Toward a statistically explicit understanding of de novo sequence assembly

@article{Howison2013TowardAS,
  title={Toward a statistically explicit understanding of de novo sequence assembly},
  author={Mark Howison and Felipe Zapata and Casey W. Dunn},
  journal={Bioinformatics},
  year={2013},
  volume={29 23},
  pages={
          2959-63
        }
}
MOTIVATION Draft de novo genome assemblies are now available for many organisms. These assemblies are point estimates of the true genome sequences. Each is a specific hypothesis, drawn from among many alternative hypotheses, of the sequence of a genome. Assembly uncertainty, the inability to distinguish between multiple alternative assembly hypotheses, can be due to real variation between copies of the genome in the sample, errors and ambiguities in the sequenced data and assumptions and… 
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29 Citations
Background Citations
16
Methods Citations
2

29 Citations

Citation Type

Citation Type

Bayesian Genome Assembly and Assessment by Markov Chain Monte Carlo Sampling
TLDR
The posterior distribution of assembly hypotheses generated by GABI as a majority-rule consensus assembly is summarized, and the posterior distribution to external assemblies of the same test data is compared, and annotate those assemblies by assigning posterior probabilities to features that are in common with GABI's assembly graph.
Extensive Error in the Number of Genes Inferred from Draft Genome Assemblies
TLDR
The magnitude of the problem, both in terms of total gene number and the number of copies of genes in specific families, and the usefulness of RNA-Seq in improving the gene annotation of draft assemblies, are investigated.
Automated ensemble assembly and validation of microbial genomes
TLDR
Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to select an assembly best tailored to their specific needs.
Automated ensemble assembly and validation of microbial genomes
TLDR
Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to select an assembly best tailored to their specific needs.
Assembly and Data Quality
TLDR
Methods to assemble sequence reads into larger pieces are described, and different strategies are used for genome, transcriptome and metagenome assemblies, and all of them greatly benefit from the inclusion of long reads.
GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments
TLDR
GMcloser is described, a tool that accurately closes gaps with a preassembled contig set or a long read set (i.e., error-corrected PacBio reads) by using likelihood-based classifiers calculated from the alignment statistics between scaffolds, contigs and paired-end reads to correctly assign contigs or long reads to gap regions of scaffolding, thereby achieving accurate and efficient gap closure.
Evaluation of de novo transcriptome assemblies from RNA-Seq data
TLDR
A model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown is developed and shown to correctly reflect assembly accuracy, as measured by REF- EVAL, a refined set of ground-truth-based scores that were developed.
Phylogenomics from Whole Genome Sequences Using aTRAM
TLDR
The use of automated Target Restricted Assembly Method (aTRAM) to assemble 1107 single‐copy ortholog genes from whole genome sequencing of sucking lice and out‐groups is demonstrated and it is demonstrated that this approach is successful at developing phylogenomic data sets from raw genome sequencing reads.
ILP-based maximum likelihood genome scaffolding
TLDR
Equipped with NSDP, SILP2 is able to scaffold large mammalian genomes, resulting in the longest and most accurate scaffolds, and the ILP formulation for the maximum likelihood model is shown to be flexible enough to handle metagenomic samples.
Evaluation of de novo transcriptome assemblies from RNA-Seq data
TLDR
This work developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown, and assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly.
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References

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