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Glen Stecher

dblp:119/6478 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
0009-0008-2331-3735ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › variant effect prediction
genetic variant effect prediction
0.212014
MEGA-MD: molecular evolutionary genetics analysis software with mutational diagnosis of amino acid variation · Bioinform. 2014
Bioinformatics and computational biology
molecular evolution
0.212014
MEGA-MD: molecular evolutionary genetics analysis software with mutational diagnosis of amino acid variation · Bioinform. 2014
Bioinformatics and computational biology › phylogenetics › phyloinformatics
phylogenetic analysis software
0.112012
MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis · Bioinform. 2012
Parallel and multicore computing
parallel computing
0.012012
MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

parallel execution · 0.3multiple sequence alignment · 0.3maximum likelihood · 0.3molecular evolutionary genetics analysis · 0.2
YearPublicationVenuePosition
2014 MEGA-MD: molecular evolutionary genetics analysis software with mutational diagnosis of amino acid variation
abstract
Computational diagnosis of amino acid variants in the human exome is the first step in assessing the disruptive impacts of non-synonymous single nucleotide variants (nsSNVs) on human health and disease. The Molecular Evolutionary Genetics Analysis software with mutational diagnosis (MEGA-MD) is a suite of tools developed to forecast the deleteriousness of nsSNVs using multiple methods and to explore nsSNVs in the context of the variability permitted in the long-term evolution of the affected position. In its graphical interface for use on desktops, it enables interactive computational diagnosis and evolutionary exploration of nsSNVs. As a web service, MEGA-MD is suitable for diagnosing variants on an exome scale. The MEGA-MD suite intends to serve the needs for conducting low- and high-throughput analysis of nsSNVs in diverse applications.
Glen Stecher, Li Liu 0035, Maxwell D. Sanderford, Daniel Peterson, Koichiro Tamura, Sudhir Kumar 0001
Bioinform.1
2012 MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis
abstract
UNLABELLED: There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. AVAILABILITY: http://www.megasoftware.net/.
Sudhir Kumar 0001, Glen Stecher, Daniel Peterson, Koichiro Tamura
Bioinform.2