{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tang F"],"funding":["National Key R&amp;D Program of China","National Key R&D Program of China"],"pagination":["470"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11092001"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<h4>Background</h4>The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequencing (LP-WGS) has been reported for the detection of AOH in a low-pass setting of less than onefold. We developed a method, termed CNVseq-AOH, for predicting the absence of heterozygosity using LP-WGS with ultra-low sequencing data, which overcomes the sparse nature of typical LP-WGS data by combing population-based haplotype information, adjustable sliding windows, and recurrent neural network (RNN). We tested the feasibility of CNVseq-AOH for the detection of AOH in 409 cases (11 AOH regions for model training and 863 AOH regions for val"],"journal":["BMC genomics"],"pubmed_title":["Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth."],"pmcid":["PMC11092001"],"funding_grant_id":["2023YFC2705600"],"pubmed_authors":["Xie X","Guo X","Yan S","Jiang T","Tang F","Sun Y","Qiao Z","Man J","Peng Z","Wang L","Peng H","Song L","Li Y","Yang Y","Wang S","Fan L","Wang X","Wang Y","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth.","description":"<h4>Background</h4>The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequencing (LP-WGS) has been reported for the detection of AOH in a low-pass setting of less than onefold. We developed a method, termed CNVseq-AOH, for predicting the absence of heterozygosity using LP-WGS with ultra-low sequencing data, which overcomes the sparse nature of typical LP-WGS data by combing population-based haplotype information, adjustable sliding windows, and recurrent neural network (RNN). We tested the feasibility of CNVseq-AOH for the detection of AOH in 409 cases (11 AOH regions for model training and 863 AOH regions for val","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-04-12T15:29:48.283Z","creation":"2026-04-07T13:17:54.774Z"},"accession":"S-EPMC11092001","cross_references":{"pubmed":["38745141"],"doi":["10.1186/s12864-024-10400-4"]}}