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Mutation classification was made based on known databases or published studies.
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These unigenes were annotated by querying against various public databases, with 34,029 unigenes found to be homologous to genes in the NCBI GenBank database and 22,945 unigenes annotated based on known proteins in the Swiss-Prot database.
Integration of this database into Gaggle software facilitated the construction of probable AMPK-regulated networks based on known and predicted molecular associations.
The 16S rRNA gene is the most popular marker gene for target sequencing, which can solve one important question related to microbial ecology as "who is there" by assigning the reads to a taxonomic lineage based on known 16S rRNA database such as green genes (DeSantis et al., 2006), SILVA (Quast et al., 2013) or RDP (Cole et al., 2014).
We annotated these millions of magnitude of transcripts generated by Cufflinks based on known genomic annotation information from different databases (e.g., UCSC and Ensembl).
The metabolic network of an organism is assembled, through automated and manual procedures [ 6, 7], based on known chemical reactions collected from genome annotation databases, such as the Kyoto Encyclopedia of Genes and Genomes [ 8].
Putative interspecies interaction networks were then identified based on known protein-protein interactions (PPIs) from existing databases, our proposed dynamic model for indirect interspecies interactions between the two organisms, and simultaneously quantified microarray data for C. albicans and zebrafish during infection.
A set of 247 reads was annotated at least to the phylum level based on known 16S rRNA genes sequences from RDP database at an E-value cutoff ≤ 0.01.
Structural classification of proteins (SCOP), is a database that classifies all proteins based on known structure, function and sequence (Murzin et al., 2005).
The Daphnia genome (http://wfleabase.org/), NCBI and Uniprot protein database were searched for the Daphnia ion channel gene models based on known human or Drosophila genes.
Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions.
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