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Updated: Jun 17, 2026

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Published on: January 19, 2017

Estimating DNA coverage and abundance in metagenomes using a gamma approximation.

Sean D Hooper¹, Daniel Dalevi, Amrita Pati

¹Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se

Bioinformatics (Oxford, England)

|December 17, 2009

Summary

This study introduces a gamma distribution model to estimate unsequenced genomic diversity in metagenomics. The model helps determine the efficacy of additional sequencing for uncovering microbial community DNA fragments.

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Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Area of Science:

Genomics
Bioinformatics
Microbial Ecology

Background:

Shotgun sequencing produces short DNA reads for organismal or metagenomic analysis.
Read assembly into contigs depends on read count, length, and genome abundance.
Low coverage in metagenomes hinders estimation of uncaptured genomic diversity.

Purpose of the Study:

To develop a statistical model for estimating unsequenced genomic diversity in metagenomes.
To assess the potential yield of additional sequencing efforts.
To provide a method for quantifying microbial community DNA fragments.

Main Methods:

Modeling metagenomes as a population of DNA fragments (bins).
Employing a gamma distribution to represent the bin population.
Evaluating model performance with simulated and real metagenomic datasets.

Main Results:

The gamma distribution model effectively fits metagenomic data.
The model allows estimation of unsequenced bins.
Demonstrated applicability on three diverse metagenomic datasets.

Conclusions:

The gamma distribution provides a flexible approach to metagenomic analysis.
This method aids in optimizing sequencing strategies for microbial community studies.
Enables better characterization of genomic diversity in complex samples.