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We expand existing clean sampling procedures using controlled artificial ice-core experiments and adapted previously established low-biomass metagenomic approaches to study glacier-ice viruses. Controlled sampling experiments drastically reduced mock contaminants including bacteria, viruses, and free DNA to background levels. Amplicon sequencing from eight depths of two Tibetan Plateau ice cores revealed common glacier-ice lineages including Janthinobacterium, Polaromonas, Herminiimonas, Flavobacterium, Sphingomonas, and Methylobacterium as the dominant genera, while microbial communities were significantly different between two ice cores, associating with different climate conditions during deposition. Separately, 355- and 14,400-year-old ice were subject to viral enrichment and low-input quantitative sequencing, yielding genomic sequences for 33 vOTUs. These were virtually all unique to this study, representing 28 novel genera and not a single species shared with 225 environmentally diverse viromes. Further, 42.4% of the vOTUs were identifiable temperate, which is significantly higher than that in gut, soil, and marine viromes, and indicates that temperate phages are possibly favored in glacier-ice environments before being frozen. In silico host predictions linked 18 vOTUs to co-occurring abundant bacteria (Methylobacterium, Sphingomonas, and Janthinobacterium), indicating that these phages infected ice-abundant bacterial groups before being archived. Functional genome annotation revealed four virus-encoded auxiliary metabolic genes, particularly two motility genes suggest viruses potentially facilitate nutrient acquisition for their hosts. Finally, given their possible importance to methane cycling in ice, we focused on Methylobacterium viruses by contextualizing our ice-observed viruses against 123 viromes and prophages extracted from 131 Methylobacterium genomes, revealing that the archived viruses might originate from soil or plants.
Pigs are one of the main sources of animal protein for humans and are commonly used for biomedical research. Over the past 9000 years, various pig breeds have been produced through natural and artificial selection [1]. According to a recent report of the Food and Agriculture Organization of the United Nations, there are currently approximately 600 pig breeds worldwide, most of which are found in Asia and Europe [2]. Breed identification of pigs is crucial for breed conservation, sustainable breeding, pork traceability, and local resource registration. Local pig breeds represent an important genetic resource with considerable genetic variability, however, most of these breeds are at risk of extinction because of a multitude of challenges, including emerging diseases, climate change, and competition from international commercial breeds [3]. Accurate breed identification is the premise of undertaking measures to alleviate such trends. Furthermore, the increasingly abundant whole-genome sequence (WGS) and single nucleotide polymorphism (SNP) chip datasets that are available in curated databases represent important resources to characterize the genetic diversity of pigs [4].
Three pig SNP chip datasets were downloaded from the Dryad Digital Repository ( ) [15] and the Figshare database ( ) [16, 17]. Breeds with less than ten individuals were removed. Table 1 shows a summary of these datasets. The three datasets were merged and SNPs with physical locations on the sex chromosomes and with minor allele frequencies lower than 0.01 were removed. The software Beagle (v5.2) [18] was used to impute sparse missing genotypes using default parameters. Crossbreds were removed and served as an independent testing set in downstream analyses. The admixture (v1.3.0) software [19] was used to investigate the genetic structure of all animals included in the chip reference dataset. Animals with admixture profiles that differed markedly from those of other animals from the same breed were removed and the remaining samples were considered purebred. Since the downloaded raw data were based on the 10.2 Sus scrofa assembly, we also generated a chip reference dataset based on the 11.1 Sus scrofa assembly [20]. A WGS dataset from the Pig Haplotype Reference Panel (PHARP) [21] was also included in this analysis. PHARP is a free genotype imputation service that comprises various pig breeds from across the globe. SNPs in the PHARP data were generated using the GATK pipeline [22] with the 11.1 Sus scrofa assembly as the reference genome.
Breed identification is a qualitative analysis, while GBC analysis is quantitative, which makes it more complex and requires detailed prior knowledge (all potential ancestral breeds) of candidate individuals. Because of this, iDIGs currently does not include a function for GBC analysis with the linear model. In addition, because GBC analyses are based on allele frequencies of breeds, sample sizes in our reference database are too small for some breeds to calculate accurate allele frequencies. In addition, because natural and artificial selection can result in multiple strains of a given breed, allele frequencies calculated from individuals in public databases may be less representative for some populations. We did attempt GBC analyses for some crossbreds, but obtained no reliable results. Since we are not sure about the true pedigree of the crossbreds (which were downloaded from public databases), we did not include these results here. To eliminate the bias introduced by public data, we designed an R package ( ) to compile reference data from user-provided data. The procedure for the construction of the reference database includes removing outliers, removing relatives, and selection of breed informative markers. This also allows users to perform GBC analysis with multiple algorithms based on their own reference data.
Data Availability: Most of the data used in this paper are freely available and downloadable from the web. Data on IUCN threat status are available in the IUCN Red List database ( ). RLS data for species lists and some trait information are available through an online portal accessible through ( ), with additional trait data available on request by using the contact form. For each species, we provide aesthetic values predicted in the present study (S3 Data) and web links to original photographic material (S4 Data). Images free of copyright can be provided on request. Other datasets used in this study (extraction from the online survey and images features analysis) and all code used for the analysis and figures are available from the GitHub Repository: _AESTHE.
Most of the data used in this paper are freely available and downloadable from the web. Data on IUCN threat status are available in the IUCN Red List database ( ). RLS data for species lists and some trait information are available through an online portal accessible through ( ), with additional trait data available on request by using the contact form. For each species, we provide aesthetic values predicted in the present study (S3 Data) and web links to original photographic material (S4 Data). Images free of copyright can be provided on request. Other datasets used in this study (extraction from the online survey and images features analysis) and all code used for the analysis and figures are available from the GitHub Repository: _AESTHE. 59ce067264
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