WG 1 Mye-Matrix

Photo: © German Biobank Node

In order to build the Mye-Matrix data framework, publicly available databases will be mined for single cell and population level transcriptome data sets related to myeloid cells and DACIs. Data and meta data will be extracted, quality controlled, expertly curated and organised in a user-friendly database to allow easier access to the available landscape of data by the broader research community. Use of algorithms such as NicheNet or regulatory network analysis on single cell data sets will be used in order to identify potential cellular interactions between myeloid cells and lymphocytic immune cell subsets with potential relevance in DACI. In a second step, transcriptomic data sets will be linked to existing literature, mined by advanced text mining approaches from public databases, e.g. PubMed.

Working Group 1 has the following specific tasks:

  1. Enumerate, extract and organise the fragmented transcriptome and meta data about the role of myeloid cells in DACI from various repositories (ArrayExpress, GEO, Human Cell Atlas etc.) and convert it into biologically and clinically meaningful biomarkers and data.
  2. Integration of novel generated transcriptomic data sets from other partners (wet lab collaborators) into the newly curated database.
  3. Establish a text mining based database of biomedical literature related to myeloid cells and DACIs and link it to a user-friendly database encompassing the afore-mentioned information in order to determine the role of myeloid cells in DACI.
  4. Prediction of novel and informative biomarkers (gene signatures or proteins), for myeloid cells associated with various DACI through bioinformatic data analysis approaches.

These tasks will be achieved by the following activities and networking tools:

  1. WG 1 will establish a bioinformatic pipeline in order to automatically mine the data repositories to extract transcriptomic data sets related to myeloid cells and DACIs.
  2. Following data mining, WG1 will generate a compendium of DACI related transcriptomic data sets and develop strategies to normalise, explore and validate (in cooperation with WG 2) these approaches.
  3. WG 1 will setup tutorials, training schools and STSMs to enable the largest possible dissemination of bioinformatic tools and techniques to the myeloid and DACI research This will be achieved through dissemination of webinars, virtual meetings and hands on demonstrations at dedicated Mye-InfoBank workshops.

At the end of the project, the created database will be published in a peer-reviewed journal and made publicly available.

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