In the study of transposable elements (TEs), the generation of a high confidence set of consensus sequences that represent the diversity of TEs found in a given genome is a key step in the path to investigating these fascinating genomic elements.
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With next-generation sequencing and other high-throughput technologies revolutionizing life sciences and health care research, data processing and interpretation, rather than data production, has become the major limiting factor for discovery and innovation. Large genomics centers, and increasingly smaller research labs, are facing significant data analysis challenges.
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The Toronto Node of C3G has developed MetaFusion, a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Designed to overcome inconsistencies among frequently used fusion callers, MetaFusion is among the first ensemble fusion calling tools currently available.
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While pursuing a Bachelor of Science in Software Engineering at McGill University, I joined the Canadian Centre for Computational Genomics as an intern in 2020. I started working at the C3G with no background whatsoever in the field of computational genomics.
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Persons living with HIV (PLWH) are at increased risk of tuberculosis (TB). HIV-associated TB is often the result of a recent infection with Mycobacterium tuberculosis (Mtb) followed by rapid progression to the disease.
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Oraly Sanchez-Ferras from the Goodman Cancer Research Centre recently published a paper in Nature Communications. She reveals how the formation of tissues proceeds through a progression of different progenitors.
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Recently, using mice with different combinations of genetic and phenotypic sex, we were able to identify sex-associated differentially methylated regions (sDMRs) that depended on the sex phenotype (see this related study).
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In recognition of Transposons Day 2021 (June 16th), let’s take a look at the importance of active mobile genomic elements (once called “junk DNA”) to human health and diseases.
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Using big data techniques to analyze the function of human genes is already helping develop treatments tailored to individual patients. The more data researchers can access from across the world, the better chances of treating even rare diseases. But privacy and consent regulations differ by country, making sharing this information across borders slow and frustrating.
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As a Bioinformatician, often I get to work with PDX cancer samples. I’ve recently been reading about samples containing genome admixture, and was revisiting strategies that we commonly use for analyzing these biological data. Presented here is a summary of the existing software tools used for this purpose.
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