RT Journal Article
SR Electronic
T1 A deep learning approach to pattern recognition for short DNA sequences
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 353474
DO 10.1101/353474
A1 Akosua Busia
A1 George E. Dahl
A1 Clara Fannjiang
A1 David H. Alexander
A1 Elizabeth Dorfman
A1 Ryan Poplin
A1 Cory Y. McLean
A1 Pi-Chuan Chang
A1 Mark DePristo
YR 2019
UL http://biorxiv.org/content/early/2019/01/28/353474.abstract
AB Motivation Inferring properties of biological sequences--such as determining the species-of-origin of a DNA sequence or the function of an amino-acid sequence--is a core task in many bioinformatics applications. These tasks are often solved using string-matching to map query sequences to labeled database sequences or via Hidden Markov Model-like pattern matching. In the current work we describe and assess an deep learning approach which trains a deep neural network (DNN) to predict database-derived labels directly from query sequences.Results We demonstrate this DNN performs at state-of-the-art or above levels on a difficult, practically important problem: predicting species-of-origin from short reads of 16S ribosomal DNA. When trained on 16S sequences of over 13,000 distinct species, our DNN achieves read-level species classification accuracy within 2.0% of perfect memorization of training data, and produces more accurate genus-level assignments for reads from held-out species than k-mer, alignment, and taxonomic binning baselines. Moreover, our models exhibit greater robustness than these existing approaches to increasing noise in the query sequences. Finally, we show that these DNNs perform well on experimental 16S mock community dataset. Overall, our results constitute a first step towards our long-term goal of developing a general-purpose deep learning approach to predicting meaningful labels from short biological sequences.Availability TensorFlow training code is available through GitHub (https://github.com/tensorflow/models/tree/master/research). Data in TensorFlow TFRecord format is available on Google Cloud Storage (gs://brain-genomics-public/research/seq2species/).Contact seq2species-interest{at}google.comSupplementary information Supplementary data are available in a separate document.