{"url":"/dataset/10-synthetic-genomics-datasets","name":"10 Synthetic Genomics Datasets","full_name":null,"description_markdown":"These are 10 synthetic genomics datasets generated with NEAT v3 (based on TP53 gene of Homo Sapiens) for the use case of benchmarking somatic variant callers. To find more about our generating framework please visit synth4bench GitHub repository.\r\n\r\nThe datasets explore intrinsic NGS data parameters for the use case of observing their effect on tumor-only somatic variant calling algorithms. From the 10 datasets, there are 5 of them with different coverage (while keeping all other parameters fixed) and 5 with varying read length. The reads in all datasets are paired-end .","description_withheld":null,"homepage":"https://zenodo.org/records/10683211","introduced_date":"2024-03-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/synth4bench-a-framework-for-generating","title":"Synth4bench: a framework for generating synthetic genomics data for the evaluation of tumor-only somatic variant calling algorithms","first_author":"Styliani-Christina Fragkouli","url":null},"license":{"name":"MIT","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Biomedical","url":"/datasets/modality/biomedical"}],"tasks":[{"name":"Sequential Pattern Mining","url":"/task/sequential-pattern-mining","datasets_with_task":"/datasets/task/sequential-pattern-mining"}],"languages":[],"variants":["10 Synthetic Genomics Datasets"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sequential-pattern-mining-on-10-synthetic","task":"Sequential Pattern Mining","dataset_variant":"10 Synthetic Genomics Datasets","rows":1,"metrics":["1 Image, 2*2 Stitching, Exact Accuracy"],"first_row_in_archive_order":{"model":"cnn","paper":"/paper/causal-analysis-of-customer-churn-using-deep-1","metrics":{"1 Image, 2*2 Stitching, Exact Accuracy":"80"},"code_links":[{"title":"DavidHason/Causal_Analysis","url":"https://github.com/DavidHason/Causal_Analysis"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/causal-analysis-of-customer-churn-using-deep-1","title":"Causal Analysis of Customer Churn Using Deep Learning","date":"2023-04-20","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}