 Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite MutationsNoa Chapal-Ilani, Yosef E Maruvka, Adam Spiro, Yitzhak Reizel, Rivka Adar, Liran I Shlush and Ehud Shapiro PLOS Computational Biology, 2013, vol. 9, issue 11, 1-17 Abstract: Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.Author Summary: The history of an organism's cells, from a single cell until any particular moment in time, can be captured by a cell lineage tree. Many fundamental open questions in biology and medicine, such as which cells give rise to metastases, whether oocytes and beta cells renew, and what is the role of stem cells in brain development and maintenance, are in fact questions about the structure and dynamics of that tree. Random mutations that occur during cell division endow each organism cell with an almost unique genomic signature. Distances between signatures capture distances in the cell lineage tree, and can be used to reconstruct that tree. On this basis, our lab developed a method for cell lineage reconstruction utilizing a panel of about 120 microsatellites. In this work, we use a large dataset of microsatellite mutations from many cells that we collected in our lab in the last few years, in order to test the performance of different distance measures and tree reconstruction algorithms. We found that the best method is not the one that gives the most accurate estimates of the mean distance, but rather the one with the lowest variance. Date: 2013 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003297 DOI: 10.1371/journal.pcbi.1003297 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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