The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Lachlan J M Coin - One of the best experts on this subject based on the ideXlab platform.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
GigaScience, 2018Co-Authors: Haotian Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Sheng Wang, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using Desktop Computer graphics processing units.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
bioRxiv, 2017Co-Authors: Haotien Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using Desktop Computer graphics processing units, making it competitive with other deep-learning basecalling algorithms.
Haotian Teng - One of the best experts on this subject based on the ideXlab platform.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
GigaScience, 2018Co-Authors: Haotian Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Sheng Wang, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using Desktop Computer graphics processing units.
Michael B Hall - One of the best experts on this subject based on the ideXlab platform.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
GigaScience, 2018Co-Authors: Haotian Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Sheng Wang, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using Desktop Computer graphics processing units.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
bioRxiv, 2017Co-Authors: Haotien Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using Desktop Computer graphics processing units, making it competitive with other deep-learning basecalling algorithms.
Tania Duarte - One of the best experts on this subject based on the ideXlab platform.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
GigaScience, 2018Co-Authors: Haotian Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Sheng Wang, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using Desktop Computer graphics processing units.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
bioRxiv, 2017Co-Authors: Haotien Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using Desktop Computer graphics processing units, making it competitive with other deep-learning basecalling algorithms.
Minh Duc Cao - One of the best experts on this subject based on the ideXlab platform.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
GigaScience, 2018Co-Authors: Haotian Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Sheng Wang, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using Desktop Computer graphics processing units.
-
chiron translating nanopore raw signal directly into nucleotide sequence using deep learning
bioRxiv, 2017Co-Authors: Haotien Teng, Michael B Hall, Tania Duarte, Minh Duc Cao, Lachlan J M CoinAbstract:Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using Desktop Computer graphics processing units, making it competitive with other deep-learning basecalling algorithms.
