180 results for "EGAS00001004448"

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• A96155B

  Whole genome sequencing for single cells for library A96155B 1028 samples; filetype=bam

  Dataset EGAD00001008236

• A96165A

  Whole genome sequencing for single cells for library A96165A 779 samples; filetype=bam

  Dataset EGAD00001008239

• A96193B

  Whole genome sequencing for single cells for library A96193B 2410 cells; filetype=bam

  Dataset EGAD00001007118

• A73044B

  Whole genome sequencing for single cells for library A73044B 2085 cells; filetype=bam

  Dataset EGAD00001007101

• A96145A

  Whole genome sequencing for single cells for library A96145A 642 samples; filetype=bam

  Dataset EGAD00001008232

• A96190A

  Whole genome sequencing for single cells for library A96190A 1833 cells; filetype=bam

  Dataset EGAD00001007620

• A95623A

  Whole genome sequencing for single cells for library A95623A 1897 cells; filetype=bam

  Dataset EGAD00001007607

• A95633B

  Whole genome sequencing for single cells for library A95633B 2259 cells; filetype=bam

  Dataset EGAD00001007104

• A95732B

  Whole genome sequencing for single cells for library A95732B 655 samples; filetype=bam

  Dataset EGAD00001008231

• A73047D

  Whole genome sequencing for single cells for library A73047D 2091 cells; filetype=bam

  Dataset EGAD00001007102

• A95668B

  Whole genome sequencing for single cells for library A95668B 1905 cells; filetype=bam

  Dataset EGAD00001007609

• A95664B

  Whole genome sequencing for single cells for library A95664B 630 samples; filetype=bam

  Dataset EGAD00001008227

• A95662A

  Whole genome sequencing for single cells for library A95662A 637 samples; filetype=bam

  Dataset EGAD00001008226

• A108879A

  Whole genome sequencing for single cells for library A108879A 1567 cells; filetype=bam

  Dataset EGAD00001007093

• A95654A

  Whole genome sequencing for single cells for library A95654A 901 samples; filetype=bam

  Dataset EGAD00001008225

• A95635D

  Whole genome sequencing for single cells for library A95635D 367 samples; filetype=bam

  Dataset EGAD00001008224

• A95632D

  Whole genome sequencing for single cells for library A95632D 644 samples; filetype=bam

  Dataset EGAD00001008222

• A95635B

  Whole genome sequencing for single cells for library A95635B 593 samples; filetype=bam

  Dataset EGAD00001008223

• A90679

  Whole genome sequencing for single cells for library A90679 1034 samples; filetype=bam

  Dataset EGAD00001008219

• A108846B

  Whole genome sequencing for single cells for library A108846B 1636 cells; filetype=bam

  Dataset EGAD00001007092

• A95628B

  Whole genome sequencing for single cells for library A95628B 1335 samples; filetype=bam

  Dataset EGAD00001008221

• A95618A

  Whole genome sequencing for single cells for library A95618A 876 samples; filetype=bam

  Dataset EGAD00001008220

• A98299B

  Whole genome sequencing for single cells for library A98299B 991 cells; filetype=bam

  Dataset EGAD00001007087

• A108837A

  Whole genome sequencing for single cells for library A108837A 1435 cells; filetype=bam

  Dataset EGAD00001007090

• A108846A

  Whole genome sequencing for single cells for library A108846A 1670 cells; filetype=bam

  Dataset EGAD00001007091

• A108768B

  Whole genome sequencing for single cells for library A108768B 1126 cells; filetype=bam

  Dataset EGAD00001007089

• A108759B

  Whole genome sequencing for single cells for library A108759B 1134 cells; filetype=bam

  Dataset EGAD00001007088

• A108757B

  Whole genome sequencing for single cells for library A108757B 1644 cells; filetype=bam

  Dataset EGAD00001007086

• How mitochondrial DNA research can benefit from data reuse through EGA?

  Mitochondria are the only cellular organelles harbouring their own DNA, which encodes for proteins essentials in its respiration process. Despite its discovery in 1960, Mitochondrial DNA is often overlooked compared to nuclear DNA. Indeed, not all sequencing technologies capture a good representation of mtDNA (mitochondrial DNA). Recently, Ed Reznik's team (The Ed Reznik Lab | Memorial Sloan Kettering Cancer Center) carried out a massive analysis and reanalysis of single cells WGS (whole genome sequencing) produced with an amplification-free protocol (Direct library prep, DLP). As they demonstrated in the paper Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes - PMC, DLP is more effective in catching mtDNA compared to classical single-cell sequencing. These data allowed the team to unravel mtDNA dynamics in tumoral and normal samples, highlighting how mtDNA and nuclear DNA are co-regulated and their ratio is critical for phenotype. Data reuse to achieve the greatest results For this project, Reznik's team re-used data from 3 studies stored at the European Genome-phenome Archive (EGAS00001006343, EGAS00001004448 and EGAS00001003190) that comprised a total of 602 datasets. All of them were initially produced by the teams of Sam Aparicio and Sohrab P. Shah at the Memorial Sloan Kettering Cancer Centre, where Ed Reznik is a promising group leader. This demonstrates, once again, that networking, collaboration and data sharing can achieve the greatest results when coupled with innovative and smart ideas. At the EGA we are proud to provide the scientific community with an infrastructure to enable all those very smart ideas out there to find the right data to be tested.

  Blog mitochondrial-dna-research

• Clonal fitness inferred from timeseries modeling of single cell cancer genomes

  Progress in defining genomic fitness landscapes in cancer by copy number alterations (CNA) has been impeded by lack of single cell and timeseries sampling. We generated 42,000 single cell whole genomes (scWGS) from breast epithelium and primary triple negative breast cancer (TNBC) patient-derived xenografts (PDX) collected during multi-year time series. Using a Wright-Fisher population genetics model, we inferred reproducible CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy, with accurate forecasting of experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDX resulted in cisplatin resistant clones that had shown low fitness in the untreated setting. By contrast high fitness clones from treatment naive controls were eradicated reflecting an inversion of the fitness landscape. Upon drug release selective pressure dynamics were reversed indicating a fitness cost of treatment resistance. Taken together, our findings reveal clonal fitness dynamics linked to CNA and therapeutic resistance in polyclonal tumours.

  Study EGAS00001004448