Reconstructing (super)trees from data sets with missing distances: Not all is lost
- Submitting institution
-
The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182619633
- Type
- D - Journal article
- DOI
-
10.1093/molbev/msv027
- Title of journal
- Molecular Biology and Evolution
- Article number
- -
- First page
- 1628
- Volume
- 32
- Issue
- 6
- ISSN
- 0737-4038
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We present a novel algorithm to build evolutionary trees from datasets with missing information. The associated freely available LASSO software is the first tool developed for this purpose that has a uniqueness guarantee. It was developed jointly with Dr. Dicks’ group at the BBSRC Quadram Institute, supported in part by her BBSRC grant. Our work featured in ScienceDaily, Feb. 6, 2015. Recently, LASSO was included in an independent comparison study involving 3 competing methods (Bhattacharjee et al, BMC Genomics, 2020). It was found to outperform them all for biological datasets with varying amounts of missing information.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -