Convergent dwarfism consequences of minipigs under independent artificial selections.

BACKGROUND: Currently, diverse minipigs have acquired a common dwarfism phenotype through independent artificial selections. Characterizing the population and genetic diversity in minipigs is important to unveil genetic mechanisms regulating their body sizes and effects of independent artificial selections on those genetic mechanisms. However, full understanding for the genetic mechanisms and phenotypic consequences in minipigs still lag behind. RESULTS: Here, using whole genome sequencing data of 41 pig breeds, including eight minipigs, we identified a large genomic diversity in a minipig population compared to other pig populations in terms of population structure, demographic signatures, and selective signatures. Selective signatures reveal diverse biological mechanisms related to body size in minipigs. We also found evidence for neural development mechanism as a minipig-specific body size regulator. Interestingly, selection signatures within those mechanisms containing neural development are also highly different among minipig breeds. Despite those large genetic variances, PLAG1, CHM, and ESR1 are candidate key genes regulating body size which experience different differentiation directions in different pig populations. CONCLUSIONS: These findings present large variances of genetic structures, demographic signatures, and selective signatures in the minipig population. They also highlight how different artificial selections with large genomic diversity have shaped the convergent dwarfism.

A chromosome-level genome assembly of the Korean minipig (Sus scrofa).

Recent advancements in sequencing and genome assembly technologies have led to rapid generation of high-quality genome assemblies for various species and breeds. Despite the importance as minipigs an animal model in biomedical research, the construction of high-quality genome assemblies of minipigs still lags behind other pig breeds. To address this problem, we constructed a high-quality chromosome-level genome assembly of the Korean minipig (KMP) utilizing multiple different types of sequencing reads and reference genomes. The KMP assembly included 19 chromosome-level sequences with a total length of 2.52 Gb and N50 of 137 Mb. Comparative analyses with the pig reference genome (Sscrofa11.1) demonstrated comparable contiguity and completeness of the KMP assembly. Additionally, genome annotation analyses identified 22,666 protein-coding genes and repetitive elements occupying 40.10% of the genome. The KMP assembly and genome annotation provide valuable resources that can contribute to various future research on minipig and other pig breeds.

Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies.

BACKGROUND: Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. RESULTS: By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. CONCLUSIONS: Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

A chromosome-level genome assembly of the Korean crossbred pig Nanchukmacdon (Sus scrofa).

As plentiful high-quality genome assemblies have been accumulated, reference-guided genome assembly can be a good approach to reconstruct a high-quality assembly. Here, we present a chromosome-level genome assembly of the Korean crossbred pig called Nanchukmacdon (the NCMD assembly) using the reference-guided assembly approach with short and long reads. The NCMD assembly contains 20 chromosome-level scaffolds with a total size of 2.38 Gbp (N50: 138.77 Mbp). Its BUSCO score is 93.1%, which is comparable to the pig reference assembly, and a total of 20,588 protein-coding genes, 8,651 non-coding genes, and 996.14 Mbp of repetitive elements are annotated. The NCMD assembly was also used to close many gaps in the pig reference assembly. This NCMD assembly and annotation provide foundational resources for the genomic analyses of pig and related species.

Reference-based read clustering improves the de novo genome assembly of microbial strains.

Constructing accurate microbial genome assemblies is necessary to understand genetic diversity in microbial genomes and its functional consequences. However, it still remains as a challenging task especially when only short-read sequencing technologies are used. Here, we present a new read-clustering algorithm, called RBRC, for improving de novo microbial genome assembly, by accurately estimating read proximity using multiple reference genomes. The performance of RBRC was confirmed by simulation-based evaluation in terms of assembly contiguity and the number of misassemblies, and was successfully applied to existing fungal and bacterial genomes by improving the quality of the assemblies without using additional sequencing data. RBRC is a very useful read-clustering algorithm that can be used (i) for generating high-quality genome assemblies of microbial strains when genome assemblies of related strains are available, and (ii) for upgrading existing microbial genome assemblies when the generation of additional sequencing data, such as long reads, is difficult.

Generation and application of pseudo-long reads for metagenome assembly.

BACKGROUND: Metagenomic assembly using high-throughput sequencing data is a powerful method to construct microbial genomes in environmental samples without cultivation. However, metagenomic assembly, especially when only short reads are available, is a complex and challenging task because mixed genomes of multiple microorganisms constitute the metagenome. Although long read sequencing technologies have been developed and have begun to be used for metagenomic assembly, many metagenomic studies have been performed based on short reads because the generation of long reads requires higher sequencing cost than short reads. RESULTS: In this study, we present a new method called PLR-GEN. It creates pseudo-long reads from metagenomic short reads based on given reference genome sequences by considering small sequence variations existing in individual genomes of the same or different species. When applied to a mock community data set in the Human Microbiome Project, PLR-GEN dramatically extended short reads in length of 101 bp to pseudo-long reads with N50 of 33 Kbp and 0.4% error rate. The use of these pseudo-long reads generated by PLR-GEN resulted in an obvious improvement of metagenomic assembly in terms of the number of sequences, assembly contiguity, and prediction of species and genes. CONCLUSIONS: PLR-GEN can be used to generate artificial long read sequences without spending extra sequencing cost, thus aiding various studies using metagenomes.

DLEB: a web application for building deep learning models in biological research.

Deep learning has been applied for solving many biological problems, and it has shown outstanding performance. Applying deep learning in research requires knowledge of deep learning theories and programming skills, but researchers have developed diverse deep learning platforms to allow users to build deep learning models without programming. Despite these efforts, it is still difficult for biologists to use deep learning because of limitations of the existing platforms. Therefore, a new platform is necessary that can solve these challenges for biologists. To alleviate this situation, we developed a user-friendly and easy-to-use web application called DLEB (Deep Learning Editor for Biologists) that allows for building deep learning models specialized for biologists. DLEB helps researchers (i) design deep learning models easily and (ii) generate corresponding Python code to run directly in their machines. DLEB provides other useful features for biologists, such as recommending deep learning models for specific learning tasks and data, pre-processing of input biological data, and availability of various template models and example biological datasets for model training. DLEB can serve as a highly valuable platform for easily applying deep learning to solve many important biological problems. DLEB is freely available at http://dleb.konkuk.ac.kr/.

Integration of multi-omics approaches for functional characterization of muscle related selective sweep genes in Nanchukmacdon.

Pig as a food source serves daily dietary demand to a wide population around the world. Preference of meat depends on various factors with muscle play the central role. In this regards, selective breeding abled us to develop "Nanchukmacdon" a pig breeds with an enhanced variety of meat and high fertility rate. To identify genomic regions under selection we performed whole-genome resequencing, transcriptome, and whole-genome bisulfite sequencing from Nanchukmacdon muscles samples and used published data for three other breeds such as Landrace, Duroc, Jeju native pig and analyzed the functional characterization of candidate genes. In this study, we present a comprehensive approach to identify candidate genes by using multi-omics approaches. We performed two different methods XP-EHH, XP-CLR to identify traces of artificial selection for traits of economic importance. Moreover, RNAseq analysis was done to identify differentially expressed genes in the crossed breed population. Several genes (UGT8, ZGRF1, NDUFA10, EBF3, ELN, UBE2L6, NCALD, MELK, SERP2, GDPD5, and FHL2) were identified as selective sweep and differentially expressed in muscles related pathways. Furthermore, nucleotide diversity analysis revealed low genetic diversity in Nanchukmacdon for identified genes in comparison to related breeds and whole-genome bisulfite sequencing data shows the critical role of DNA methylation pattern in identified genes that leads to enhanced variety of meat. This work demonstrates a way to identify the molecular signature and lays a foundation for future genomic enabled pig breeding.