DATA-DRIVEN ALGORITHMS FOR CHARACTERIZING STRUCTURAL VARIATION IN METAGENOMIC DATA

Abstract

Sequence differences between the strains of bacteria comprising host-associated and environmental microbiota may play a role in community assembly and influence the resilience of microbial communities to disturbances. Tools for characterizing strain-level variation within microbial communities, however, are limited in scope, focusing on just single nucleotide poly-morphisms, or relying on reference-based analyses that miss complex structural variants. In this thesis, we describe data-driven methods to characterize variation in metagenomic data.

In the first part of the thesis, I present our analysis of the structural variants identified from metagenomic whole genome shotgun sequencing data. I begin by describing the power of assembly graph analysis to detect over 9 million structural variants such as insertion/deletion, repeat copy-number changes, and mobile elements, in nearly 1,000 metagenomes generated as a part of the Human Microbiome Project. Next, I describe Binnacle, which is a structural variant aware binning algorithm. To improve upon the fragmented nature of assemblies, metagenomic binning is performed to cluster contigs that is likely to have originated from the same genome. We show that binning “graph-based” scaffolds, rather than contigs, improves the quality of the bins, and captures a broader set of the genes of the genomes being reconstructed. Finally, we present a case study of the microbial mats from the Yellowstone National Park. The cyanobacterium Synechococcus is abundant in these mats along a stable temperature gradient from ∼ 50oC to ∼ 65oC and plays a key role in fixing carbon and nitrogen. Previous studies have isolated and generated good quality reference sequences of two major Synechococcus spp. that share a very high genomic content; OS-A and OS-B’. Despite the high abundance of the Synechococcus spp., metagenomic assembly of these organisms is challenging due to the large number of rearrangements between them. We explore the genomic diversity of the Synechococcus spp. using a reference genome, reliant assembly and scaffolding. We also highlight that the variants we detect can be used to fingerprint the local biogeography of the hot spring.

In the second part of the thesis, I present our analysis of amplicon sequencing data, specifically the 16S rRNA gene sequences. I begin by describing SCRAPT (Sample, Cluster, Recruit, AdaPt and iTerate), which is a fast iterative algorithm for clustering large 16S rRNA gene datasets. We also show that SCRAPT is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST and runs orders of magnitude faster than existing methods. Finally, we study the impact of transitive annotation on taxonomic classifiers. Taxonomic labels are assigned using machine learning algorithms trained to recognize individual taxonomic groups based on training sequences with known taxonomic labels. Ideally, the training data should rely on experimentally verified-formal taxonomic labels however, the labels associated with sequences in biological databases are most commonly the result of computational predictions– “transitive annotation.” We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. We also discuss key factors that affect the resilience of classifiers to transitively annotated training data and propose best practices to avoid the artifacts described in this thesis.