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Prusokiene A, Prusokas A, Retkute R. Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes. NAR Genom Bioinform 2023; 5:lqad077. [PMID: 37608801 PMCID: PMC10440785 DOI: 10.1093/nargab/lqad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.
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Affiliation(s)
- Alisa Prusokiene
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
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2
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Wen L, Tang F. Recent advances on single-cell sequencing technologies. Precision Clinical Medicine 2022; 5:pbac002. [PMID: 35821681 PMCID: PMC9267251 DOI: 10.1093/pcmedi/pbac002] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 11/15/2022] Open
Abstract
ABSTRACT
Single-cell omics sequencing was first achieved for transcriptome in 2009, which was followed by fast development of technologies for profiling genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications and so on, in an individual cell. In this review we mainly focus on the recent progresses in four topics in single cell omics field- single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics, and third-generation sequencing platform-based single cell omics sequencing. We also discuss the potential applications and future directions of these single cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.
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Affiliation(s)
- Lu Wen
- ICG, BIOPIC, School of Life Sciences, Peking University, Beijing, PR China
| | - Fuchou Tang
- ICG, BIOPIC, School of Life Sciences, Peking University, Beijing, PR China
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3
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Lyne AM, Perie L. Comparing Phylogenetic Approaches to Reconstructing Cell Lineage From Microsatellites With Missing Data. IEEE/ACM Trans Comput Biol Bioinform 2021; 18:2291-2301. [PMID: 32386163 DOI: 10.1109/tcbb.2020.2992813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Due to the imperfect fidelity of DNA replication, somatic cells acquire DNA mutations at each division which record their lineage history. Microsatellites, tandem repeats of DNA nucleotide motifs, mutate more frequently than other genomic regions and by observing microsatellite lengths in single cells and implementing suitable inference procedures, the cell lineage tree of an organism can be reconstructed. Due to recent advances in single cell Next Generation Sequencing (NGS) and the phylogenetic methods used to infer lineage trees, this work investigates which computational approaches best exploit the lineage information found in single cell NGS data. We simulated trees representing cell division with mutating microsatellites, and tested a range of available phylogenetic algorithms to reconstruct cell lineage. We found that distance-based approaches are fast and accurate with fully observed data. However, Maximum Parsimony and the computationally intensive probabilistic methods are more robust to missing data and therefore better suited to reconstructing cell lineage from NGS datasets. We also investigated how robust reconstruction algorithms are to different tree topologies and mutation generation models. Our results show that the flexibility of Maximum Parsimony and the probabilistic approaches mean they can be adapted to allow good reconstruction across a range of biologically relevant scenarios.
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4
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Tao L, Raz O, Marx Z, Ghosh MS, Huber S, Greindl-Junghans J, Biezuner T, Amir S, Milo L, Adar R, Levy R, Onn A, Chapal-Ilani N, Berman V, Ben Arie A, Rom G, Oron B, Halaban R, Czyz ZT, Werner-Klein M, Klein CA, Shapiro E. Retrospective cell lineage reconstruction in humans by using short tandem repeats. Cell Rep Methods 2021; 1:None. [PMID: 34341783 PMCID: PMC8313865 DOI: 10.1016/j.crmeth.2021.100054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/17/2021] [Accepted: 06/24/2021] [Indexed: 12/18/2022]
Abstract
Cell lineage analysis aims to uncover the developmental history of an organism back to its cell of origin. Recently, novel in vivo methods utilizing genome editing enabled important insights into the cell lineages of animals. In contrast, human cell lineage remains restricted to retrospective approaches, which still lack resolution and cost-efficient solutions. Here, we demonstrate a scalable platform based on short tandem repeats targeted by duplex molecular inversion probes. With this human cell lineage tracing method, we accurately reproduced a known lineage of DU145 cells and reconstructed lineages of healthy and metastatic single cells from a melanoma patient who matched the anatomical reference while adding further refinements. This platform allowed us to faithfully recapitulate lineages of developmental tissue formation in healthy cells. In summary, our lineage discovery platform can profile informative somatic mutations efficiently and provides solid lineage reconstructions even in challenging low-mutation-rate healthy single cells.
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Affiliation(s)
- Liming Tao
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Ofir Raz
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Zipora Marx
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Manjusha S. Ghosh
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Sandra Huber
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Julia Greindl-Junghans
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Tamir Biezuner
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Shiran Amir
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Lilach Milo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Rivka Adar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Ron Levy
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Amos Onn
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Noa Chapal-Ilani
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Veronika Berman
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Asaf Ben Arie
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Guy Rom
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Barak Oron
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Ruth Halaban
- Department of Dermatology, Yale University School of Medicine, New Haven, CT 06520-8059, USA
| | - Zbigniew T. Czyz
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Melanie Werner-Klein
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Christoph A. Klein
- Experimental Medicine and Therapy Research, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
- Division of Personalized Tumor Therapy, Fraunhofer Institute for Experimental Medicine and Toxicology Regensburg, Am Biopark 9, 93053 Regensburg, Germany
| | - Ehud Shapiro
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
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Bramlett C, Jiang D, Nogalska A, Eerdeng J, Contreras J, Lu R. Clonal tracking using embedded viral barcoding and high-throughput sequencing. Nat Protoc 2020; 15:1436-1458. [PMID: 32132718 PMCID: PMC7427513 DOI: 10.1038/s41596-019-0290-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 12/21/2019] [Indexed: 11/09/2022]
Abstract
Embedded viral barcoding in combination with high-throughput sequencing is a powerful technology with which to track single-cell clones. It can provide clonal-level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. Here, we present a detailed protocol for a viral barcoding procedure that includes the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can be completed within a few weeks. This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and enables precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.
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Affiliation(s)
- Charles Bramlett
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Du Jiang
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Anna Nogalska
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Jiya Eerdeng
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Jorge Contreras
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Rong Lu
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA.
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6
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Wei CJY, Zhang K. RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells. Genome Res 2020; 30:602-610. [PMID: 32127417 PMCID: PMC7197472 DOI: 10.1101/gr.255851.119] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 02/27/2020] [Indexed: 12/02/2022]
Abstract
Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single-cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of in vitro noise through polymerase slippage inherent in DNA amplification, which we characterized to be approximately 10–100× higher than the in vivo replication mutation rate. Here, we present RETrace, a method for simultaneously capturing both microsatellites and methylation-informative cytosines to characterize both lineage and cell type, respectively, from the same single cell. An important unique feature of RETrace was the introduction of linear amplification of microsatellites in order to reduce in vitro amplification noise. We further coupled microsatellite capture with single-cell reduced representation bisulfite sequencing (scRRBS), to measure the CpG methylation status on the same cell for cell type inference. When compared to existing retrospective lineage tracing methods, RETrace achieved higher accuracy (88% triplet accuracy from an ex vivo HCT116 tree) at a higher cell division resolution (lowering the required number of cell division difference between single cells by approximately 100 divisions). Simultaneously, RETrace demonstrated the ability to capture on average 150,000 unique CpGs per single cell in order to accurately determine cell type. We further formulated additional developments that would allow high-resolution mapping on microsatellite-stable cells or tissues with RETrace. Overall, we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells.
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Affiliation(s)
- Christopher Jen-Yue Wei
- Department of Bioengineering, University of California San Diego, La Jolla, California 92093, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, California 92093, USA
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7
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Frieda KL, Linton JM, Hormoz S, Choi J, Chow KHK, Singer ZS, Budde MW, Elowitz MB, Cai L. Synthetic recording and in situ readout of lineage information in single cells. Nature 2017; 541:107-111. [PMID: 27869821 PMCID: PMC6487260 DOI: 10.1038/nature20777] [Citation(s) in RCA: 263] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 11/11/2016] [Indexed: 12/13/2022]
Abstract
Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.
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Affiliation(s)
- Kirsten L Frieda
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - James M Linton
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Sahand Hormoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Joonhyuk Choi
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Ke-Huan K Chow
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Zakary S Singer
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Mark W Budde
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, Pasadena, California 91125, USA
| | - Long Cai
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
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8
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Perié L, Duffy KR. Retracing thein vivohaematopoietic tree using single-cell methods. FEBS Lett 2016; 590:4068-4083. [DOI: 10.1002/1873-3468.12299] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 07/08/2016] [Accepted: 07/09/2016] [Indexed: 12/14/2022]
Affiliation(s)
- Leïla Perié
- Institut Curie; PSL Research University; CNRS UMR168; Paris France
- Sorbonne Universités; UPMC Univ Paris 06; France
| | - Ken R. Duffy
- Hamilton Institute; Maynooth University; Co Kildare Ireland
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9
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Abstract
The frequency of cancer is postulated to be proportional to the number of cells an animal possesses, as each cell is similarly exposed to mutagens with every cell division. Larger animals result from more cell divisions with more mutagenic exposure, and hence are expected to have higher frequencies of cancer. Yet, as stipulated by Peto's paradox, larger animals do not have the higher rates of cancers seen in smaller animals despite the significant differences in cell numbers and a longer lifetime that would expose larger animals to more mutagens. The rates of cancer appear to be inversely proportional to animal body size, which scales inversely with specific metabolic rates of mammals. Studies over the past 20 years have linked oncogenes and tumour suppressors to alterations in cancer metabolism, and conversely, mutations in metabolic genes have been documented to trigger tumorigenesis. The by-products and intermediates of metabolism, such as reactive oxygen species, oxoglutarate, citrate and acetate, all have the potential to mutate and alter the genome or epigenome. On the basis of these general observations, it is proposed that metabolic rates correlate with mutagenic rates, which are higher in small animals and give the mechanistic basis for Peto's paradox. The observations discussed in this overview collectively indicate that specific metabolic rate varies inversely with body size, which seems to support the hypothesis that metabolism drives tumorigenesis and accounts for Peto's paradox.
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Affiliation(s)
- Chi V Dang
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19072, USA
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10
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Abstract
Cellular barcoding is a recently rediscovered tool to trace the clonal output of individual cells with genetically distinct and heritable DNA sequences. Each year a few dozens of papers are published using the cellular barcoding technique. Those publications largely focus on mutually related issues, namely: counting cells capable of clonal proliferation and expansion, monitoring clonal dynamics in time, tracing the origin of differentiated cells, characterizing the differentiation potential of stem cells and similar topics. Apart from their biological content, claims and conclusions, these studies show remarkable diversity in technical aspects of the barcoding method and sometimes in major conclusions. Although a diversity of approaches is quite usual in data analysis, deviant handling of barcode data might directly affect experimental results and their biological interpretation. Here, we will describe typical challenges and caveats in cellular barcoding publications available so far.
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Affiliation(s)
- Leonid V Bystrykh
- Laboratory of Ageing Biology and Stem Cells, European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, Building 3226, Groningen, 9713, AV, The Netherlands.
| | - Mirjam E Belderbos
- Laboratory of Ageing Biology and Stem Cells, European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, Building 3226, Groningen, 9713, AV, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, Groningen, The Netherlands
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Press MO, Carlson KD, Queitsch C. The overdue promise of short tandem repeat variation for heritability. Trends Genet 2014; 30:504-12. [PMID: 25182195 DOI: 10.1016/j.tig.2014.07.008] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 07/23/2014] [Accepted: 07/24/2014] [Indexed: 12/11/2022]
Abstract
Short tandem repeat (STR) variation has been proposed as a major explanatory factor in the heritability of complex traits in humans and model organisms. However, we still struggle to incorporate STR variation into genotype-phenotype maps. We review here the promise of STRs in contributing to complex trait heritability and highlight the challenges that STRs pose due to their repetitive nature. We argue that STR variants are more likely than single-nucleotide variants to have epistatic interactions, reiterate the need for targeted assays to genotype STRs accurately, and call for more appropriate statistical methods in detecting STR-phenotype associations. Lastly, we suggest that somatic STR variation within individuals may serve as a read-out of disease susceptibility, and is thus potentially a valuable covariate for future association studies.
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Affiliation(s)
- Maximilian O Press
- Department of Genome Sciences, University of Washington, Foege Building S-250, Box 355065, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Keisha D Carlson
- Department of Genome Sciences, University of Washington, Foege Building S-250, Box 355065, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Foege Building S-250, Box 355065, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA.
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12
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Spiro A, Cardelli L, Shapiro E. Lineage grammars: describing, simulating and analyzing population dynamics. BMC Bioinformatics 2014; 15:249. [PMID: 25047682 PMCID: PMC4223406 DOI: 10.1186/1471-2105-15-249] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Accepted: 07/07/2014] [Indexed: 11/17/2022] Open
Abstract
Background Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time. Results We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals. Conclusions We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as “black boxes” in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.
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Affiliation(s)
| | | | - Ehud Shapiro
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
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