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The SW algorithm integrates multiple networks, each of which is computed from a dataset, into a single combined network that it then uses to infer gene function.
An OTU is a monophyletic group of species (possibly one) that will result, in the final dataset, into a single taxon labelled with the OTU name.
More stringent values caused disconnection of the most diverged sequences, while more permissive values caused over-compaction of the whole dataset into a single cluster with only a few outliers.
More stringent values caused disconnection of the most diverged sequences, while more permissive values, such as 10-5 usearlierier for the analysis of relation of HEN1 to other COGs, caused over-compaction of the whole dataset into a single cluster with only a few outliers.
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To facilitate this large analysis, we developed a novel method that combines or multiplexes the separate IMT (mIMT) datasets into a single super dataset for subsequent model-based clustering and co-regulation analysis.
Additionally, a combined alignment was created by concatenating our amino acid and nucleotide datasets into a single matrix.
Due to the presence of common samples, we merged the Desmedt and Sotiriou datasets into a single one and removed from it the patients that were also included in the Miller dataset.
To investigate how expect-value cutoff with TBLASTN affected the number of identified homologs for different genes, a nucleotide database was created by combining all GOS datasets into a single FASTA file and then randomly subsampling to 46,052 sequences using the daisychopper (http://www.genomics.ceh.ac.uk/GeneSwytch/Tools.html) tool.
Future developments will include methods for combining several different datasets into a single module-finding problem.
We normalized all datasets into a single matrix using the RMA package (Bolstad et al., 2003).
We can thus transparently combine information from multiple diverse datasets into a single score.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com