4210 results for "*geneti*"

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• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• New review highlights how data reanalysis is transforming rare disease diagnosis

  A newly published review, Advancing the diagnosis of rare neuromuscular and neurological diseases through the collaborative Solve-RD research framework, reflects on one of Europe's largest collaborative efforts to improve the diagnosis of rare diseases through systematic genomic reanalysis. Rather than focusing on generating new sequencing data alone, the review highlights a simple but powerful idea: genomic data continue to gain value over time. As disease knowledge expands, analytical methods improve, and new technologies become available, datasets that once yielded no diagnosis can help “solve families” - that is, identify the genetic cause of disease in families who had previously remained without an answer - years after they were first generated. It is a compelling reminder that the diagnostic journey does not necessarily end with an initial negative result. Solve-RD: a collaborative approach to unsolved rare diseases Launched in 2018, Solve-RD brought together clinicians, researchers and data scientists across Europe to systematically revisit genomic data from patients with previously unsolved rare diseases. By combining expertise across the European Reference Networks (ERNs) with complementary approaches such as RNA sequencing and other multi-omics technologies, the consortium created a framework for large-scale collaborative reanalysis. The results demonstrate the value of this approach. In one of the consortium's largest studies, 506 of more than 6,000 previously unsolved families received a genetic diagnosis through systematic reanalysis, as reported in the consortium's flagship Nature Medicine publication. More recently, a complementary RNA sequencing study released in MedRxiv provided diagnoses for 19 of 248 families whose DNA analyses had previously been inconclusive, while also identifying several novel candidate disease genes. From archived data to new diagnoses These achievements depended on more than new analytical methods—they relied on the continued availability of genomic data. Throughout the Solve-RD project, datasets were securely archived at the European Genome-phenome Archive (EGA), enabling long-term preservation and controlled access for future research and reanalysis. More than 750,000 files, representing over 800 TB of data, were deposited in the archive. Today, the Solve-RD collection remains the second largest collection of datasets at the EGA and discoverable under study EGAS00001003851, allowing authorised researchers to request access through the project's Data Access Committee. Looking ahead While the review reflects on the achievements of Solve-RD, its message extends beyond a single project. It shows how systematic reanalysis, collaborative interpretation and the responsible reuse of existing data can continue generating diagnoses long after those data were first produced. Building on the collaborative foundations established through RD-Connect, Solve-RD and EJP RD, the European Rare Diseases Research Alliance (ERDERA) is now expanding this model across Europe. ERDERA has already brought together 10,000 harmonised genomic and phenotypic datasets from unsolved rare disease cases into a secure European resource, with the long-term ambition of integrating 100,000 genomic and multi-omics datasets to accelerate rare disease research and diagnosis. The EGA is actively working with ERDERA partners to support the operation of this large-scale effort: from aiding in coordinating and processing data submissions to enabling their secure archival and controlled reuse. This support extends across genomic, transcriptomic and other multi-omics data, including emerging technologies such as long-read sequencing, optical genome mapping and novel omics approaches. By helping centres submit and manage increasingly diverse datasets at scale, the EGA supports their integration into a sustainable resource that can be revisited as technologies evolve and new research questions emerge.

  Blog data-reanalysis-is-transforming-rare-disease-diagnosis

• Whole-genome sequences of Korean populations generated by using long- and short-read sequencing technologies.

  We newly sequenced whole-genome of Korean participants by using long- and short-read sequencing platforms (n = 3 and n = 100 for long- and short-read sequencing, respectively). With these data, generated by Sequel II HiFi system (Pacific Bioscience) and DNBSEQ-T7 (MGI) sequencers, we identified a set of structural variations common in Korean population as well as the genetic features distinct from other ethnicities.

  Study EGAS50000000375

• Epigenetic Heterogeneity in Breast Tumors with Implications for Disease Diagnosis and Monitoring

  This study investigates epigenetic heterogeneity in human breast cancer using complementary single-cell and bulk epigenomic approaches. Human breast tumor samples were profiled using whole-genome bisulfite sequencing, enzyme-mediated methylation sequencing of sorted tumor-cell populations, single-nucleus CUT&Tag, and single-cell RNA and chromatin-accessibility multiome profiling. These data were used to characterize recurrent epigenetic cell states and intra-tumor heterogeneity in breast cancer.

  Study EGAS50000002070

• NGS in FACSorted populations from AML patients

  FACS-enriched bone marrow cell populations from 22 AML patients were analyzed using a custom panel targeting 56 genes recurrently mutated in myeloid neoplasms. DNA was released from a median of 60,000 sorted cells by incubation with 2.5 μL proteinase K and 2 μL RNase A (Thermo Fisher Scientific) for 30 minutes at 37°C, purified using 2× AMPure XP beads (Beckman Coulter, Brea, CA), and stored in pH 8 elution buffer at −20°C. DNA quantity was measured using the Qubit dsDNA BR Assay on a Qubit 3.0 fluorometer (Thermo Fisher Scientific), and quality was assessed using the Genomic DNA ScreenTape assay on a 4200 TapeStation system (Agilent Technologies, Santa Clara, CA). Libraries were prepared from at least 20 ng of DNA according to the SOPHiA GENETICS protocol and sequenced on an Illumina MiSeq using 2 × 251-cycle paired-end reads. At least 95% of target bases achieved ≥1,000× coverage. FASTQ files were analyzed in SOPHiA DDM using the GRCh37/hg19 reference genome for alignment, variant calling, and annotation. Variants were retained when the variant allele frequency was ≥2%, coverage was ≥100 reads, and the alternate allele was supported on both strands. Synonymous, intronic, intergenic, 5′ untranslated-region, and recurrent panel-artifact calls were excluded. The remaining variants were classified according to Association for Molecular Pathology and American College of Medical Genetics and Genomics guidance using VarSome, ClinVar, COSMIC, and dbSNP. Only pathogenic or likely pathogenic variants were analyzed. All retained calls were visually confirmed in Integrative Genomics Viewer v2.11.3, and only variants previously reported as somatic in hematopoietic or myeloid neoplasms were retained.

  Study EGAS50000002132

• Single-Cell Multiomic and Whole-Genome Profiling of Primary and Metastatic Colorectal Cancer

  This study contains controlled-access single-cell multiomic sequencing (scRNA-seq and scATAC-seq) data generated from patients with colorectal cancer, including 33 primary tumours, 11 metastatic lesions, and 7 adjacent normal tissue samples. Whole-genome sequencing (WGS) data are available for the tumour samples. These data were generated to investigate the genomic and epigenomic landscape of colorectal cancer and to identify recurrent regulatory alterations associated with tumour progression and metastasis.

  Study EGAS50000002048

• NNI ET Imaging Genetic DAC

  Dac EGAC50000001134

• Weizmann Breast Cancer Epigenetics Data Access Committee

  Data Access Committee responsible for reviewing requests for controlled access to human sequencing data generated in the study "Epigenetic Heterogeneity in Breast Tumors with Implications for Disease Diagnosis and Monitoring".

  Dac EGAC50000001136

• Large-scale single-cell phylogenetic mapping of clonal evolution in the human aging esophagus

  SMART-PTA single-cell whole-genome and full transcriptome sequencing form normal esophagus samples

  Dataset EGAD50000002573

• Long-read whole genome sequencing of gastric cancer

  Gastric cancer (GC) remains a significant health challenge globally, with a particularly high incidence in Asia, including Singapore. Understanding the genomic landscape of normal gastric and the different GC subtypes along with its precursor lesions such as intestinal metaplasia (IM) as well as GC metastatic sites is crucial for advancing diagnostics, prognostics, and therapeutics. Long-read sequencing technologies offer an opportunity to delve deeper into the genomic and epigenetic alterations underlying these conditions and to complement existing short-read omics data. By integrating these data with previously generated in-house multi-omics data (such as RNA sequencing and whole exome sequencing), we hope to better elucidate the molecular mechanism underlying GC initiation and progression.

  Study EGAS50000001607