In this issue: GA4GH standards enable the responsible sharing of human genomic and biomedical data

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

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Author Correction: Deep transfer learning for reducing health care disparities arising from biomedical data inequality

Nature Communications ◽

10.1038/s41467-020-20480-x ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Yan Gao ◽

Yan Cui

Keyword(s):

Health Care ◽

Transfer Learning ◽

Health Care Disparities ◽

Biomedical Data

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20480-x

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Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

Genetics in Medicine ◽

10.1038/s41436-020-01084-8 ◽

2021 ◽

Author(s):

Shilpa Nadimpalli Kobren ◽

◽

Dustin Baldridge ◽

Matt Velinder ◽

Joel B. Krier ◽

...

Keyword(s):

Online Survey ◽

Variant Calling ◽

Theoretical Method ◽

The United States ◽

Genomic Sequencing ◽

Biomedical Data ◽

Sequencing Data ◽

Multimodal Data ◽

Undiagnosed Diseases ◽

Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

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A machine learning case–control classifier for schizophrenia based on DNA methylation in blood

Translational Psychiatry ◽

10.1038/s41398-021-01496-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chathura J. Gunasekara ◽

Eilis Hannon ◽

Harry MacKay ◽

Cristian Coarfa ◽

Andrew McQuillin ◽

...

Keyword(s):

Dna Methylation ◽

Population Based ◽

Case Control ◽

Training Data ◽

Polygenic Risk Score ◽

Reverse Causality ◽

Cell Type Specificity ◽

Data Set ◽

Human Genomic ◽

Genomic Regions

AbstractEpigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case–control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a “risk distance” to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10−12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants.

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Publisher Correction: Similarity-driven multi-view embeddings from high-dimensional biomedical data

Nature Computational Science ◽

10.1038/s43588-021-00049-4 ◽

2021 ◽

Author(s):

Brian B. Avants ◽

Nicholas J. Tustison ◽

James R. Stone

Keyword(s):

High Dimensional ◽

Biomedical Data

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Genomic Tackling of Human Satellite DNA: Breaking Barriers through Time

International Journal of Molecular Sciences ◽

10.3390/ijms22094707 ◽

2021 ◽

Vol 22 (9) ◽

pp. 4707

Author(s):

Mariana Lopes ◽

Sandra Louzada ◽

Margarida Gama-Carvalho ◽

Raquel Chaves

Keyword(s):

Human Genome ◽

Satellite Dna ◽

Repetitive Sequences ◽

Nucleotide Composition ◽

Genomic Component ◽

Genomic Studies ◽

Human Genomic ◽

Definition Of ◽

High Degree

(Peri)centromeric repetitive sequences and, more specifically, satellite DNA (satDNA) sequences, constitute a major human genomic component. SatDNA sequences can vary on a large number of features, including nucleotide composition, complexity, and abundance. Several satDNA families have been identified and characterized in the human genome through time, albeit at different speeds. Human satDNA families present a high degree of sub-variability, leading to the definition of various subfamilies with different organization and clustered localization. Evolution of satDNA analysis has enabled the progressive characterization of satDNA features. Despite recent advances in the sequencing of centromeric arrays, comprehensive genomic studies to assess their variability are still required to provide accurate and proportional representation of satDNA (peri)centromeric/acrocentric short arm sequences. Approaches combining multiple techniques have been successfully applied and seem to be the path to follow for generating integrated knowledge in the promising field of human satDNA biology.

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