cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/102584
title: Supporting data for "De novo assembly and characterization of a highly degenerated ZW sex chromosome in the fish <i>Megaleporinus macrocephalus</i>"

description: <i>Megaleporinus macrocephalus</i> (piauçu) is a Neotropical fish within Characoidei that presents a well-established heteromorphic ZZ/ZW sex-determination system and thus, constitutes a good model for studying W and Z chromosomes in fishes. We used PacBio reads and Hi-C to assemble a chromosome-level reference genome for <i>M. macrocephalus</i>. We generated family segregation information to construct a genetic map, pool-seq of males and females to characterize its sex system, and RNA-seq to highlight candidate genes of <i>M. macrocephalus</i> sex determination. <br>The <i>M. macrocephalus</i> reference genome is 1,282,030,339 bp in length and has a contig and scaffold N50 of 5.0 Mb and 45.03 Mb, respectively. Based on patterns of recombination suppression, coverage, Fst, and sex-specific SNPs, three major regions were distinguished in the sex chromosome: W-specific (highly differentiated), Z-specific (in degeneration), and PAR. The sex chromosome gene repertoire was composed of genes from the TGF- family (<i>amhr2, bmp7</i>) and Wnt/-catenin pathway (<i>wnt4, wnt7a</i>), and some of them were differentially expressed. <br>The chromosome-level genome of piauçu exhibits high quality, establishing a valuable resource for advancing research within the group. Our discoveries offer insights into the evolutionary dynamics of Z and W sex chromosomes in fish, emphasizing ongoing degenerative processes and indicating complex interactions between Z and W sequences in specific genomic regions. Notably, <i>amhr2</i> and <i>bmp7</i> are potential candidate genes for sex determination in <i>M. macrocephalus</i>.
DOI: 10.5524/102587
title: Supporting data for "AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data"

description: The vast number of viral genome sequences generated during the latest pandemic has presented new challenges for computational analysis. Analyzing millions of viral genomes in multi-FASTA format is computationally demanding, especially when using alignment-based methods. Most existing methods are not designed to handle such large datasets, often requiring the analysis to be divided into smaller parts to obtain results using available computational resources.<br>We introduce AltaiR, a toolkit for analyzing multiple sequences in multi-FASTA format using exclusively alignment-free methodologies. AltaiR enables the identification of singularity and similarity patterns within sequences and computes static and temporal dynamics without restrictions on the number or size of input sequences. It automatically filters low-quality, biased, or deviant data. We demonstrate AltaiRs capabilities by analyzing more than 1.5 million full SARS-CoV-2 sequences, revealing interesting observations regarding viral genome characteristics over time, such as shifts in nucleotide composition, decreases in average Kolmogorov sequence complexity, and the evolution of the smallest sequences not found in the human host.<br>AltaiR can identify temporal characteristics and trends in large numbers of sequences, making it ideal for scenarios involving endemic or epidemic outbreaks with vast amounts of available sequence data. Implemented in C with multi-threading and methodological optimizations, AltaiR is computationally efficient, flexible, and dependency-free. It accepts any sequence in FASTA format, including amino acid sequences. The complete toolkit is freely provided at https://github.com/cobilab/altair.
DOI: 10.5524/102589
title: Supporting data for "The chromosome-level genome assembly of an endangered herb Bergenia scopulosa provides insights into local adaptation and genomic vulnerability under climate change"

description: Global climate change poses severe threats to biodiversity and ecosystem stability. Rapid climate oscillations potentially lead to species geographic range shifts, population declines and even extinctions. The rare and endangered species, being critical components of regional biodiversity, hold the key to understanding local adaptation and evolutionary processes shaping species distributions. Therefore, assessing the evolutionary mechanisms of local adaptation and population vulnerability under climate change is crucial for developing conservation strategies of endangered species.br /In this study, we assembled a high-quality, chromosome-level genome of the rare and endangered herb iBergenia scopulosa/i in the Qinling Mountains in East Asia and re-sequenced 37 individual genomes spanning its entire geographic distributional ranges. By integrating population genetics, landscape genomics, and climate datasets, a substantial number of adaptive SNP loci associated with climate variables were identified. The genotype-environment association analysis showed that some cold-tolerant genes have played pivotal roles in cold environmental adaptation of iB. scopulosa/i. These findings are further corroborated through evolutionary analysis of gene family and qPCR validation. Population genomic analysis revealed two distinct genetic lineages in iB. scopulosa/i. The western lineage showed higher genomic vulnerability and more rare cold-tolerance alleles, suggesting its heightened sensitivity to impending climate shifts and should be given priority conservation in the management practices.br /These findings provide novel insights into local adaptation and genomic vulnerability of iB. scopulosa/i under climate change in the Qinling Mountains in East Asia. Additionally, the study also offers valuable guidance for formulating conservation strategies for the rare and endangered plants.
DOI: 10.5524/102594
title: Supporting data for "SMARTER-database: a tool to integrate SNP array datasets for sheep and goat breeds"

description: Underutilized sheep and goat breeds have the ability to adapt to challenging environments due to their genetic composition. Integrating publicly available genomic datasets with new data will facilitate genetic diversity analyses, however this process is complicated by the fact that data could refer to outdated assembly versions or encoded in different formats. Here we present the SMARTER-database, a collection of tools and scripts to standardize genomic data and metadata on global small ruminant populations with a focus on reproducibility. It harmonizes genotypes for about 12,000 sheep and 6,000 goats to a uniform coding and assembly version. Users can access this data via FTP and interact with it through a REST API, a web interface, or a custom R-package, enabling efficient filtering and selection of samples. These tools will empower researchers to focus on the crucial aspect of adaptation, leveraging the rich dataset provided by the SMARTER-database.<br> Datasets is composed by all samples imported with SMARTER project. However there are additional metadata that could be used to subset the dataset according user's need. Those data are made available to SMARTER Data Portal and SMARTER API. Samples IDs and breed code available through these interface are the same used in the genoypes dataset and could be used to subset data accordinag user needs.
DOI: 10.5524/102598
title: Supporting data for "Mutation Impact on mRNA Versus Protein Expression across Human Cancers"

description: Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely long- tail driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.
DOI: 10.5524/102600
title: Supporting data for "Near Telomere-to-Telomere Genome Assembly of Mongolian Cattle: Implications for Population Genetic Variation and Beef quality"

description: Mongolian cattle, a unique breed indigenous to China, represent valuable genetic resources and serve as important sources of meat and milk. However, there is a lack of high-quality genomes in cattle, which limits biological research and breeding improvement. <br>In this study, we conducted whole-genome sequencing on a Mongolian bull. This effort yielded a 3.1 Gb Mongolian cattle genome sequence, with a BUSCO integrity assessment of 95.9%. The assembly achieved both Contig N50 and Scaffold N50 values of 110.9 Mb, with only 8 gaps identified across the entire genome. Additionally, we successfully assembled the Y chromosome among the 31 chromosomes. Notably, 3 chromosomes were identified as having telomeres at both ends. The annotation data includes 54.31% repetitive sequences, 29,794 coding genes. Furthermore, a population genetic variation analysis was conducted on 332 individuals from 56 breeds, through which we identified variant loci and potentially discovered genes associated with the formation of marbling patterns in beef, predominantly located on chromosome 12. <br>This study produced a genome with high continuity, completeness, and accuracy, marking the first assembly and annotation of a near telomere-to-telomere genome in cattle. Based on this, we generated a variant database comprising 332 individuals. The assembly of the genome and the analysis of population variants provide significant insights into cattle evolution and enhance our understanding of breeding selection.
DOI: 10.5524/102558
title: Supporting data for "The genetics of epigenetics in the bovine pangenome era"

description: Most DNA methylation studies have used a single reference genome with little attention paid to the bias introduced due to the reference chosen. Genetic variation, including single nucleotide polymorphism (SNPs) and structural variants (SVs), can lead to differences in methylation sites (CpGs) between individuals of the same species. We analysed whole genome bisulfite sequencing (WGBS) data from the fetal liver of Angus (<i>Bos taurus taurus</i>), Brahman (<i>Bos taurus indicus</i>) and reciprocally crossed samples. Using reference genomes for each breed from the Bovine Pangenome Consortium, we investigated the influence of reference genome choice on the breed- and parent-of-origin effects in methylome analyses. <br>Our findings revealed that about 75% of CpG sites were shared between Angus and Brahman, ~5% were breed-specific, and ~20% were unresolved. We demonstrated up to 2% quantification bias in global methylation when an incorrect reference genome was used. Furthermore, we found that SNPs and SVs were 8-fold (p-value &lt; 5×10^(-324)) and 1.13-fold (p-value &lt; 5×10^(-324)) higher in CpGs, respectively, compared to the rest of the genome. We found a poor association between differentially methylated regions (DMRs) and differentially expressed genes (DEGs) and suggest that DMRs may be impacting enhancers that target these DEGs. DMRs overlapped with imprinted genes, of which one, <i>Dgat1</i>, which is important for fat metabolism and weight gain, was found in the breed-specific and sire-of-origin comparisons. <br>This work demonstrates the need to consider reference genome effects to explore genetic and epigenetic differences accurately and identify DMRs involved in controlling certain genes.
DOI: 10.5524/102567
title: Chromosome-level genome assemblies of the littorinid marine snail <i>Littorina brevicula</i>

description: <i>Littorina brevicula</i> is a species of periwinkle sea snail, a marine gastropod mollusk in the family Littorinidae, which includes winkles or periwinkles, and is native to the Northwest Pacific. It ranges from Hong Kong to the southern Kurile Islands, Russia. In its native habitat it is found on rocky shores where it feeds on littoral algae. It has a sturdy shell and the adult shell is dextrally coiled, has 5-6 whorls with prominent spiral keels. Between the keels are many small spiral ribs. The sculpture includes widely spaced, slanted growth lines, intersecting the spiral keels. The shell color may be yellow-brown, green, or olive, with blotches of dark brown or black. The interior of the mouth is lilac or violet. The shell reaches 18 mm in length, and about 17 mm in height [Description from: Golikov et al. 1976.]. <br> High-quality genomes are the base to facilitate littorinid snails to achieve their maximum potential as true ecological and evolutionary models, so here we present a publicly available high-quality chromosome-level littorinid genome for <i>Littorina brevicula</i>, with 197.66Gb (~212-fold coverage) of sequence data, assembled into 17 pseudochromosomes. The final, chromosome-level genome assembly spans 927.94Mb, with contig N50 of 3.43Mb. The BUSCO results indicated high genome assembly completeness, with 888 out of 954 (93.1%) metazoan single-copy core genes present in the genome assemblies of <i>L. brevicula</i>. Repetitive elements composed 47.25% (438.52Mb) of the genome, and a total of 29,335 coding genes were predicted.
DOI: 10.5524/102572
title: Supporting data for "StereoSiTE: A framework to spatially and quantitatively profile the cellular neighborhood organized iTME"

description: Spatial Transcriptome (ST) technologies are emerging as powerful tools for studying tumor biology. However, existing tools for analyzing ST data are limited, as they mainly rely on algorithms developed for single-cell RNA sequencing (scRNAseq) data and do not fully utilize the spatial information. While some algorithms have been developed for ST data, they are often designed for specific tasks, lacking a comprehensive analytical framework for leveraging spatial information.<br>In this study, we present StereoSiTE, an analytical framework that combines open-source bioinformatics tools with custom algorithms to accurately infer the functional Spatial Cell Interaction Intensity (SCII) within the Cellular Neighborhood (CN) of interest. We applied StereoSiTE to decode ST datasets from xenograft models and found that the CN efficiently distinguished different cellular contexts, while the SCII analysis provided more precise insights into intercellular interactions by incorporating spatial information. By applying StereoSiTE to multiple samples, we successfully identified a CN region dominated by neutrophils, suggesting their potential role in remodeling the immune Tumor MicroEnvironment (iTME) after treatment. Moreover, the SCII analysis within the CN region revealed neutrophil-mediated communication, supported by pathway enrichment, transcription factor regulon activities, and protein-protein interactions.<br>StereoSiTE represents a promising framework for unraveling the mechanisms underlying treatment response within the iTME by leveraging CN-based tissue domain identification and SCII-inferred spatial intercellular interactions. The software is designed to be scalable, modular, and user-friendly, making it accessible to a wide range of researchers.
DOI: 10.5524/102574
title: Supporting data for "vEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy"

description: As software and hardware have developed, so has the scale of research into volume electron microscopy (vEM), leading to ever-increasing resolution. Usually, data collection is followed by image stitching: the same area is subjected to high-resolution imaging with a certain overlap, and then the images are stitched together to achieve ultrastructure with large scale and high resolution simultaneously. However, there is currently no perfect method for image stitching, especially when the global feature distribution of the sample is uneven and the feature points of the overlap area cannot be matched accurately, which results in the ghosting of the fusion area. <br>We have developed a novel algorithm called vEMstitch to solve these problems, aiming for seamless and clear stitching of high-resolution images. In vEMstitch, the image transformation model is constructed as a combination of global rigid and local elastic transformation using weighted pixel displacement fields. Specific local geometric constraints and feature re-extraction strategies are incorporated to ensure that the transformation model accurately and completely reflects the characteristics of biological distortions. To demonstrate the applicability of vEMstitch, we conducted thorough testing on simulated datasets involving different transformation combinations, consistently showing promising performance. Furthermore, in real data sample experiments, vEMstitch successfully gives clear ultrastructure in the stitching region, reaffirming the effectiveness of the algorithm. <br>vEMstitch serves as a valuable tool for large-field and high-resolution image stitching. The clear stitched regions facilitate better visualization and identification in volume EM analysis. The source code is available at https://github.com/HeracleBT/vEMstitch.
DOI: 10.5524/102578
title: The genomic data of Longhuihong (Citrus sinensis [L.] Osb. cv. LHH)

description: Sweet orange (<i>Citrus sinensis</i><em> </em>Osbeck) is a fruit crop of high nutritional value that is widely consumed around the world. However, its susceptibility to low-temperature stress limits its cultivation and production in regions prone to frost damage, severely impacting the sustainable development of the sweet orange industry. Therefore, developing cold-resistant sweet orange varieties is of great necessity. Traditional hybrid breeding methods are not feasible due to the polyembryonic phenomenon in sweet oranges, necessitating the enhancement of its germplasm through molecular breeding. High-quality reference genomes are valuable for studying crop resistance to biotic and abiotic stresses. However, the lack of genomic resources for cold-resistant sweet orange varieties has hindered the progress in developing such varieties and researching their molecular mechanisms of cold resistance. <br>This study integrated PacBio HiFi, ONT, Hi-C, and Illumina sequencing data to assemble telomere-to-telomere (T2T) reference genomes for the cold-resistant sweet orange mutant Longhuihong (<i>Citrus sinensis</i> [L.] Osb. cv. LHH). Comprehensive evaluations based on multiple criteria revealed that the genome exhibits high continuity, completeness, and accuracy. The genome size was calculated as 340.28 Mb with contig N50 of 39.31 Mb. In total, 31,456 gene models were annotated in the genomes. Leveraging this assembled genome in comparative genomics analysis with another cultivar genome we have examined the evolutionary history of the sweet orange genome.
DOI: 10.5524/102585
title: Supporting data for "PhysiMeSS - A New PhysiCell Add-on for Extracellular Matrix Modelling"

description: The extracellular matrix is a complex assembly of macromolecules, such as collagen fibres, which provide structural support for surrounding cells. In the context of cancer metastasis, it represents a barrier for the cells, that the migrating cells need to degrade to leave the primary tumour and invade further tissues. Agent-based frameworks, such as PhysiCell, are often used to represent the spatial dynamics of tumour evolution. However, typically they only implement cells as agents, which are represented by either a circle (2D) or a sphere (3D). To accurately represent the extracellular matrix as a network of fibres, we require a new type of agent represented by a segment (2D) or a cylinder (3D). <br>In this article, we present PhysiMeSS, an add-on of PhysiCell, which introduces a new type of agent to describe fibres, and their physical interactions with cells and other fibres. PhysiMeSS implementation is publicly available in the PhysiMeSS GitHub repository and in the official PhysiCell repository. We also provide simple examples to describe the extended possibilities of this new framework. We hope that this tool will serve to tackle important biological questions such as diseases linked to dis-regulation of the extracellular matrix, or the processes leading to cancer metastasis.
DOI: 10.5524/102588
title: Supporting data for "Learning A Generalized Graph Transformer for Protein Function Prediction in Dissimilar Sequences"

description: In the face of a growing disparity between high-throughput sequence data and low-throughput experimental studies, the emerging field of deep learning stands as a promising alternative. Generally, many data-driven approaches are capable of facilitating fast and accurate predictions of protein functions. Nevertheless, the inherent statistical nature of deep learning techniques may limit their generalization capabilities when applied to novel non-homologous proteins that diverge significantly from existing ones. <br>In this work, we propose a novel, generalized approach named Graph Adversarial Learning with Alignment (GALA) for protein function prediction. Our GALA model integrates a graph transformer architecture with an attention pooling module to extract information from both protein sequences and structures, facilitating unified learning of protein structural representations. Particularly noteworthy, GALA incorporates a domain discriminator conditioned on both representations and predicted probabilities, which undergoes adversarial training to ensure representation invariance across diverse environments. To optimize the model with abundant label information, we generate label embeddings in the hidden space, explicitly aligning them with protein representations. Benchmarked on datasets derived from PDB database and Swiss-Prot database, our GALA achieves performance comparable to several state-of-the-art methods. Furthermore, GALA demonstrates outstanding interpretability by identifying key functional residues associated with GO terms through class activation mapping. <br>GALA, which leverages adversarial learning and label embedding alignment to acquire domain-invariant protein representations, exhibits outstanding generalizability in function prediction for proteins from previously unseen sequence space. By utilizing the structures predicted by AlphaFold2, GALA holds significant potential for function annotation in newly discovered sequences. Implementations of our GALA can be found at https://github.com/fuyw-aisw/GALA.