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In this review we discuss recent developments in knowledge-based metabolic engineering strategies, that is the gathering and mining of genomic, transcriptomic, proteomic and metabolomic data to generate models of metabolic pathways that help to define and refine optimal intervention strategies.
TO, SW and DG performed the computational mining of genomic variations.
We performed in-silico data mining of genomic variations in OR genes and segregating pseudogenes, including single nucleotide polymorphisms, small indels (< 100 bp) and structural variations.
Data mining of genomic databases and bioinformatic predictions allowed us to determine that plants also possess lipocalins, which were classified as temperature-induced lipocalins (TILs) and chloroplastic lipocalins (CHLs) [ 4].
However, annotation was enhanced through the mining of genomic data available for H. contortus (http://www.sanger.ac.uk; August 2008), ultimately increasing the percentage of annotated sequences to ~50% for the L3 and xL3 datasets (see Results section).
Data mining of genomic BAC and MAGI sequences containing ZmPti1b and - d indicated that the corresponding genes possess nearly identical exon/intron structures as compared to ZmPti1a (data not shown).
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Comprehensive mining of bacterial genomic databases recovered 63 genomes affiliated to Pseudomonas fluorescens (fluorescent pseudomonads) and 10 genomes affiliated to Stenotrophomonas maltophilia (stenotrophomonads).
Grounded in phylogenetic analyses of protein sequences of key catabolic protein families and of proteins of documented function, AromaDeg allows query and data mining of novel genomic, metagenomic or metatranscriptomic data sets.
In the post-genomics era, the analysis of human genetic data is often combined with the mining of functional genomic data to facilitate the identification of potential causative genes [ 1, 2].
These may be used for many important research applications such as the data mining of large genomic resources, identification of candidate disease associated genes and variations within them, and the annotation of genome-wide experiments such as microarray studies.
Chado is a product of the Generic Model Organism Database (GMOD) project (http://www.gmod.org) which builds and coordinates construction and distribution of a suite of tools that support storing, mining and visualization of genomic, genetic and other biological data.
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