Genomic projects focused on rare cancers encounter the limitation of availability of high-quality biological material suitable for such studies. This translates in small series of samples usually underpowered to draw meaningful conclusions. Thus, facilitating the integration of independent datasets into larger sample series is of the upmost importance. We provide a full spectrum of data and tools to maximize reuse potential for a wide range of users: raw sequencing reads, data notes and processed data, interactive computational notebooks, interactive tumor maps, and reproducible bioinformatics pipelines. We have also reprocessed available raw sequencing datasets to homogenize independently published series using our reproducible bioinformatics pipelines.

Our raw data are available on European Genome-Phenome Archive. To get access please contact us at rcg@iarc.who.int and fill the Data Access Agreement (DAA) template of the project for which you would like to get the raw data (available below in the corresponding projet section)

Once access has been granted, data can be downloaded using the EGA python client (see detailed instructions here and video tutorial here)

If you use our data please acknowledge the rare cancers genomics initiative in the acknowledgments section, for example: « The results shown here are in part based upon data generated by the Rare Cancers Genomics initiative (www.rarecancersgenomics.com) »

Study EGAS00001003699 from paper: Alcala et al. 2019 Nat Comms

• – Whole genome and exome sequencing dataset:  EGAD00001005087
• – RNA-sequencing dataset: EGAD00010001719
• – Infinium EPIC 850K DNA methylation beadchip dataset: EGAD00010001720

Download LungNENomics DAA template

Study EGAS00001004812 from paper: Mangiante et al. 2023 Nat Genet

• – Whole genome sequencing dataset:  EGAD00001007023
• – RNA-sequencing dataset: EGAD00001007024
• – Infinium EPIC 850K DNA methylation beadchip dataset: EGAD00010002053

Download MESOMICS DAA template

• – Gigascience (Gabriel et al. 2020)
• – GigaDB (Gabriel et al. 2020, Alcala et al. 2019)
• – Github IARCbioinfo DRMetrics
• – R markdown
• – Jupyter notebook
• – Nextjournal notebook

Organoids:

• – Gigascience (Alcala et al. 2024)
• – GigaDB dataset (Alcala et al. 2024)
• – Github of the organoids paper (Dayton et al. biorxiv 2022)

• – Gigascience (Di Genova et al. 2023)
• – GigaDB
• – Github mesomics data notes

Tumor map is an interactive browser that allows biologists, who may not have computational expertise, to richly explore the results of high-throughput cancer genomics experiments on thousands of patient samples.

• Map for lungNENomics project

• Archetypes Map for MESOMICS project