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Matthew R. Huska

Researcher at Max Delbrück Center for Molecular Medicine

Publications -  30
Citations -  3000

Matthew R. Huska is an academic researcher from Max Delbrück Center for Molecular Medicine. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 12, co-authored 20 publications receiving 1954 citations. Previous affiliations of Matthew R. Huska include Max Planck Society & University of Ottawa.

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Pan-cancer analysis of whole genomes

Peter J. Campbell, +1332 more
- 06 Feb 2020 - 
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
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DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction

TL;DR: This work shows that alternative functional programs of hematopoietic stem cells (HSCs) are governed by gradual differences in methylation levels and identifies DNA methylation as an essential epigenetic mechanism to protect stem cells from premature activation of predominant differentiation programs.
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Genomic basis for RNA alterations in cancer

TL;DR: The most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Gome Atlas (TCGA) was presented in this article.
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MedlineRanker: flexible ranking of biomedical literature

TL;DR: The MedlineRanker webserver is implemented, which allows a flexible ranking of Medline for a topic of interest without expert knowledge, and shows that the tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time.
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PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs

TL;DR: PromiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features, increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation.