Metagenomic biomarker discovery and explanation.
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ABSTRACT:
SUBMITTER: Segata N
PROVIDER: S-EPMC3218848 | biostudies-other | 2011 Jun
REPOSITORIES: biostudies-other
Publications
Metagenomic biomarker discovery and explanation.
Segata Nicola N Izard Jacques J Waldron Levi L Gevers Dirk D Miropolsky Larisa L Garrett Wendy S WS Huttenhower Curtis C
Genome biology 20110624 6
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 provid ...[more]
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Project description:As part of the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) a dataset was obtained from 35 participants, including 31 Myotonic Dystrophy type 1 (DM1) cases and four unaffected controls. All DM1 cases in this research were heterozygous for the abnormally expanded CTG repeat. The mode of the length of the DM1 CTG expansion (Modal Allele Length, MAL) was determined by small-pool PCR of blood DNA for 35/36 patients. For this work we did not attempt to measure the repeat length from muscle, due to a very high degree of repeat instability in muscle cells, and associated difficulties in its experimental measurement. One patient refused blood donation. For each of the 35 blood-donating patients mRNA expression profiling of blood was performed using Affymetrix GeneChip™ Human Exon 1.0 ST microarray. For 28 of 36 patients a successful quadriceps muscle biopsy was obtained. The muscle tissue was mRNA profiled using the same type of microarray. In total, a complete set of samples (blood and muscle) was obtained for 27 of 36 patients; samples were given a disease staging score based on muscle impairment rating. mRNA profiling was carried out by the GeneLogic service lab (on a fee-for-service basis) using standard Affymetrix hybridisation protocol.
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Project description:As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community's phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community's phenotype. In this study, we have proposed a novel method, MetaBoot, that combines the techniques of mRMR (minimal redundancy maximal relevance) and bootstrapping, for discover of non-redundant biomarkers for microbial communities through mining of metagenomic data. MetaBoot has been tested and compared with other methods on well-designed simulated datasets considering normal and gamma distribution as well as publicly available metagenomic datasets. Results have shown that MetaBoot was robust across datasets of varied complexity and taxonomical distribution patterns and could also select discriminative biomarkers with quite high accuracy and biological consistency. Thus, MetaBoot is suitable for robustly and accurately discover taxonomical biomarkers for different microbial communities.
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Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes. Adult earthworms (E. fetida) were exposed in soil spiked with TNT (0, 6, 12, 24, 48, or 96 mg/kg) or RDX (8, 16, 32, 64, or 128 mg/kg) for 0, 4 or 14 days. The 4-day treatment was repeated with RDX concentration being 2, 4, 8, 16 or 32 mg/kg soil. Each treatment originally had 10 replicate worms with 8-10 survivors at the end of exposure. Total RNA was isolated from the surviving worms. A total of 248 worm RNA samples were hybridized to a custom-designed oligo array using Agilentâs one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript (Gong et al. 2009). After hybridization and scanning, gene expression data were acquired using Agilentâs Feature Extraction Software (v.9.1.3). The 248-array dataset consists of three worm groups: 32 untreated controls, 96 TNT-treated, and 120 RDX-treated.
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Project description:The endometrium undergoes profound progesterone-driven remodeling during the secretory phase of the menstrual cycle in a process called decidualization. In the absence of pregnancy, circulating progesterone levels fall and tissue-wide inflammation and influx of neutrophils precede tissue breakdown and menstrual shedding. These changes are accompanied by wide-scale transcriptomic changes that co-ordinate temporal changes throughout the secretory phase. Here, we sequenced whole endometrial biopsies to identify molecular biomarkers that mark specific stages of the secretory phase, including pre-menstrual tissues. 20 biopsies were timed to specific phases of the secretory cycle based on the donor’s reported LH, the physical morphology of tissues observed via IHC and serum progesterone levels.
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