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Traditional genome-wide analysis of gene expression of organisms under different conditions or, in the case of pathogens, at different life cycle stages, has mainly been carried out by microarrays, suppression subtractive hybridization (SSH), and cDNA-AFLP methods [ 9- 12].
To confirm relevance of our findings to those of previous studies, we examined SAP expression of organisms grown in the expression medium and found similar results (data not shown).
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An effective way to determine these genes and gene-regulatory miRNAs are genome-wide studies of expression patterns, as it is well known that expression profiles of organisms change with age [ 2, 5].
It is particularly well suited for the de novo detection of splice junctions and allows genome-wide qualitative expression profiling of organisms with unknown genome sequence.
These new technologies enable the de novo reconstruction of the transcriptome for a non-model organism [ 11], leading to novel opportunities for expression profiling of organisms lacking any genome or transcriptome sequence information [ 11, 12].
RNA-seq is increasingly used to study gene expression of various organisms.
Microarrays have become indispensable tools for studying the gene expression of particular organisms on a genomic scale.
As research into alternative splicing reveals the fundamental importance of this phenomenon in the genome expression of higher organisms, there is an increasing need for a standardized, consistent and unique identifier for alternatively spliced isoforms.
Another drawback that all approaches for modeling gene expression of eukaryotic organisms suffer from, is the inability to include all the factors that regulate gene expression [ 53].
Similarly, for estimating the expression of diploid organisms at the haplotype, isoform and gene levels, specific tools are needed to be part of the RNA-seq pipeline, such as MMSEQ [ 39].
Finding how these all affect the phenotypic expression of an organism is complicated.
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