News
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Transcriptome Analysis Provides Genomic Insight into Emu Fat Metabolism
Understanding gene expression in emu fat tissue could improve production of emu oil and increase its potential health benefits.
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Constructing novel next-generation physical maps using Physlr
Researchers from the GSC’s Birol lab have developed a tool capable of generating next-generation physical maps from whole-genome sequencing (WGS) data.
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Addressing challenges associated with genome analysis when nucleic acids are extracted from formalin-fixed paraffin-embedded tissue samples
Somatic variant calling from formalin-fixed paraffin-embedded (FFPE) genome sequencing data can be improved using computational and machine learning approaches.
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Whole Genome and Transcriptome Analysis Benefits Clinical Cancer Care
A prospective study of 570 Personalized OncoGenomic (POG) patients demonstrates how whole genome and transcriptome analysis can impact cancer treatment.
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Unique repressive chromatin state to myelo-erythroid lineage is linked to regulation of blood cell differentiation
Lineage-selective genome-wide repressive histone modification signature unique to myelo/erythroid lineage involved in hematopoietic differentiation.
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Looking at the genomic features of lung-recurrent, hormone-sensitive prostate cancer
The GSC’s Dr. Alexander Wyatt Lab assesses the genomic features of lung-recurrent, hormone-sensitive prostate cancer.
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Genomic Analysis of Adrenocortical Cancer Identifies Treatment Targets
Results of whole-genome and transcriptome analysis identifies two genomic targets for potential treatment of adrenocortical cancer, a rare disease with limited therapeutic options.
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Genome polishing: ntEdit+Sealer produces high quality long-read genome assemblies as a genome finishing protocol
The Birol Lab publishes ntEdit+Sealer, an alignment-free genome finishing protocol that corrects sequencing errors and fills gaps to produce highly accurate long-read genome assemblies.
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Predicting Patient Response to Cancer Immunotherapy with Neoantigens
A study from the Karsan lab concludes that predicting patient response to immunotherapy is not improved using tumour neoantigen counts from noncoding DNA.