511885715648532], [0.001584893325343728], [0.050118714570999146]], [[0.00019952621369156986], [0.0006309572490863502], [0.0003162277571391314]]]>
One hot encodings
You may also want to encode the genome sequence data (which consists of A T C G bases) using a one hot encoder. There's a built in operation that can help with this.
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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2022-01-10 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-01-10 UTC."],[],[],null,["\u003cbr /\u003e\n\n|--------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|\n| [View on TensorFlow.org](https://www.tensorflow.org/io/tutorials/genome) | [Run in Google Colab](https://colab.research.google.com/github/tensorflow/io/blob/master/docs/tutorials/genome.ipynb) | [View source on GitHub](https://github.com/tensorflow/io/blob/master/docs/tutorials/genome.ipynb) | [Download notebook](https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/genome.ipynb) |\n\nOverview\n--------\n\nThis tutorial demonstrates the [`tfio.genome`](https://www.tensorflow.org/io/api_docs/python/tfio/genome) package that provides commonly used genomics IO functionality--namely reading several genomics file formats and also providing some common operations for preparing the data (for example--one hot encoding or parsing Phred quality into probabilities).\n\nThis package uses the [Google Nucleus](https://github.com/google/nucleus) library to provide some of the core functionality.\n\nSetup\n-----\n\n try:\n %tensorflow_version 2.x\n except Exception:\n pass\n !pip install -q tensorflow-io\n\n import tensorflow_io as tfio\n import tensorflow as tf\n\nFASTQ Data\n----------\n\nFASTQ is a common genomics file format that stores both sequence information in addition to base quality information.\n\nFirst, let's download a sample `fastq` file. \n\n # Download some sample data:\n curl -OL https://raw.githubusercontent.com/tensorflow/io/master/tests/test_genome/test.fastq\n\n```\n% Total % Received % Xferd Average Speed Time Time Time Current\n Dload Upload Total Spent Left Speed\n100 407 100 407 0 0 2035 0 --:--:-- --:--:-- --:--:-- 2035\n```\n\n### Read FASTQ Data\n\nNow, let's use [`tfio.genome.read_fastq`](https://www.tensorflow.org/io/api_docs/python/tfio/genome/read_fastq) to read this file (note a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API coming soon). \n\n fastq_data = tfio.genome.read_fastq(filename=\"test.fastq\")\n print(fastq_data.sequences)\n print(fastq_data.raw_quality)\n\n```\ntf.Tensor(\n[b'GATTACA'\n b'CGTTAGCGCAGGGGGCATCTTCACACTGGTGACAGGTAACCGCCGTAGTAAAGGTTCCGCCTTTCACT'\n b'CGGCTGGTCAGGCTGACATCGCCGCCGGCCTGCAGCGAGCCGCTGC' b'CGG'], shape=(4,), dtype=string)\ntf.Tensor(\n[b'BB\u003eB@FA'\n b'AAAAABF@BBBDGGGG?FFGFGHBFBFBFABBBHGGGFHHCEFGGGGG?FGFFHEDG3EFGGGHEGHG'\n b'FAFAF;F/9;.:/;999B/9A.DFFF;--\u003e.AAB/FC;9-@-=;=.' b'FAD'], shape=(4,), dtype=string)\n```\n\nAs you see, the returned `fastq_data` has `fastq_data.sequences` which is a string tensor of all sequences in the fastq file (which can each be a different size) along with `fastq_data.raw_quality` which includes Phred encoded quality information about the quality of each base read in the sequence.\n\n### Quality\n\nYou can use a helper op to convert this quality information into probabilities if you are interested. \n\n quality = tfio.genome.phred_sequences_to_probability(fastq_data.raw_quality)\n print(quality.shape)\n print(quality.row_lengths().numpy())\n print(quality)\n\n```\nWARNING: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.\nInstructions for updating:\nUse fn_output_signature instead\n(4, None, 1)\n[ 7 68 46 3]\n\u003ctf.RaggedTensor [[[0.0005011872854083776], [0.0005011872854083776], [0.0012589251855388284], [0.0005011872854083776], [0.0007943279924802482], [0.00019952621369156986], [0.0006309572490863502]], [[0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.00019952621369156986], [0.0007943279924802482], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.0003162277571391314], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.0001584893325343728], [0.00012589251855388284], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0006309572490863502], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.00012589251855388284], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00019952621369156986], [0.00012589251855388284], [0.00012589251855388284], [0.0003981070767622441], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.00019952621369156986], [0.00012589251855388284], [0.0002511885541025549], [0.0003162277571391314], [0.0001584893325343728], [0.015848929062485695], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00012589251855388284], [0.0002511885541025549], [0.0001584893325343728], [0.00012589251855388284], [0.0001584893325343728]], [[0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.002511885715648532], [0.00019952621369156986], [0.03981072083115578], [0.003981071058660746], [0.002511885715648532], [0.050118714570999146], [0.003162277629598975], [0.03981072083115578], [0.002511885715648532], [0.003981071058660746], [0.003981071058660746], [0.003981071058660746], [0.0005011872854083776], [0.03981072083115578], [0.003981071058660746], [0.0006309572490863502], [0.050118714570999146], [0.0003162277571391314], [0.00019952621369156986], [0.00019952621369156986], [0.00019952621369156986], [0.002511885715648532], [0.06309572607278824], [0.06309572607278824], [0.0012589251855388284], [0.050118714570999146], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.03981072083115578], [0.00019952621369156986], [0.0003981070767622441], [0.002511885715648532], [0.003981071058660746], [0.06309572607278824], [0.0007943279924802482], [0.06309572607278824], [0.001584893325343728], [0.002511885715648532], [0.001584893325343728], [0.050118714570999146]], [[0.00019952621369156986], [0.0006309572490863502], [0.0003162277571391314]]]\u003e\n```\n\n### One hot encodings\n\nYou may also want to encode the genome sequence data (which consists of `A` `T` `C` `G` bases) using a one hot encoder. There's a built in operation that can help with this. \n\n one_hot = tfio.genome.sequences_to_onehot(fastq_data.sequences)\n print(one_hot)\n print(one_hot.shape)\n\n```\n\u003ctf.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]]]\u003e\n(4, None, 4)\n``` \n\n print(tfio.genome.sequences_to_onehot.__doc__)\n\n```\nConvert DNA sequences into a one hot nucleotide encoding.\n\n Each nucleotide in each sequence is mapped as follows:\n A -\u003e [1, 0, 0, 0]\n C -\u003e [0, 1, 0, 0]\n G -\u003e [0 ,0 ,1, 0]\n T -\u003e [0, 0, 0, 1]\n\n If for some reason a non (A, T, C, G) character exists in the string, it is\n currently mapped to a error one hot encoding [1, 1, 1, 1].\n\n Args:\n sequences: A tf.string tensor where each string represents a DNA sequence\n\n Returns:\n tf.RaggedTensor: The output sequences with nucleotides one hot encoded.\n```"]]
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