DNA Datasets Research Articles

Clinal variation in autosomal satellite DNA clusters across a contact zone in Barker Frogs.

Contact zones between genetically divergent lineages offer a unique opportunity to explore gene flow and speciation dynamics. Because satellite DNAs (satDNAs) have high evolutionary rates, they may be useful for comparing related taxa and assessing contact zones. Here, we analyzed the distribution of chromosomal clusters of PcP190 satDNA across a contact zone between two distinct genetic lineages of a Neotropical species complex of frogs. The parental lineages, Physalaemus ephippifer and lineage 1B (L1B), exhibited pronounced divergence in the number of PcP190 clusters. We further expanded the geographic scope of this species complex by including two additional Brazilian localities in previously available cytogenetic and mitochondrial DNA datasets. The contact zone exhibited remarkable variation in the chromosomal distribution of PcP190 clusters among the autosomes; the only fixed autosomal site was found on chromosome 3. The highest numbers of PcP190 clusters were observed in specimens collected at sites near (approximately 320 km from) the distribution of L1B, whereas specimens from Dom Eliseu, which is near (approximately 330 km from) the distribution of P. ephippifer, had the lowest numbers of such clusters. Mitochondrial haplotypes also exhibited geographical variation across sites. Our findings expand the known contact zone of these Physalaemus lineages from 1,500 km2 to over 6,200 km2, demonstrating its extensive area, and emphasize the usefulness of satDNAs in studying contact zones.

Elliptic geometry-based kernel matrix for improved biological sequence classification

Protein sequence classification plays a pivotal role in bioinformatics as it enables the comprehension of protein functions and their involvement in diverse biological processes. While numerous machine learning models have been proposed to tackle this challenge, traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences. These limitations stem from operating within high-dimensional non-Euclidean spaces. To address this issue, we introduce the application of the elliptic geometry-based approach for protein sequence classification. First, we transform the problem in elliptic geometry and integrate it with the Gaussian kernel to map the problem into the Mercer kernel. The Gaussian-Elliptic approach allows for the implicit mapping of data into a higher-dimensional feature space, enabling the capture of complex nonlinear relationships. This feature becomes particularly advantageous when dealing with hierarchical or tree-like structures commonly encountered in biological sequences. Experimental results highlight the effectiveness of the proposed model in protein sequence classification, showcasing the advantages of utilizing elliptic geometry in bioinformatics analyses. It outperforms state-of-the-art methods by achieving 76% and 84% accuracies for DNA and Protein datasets, respectively. Furthermore, we provide theoretical justifications for the proposed model. This study contributes to the burgeoning field of geometric deep learning, offering insights into the potential applications of elliptic representations in the analysis of biological data.

Phylogeny, biogeography and morphological evolution of the treehopper‐like leafhoppers (Hemiptera: Cicadellidae) Megophthalminae and Ulopinae

AbstractRecent phylogenetic analyses of anchored‐hybrid, transcriptomic and morphological data have consistently recovered a clade comprising the three previously recognized families of treehoppers (Hemiptera), Aetalionidae, Melizoderidae and Membracidae, as well as two groups traditionally included in the leafhopper family Cicadellidae as subfamilies Megophthalminae and Ulopinae. To reconstruct the phylogeny of these two groups of treehopper‐like leafhoppers, maximum likelihood and multi‐species coalescent analyses were performed on a molecular DNA dataset consisting of ~700 anchored hybrid loci representing 84 terminal taxa. Analyses based on different dataset subsets and approaches yielded largely congruent topologies, although the relationships among Megophthalminae, Ulopinae and treehoppers are still unstable. The monophyly of both subfamilies is strongly supported, but several tribes, including Agalliini, Cephalelini, Megophthalmini and Ulopini, are recovered as non‐monophyletic. The origin of Megophthalminae and Ulopinae was estimated as early Cretaceous (~140 million years ago), and the divergence within each subfamily began in the mid‐Cretaceous. Continental‐scale biogeographic structure is evident in these two groups, with genera occurring on the same continent tending to group together regardless of tribal placement, suggesting that extensive morphological convergence occurred among faunas inhabiting different regions. Ancestral microhabitat reconstruction suggested that megophthalmine and ulopine leafhoppers originally lived on trees or shrubs and later several groups evolved independently to inhabit leaf litter and soil. Convergent modifications of the ocelli, forewings and hindwings accompanied changes in microhabitat preference.

Multiplexed Random Access Approach to DNA Microspheres for High‐Capacity Data Storage

AbstractWith the rapid growth of modern information data, DNA has emerged as a novel medium for data storage due to its durability, replicability, sustainability, and high density. This study focuses on the compatibility, high capacity, and random access of DNA data storage systems. A calcium phosphate (CaP) mineralized fluorescent SiO2 DNA (FSD@CaP) microspheres is developed by encapsulating fluorescence‐labeled encoding DNA files onto the surface of the SiO2 microspheres that are intrinsically sized and fluorescent as addressed two‐dimensional (2D) barcodes. The various sizes of SiO2 and various fluorescence‐labeled encoding DNA files forming 2D barcodes of FSD@CaP. The 2D barcodes enhance the system's compatibility while allowing multiplexed random access to DNA files. The high‐capacity DNA storage permits effective single access to the DNA file using a minimal number of FSD@CaP microspheres, without impacting the matrix of the system. Ultimately, random access to arbitrary DNA files within a complex pool of distinct 2D barcode‐tagged FSD@CaP microspheres is demonstrated by utilizing a flow cytometer for multiplexed sorting. Consequently, the strategy of this storage system provides a scalable concept for compatibility and multiplexed random access to DNA data sets.

Genetic Structure of Monochamus alternatus (Hope) in Qinling-Daba Mountains and Expansion Trend: Implications for Pest Prevention and Management.

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, severely threatens global pine forests. Monochamus alternatus is the primary vector of B. xylophilus in East Asia. Understanding the population structure and evolutionary forces of vector insects is critical for establishing effective PWD management strategies. The present work explores the genetic structure and phylogenetic relationships of 20 populations of M. alternatus from the Qinling-Daba Mountains (QDM) in China using the mitochondria DNA dataset, supplemented by ecological niche modeling (ENM). All M. alternatus populations were categorized into three phylogeographic clusters (Clade A, Clade B, and Clade C), with Clade A and Clade B corresponding to the western and eastern QDM, respectively. The results of divergence time estimation concur with environmental changes induced by Quaternary glacial climate oscillations in QDM of China. M. alternatus populations exhibited significant genetic differentiation, with expansion in their population size. Ecological niche modeling (ENM) demonstrated that precipitation and temperature significantly influence the distribution of M. alternatus and the species is anticipated to grow into higher latitude and higher altitude regions in the future. In a nutshell, exploring the genetic structure and evolutionary dynamics of M. alternatus can provide valuable insights into the prevention and occurrence of B. xylophilus. These findings also serve as a reference for research on population differentiation and phylogeography of other species in QDM and adjacent areas.

A hybrid approach of ensemble learning and grey wolf optimizer for DNA splice junction prediction.

DNA splice junction classification is a crucial job in computational biology. The challenge is to predict the junction type (IE, EI, or N) from a given DNA sequence. Predicting junction type is crucial for understanding gene expression patterns, disease causes, splicing regulation, and gene structure. The location of the regions where exons are joined, and introns are removed during RNA splicing is very difficult to determine because no universal rule guides this process. This study presents a two-layer hybrid approach inspired by ensemble learning to overcome this challenge. The first layer applies the grey wolf optimizer (GWO) for feature selection. GWO's exploration ability allows it to efficiently search a vast feature space, while its exploitation ability refines promising areas, thus leading to a more reliable feature selection. The selected features are then fed into the second layer, which employs a classification model trained on the retrieved features. Using cross-validation, the proposed method divides the DNA splice junction dataset into training and test sets, allowing for a thorough examination of the classifier's generalization ability. The ensemble model is trained on various partitions of the training set and tested on the remaining held-out fold. This process is performed for each fold, comprehensively evaluating the classifier's performance. We tested our method using the StatLog DNA dataset. Compared to various machine learning models for DNA splice junction prediction, the proposed GWO+SVM ensemble method achieved an accuracy of 96%. This finding suggests that the proposed ensemble hybrid approach is promising for DNA splice junction classification. The implementation code for the proposed approach is available at https://github.com/EFHamouda/DNA-splice-junction-prediction.

Mitochondrial phylogenomics reveals the sister relationship between the endogean Mediterranean raymondionymine weevils and the remaining 51,000+ Curculionidae (Coleoptera)

The tribe Raymondionymini has long been neglected in phylogenetic studies. The tribe is characterized by uncertain monophyly, fluctuating taxonomic status, and a composition prone to instability. All raymondionymine weevils are wingless and have eyes either completely absent or, rarely, consisting of a single ommatidium. With body lengths predominantly below three millimeters, they inhabit deep soil environments and are infrequently collected. The core of this tribe comprises nine genera distributed in Europe and around the Mediterranean region and encompassing 76 species, while six additional genera include 17 species distributed in USA (California), Mexico, Ecuador, Venezuela, Russian Far East, and Madagascar. Here, we present eight new mitogenomes, complemented by one publicly available, encompassing all but two Mediterranean genera of raymondionymine weevils. We used publicly available Curculionoidea mitogenomes to compile an all-inclusive dataset with 391 terminals and a reduced dataset with 61 terminals representing main families of Curculionoidea and subfamilies within Curculionidae. Our maximum likelihood and Bayesian phylogenetic analyses, employing both DNA and amino acids datasets under alternative partition schemes, consistently produced congruent phylogenies. Our results show that the Mediterranean raymondionymines form a strongly supported clade, and their easternmost and morphologically distinct genus Ubychia is sister to the rest of them. Most notably, our results consistently recover a sister relationship between the clade of Mediterranean raymondionymine weevils and a clade encompassing all remaining Curculionidae. Consequently, we propose a revision of weevil taxonomy: (i) Our target group is removed from the non-monophyletic subfamily Brachycerinae; (ii) this clade is resurrected to its former subfamily level within Curculionidae, as the subfamily Raymondionyminaestat. rev; (iii) the nine Mediterranean genera Alaocephala, Alaocyba, Coiffaitiella, Derosasius, Ferreria, Raymondiellus, Raymondionymus, Tarattostichus, and Ubychia compose Raymondionyminaestat. rev; (iv) and non-Mediterranean genera Alaocybites, Bordoniola, Gilbertiola, Homosomus, Neoubychia, and Schizomicrus are considered as “incertae sedis” pending further phylogenetic corroboration. We hypothesize that the remaining Brachycerinae and the non-Mediterranean representatives within Raymondionyminae constitute a series of species-poor early-diverging lineages representing currently unrecognized subfamilies of Curculionidae.

Nonlinear Analysis of Machine Learning Applicability for Survival Analysis of Lung Cancer Patients

This study about looks into how nonlinear machine learning (ML) can be utilized to foresee the survival of lung cancer patients in arrange to create expectations more exact and make strides understanding comes about. Lung cancer is still one of the foremost deadly types of cancer within the world, so better approaches ought to be found to precisely analyze and arrange medicines. Whereas conventional measurements strategies have made a difference us get it survival examination, they aren't continuously great at catching the complicated, nonlinear connections that exist in organic information. The think about looks at how to combine diverse machine learning models, like Irregular Woodlands, Slope Boosting Machines, and Neural Systems, to see at nonlinear designs and connections in DNA and clinical datasets of lung cancer cases. A part of consideration is paid to methods for choosing highlights and utilizing progressed arrangement strategies to bargain with expansive sums of information and lower clamor. We utilize strict cross-validation strategies and comparison investigation to check how well ML models work compared to conventional factual strategies. To see how well a demonstrate can anticipate how long a understanding will live, measurements just like the Concordance File, Brier Score, and Log-Rank Test are utilized. The comes about appear that machine learning models, particularly those that are great at recognizing nonlinearities, do much way better than standard strategies, making gauges that are more exact and dependable. The comes about appear that machine learning has the ability to alter the way survival analysis is worn out cancer by giving personalized data around forecast and making it easier to select centered treatments. Within the future, analysts will center on combining information from numerous omics and making machine learning models that can be caught on. This will make these progressed examination devices indeed more valuable in clinical settings and boost believe in them. This work includes to the developing sum of information that machine learning is an vital device for moving forward cancer inquire about and quiet care.

Does cytomegalovirus infection and antiviral therapy affect prognosis of biliary atresia? A real-world retrospective cohort study.

To explore the impacts of cytomegalovirus (CMV) infection and antiviral treatment (AVT) on native liver survival (NLS) in biliary atresia (BA) infants. This retrospective cohort study included infants diagnosed as BA between January 2015 and December 2021 at Hunan Children's Hospital. CMV infection was defined by DNA polymerase chain reaction alone (DNA data set) and combination of DNA and immunoglobulin M (CMV data set). In the DNA data set of 330 patients, 234 patients (70.9%) survived with their native liver in 2 years, with 113 (73.9%) in the DNA- cohort, 70 (65.4%) in the DNA+ andAVT- cohort and 51 (72.9%) in the DNA+ andAVT+ cohort, without significant differences by log-rank tests. In patients administrated between 2015 and March 2019, there were 206 evaluable patients in the DNA data set, with rates of 5-year NLS of 68.3% in the DNA- cohort, similar to that in the DNA+ andAVT+ cohort (62.2%, p = 0.546), but significantly higher than that in the DNA+ andAVT- cohort (51.4%, p = 0.031). Similar trends were also observed in the CMV data set, although statistically insignificant. CMV infection before or on the day of HPE can reduce the rate of 5-year NLS and AVT was recommended for CMV-infectedBA infants.

Pathogen dynamics and discovery of novel viruses and enzymes by deep nucleic acid sequencing of wastewater

Wastewater contains an extensive reservoir of genetic information, yet largely unexplored. Here, we analyzed by high-throughput sequencing total nucleic acids extracted from wastewater samples collected during a 17 month-period in Berlin, Germany. By integrating global wastewater datasets and applying a novel computational approach to accurately identify viral strains within sewage RNA-sequencing data, we demonstrated the emergence and global dissemination of a specific astrovirus strain. Astrovirus abundance and sequence variation mirrored temporal and spatial patterns of infection, potentially serving as footprints of specific timeframes and geographical locations. Additionally, we revealed more than 100,000 sequence contigs likely originating from novel viral species, exhibiting distinct profiles in total RNA and DNA datasets and including undescribed bunyaviruses and parvoviruses. Finally, we identified thousands of new CRISPR-associated protein sequences, including Transposase B (TnpB), a class of compact, RNA-guided DNA editing enzymes. Collectively, our findings underscore the potential of high-throughput sequencing of total nucleic acids derived from wastewater for a broad range of applications.

Unravelling reference bias in ancient DNA datasets.

The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.

Genomic insights into the critically endangered King Island scrubtit.

Small, fragmented or isolated populations are at risk of population decline due to fitness costs associated with inbreeding and genetic drift. The King Island scrubtit Acanthornis magna greeniana is a critically endangered subspecies of the nominate Tasmanian scrubtit A. m. magna, with an estimated population of < 100 individuals persisting in three patches of swamp forest. The Tasmanian scrubtit is widespread in wet forests on mainland Tasmania. We sequenced the scrubtit genome using PacBio HiFi and undertook a population genomic study of the King Island and Tasmanian scrubtits using a double-digest restriction site-associated DNA (ddRAD) dataset of 5,239 SNP loci. The genome was 1.48 Gb long, comprising 1,518 contigs with an N50 of 7.715Mb. King Island scrubtits formed one of four overall genetic clusters, but separated into three distinct subpopulations when analysed independently of the Tasmanian scrubtit. Pairwise FST values were greater among the King Island scrubtit subpopulations than among most Tasmanian scrubtit subpopulations. Genetic diversity was lower and inbreeding coefficients were higher in the King Island scrubtit than all except one of the Tasmanian scrubtit subpopulations. We observed crown baldness in 8/15 King Island scrubtits, but 0/55 Tasmanian scrubtits. Six loci were significantly associated with baldness, including one within the DOCK11 gene which is linked to early feather development. Contemporary gene flow between King Island scrubtit subpopulations is unlikely, with further field monitoring required to quantify the fitness consequences of its small population size, low genetic diversity and high inbreeding. Evidence-based conservation actions can then be implemented before the taxon goes extinct.

Phylogenetic position and plastid genome structure of Vietorchis, a mycoheterotrophic genus of Orchidaceae (subtribe Orchidinae) endemic to Vietnam

The orchid genus Vietorchis comprises three species, all discovered in the 21 century. Each of these species is achlorophyllous, mycoheterotrophic and is known to be endemic to Vietnam. The type species of the genus, V. aurea, occurs in a single location in northern Vietnam within a lowland limestone karstic area. Vietorchis furcata and V. proboscidea, in contrast, are confined to mountains of southern Vietnam, far away from any limestone formations. Taxonomic placement of Vietorchis remained uncertain for the reason of inconclusive morphological affinities. At the same time, the genus has never been included into molecular phylogenetic studies. We investigate the phylogenetic relationships of two species of Vietorchis (V. aurea and V. furcata) based on three DNA datasets: (1) a dataset comprising two nuclear regions, (2) a dataset comprising two plastid regions, and (3) a dataset employing data on the entire plastid genomes. Our phylogenetic reconstructions support the placement of Vietorchis into the subtribe Orchidinae (tribe Orchideae, subfamily Orchidoideae). This leads to a conclusion that the previously highlighted similarities in the rhizome morphology between Vietorchis and certain mycoheterotrophic genera of the subfamilies Epidendroideae and Vanilloideae are examples of a convergence. Vietorchis is deeply nested within Orchidinae, and therefore the subtribe Vietorchidinae is to be treated as a synonym of Orchidinae. In the obtained phylogenetic reconstructions, Vietorchis is sister to the photosynthetic genus Sirindhornia. Sirindhornia is restricted to limestone mountains, which allows to speculate that association with limestone karst is plesiomorphic for Vietorchis. Flower morphology is concordant with the molecular data in placing Vietorchis into Orchidinae and strongly supports the assignment of the genus to one of the two major clades within this subtribe. Within this clade, however, Vietorchis shows no close structural similarity with any of its genera; in particular, the proximity between Vietorchis and Sirindhornia has never been proposed. Finally, we assembled the plastid genome of V. furcata, which is 65969 bp long and contains 45 unique genes, being one of the most reduced plastomes in the subfamily Orchidoideae. The plastome of Vietorchis lacks any rearrangements in comparison with the closest studied autotrophic species, and possesses substantially contracted inverted repeats. No signs of positive selection acting on the protein-coding plastid sequences were detected.

Phylogenetic insights into the genetic legacies of Hungarian-speaking communities in the Carpathian Basin

This study focuses on exploring the uniparental genetic lineages of Hungarian-speaking minorities residing in rural villages of Baranja (Croatia) and the Zobor region (Slovakia). We aimed to identify ancestral lineages by examining genetic markers distributed across the entire mitogenome and on the Y-chromosome. This allowed us to discern disparities in regional genetic structures within these communities. By integrating our newly acquired genetic data from a total of 168 participants with pre-existing Eurasian and ancient DNA datasets, our goal was to enrich the understanding of the genetic history trajectories of Carpathian Basin populations. Our findings suggest that while population-based analyses may not be sufficiently robust to detect fine-scale uniparental genetic patterns with the sample sizes at hand, phylogenetic analysis of well-characterized Y-chromosomal Short Tandem Repeat (STR) data and entire mitogenome sequences did uncover multiple lineage ties to far-flung regions and eras. While the predominant portions of both paternal and maternal DNA align with the East-Central European spectrum, rarer subhaplogroups and lineages have unveiled ancient ties to both prehistoric and historic populations spanning Europe and Eastern Eurasia. This research augments the expansive field of phylogenetics, offering critical perspectives on the genetic constitution and heritage of the communities in East-Central Europe.

Listening to life: Sonification for enhancing discovery in biological research.

Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

Accurate identification of protein nucleic acid binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large scale protein language model. Specifically, our multi modal approach leverages a contrastive learning technique and atom wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state of the art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1 Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.

Global conservation genomics of blue whales calls into question subspecies taxonomy and refines knowledge of population structure

AbstractBlue whales (Balaenoptera musculus) are the largest living animal and, like other baleen whales, became endangered due to whaling. Here, we used population genomics to infer the number, distribution and other characteristics of subspecies and populations. We used the largest DNA dataset in blue whales, both in terms of genomic markers (16,661 SNPs and mtDNA) and geographic coverage (n = 276 for SNPs; n = 531 for mtDNA). We found greatest divergence among the eastern Pacific, Indo‐western Pacific and Antarctic blue whales. There were indications that natural selection in different environments promoted divergence among these groupings. Within these regions, there was divergence between the eastern North and eastern South Pacific, and among the eastern Indian Ocean, the western South Pacific and the northern Indian Ocean. There was no divergence within the Antarctic. These findings are consistent with the current classification of Antarctic and Indo‐western Pacific blue whales in the Southern Hemisphere as different subspecies but call into question the subspecies taxonomy of eastern Pacific blue whales. The study shows that opposite breeding seasons on either side of the equator do not necessarily inhibit connectivity across the equator, and reinforces that population structure needs to be well understood to conserve the diversity within species.

Population genetics reveals new introgression in the nucleus herd of min pigs.

Min pigs are a unique genetic resource among local pig breeds in China. They have more excellent characteristics in cold and stress resistance, good meat quality, and a high reproductive rate. However, the genetic structure and driving factors remain unclear in the nucleus herd. In this study, the genetic diversity of Min pigs was studied to reveal the formation mechanism of its unique genetic structure. We hope to protect and develop the genetic resources of Min pigs. We analyzed different types of genes to identify the genetic structure and gene introgression pattern of Min pigs. The nuclear DNA dataset includes information on 21 microsatellite loci and 6 Y-chromosome genes, and the mitochondrial D-loop gene is selected to represent maternal lineages. The above genes are all from the nucleus herd of Min pigs. The results of genetic structure identification and analysis of potential exogenous gene introgression patterns indicate that the nucleus herd of Min pigs maintains a high level of genetic diversity (polymorphism information content = 0.713, expected heterozygosity = 0.662, observed heterozygosity = 0.612). Compared with other Asian pig breeds, the formation of Min pig breeds is more special. Gene introgression from European pig breeds to Min pigs has occurred, which is characterized by complete introgression of paternal genes and incomplete introgression of maternal genes. Gene introgression caused by cross-breeding is not the main factor leading to the formation of the current genetic structure of Min pigs, but this process has increased the level of genetic diversity in the nucleus herd. Compared with the influence of gene introgression, our research suggest that artificial selection and environmental adaptive evolution make Min pigs form unique genetic characteristics.