Juan Carlos Mier
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Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic
Jimin Park
Daniel Cook
Lucas Brambrink
Joshua Gardner
Brandy McNulty
Samuel Sacco
Ayse Keskus
Asher Bryant
Tanveer Ahmad
Jyoti Shetty
Yongmei Zhao
Bao Tran
Giuseppe Narzisi
Adrienne Helland
Byunggil Yoo
Irina Pushel
Lisa Lansdon
Chengpeng Bi
Adam Walter
Margaret Gibson
Tomi Pastinen
Rebecca Reiman
Sharvari Mankame
Rhyker Ranallo-Benavidez
Christine Brown
Nicolas Robine
Floris Barthel
Midhat S. Farooqi
Karen Miga
Andrew Carroll
Mikhail Kolmogorov
Benedict Paten
Kishwar Shafin
Nature Biotechnology (2025)
Preview abstract
Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor–normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor–normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.
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Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic
Jimin Park
Daniel E. Cook
Lucas Brambrink
Joshua Gardner
Brandy McNulty
Samuel Sacco
Ayse G. Keskus
Asher Bryant
Tanveer Ahmad
Jyoti Shetty
Yongmei Zhao
Bao Tran
Giuseppe Narzisi
Adrienne Helland
Byunggil Yoo
Irina Pushel
Lisa A. Lansdon
Chengpeng Bi
Adam Walter
Margaret Gibson
Tomi Pastinen
Rebecca Reiman
Sharvari Mankame
T. Rhyker Ranallo-Benavidez
Christine Brown
Nicolas Robine
Floris P. Barthel
Midhat S. Farooqi
Karen H. Miga
Andrew Carroll
Mikhail Kolmogorov
Benedict Paten
Kishwar Shafin
Nature Biotechnology (2025)
Preview abstract
Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor–normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor–normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.
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Deep Perceptual Image Quality Assessment for Compression
ICIP 2021 International Conference on Image Processing (2021)
Preview abstract
Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This makes the image quality metric an integral part of any imaging system. While the existing full-reference metrics such as PSNR and SSIM may be less sensitive to perceptual quality, the recently introduced learning methods may fail to generalize to unseen data. In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods. We show that the proposed model can effectively learn from thousands of examples available in the new dataset, and consequently it generalizes better to other unseen datasets of human perceptual preference. The CIQA dataset can be found at https://github.com/googleresearch/google-research/tree/master/CIQA
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