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The GO was determined using on-line functional annotation of DAVID Bioinformatics Resources 2008 (NIAID/NIH, USA).
Enrichment analyses of GO categories using Fisher exact tests were performed using Blast2go v.3.1.2 based on the annotations of the reference isolate PH-1, and categorizing the molecular function of each domain of the genes in the hotspots relative to the whole genome.
In each comparison, the same assembly-to-annotation pipeline was used on each of the genomes being compared.
Putative homologs of genes targeted by miR156/157 and miR172 were identified using the annotations on ORCAE (http://bioinformatics.psb.ugent.be/orcae/overview/Eugra) and Eucgenie (http://www.eucgenie.org/), respectively.
In order to exclude possible transposable element (TE) sequences embedded in introns, we used the TE annotations based on the BLASTEr/tblastx analysis by Quesneville et al. [ 63] (GFF3 files available at [ 64]).
The method is based on a 3D reconstruction of the bounding box using the provided annotations, based on the geometric properties of the elements involved.
This ultimately skews VQSR based on the annotations used to hard filter the variants during bootstrapping, but communities lacking sufficient data sources may find this procedure to be an acceptable alternative.
Based on gene neighborhoods, we used the functional annotation of the genes in clusters to identify the general metabolic network represented by these clusters to guide the construction of a metabolite library of potential effectors of this TR.
A total of 6,400 common probesets were identified on both microarrays, using the annotation-files provided by the array-manufacturer.
To determine whether any gene class was over-represented in this group (n=382) of DNMT3L-dependent oocyte gDMR genes, we used the functional annotation clustering tool on the DAVID platform (Huang et al., 2009).
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