Asymptotic Approaches to Discovering Cancer Genomic Signatures

2017 ◽  
pp. 23-36
Author(s):  
Maciej Pietrzak ◽  
Grzegorz A. Rempala
Keyword(s):  
Genomic Signatures

scholarly journals Genomic signatures of local adaptation to the degree of environmental predictability in rotifers

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Lluis Franch-Gras ◽  
Christoph Hahn ◽  
Eduardo M. García-Roger ◽  
María José Carmona ◽  
Manuel Serra ◽  
...  
Keyword(s):  
Local Adaptation ◽  
Genomic Signatures ◽  
Environmental Predictability

scholarly journals Genomic signatures of clonality in the deep water kelpLaminaria rodriguezii

2021 ◽  
Vol 30 (8) ◽  
pp. 1806-1822
Author(s):  
Lauric Reynes ◽  
Thierry Thibaut ◽  
Stéphane Mauger ◽  
Aurélie Blanfuné ◽  
Florian Holon ◽  
...  
Keyword(s):  
Deep Water ◽  
Genomic Signatures

scholarly journals Comparative insights into genome signatures of ferric iron oxide- and anode-stimulated Desulfuromonas spp. strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yong Guo ◽  
Tomo Aoyagi ◽  
Tomoyuki Hori
Keyword(s):  
Iron Oxide ◽  
Genome Rearrangement ◽  
Ferric Iron ◽  
Electron Acceptors ◽  
Extracellular Electron Transfer ◽  
Redox Potentials ◽  
Bacterial Strains ◽  
Biosynthesis Gene Cluster ◽  
Genomic Signatures ◽  
Flagellar Biosynthesis

Abstract Background Halotolerant Fe (III) oxide reducers affiliated in the family Desulfuromonadaceae are ubiquitous and drive the carbon, nitrogen, sulfur and metal cycles in marine subsurface sediment. Due to their possible application in bioremediation and bioelectrochemical engineering, some of phylogenetically close Desulfuromonas spp. strains have been isolated through enrichment with crystalline Fe (III) oxide and anode. The strains isolated using electron acceptors with distinct redox potentials may have different abilities, for instance, of extracellular electron transport, surface recognition and colonization. The objective of this study was to identify the different genomic signatures between the crystalline Fe (III) oxide-stimulated strain AOP6 and the anode-stimulated strains WTL and DDH964 by comparative genome analysis. Results The AOP6 genome possessed the flagellar biosynthesis gene cluster, as well as diverse and abundant genes involved in chemotaxis sensory systems and c-type cytochromes capable of reduction of electron acceptors with low redox potentials. The WTL and DDH964 genomes lacked the flagellar biosynthesis cluster and exhibited a massive expansion of transposable gene elements that might mediate genome rearrangement, while they were deficient in some of the chemotaxis and cytochrome genes and included the genes for oxygen resistance. Conclusions Our results revealed the genomic signatures distinctive for the ferric iron oxide- and anode-stimulated Desulfuromonas spp. strains. These findings highlighted the different metabolic abilities, such as extracellular electron transfer and environmental stress resistance, of these phylogenetically close bacterial strains, casting light on genome evolution of the subsurface Fe (III) oxide reducers.

scholarly journals Capturing genomic signatures of DNA sequence variation using a standard anonymous microarray platform

2006 ◽  
Vol 34 (18) ◽  
pp. e121-e121 ◽  
Author(s):  
C. H. Cannon ◽  
C. S. Kua ◽  
E. K. Lobenhofer ◽  
P. Hurban
Keyword(s):  
Dna Sequence ◽  
Sequence Variation ◽  
Microarray Platform ◽  
Genomic Signatures ◽  
Dna Sequence Variation

scholarly journals GATHER: a systems approach to interpreting genomic signatures

2006 ◽  
Vol 22 (23) ◽  
pp. 2926-2933 ◽  
Author(s):  
Jeffrey T. Chang ◽  
Joseph R. Nevins
Keyword(s):  
Systems Approach ◽  
Genomic Signatures

Genomic signatures in T-cell lymphoma: How can these improve precision in diagnosis and inform prognosis?

Blood Reviews ◽  
2016 ◽  
Vol 30 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Javeed Iqbal ◽  
Ryan Wilcox ◽  
Hina Naushad ◽  
Joseph Rohr ◽  
Tayla B. Heavican ◽  
...  
Keyword(s):  
T Cell ◽  
Cell Lymphoma ◽  
T Cell Lymphoma ◽  
Genomic Signatures

Abstract 187: Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures

2021 ◽  
Author(s):  
Derek Van Booven ◽  
Victor Sandoval ◽  
Oleksander Kryvenko ◽  
Madhumita Parmar ◽  
Andres Briseño ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Learning System ◽  
Gleason Grading ◽  
Genomic Signatures
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