TASBE Flow Analytics: A Package for Calibrated Flow Cytometry Analysis.

Flow cytometry is a powerful method for high-throughput precision measurement of cell fluorescence and size. Effective use of this tool for quantification of synthetic biology devices and circuits, however, generally requires careful application of complex multistage workflows for calibration, filtering, and analysis with appropriate statistics. The TASBE Flow Analytics package provides a free, open, and accessible implementation of such workflows in a form designed for high-throughput analysis of large synthetic biology data sets. Given a set of experimental samples and controls, this package can process them to output calibrated data, quantitative analyses and comparisons, automatically generated figures, and detailed debugging and diagnostic reports in both human-readable and machine-readable forms. TASBE Flow Analytics can be used through a simple user-friendly interactive Excel interface, as a library supporting Matlab, Octave, or Python interactive sessions, or as a component integrated into automated workflows.

[1]  Mario Roederer,et al.  Compensation in Flow Cytometry , 2002, Current protocols in cytometry.

[2]  Drew Endy,et al.  Measuring the activity of BioBrick promoters using an in vivo reference standard , 2009, Journal of biological engineering.

[3]  S Demers,et al.  Analyzing multivariate flow cytometric data in aquatic sciences. , 1992, Cytometry.

[4]  Jacob Beal,et al.  Reducing DNA context dependence in bacterial promoters , 2017, PloS one.

[5]  Adolfas K Gaigalas,et al.  Toward quantitative fluorescence measurements with multicolor flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[6]  John Ferbas,et al.  Mixture modeling approach to flow cytometry data , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Nir Friedman,et al.  Linking stochastic dynamics to population distribution: an analytical framework of gene expression. , 2006, Physical review letters.

[8]  Jeffrey J. Tabor,et al.  FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units. , 2016, ACS synthetic biology.

[9]  M Roederer,et al.  Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. , 2001, Cytometry.

[10]  Jacob Beal,et al.  An end-to-end workflow for engineering of biological networks from high-level specifications. , 2012, ACS synthetic biology.

[11]  R. Weiss,et al.  CRISPR transcriptional repression devices and layered circuits in mammalian cells , 2014, Nature Methods.

[12]  Jacob Beal,et al.  Accurate predictions of genetic circuit behavior from part characterization and modular composition. , 2015, ACS synthetic biology.

[13]  Cliburn Chan,et al.  Statistical mixture modeling for cell subtype identification in flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[15]  Raphael Gottardo,et al.  flowClust: a Bioconductor package for automated gating of flow cytometry data , 2009, BMC Bioinformatics.

[16]  J. Tait,et al.  A new microparticle size calibration standard for use in measuring smaller microparticles using a new flow cytometer , 2011, Journal of thrombosis and haemostasis : JTH.

[17]  Lili Wang,et al.  Formalization of the MESF unit of fluorescence intensity , 2004, Cytometry. Part B, Clinical cytometry.

[18]  U. Resch‐Genger.  Standardization and quality assurance in fluorescence measurements I : techniques , 2008 .

[19]  Mario Roederer,et al.  Gating‐ML: XML‐based gating descriptions in flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[20]  Raphael Gottardo,et al.  Automated gating of flow cytometry data via robust model‐based clustering , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  Jacob Beal,et al.  Quantification of bacterial fluorescence using independent calibrants , 2018, PloS one.

[22]  A. L. Koch,et al.  Deduction of the cell volume and mass from forward scatter intensity of bacteria analyzed by flow cytometry , 1996 .

[23]  Jacob Beal,et al.  A standard-enabled workflow for synthetic biology. , 2017, Biochemical Society transactions.

[24]  Jacob Beal,et al.  Model-driven engineering of gene expression from RNA replicons. , 2015, ACS synthetic biology.

[25]  Jacob Beal,et al.  Signal-to-Noise Ratio Measures Efficacy of Biological Computing Devices and Circuits , 2015, Front. Bioeng. Biotechnol..

[26]  A. L. Koch,et al.  The logarithm in biology. 1. Mechanisms generating the log-normal distribution exactly. , 1966, Journal of theoretical biology.

[27]  Greg Finak,et al.  OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis , 2014, PLoS Comput. Biol..

[28]  J. Beal.  Biochemical complexity drives log-normal variation in genetic expression , 2017 .

[29]  Nathan J Hillson,et al.  The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization. , 2017, ACS synthetic biology.

[30]  Christopher A. Voigt,et al.  Genetic circuit design automation , 2016, Science.

[31]  James Alastair McLaughlin,et al.  SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology. , 2018, ACS synthetic biology.