Name
Title | ||
|---|---|---|
Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data. |
Abstract | ||
|---|---|---|
Single-molecule techniques have emerged as incisive approaches for addressing a wide range of questions arising in contemporary biological research [Trends Biochem Sci 38:30-37, 2013; Nat Rev Genet 14:9-22, 2013; Curr Opin Struct Biol 2014, 28C:112-121; Annu Rev Biophys 43:19-39, 2014]. The analysis and interpretation of raw single-molecule data benefits greatly from the ongoing development of sophisticated statistical analysis tools that enable accurate inference at the low signal-to-noise ratios frequently associated with these measurements. While a number of groups have released analysis toolkits as open source software [J Phys Chem B 114:5386-5403, 2010; Biophys J 79:1915-1927, 2000; Biophys J 91:1941-1951, 2006; Biophys J 79:1928-1944, 2000; Biophys J 86:4015-4029, 2004; Biophys J 97:3196-3205, 2009; PLoS One 7:e30024, 2012; BMC Bioinformatics 288 11(8):S2, 2010; Biophys J 106:1327-1337, 2014; Proc Int Conf Mach Learn 28:361-369, 2013], it remains difficult to compare analysis for experiments performed in different labs due to a lack of standardization.Here we propose a standardized single-molecule dataset (SMD) file format. SMD is designed to accommodate a wide variety of computer programming languages, single-molecule techniques, and analysis strategies. To facilitate adoption of this format we have made two existing data analysis packages that are used for single-molecule analysis compatible with this format.Adoption of a common, standard data file format for sharing raw single-molecule data and analysis outcomes is a critical step for the emerging and powerful single-molecule field, which will benefit both sophisticated users and non-specialists by allowing standardized, transparent, and reproducible analysis practices. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s12859-014-0429-4 | BMC Bioinformatics |
Keywords | Field | DocType |
computational biology,microarrays,kinetics,bioinformatics,algorithms,microscopy | Computer science,Bioinformatics,Open source software,Statistical analysis | Journal |
Volume | Issue | ISSN |
16 | 1 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Max Greenfeld | 1 | 0 | 0.34 |
| Jan-Willem van de Meent | 2 | 110 | 13.68 |
| Dmitri S. Pavlichin | 3 | 14 | 2.54 |
| Hideo Mabuchi | 4 | 87 | 22.07 |
| Chris H. Wiggins | 5 | 94 | 6.07 |
| Ruben L. Gonzalez | 6 | 17 | 1.23 |
| Daniel Herschlag | 7 | 0 | 0.34 |