컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
개요
이 튜토리얼은 일반적으로 사용되는 게놈 IO 기능을 제공하는 tfio.genome 패키지를 시연합니다. 즉, 여러 게놈 파일 형식을 읽고 데이터를 준비하기 위한 몇 가지 일반적인 연산도 제공합니다(예: 원-핫 인코딩 또는 Phred 품질을 확률로 구문 분석).
이 패키지는 Google Nucleus 라이브러리를 사용하여 일부 핵심 기능을 제공합니다.
설정
try:
%tensorflow_version 2.x
except Exception:
pass
!pip install -q tensorflow-io
import tensorflow_io as tfio
import tensorflow as tf
FASTQ 데이터
FASTQ는 기본 품질 정보와 함께 두 가지 시퀀스 정보를 모두 저장하는 일반적인 게놈 파일 형식입니다.
먼저, 샘플 fastq 파일을 다운로드하겠습니다.
# 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 1000 0 --:--:-- --:--:-- --:--:-- 1000
FASTQ 데이터 읽기
이제 tfio.genome.read_fastq를 사용하여 이 파일을 읽겠습니다(tf.data API는 곧 제공 예정).
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)
보는 바와 같이 반환된 fastq_data에는 시퀀스에서 읽은 각 기본 정보의 품질에 관한 Phred 인코딩 품질 정보를 포함하는 fastq_data.raw_quality와 함께 fastq 파일(각각 크기가 다를 수 있음)에 있는 모든 시퀀스의 문자열 텐서인 fastq_data.sequences가 있습니다.
품질
관심이 있는 경우 도우미 op를 사용하여 이 품질 정보를 확률로 변환할 수 있습니다.
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], [0.0012589250691235065], [0.0005011872854083776], [0.0007943279924802482], [0.00019952619913965464], [0.0006309573072940111]], [[0.0006309573072940111], [0.0006309573072940111], [0.0006309573072940111], [0.0006309573072940111], [0.0006309573072940111], [0.0005011872854083776], [0.00019952619913965464], [0.0007943279924802482], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.0003162277862429619], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952619913965464], [0.00019952619913965464], [0.0001584893325343728], [0.00019952619913965464], [0.0001584893325343728], [0.00012589251855388284], [0.0005011872854083776], [0.00019952619913965464], [0.0005011872854083776], [0.00019952619913965464], [0.0005011872854083776], [0.00019952619913965464], [0.0006309573072940111], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.00012589251855388284], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00019952619913965464], [0.00012589251855388284], [0.00012589251855388284], [0.00039810704765841365], [0.0002511885832063854], [0.00019952619913965464], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952619913965464], [0.0001584893325343728], [0.00019952619913965464], [0.00019952619913965464], [0.00012589251855388284], [0.0002511885832063854], [0.0003162277862429619], [0.0001584893325343728], [0.015848929062485695], [0.0002511885832063854], [0.00019952619913965464], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00012589251855388284], [0.0002511885832063854], [0.0001584893325343728], [0.00012589251855388284], [0.0001584893325343728]], [[0.00019952619913965464], [0.0006309573072940111], [0.00019952619913965464], [0.0006309573072940111], [0.00019952619913965464], [0.0025118854828178883], [0.00019952619913965464], [0.03981072083115578], [0.003981070592999458], [0.0025118854828178883], [0.050118714570999146], [0.003162277629598975], [0.03981072083115578], [0.0025118854828178883], [0.003981070592999458], [0.003981070592999458], [0.003981070592999458], [0.0005011872854083776], [0.03981072083115578], [0.003981070592999458], [0.0006309573072940111], [0.050118714570999146], [0.0003162277862429619], [0.00019952619913965464], [0.00019952619913965464], [0.00019952619913965464], [0.0025118854828178883], [0.06309573352336884], [0.06309573352336884], [0.0012589250691235065], [0.050118714570999146], [0.0006309573072940111], [0.0006309573072940111], [0.0005011872854083776], [0.03981072083115578], [0.00019952619913965464], [0.00039810704765841365], [0.0025118854828178883], [0.003981070592999458], [0.06309573352336884], [0.0007943279924802482], [0.06309573352336884], [0.00158489344175905], [0.0025118854828178883], [0.00158489344175905], [0.050118714570999146]], [[0.00019952619913965464], [0.0006309573072940111], [0.0003162277862429619]]]>
원-핫 인코딩
또한, 원-핫 인코더를 사용하여 A T C G 염기 서열로 구성된 게놈 시퀀스 데이터를 인코딩할 수도 있습니다. 인코딩에 도움이 되는 내장 연산이 있습니다.
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.
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.
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2022-01-24(UTC)
[null,null,["최종 업데이트: 2022-01-24(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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