A robust machine learning approach for missing persons cases with high genotyping errors
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ABSTRACT:
PROVIDER: PRJNA862502 | ENA |
REPOSITORIES: ENA
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Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.
2022-07-27 | GSE209804 | GEO
Project description:Background: Tuberculosis (TB) among persons living with HIV (PLWH) poses diagnostic challenges. Although several transcriptional signatures have recently been identified as promising tool for TB diagnosis, data are limited in persons with advanced HIV. Methodology: Reads were aligned to the human transcriptome (GRCh38 version 100), comprising both mRNA and ncRNA, with Salmon v1.2.0. A Random Forest algorithm with “leave-one-out” cross-validation was applied in the variance stabilizing transformation gene expression. The identified biomarkers were compared to previous TB transcriptional signatures using the Area Under the Curve (AUC). Results: Functional analysis indicated that common upregulated pathways in TB/HIV patients were associated with Toll-like receptor cascades and neutrophil degranulation. A machine learning decision tree algorithm identified the expression values from RAB20 and INSL3 as most informative for classifying TB status in PLWH. Only these two genes were able to correctly classify all samples.
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Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.
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Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.
2023-06-01 | GSE218901 | GEO
Project description:In order to identified as candidate biomarkers for early diagonosis or as therapeutic targets in late damages of ionizing radiation, gene expression profile was analyzed in the peripheral blood of three 60Co γ-ray accidently-exposed persons. The results showed that there were 285 up-regulated and 446 down-regulated genes in irradiated samples compared with control samples. The large majority of those differentially expressed genes encode proteins that are associated with immune response, inflammation, cell structure, oxidative stress, neuro-hormone regulation, reproduction, susceptibility of psychiatric disorders, and transcriptional regulators. The expressions of IL3, KDR, CEACAM8 and OSM were validated by real-time RT-PCR method. The findings of our study should help us understanding the molecular mechanisms underlying the late effects of ionizing radiation and to develop better diagonostic and therapeutic strategies for those damages. Fresh blood samples were collected from three 60Coγ-Ray accidentally-exposed persons(3 years post irradiation) and three non-irradiated healthy donors. Leucocytes cells were isolated and total RNA was extracted. Four RNA samples, from three accidently-exposed persons and a RNA mixture extracted from a combination of three healthy donors` leucocytes (ratio 1:1:1, each 3.3×106 cells) in EL buffer, were subjected to Agilent gene expression microarray assay.
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