ArticleEvolutionary bioinformatics online2015
SUMAC: Constructing Phylogenetic Supermatrices and Assessing Partially Decisive Taxon Coverage.
Article in Evolutionary bioinformatics online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Article
- Genome size and endopolyploidy evolution across the moss phylogeny.Annals of botany · 2020Article
- Gymnosperms on the EDGE.Scientific reports · 2018Article
- Resurrection of the genus Aphyllon for New World broomrapes (Orobanche s.l., Orobanchaceae).PhytoKeys · 2016Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The amount of phylogenetically informative sequence data in GenBank is growing at an exponential rate, and large phylogenetic trees are increasingly used in research. Tools are needed to construct phylogenetic sequence matrices from GenBank data and evaluate the effect of missing data. Supermatrix Constructor (SUMAC) is a tool to data-mine GenBank, construct phylogenetic supermatrices, and assess the phylogenetic decisiveness of a matrix given the pattern of missing sequence data. SUMAC calculates a novel metric, Missing Sequence Decisiveness Scores (MSDS), which measures how much each individual missing sequence contributes to the decisiveness of the matrix. MSDS can be used to compare supermatrices and prioritize the acquisition of new sequence data. SUMAC constructs supermatrices either through an exploratory clustering of all GenBank sequences within a taxonomic group or by using guide sequences to build homologous clusters in a more targeted manner. SUMAC assembles supermatrices for any taxonomic group recognized in GenBank and is optimized to run on multicore computer systems by parallelizing multiple stages of operation. SUMAC is implemented as a Python package that can run as a stand-alone command-line program, or its modules and objects can be incorporated within other programs. SUMAC is released under the open source GPLv3 license and is available at https://github.com/wf8/sumac.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.