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MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets
Open accessAbstract
We present the latest version of the Molecular Evolutionary Genetics Analysis (M ega ) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, M ega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in M ega . The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit M ega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line M ega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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Metadata
- Authors
- Kumar, S., Stecher, G., & Tamura, K.
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-03-22
- Volume
- 33
- Issue
- 7
- Pages
- 1870-1874
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2311203695
- Cited by
- 44,059
- References
- 1
- FWCI
- 2215.16
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.