David H. Alexander
David Alexander works on applications of deep learning in genomics as a member of the Brain Genomics Team. Before joining Google, he led a team of data scientists and software engineers at Pacific Biosciences. His research interests include computational statistics, machine learning, and programming languages. He received his Ph.D. from UCLA's Department of Biomathematics, under the supervision of Professor Kenneth Lange.
Authored Publications
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A deep learning approach to pattern recognition for short DNA sequences
Akosua Busia
Clara Fannjiang
Lizzie Dorfman
Mark DePristo
bioArxiv (2018)
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Sequence-to-sequence alignment is a widely-used analysis method in bioinformatics. One common use of sequence alignment is to infer information about an unknown query sequence from the annotations of similar sequences in a database, such as predicting the function of a novel protein sequence by aligning to a database of protein families or predicting the presence/absence of species in a metagenomics sample by aligning reads to a database of reference genomes. In this work we describe a deep learning approach to solve such problems in a single step by training a deep neural network (DNN) to predict the database-derived labels directly from the query sequence. We demonstrate the value of this DNN approach on a hard problem of practical importance: determining the species of origin of next-generation sequencing reads from 16s ribosomal DNA. In particular, we show that when trained on 16s sequences from more than 13,000 distinct species, our DNN can predict the species of origin of individual reads more accurately than existing machine learning baselines and alignment-based methods like BWA or BLAST, achieving absolute performance within 2.0% of perfect memorization of the training inputs. Moreover, the DNN remains accurate and outperforms read alignment approaches when the query sequences are especially noisy or ambiguous. Finally, these DNN models can be used to assess metagenomic community composition on a variety of experimental 16s read datasets. Our results are a first step towards our long-term goal of developing a general-purpose deep learning model that can learn to predict any type of label from short biological sequences.
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A universal SNP and small-indel variant caller using deep neural networks
Scott Schwartz
Dan Newburger
Jojo Dijamco
Nam Nguyen
Pegah T. Afshar
Sam S. Gross
Lizzie Dorfman
Mark A. DePristo
Nature Biotechnology (2018)
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Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.
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