Hierarchical and Spatially Explicit Clustering of DNA Sequences with BAPS Software

By Lu Cheng, Thomas Richard Connor, Jukka Sirén, David M. Aanensen, Jukka Corander. Molecular biology and evolution, 2013

602 citations recorded by the source corpus.

Phylogeographical analyses have become commonplace for a myriad of organisms with the advent of cheap DNA sequencing technologies. Bayesian model-based clustering is a powerful tool for detecting important patterns in such data and can be used to decipher even quite subtle signals of systematic differences in molecular variation. Here, we introduce two upgrades to the Bayesian Analysis of Population Structure (BAPS) software, which enable 1) spatially explicit modeling of variation in DNA sequences and 2) hierarchical clustering of DNA sequence data to reveal nested genetic population structures. We provide a direct interface to map the results from spatial clustering with Google Maps using the portal http://www.spatialepidemiology.net/ and illustrate this approach using sequence data from Borrelia burgdorferi. The usefulness of hierarchical clustering is demonstrated through an analysis of the metapopulation structure within a bacterial population experiencing a high level of local horizontal gene transfer. The tools that are introduced are freely available at http://www.helsinki.fi/bsg/software/BAPS/.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/33a3ea5fcd0fc8bee20a09922f13a8c20dda501f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Gerry Tonkin-Hill
2. Stephen D. Bentley
3. John Lees
4. Simon D. W. Frost
5. Simon R. Harris
6. Jukka Corander
7. Sergei L. Kosakovsky Pond

In Molecular Biology

1. Gerry Tonkin-Hill
2. Simon R. Harris
3. John Lees
4. Jukka Corander
5. Simon D. W. Frost
6. Stephen D. Bentley
7. Sergei L. Kosakovsky Pond

Citations in the corpus

• Fast hierarchical Bayesian analysis of population structure (2018, methodology): “By leveraging ideas from both the BHC (26) and the hierBAPS (15) algorithms, along with a fast sparse matrix based implementation, fastbaps enables efficient model-based clustering of large alignments that were previously in-feasible to analyze using existing methods.”
• Fast and flexible bacterial genomic epidemiology with PopPUNK (2018, methodology): “…of clusters from genomic data can be performed by partitioning a phylogeny (Prosperi et al. 2011) , but has more typically used complex population structure analysis models: initially STRUCTURE (Pritchard et al. 2000) or BAPS (Corander et al. 2008) , and more recently hierBAPS (Cheng et al. 2013) .”