BACKGROUND: Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets. FINDINGS: For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets. CONCLUSION: With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.

Comment on doi: 10.1093/gigascience/giab029.

Comment on doi: 10.1093/gigascience/giab069.

BACKGROUND: Channa argus and Channa maculata are the main cultured species of the snakehead fish family, Channidae. The relationship between them is close enough that they can mate; however, their temperature adaptability is quite different. RESULTS: In this study, we sequenced and assembled the whole genomes of C. argus and C. maculata and obtained chromosome-level genome assemblies of 630.39 and 618.82 Mb, respectively. Contig N50 was 13.20 and 21.73 Mb, and scaffold N50 was 27.66 and 28.37 Mb, with 28,054 and 24,115 coding genes annotated for C. argus and C. maculata, respectively. Our analyses showed that C. argus and C. maculata have 24 and 21 chromosomes, respectively. Three pairs of chromosomes in C. argus correspond to 3 chromosomes in C. maculata, suggesting that 3 chromosomal fusion events occurred in C. maculata. Comparative analysis of their gene families showed that some immune-related genes were unique or expandable to C. maculata, such as genes related to herpes simplex infection. Analysis of the transcriptome differences related to temperature adaptation revealed that the brain and liver of C. argus rapidly produced more differentially expressed genes than C. maculata. Genes in the FoxO signalling pathway were significantly enriched in C. argus during the cooling process (P < 0.05), and the expression of 3 transcription factor genes in this pathway was significantly different between C. argus and C. maculata (P < 0.01). CONCLUSIONS: C. maculata may have higher resistance to certain diseases, whereas C. argus has a faster and stronger response to low-temperature stress and thus has better adaptability to a low-temperature environment. This study provides a high-quality genome research platform for follow-up studies of Channidae and provides important clues regarding differences in the low-temperature adaptations of fish.

BACKGROUND: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. RESULTS: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. CONCLUSIONS: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? RESULTS: Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. CONCLUSION: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.

BACKGROUND: High-quality genomic resources facilitate investigations into behavioral ecology, morphological and physiological adaptations, and the evolution of genomic architecture. Lizards in the genus Sceloporus have a long history as important ecological, evolutionary, and physiological models, making them a valuable target for the development of genomic resources. FINDINGS: We present a high-quality chromosome-level reference genome assembly, SceUnd1.0 (using 10X Genomics Chromium, HiC, and Pacific Biosciences data), and tissue/developmental stage transcriptomes for the eastern fence lizard, Sceloporus undulatus. We performed synteny analysis with other snake and lizard assemblies to identify broad patterns of chromosome evolution including the fusion of micro- and macrochromosomes. We also used this new assembly to provide improved reference-based genome assemblies for 34 additional Sceloporus species. Finally, we used RNAseq and whole-genome resequencing data to compare 3 assemblies, each representing an increased level of cost and effort: Supernova Assembly with data from 10X Genomics Chromium, HiRise Assembly that added data from HiC, and PBJelly Assembly that added data from Pacific Biosciences sequencing. We found that the Supernova Assembly contained the full genome and was a suitable reference for RNAseq and single-nucleotide polymorphism calling, but the chromosome-level scaffolds provided by the addition of HiC data allowed synteny and whole-genome association mapping analyses. The subsequent addition of PacBio data doubled the contig N50 but provided negligible gains in scaffold length. CONCLUSIONS: These new genomic resources provide valuable tools for advanced molecular analysis of an organism that has become a model in physiology and evolutionary ecology.