Biology subjects

Meleshko, D.

Publications and source records attributed to Meleshko, D..

2 recordsLinked to original sources

bioRxiv2018

American Gut: an Open Platform for Citizen-Science Microbiome Research

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.\n\nImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

microbiology↗

bioRxiv2017

Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Metagenomics

Emerging Linked-Read technologies (aka Read-Cloud or barcoded short-reads) have revived interest in standard short-read technology as a viable way to understand large-scale structure in genomes and metagenomes. Linked-Read technologies, such as the 10X Chromium system, use a microfluidic system and a set of specially designed 3 barcodes (aka UIDs) to tag short DNA reads which were originally sourced from the same long fragment of DNA; subsequently, these specially barcoded reads are sequenced on standard short read platforms. This approach results in interesting compromises. Each long fragment of DNA is covered only sparsely by short reads, no information about the relative ordering of reads from the same fragment is preserved, and typically each 3 barcode matches reads from 2-20 long fragments of DNA. However, compared to long read platforms like those produced by Pacific Biosciences and Oxford Nanopore the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads.\n\nThe use of Linked-Reads presents a new set of algorithmic challenges. In this paper, we formally describe one particular issue common to all applications of Linked-Read technology: the deconvolution of reads with a single 3 barcode into clusters that correspond to a single long fragment of DNA. We introduce Minerva, A graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results: improving the specificity of taxonomic assignments, and by improving clustering of related sequences. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.

bioinformatics↗