Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2020Remembering Joanna McKittrickcitations
  • 2020The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia76citations

Places of action

Chart of shared publication
Ferreira, José Maria Da Fonte
1 / 456 shared
Fahrenholtz, Bill
1 / 1 shared
Dickey, Elizabeth
1 / 1 shared
Ohji, Tatsuki
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Viehland, Dwight
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Zhou, Yanchun
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Foreman, Jonathon
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Wu, Yiquan
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Klein, Lisa
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Mauro, John
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Riedel, Ralf
1 / 33 shared
Brennecka, Geoff
1 / 2 shared
Mechinaud, Francoise
1 / 1 shared
Khaw, Seong Lin
1 / 1 shared
Wallach, Elise
1 / 1 shared
Sutton, Rosemary
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Bartolo, Ray C.
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Brooks, Ian
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Petrovic, Vida
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Ludlow, Louise E. A.
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Lonsdale, Andrew
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Challis, Jackie
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Schmidt, Breon
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Hawkins, Anthony
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Oshlack, Alicia
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Venn, Nicola C.
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Zhu, Andrea
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Davidson, Nadia M.
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Chart of publication period
2020

Co-Authors (by relevance)

  • Ferreira, José Maria Da Fonte
  • Fahrenholtz, Bill
  • Dickey, Elizabeth
  • Ohji, Tatsuki
  • Viehland, Dwight
  • Zhou, Yanchun
  • Foreman, Jonathon
  • Wu, Yiquan
  • Xie, Rongjun
  • Klein, Lisa
  • Mauro, John
  • Riedel, Ralf
  • Brennecka, Geoff
  • Mechinaud, Francoise
  • Khaw, Seong Lin
  • Wallach, Elise
  • Sutton, Rosemary
  • Bartolo, Ray C.
  • Brooks, Ian
  • Petrovic, Vida
  • Ludlow, Louise E. A.
  • Lonsdale, Andrew
  • Challis, Jackie
  • Schmidt, Breon
  • Hawkins, Anthony
  • Oshlack, Alicia
  • Venn, Nicola C.
  • Zhu, Andrea
  • Davidson, Nadia M.
OrganizationsLocationPeople

article

The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  • Mechinaud, Francoise
  • Khaw, Seong Lin
  • Wallach, Elise
  • Martin, Michelle
  • Sutton, Rosemary
  • Bartolo, Ray C.
  • Brooks, Ian
  • Petrovic, Vida
  • Ludlow, Louise E. A.
  • Lonsdale, Andrew
  • Challis, Jackie
  • Schmidt, Breon
  • Hawkins, Anthony
  • Oshlack, Alicia
  • Venn, Nicola C.
  • Zhu, Andrea
  • Davidson, Nadia M.
Abstract

<jats:title>Abstract</jats:title><jats:p>Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1, and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard-of-care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard-of-care diagnostics. KMT2A (MLL) rearrangements, although usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard-of-care testing in 9 patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the most common IKZF1 deletion from RNA-seq data and validated this using an RQ-PCR assay. We applied an expression classifier to identify Philadelphia chromosome–like B-ALL patients. T-ALL proved a rich source of novel gene fusions, which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.</jats:p>

Topics
  • impedance spectroscopy