Improvements in machine learning for predicting taxon, phenotype and function from genetic sequences

Zhengqiao Zhao

doi:10.17918/00000198

Advances in DNA sequencing, as well as the rise of shotgun metagenomics and metabolomics, are rapidly producing complex microbiome datasets for studies of human health and the environment. The large-scale sampling of DNA/RNA from microbes provides a window into the microbiome's interactions with its host and habitat, enables us to predict phenotypic traits of the host/microbiome, aids the discovery of emergent biological function, and supports the medical diagnosis. Researchers try to extract features from DNA/RNA sequencing data and make 1) taxonomic predictions ("Who is there"), 2) function annotations ("What they are doing") and 3) host/microbiome phenotype predictions. This work is to explore different computational methods to address challenges in these three fields. First, taxonomic classification relies on NCBI RefSeq database sequences, which are being added at an exponential rate. Therefore, the incremental learning concept is especially important. Although the incremental naive Bayes classifier (NBC) is a decade old concept, it has not been applied to taxonomic classification in the metagenomics field. In this work, I compare the classification accuracy and runtime of the proposed incremental learning implementation of NBC with the performance of the traditional implementation of NBC and demonstrate a proof of concept of how incremental learning can make taxonomic classification much more efficient in its training process, significantly reducing computation while maintaining accuracy. In addition to predicting taxonomic labels for metagenomic samples, researchers are also interested in identifying different subtypes for one virus since mutations can be introduced during the transmission. "Oligotyping" is an entropy analysis tool developed for subtyping taxonomic units based on 16S rRNA sequences. "Oligotyping" was formulated because the 16S rRNA gene is very conservative and there are only very few mutations in the 16S rRNA gene for some lineages. The SARS-CoV-2 genome, being months old, also has a relatively small amount of mutations. Therefore, the entropy analysis developed for 16S rRNA sequences can be adapted for SARS-CoV-2 viral genome subtyping. However, other researchers were only looking at sequence similarity (and subsequent trees) or important single nucleotide variants individually between the genomes. To my knowledge, I am the first to draw on the "Oligotyping" concept to group mutations as a "barcode" of the viral genome and extend it to define subtypes for SARS-CoV-2 viral genomes. I further add error correction to account for ambiguities in the sequences and, optionally, apply further compression by identifying patterns of base entropy correlation. I demonstrate its application in spatiotemporal analyses of real world SARS-CoV-2 sequences responsible for the COVID-19 pandemic. My method is validated by comparing the subtypes defined to similar subtypes discovered in other literature. Third, microbial survey data is not used efficiently for phenotype prediction. For example, a precise Crohn's disease prediction model can help diagnostics given stool samples collected from subjects. To predict Crohn's disease (or another phenotype) from microbiome composition, researchers usually start by grouping sequences that look similar together into an Operation Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV) and subsequently learn samples by examining OTU occurrences in different phenotypes. However, only looking at sequence similarity ignores the sequential information contained in DNA sequences. Bioinformatics has been inspired by successes in deep learning applications in Natural Language Processing (NLP). Both convolutional neural network (CNN) and recurrent neural network (RNN) have been utilized to learn DNA sequential information for applications such as transcription factor binding site classification. In my work, I propose to adapt deep learning architectures (such as RNN and attention mechanism) that have been widely used in NLP to develop a "phenotype" classifier. This Read2Pheno classifier can predict "phenotype" based on 16S rRNA reads. I demonstrate how the sequential information learned by the proposed model can provide insights on informative regions in DNA sequences/reads while making accurate predictions. The model is validated by comparing its accuracy with other baseline methods such as a random forest model trained with various features (standard OTU/ASV table and k-mers). Forth, there have been different deep learning based functional annotation models proposed recently. However, these models can only output one class of function annotation predictions, such as Gene Ontology (GO). It is convenient to have a tool that can output function predictions for both function annotation databases. In this work, I first extend the proposed Read2Pheno model to a function prediction model, AttentionGO, and compare the performance with both alignment based and deep learning based models to show that the proposed model can achieve comparable performance with additional interpretability. Second, I explore the possibility of using the proposed AttentionGO classifier in a multi-task learning model to predict three branches of GO terms and KEGG Orthology terms simultaneously. The multi-task learning model is compared with single-task models trained with individual tasks to demonstrate performance improvement.

Improvements in machine learning for predicting taxon, phenotype and function from genetic sequences

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Improvements in machine learning for predicting taxon, phenotype and function from genetic sequences

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