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Ontological protein classification and clustering of the Drosophila cardiac dataset were conducted using the Database for the Annotation, Visualization and Integration of Data (DAVID) (http://david.abcc.ncifcrf.gov/) and ProteinCenter (Proxeon).
The alignments of these dataset were conducted using ClustalW 1.74 [ 43].
GenBank accession numbers for all planthopper sequences are provided in Table 5. Phylogenetic analyses of the bacterial dataset were conducted using Maximum Likelihood (ML) and Bayesian Inference (BI) reconstruction methodologies.
Bayesian phylogenetic analyses of the combined chloroplast dataset and the ITS dataset were conducted using MrBayes version 3.1.2 [ 52] with the GTR+ G + I and GTR+ G models of nucleotide substitution, respectively.
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To do so, secondary analyses of the ICILS 2013 dataset are conducted using the student data set from the 21 education systems that participated in the study.
ML phylogeny on each dataset was conducted using RAxML (Stamatakis 2006) with the GTRGAMMA model.
Maximum likelihood (ML) analysis of the dataset was conducted using RAxML version 7.0.3 [ 51], using GTR+GAMMA+P-Invar model parameters (4 gamma categories).
Successful segmentation of the dataset was conducted using a combination of level-set segmentation filters, combined in a sequential processing pipeline to refine the segmentation at each stage.
The final likelihood analysis of the joint dataset was conducted using RaxML [ 63] under a GTR+Г+I model with 1,000 nonparametric bootstrap iterations, adopting the partitioning strategy preferred by Bayes factor comparisons and using a 16-state secondary structure model for the stem regions of 28S.
Pathway analysis of the discovery GWAS dataset was conducted using the SNP ratio test (SRT) SRT was used to investigate the associations with breast cancer for 212 pathways and their genes (approximately 4,700) taken from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (05/12/08) [ 39].
Ontological and functional domain comparisons between Drosophila and mouse proteomic datasets were conducted using Ontologizer 2.0- a multifunctional software tool for GO term enrichment analysis and data exploration [22].
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