Engineering topics

Egan, Rob

Publications and source records attributed to Egan, Rob.

22 records · Page 2

DOE OSTI2014

MetaBAT

Assembling individual genomes from shotgun metagenomic sequences derived from complex microbial communities is so far one of the most challenging problems in bioinformatics.As it is impractical to directly assemble full-length genomes, a first step that groups contigs from the same organisms, called metagenome binning, has been developed to provide insights of individual organisms. However, current binning methods perform poorly in the context of large complex community, and as a result they fail to recover many novel genomes. To overcome this limitation, we developed integrated software, called MetaBAT, which automatically forms hundreds of individual genome bins from metagenome contigs. Probabilistic models of abundance and tetranucleotide frequency were trained by extensive empirical studies and integrated to decide the membership of contigs iteratively. To test the performance of MetaBAT, we applied MetaBAT to both synthetic and several large-scale real world metagenome datasets. By using two independent metrics, we demonstrate that in all the data sets tested MetaBAT achieves good sensitivity (16~87%) and very high specificity (56~99%) in forming genome bins. Further analyses of the novel genomes recovered from the human gut microbiome suggest a subset of these genomes are potentially associated with pathological conditions. In conclusion, we believe MetaBAT is a powerful tool

Kang, Dongwan↗

DOE OSTI2014

MetaBAT: Metagenome Binning based on Abundance and Tetranucleotide frequence

Grouping large fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. On synthetic datasets MetaBAT on average achieves 98percent precision and 90percent recall at the strain level with 281 near complete unique genomes. Applying MetaBAT to a human gut microbiome data set we recovered 176 genome bins with 92percent precision and 80percent recall. Further analyses suggest MetaBAT is able to recover genome fragments missed in reference genomes up to 19percent, while 53 genome bins are novel. In summary, we believe MetaBAT is a powerful tool to facilitate comprehensive understanding of complex microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Dynamics of Sequence -Discrete Bacterial Populations Inferred Using Metagenomes

From a multi-year metagenomic time series of two dissimilar Wisconsin lakes we have assembled dozens of genomes using a novel approach that bins contigs into distinct genome based on sequence composition, e.g. kmer frequencies, and contig coverage patterns at various times points. Next, we investigated how these genomes, which represent sequence-discrete bacterial populations, evolved over time and used the time series to discover the population dynamics. For example, we explored changes in single nucleotide polymorphism (SNP) frequencies as well as patterns of gene gain and loss in multiple populations. Interestingly, SNP diversity was purged at nearly every genome position in some populations during the course of this study, suggesting these populations may have experienced genome-wide selective sweeps. This represents the first direct, time-resolved observations of periodic selection in natural populations, a key process predicted by the ecotype model of bacterial diversification.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

FASTERp: A Feature Array Search Tool for Estimating Resemblance of Protein Sequences

Metagenome sequencing efforts have provided a large pool of billions of genes for identifying enzymes with desirable biochemical traits. However, homology search with billions of genes in a rapidly growing database has become increasingly computationally impractical. Here we present our pilot efforts to develop a novel alignment-free algorithm for homology search. Specifically, we represent individual proteins as feature vectors that denote the presence or absence of short kmers in the protein sequence. Similarity between feature vectors is then computed using the Tanimoto score, a distance metric that can be rapidly computed on bit string representations of feature vectors. Preliminary results indicate good correlation with optimal alignment algorithms (Spearman r of 0.87, ~;;1,000,000 proteins from Pfam), as well as with heuristic algorithms such as BLAST (Spearman r of 0.86, ~;;1,000,000 proteins). Furthermore, a prototype of FASTERp implemented in Python runs approximately four times faster than BLAST on a small scale dataset (~;;1000 proteins). We are optimizing and scaling to improve FASTERp to enable rapid homology searches against billion-protein databases, thereby enabling more comprehensive gene annotation efforts.

59 BASIC BIOLOGICAL SCIENCES↗