Analyzing genome coverage profiles with applications to quality control in metagenomics

@article{Lindner2013AnalyzingGC,
  title={Analyzing genome coverage profiles with applications to quality control in metagenomics},
  author={Martin Michael Serenus Lindner and Maximilian Kollock and Franziska Zickmann and Bernhard Y. Renard},
  journal={Bioinformatics},
  year={2013},
  volume={29 10},
  pages={
          1260-7
        }
}
MOTIVATION Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for… 

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References

SHOWING 1-10 OF 23 REFERENCES

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads

TLDR
A unified probabilistic framework by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes using the Mixture Model theory (GRAMMy), which is demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets.

Estimating DNA coverage and abundance in metagenomes using a gamma approximation

TLDR
A gamma distribution is employed to model a metagenome as a population of DNA fragments (bins), each of which may be covered by one or more reads, and the number of bins that were not sequenced and that could potentially be revealed by additional sequencing is estimated.

ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads

TLDR
The readDepth package for R is presented, which can detect copy number alterations by measuring the depth of coverage obtained by massively parallel sequencing of the genome, and demonstrates a method for inferring copy number using reads generated by whole-genome bisulfite sequencing, thus enabling integrative study of epigenomic and copy number alteration.

Metagenomic abundance estimation and diagnostic testing on species level

TLDR
Genome Abundance Similarity Correction (GASiC) is developed, a method to estimate true genome abundances via read alignment by considering reference genome similarities in a non-negative LASSO approach, and its superior performance over existing methods on simulated benchmark data as well as on real data.

Qualimap: evaluating next-generation sequencing alignment data

TLDR
Qualimap is a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties, and takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data.

Classification of metagenomic sequences: methods and challenges

TLDR
The premise, methodologies, advantages, limitations and challenges of various methods available for binning of metagenomic datasets obtained using the shotgun sequencing approach are discussed.

Confidence-based Somatic Mutation Evaluation and Prioritization

TLDR
An algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS, which accurately discriminates true mutations from erroneous calls and enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics.

MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads

TLDR
MetaVelvet succeeded to generate higher N50 scores and smaller chimeric scaffolds than any compared single-genome assemblers, produce high-quality scaffolds as well as the separate assembly using Velvet from isolated species sequence reads, and MetaVelvet reconstructed even relatively low-coverage genome sequences as scaffolds.

A human gut microbial gene catalogue established by metagenomic sequencing

TLDR
The Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals are described, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species.

Mason – A Read Simulator for Second Generation Sequencing Data

TLDR
A read simulator software for Illumina, 454 and Sanger reads that has been written with performance in mind and can sample reads from large genomes.