ArticleBioinformatics (Oxford, England)2013
Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Graph Traversal Edit Distance and Extensions.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Efficient Synergistic Single-Cell Genome Assembly.Frontiers in bioengineering and biotechnology · 2016Article
- PCR-activated cell sorting for cultivation-free enrichment and sequencing of rare microbes.PloS one · 2015Article
- Ensemble analysis of adaptive compressed genome sequencing strategies.BMC bioinformatics · 2014Article
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Authors and funding
4 authors.
Funding
Abstract
motivationIdentification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells.
resultsHere, we present a novel divide and conquer method to sequence and de novo assemble all distinct genomes present in a microbial sample with a sequencing cost and computational complexity proportional to the number of genome types, rather than the number of cells. The method is implemented in a tool called Squeezambler. We evaluated Squeezambler on simulated data. The proposed divide and conquer method successfully reduces the cost of sequencing in comparison with the naïve exhaustive approach. AVAILABILITY: Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.
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