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In addition to uncovering three genomic subclasses of metastastic melanomas, we delineated 39 focal and recurrent regions of amplification and deletions, many of which encompassed resident genes that have not been implicated in cancer or metastasis.
High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype.
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We present an analysis of genomic features among tumors in each group, and show how these proteomically-defined subclasses of GBM compare with genomic subclasses arising from integrated analysis of primary GBM in data from The Cancer Genome Atlas.
In view of the highly rearranged nature of the metastatic melanoma genome, we next asked whether metastases were comprised of distinct genomic subclasses by genomic non-negative matrix factorization (gNMF), an unsupervised classification algorithm modified for array-CGH data [16], [17].
Instead, when intersected with survival outcome available on a subset of these samples, K3-3 subclappearedared to have a significant survival advantage by Kaplan-Meier analysis (Supplemental Figure S2), suggesting that these genomic subclasses likely represent biologically- and clinically-relevant subpopulations.
Among the 10 most cited articles, 7 reported on genomic analyses, molecular subclasses of glioblastoma and/or stem cells.
We then tested whether the 968 genomic delegates could point to subclasses of melanoma metastases linked to structural alterations of the cancer cell genome.
To extend their study, we examined the genomic organization of SKP1 genes in the moss P. patens and in 16 monocot and eudicot genomes and defined 4 subclasses of SKP1 genes, among which 1 subclass corresponds to SKP1 genes with 1 intron at a conserved position and contains the rice OSK1 and OSK20, suggesting orthology relationships between these genes.
Further, protein and genomic features suggest functional differences between subclasses of PIWI proteins and provide a basis for future analyses.
Microarray analyses have revealed subclasses of glioblastoma identified by congruence of genomic features that would otherwise be indistinguishable by morphology (reviewed in Chen et al, 2012).
Moreover, this analysis has exploited the deep sampling and high resolution of ChIP-Seq to identify a novel class of genomic NRSF-binding sites, suggesting the existence of different subclasses of genes regulated by the same factor [ 32].
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