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Waldron, Levi

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Waldron

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Waldron, Levi

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2011 - 20137

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Author's Publications: search.filters.isAuthorOfPublication.466b1ddc-960a-456b-bfde-f6e75dbc25f0

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Now showing 1 - 7 of 7
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    Metagenomic biomarker discovery and explanation
    (Springer Science + Business Media, 2011) Segata, Nicola; Izard, Jacques Georges; Waldron, Levi; Gevers, Dirk; Miropolsky, Larisa; Garrett, Wendy; Huttenhower, Curtis
    This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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    A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets
    (Public Library of Science, 2013) Koren, Omry; Knights, Daniel; Gonzalez, Antonio; Waldron, Levi; Segata, Nicola; Knight, Rob; Huttenhower, Curtis; Ley, Ruth E.
    Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.
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    Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues
    (BioMed Central, 2012) Waldron, Levi; Simpson, Peter; Parmigiani, Giovanni; Huttenhower, Curtis
    Background: With over 20 million formalin-fixed, paraffin-embedded (FFPE) tissue samples archived each year in the United States alone, archival tissues remain a vast and under-utilized resource in the genomic study of cancer. Technologies have recently been introduced for whole-transcriptome amplification and microarray analysis of degraded mRNA fragments from FFPE samples, and studies of these platforms have only recently begun to enter the published literature. Results: The Emerging Technologies for Translational Bioinformatics symposium on gene expression profiling for archival tissues featured presentations of two large-scale FFPE expression profiling studies (each involving over 1,000 samples), overviews of several smaller studies, and representatives from three leading companies in the field (Illumina, Affymetrix, and NuGEN). The meeting highlighted challenges in the analysis of expression data from archival tissues and strategies being developed to overcome them. In particular, speakers reported higher rates of clinical sample failure (from 10% to 70%) than are typical for fresh-frozen tissues, as well as more frequent probe failure for individual samples. The symposium program is available at http://www.hsph.harvard.edu/ffpe. Conclusions: Multiple solutions now exist for whole-genome expression profiling of FFPE tissues, including both microarray- and sequencing-based platforms. Several studies have reported their successful application, but substantial challenges and risks still exist. Symposium speakers presented novel methodology for analysis of FFPE expression data and suggestions for improving data recovery and quality assessment in pre-analytical stages. Research presentations emphasized the need for careful study design, including the use of pilot studies, replication, and randomization of samples among batches, as well as careful attention to data quality control. Regardless of any limitations in quantitave transcriptomics for FFPE tissues, they are often the only biospecimens available for large patient populations with long-term history and clinical follow-up. Current challenges can be expected to remain as RNA sequencing matures, and they will thus motivate ongoing research efforts into noise reduction and identification of robust, translationally relevant biological signals in expression data from FFPE tissues.
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    Metagenomic microbial community profiling using unique clade-specific marker genes
    (2012) Segata, Nicola; Waldron, Levi; Ballarini, Annalisa; Narasimhan, Vagheesh; Jousson, Olivier; Huttenhower, Curtis
    Metagenomic shotgun sequencing data can identify microbes populating a microbial community and their proportions, but existing taxonomic profiling methods are inefficient for increasingly large datasets. We present an approach that uses clade-specific marker genes to unambiguously assign reads to microbial clades more accurately and >50× faster than current approaches. We validated MetaPhlAn on terabases of short reads and provide the largest metagenomic profiling to date of the human gut
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    Composition of the Adult Digestive Tract Bacterial Microbiome Based on Seven Mouth Surfaces, Tonsils, Throat and Stool Samples
    (BioMed Central, 2012) Segata, Nicholas; Haake, Susan Kinder; Mannon, Peter; Lemon, Katherine; Waldron, Levi; Gevers, Dirk; Huttenhower, Curtis; Izard, Jacques Georges
    Background: To understand the relationship between our bacterial microbiome and health, it is essential to define the microbiome in the absence of disease. The digestive tract includes diverse habitats and hosts the human body's greatest bacterial density. We describe the bacterial community composition of ten digestive tract sites from more than 200 normal adults enrolled in the Human Microbiome Project, and metagenomically determined metabolic potentials of four representative sites. Results: The microbiota of these diverse habitats formed four groups based on similar community compositions: buccal mucosa, keratinized gingiva, hard palate; saliva, tongue, tonsils, throat; sub- and supra-gingival plaques; and stool. Phyla initially identified from environmental samples were detected throughout this population, primarily TM7, SR1, and Synergistetes. Genera with pathogenic members were well-represented among this disease-free cohort. Tooth-associated communities were distinct, but not entirely dissimilar, from other oral surfaces. The Porphyromonadaceae, Veillonellaceae and Lachnospiraceae families were common to all sites, but the distributions of their genera varied significantly. Most metabolic processes were distributed widely throughout the digestive tract microbiota, with variations in metagenomic abundance between body habitats. These included shifts in sugar transporter types between the supragingival plaque, other oral surfaces, and stool; hydrogen and hydrogen sulfide production were also differentially distributed. Conclusions: The microbiomes of ten digestive tract sites separated into four types based on composition. A core set of metabolic pathways was present across these diverse digestive tract habitats. These data provide a critical baseline for future studies investigating local and systemic diseases affecting human health.
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    Microbial Community Function and Biomarker Discovery in the Human Microbiome
    (BioMed Central, 2011) Abubucker, Sahar; Goll, Johannes; Schubert, Alyxandria M; Cantarel, Brandi L; Rodriguez-Mueller, Beltran; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T; Methé, Barbara; Schloss, Patrick D; Gevers, Dirk; Mitreva, Makedonka; Izard, Jacques Georges; Waldron, Levi; Zucker, Jeremy Daniel Hofeld; Garrett, Wendy; Huttenhower, Curtis; Segata, Nicola
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    curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome
    (Oxford University Press, 2013) Ganzfried, Benjamin Frederick; Riester, Markus; Haibe-Kains, Benjamin; Risch, Thomas; Tyekucheva, Svitlana; Jazic, Ina; Wang, Xin; Ahmadifar, Mahnaz; Birrer, Michael J.; Parmigiani, Giovanni; Huttenhower, Curtis; Waldron, Levi
    This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer. Database URL: http://bcb.dfci.harvard.edu/ovariancancer