{"database":"EGA","file_versions":[],"scores":null,"additional":{"omics_type":["Genomics"],"contact_person":["María Luisa Maestro"],"full_dataset_link":["https://ega-archive.org/dacs/EGAC00001000124"],"host":["EGA"],"description":["EGA DAC EGAC00001000124"],"repository":["EGA"],"email":["mmaestro.hcsc@salud.madrid.org"],"pubmed_abstract":["<h4>Background</h4>Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.<h4>Results</h4>We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.<h4>Conclusions</h4>seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow."],"pubmed_title":["seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing."],"pubmed_authors":["Mosen-Ansorena David D, Telleria Naiara N, Veganzones Silvia S, De la Orden Virginia V, Maestro Maria Luisa ML, Aransay Ana M AM"],"name_synonyms":["fbwd4, Dmel_CG5060, Slc20a3, l(2)04454, DmelCG1772, Dmel_CG5067, dac, CIB1, shsf3, E(Dl)D49, shfm3, Decapo., fet, E(Sev-CycE)2B, CG5067, p21[dacapo], Fbw4, FBW4, dactylin, Cic, E130112M23Rik, Dach, CDKN2B, cdi4, mKIAA0306, SHFM3, Dac, DAC, p27[Dap], dactylyn, dacapo/cyclin-dependent kinase interactor 4, AI182278, p21, CG5060, CG43122, 1200010B10Rik, CG1772, FBWD4, Dap, Cdi4, CDI4, CES5A1, p27, P15, fbw4, SHSF3, Ctp, DmelCG43122"],"pubmed_title_synonyms":["thymus nucleic acid, primary cancer, DNS, Malignant Neoplasm, (Deoxyribonucleotide)n, determination, Malignancy, DNAn+1, Neoplasms, number, Benign Neoplasm, Double Stranded, Benign Neoplasms, Deoxyribonucleic acid, Cancers, Double-Stranded, Tumor, Malignant, presence, malignant tumor, Deoxyribonucleic acids, (Deoxyribonucleotide)n+m, Neoplasias, count in organism, MT, Benign, Deoxyribonucleic Acid, malignant neoplasm, ds DNA, cardinality, chemical analysis, Desoxyribonukleinsaeure, Neoplasm, Double-Stranded DNA, Malignancies, (Deoxyribonucleotide)m, assay, DNA, deoxyribonucleic acids, DNAn, ds-DNA, desoxyribose nucleic acid, Neoplasia, Cancer, Tumors, Malignant Neoplasms."],"pubmed_abstract_synonyms":["Scientific Bias, determination, False, Ecological Fallacy, Neoplasms, Truncation, number, Benign Neoplasm, Gene, Systematic Bias, Tumor, composed of, Malignant, presence, Epidemiologic Biase, Work Flows., Mood, Ecological, Work Flow, INSL3R, Fallacies, average, Ecological Biases, F, Gene Expressions, Gpr106, Malignancy, Genomes, availability, Moods, Aggregation, composition, RXFPR2, procedures, Expressions, Neoplasias, GREAT, malignant neoplasm, Statistical Biases, Great, Ecological Bias, Malignancies, Expression, Systematic, Epidemiologic Biases, GPR106, HHT1, Cancer, Tumors, Biase, Fallacy, adequate, Malignant Neoplasm, Scientific, Edg, Affects, Epidemiologic, Truncation Biases, Ecological Fallacies, compositionality, results, Outcome Measurement Errors, read, count in organism, Workflows, Experimental, MT, Benign, chemical analysis, Neoplasm, Errors, background, techniques, Truncation Bias, END, LGR8, primary cancer, Lgr8, Bias, content, Biases, Benign Neoplasms, Cancers, whole genome, Outcome Measurement, LGR8.1, malignant tumor, introduction, Malignant Neoplasms, Experimental Bias, Outcome Measurement Error, Error, Aggregation Bias, structure, cardinality, Statistical Bias, assay, Statistical, ORW1, Neoplasia, methodology"],"additional_accession":[]},"is_claimable":false,"name":"CIC bioGUNE´s DAC (CbGDAC)","description":"Data Access Committee EGAC00001000124","dates":{"output":"2025-1-9"},"accession":"EGAC00001000124","cross_references":{"TAXONOMY":["9606"],"pubmed":["24597965"],"EGA":["EGAS00001000558","EGAS00001000646","EGAD00010000492","EGAD00001000643","EGAD00001000642","EGAD00010000494"]}}