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MetaDecoder: A novel method for clustering metagenomic contigs
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This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Metagenomics is a powerful technique for studying complex microbial communities. The key computational step in this method is clustering genomic sequences from mixed samples into potential microbial genomes, but accurately classifying sequences from complex metagenomes remains challenging. Some tools depend on k-mer frequency and coverage, but such methods struggle to distinguish between similar genomes. Methods that address the similar genomes problem, like ones that rely on single-copy marker genes, in turn struggle with complex datasets. The newly developed MetaDecoder balances these challenges by using both types of methods broken into two steps. First, MetaDecoder simplifies the dataset by generating preliminary groups of sequences with the Dirichlet Process Gaussian Mixture Model (DPGMM). Then, these preliminary clusters are clustered further with a k-mer frequency probabilistic model and a modified Gaussian Mixture Model of single-copy marker gene coverage..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Subject:
Biology
Life Science
Material Type:
Diagram/Illustration
Reading
Provider:
Research Square
Provider Set:
Video Bytes
Date Added:
05/18/2022
Nonlinear Econometric Analysis
Conditional Remix & Share Permitted
CC BY-NC-SA
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This course presents micro-econometric models, including large sample theory for estimation and hypothesis testing, generalized method of moments (GMM), estimation of censored and truncated specifications, quantile regression, structural estimation, nonparametric and semiparametric estimation, treatment effects, panel data, bootstrapping, simulation methods, and Bayesian methods. The methods are illustrated with economic applications.

Subject:
Economics
Mathematics
Social Science
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Chernozhukov, Victor
Newey, Whitney
Date Added:
09/01/2007