Dataset Information

Bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS).

ABSTRACT:

Summary

We present bammds, a practical tool that allows visualization of samples sequenced by second-generation sequencing when compared with a reference panel of individuals (usually genotypes) using a multidimensional scaling algorithm. Our tool is aimed at determining the ancestry of unknown samples-typical of ancient DNA data-particularly when only low amounts of data are available for those samples.

Availability and implementation

The software package is available under GNU General Public License v3 and is freely available together with test datasets https://savannah.nongnu.org/projects/bammds/. It is using R (http://www.r-project.org/), parallel (http://www.gnu.org/software/parallel/), samtools (https://github.com/samtools/samtools).

Contact

bammds-users@nongnu.org

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Malaspinas AS

PROVIDER: S-EPMC4184259 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS).

Malaspinas Anna-Sapfo AS Tange Ole O Moreno-Mayar José Víctor JV Rasmussen Morten M DeGiorgio Michael M Wang Yong Y Valdiosera Cristina E CE Politis Gustavo G Willerslev Eske E Nielsen Rasmus R

Bioinformatics (Oxford, England) 20140628 20

<h4>Summary</h4>We present bammds, a practical tool that allows visualization of samples sequenced by second-generation sequencing when compared with a reference panel of individuals (usually genotypes) using a multidimensional scaling algorithm. Our tool is aimed at determining the ancestry of unknown samples-typical of ancient DNA data-particularly when only low amounts of data are available for those samples.<h4>Availability and implementation</h4>The software package is available under GNU G ...[more]

PMID: 24974206

Similar Datasets

Project description:BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.