GigaByte (Hong Kong, China)最新文献

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDS^COVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDS^COVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDS^COVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDS^COVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development.

Availability & implementation: The MPDS^COVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

The fast-paced development of machine learning (ML) and its increasing adoption in research challenge researchers without extensive training in ML. In neuroscience, ML can help understand brain-behavior relationships, diagnose diseases and develop biomarkers using data from sources like magnetic resonance imaging and electroencephalography. Primarily, ML builds models to make accurate predictions on unseen data. Researchers evaluate models' performance and generalizability using techniques such as cross-validation (CV). However, choosing a CV scheme and evaluating an ML pipeline is challenging and, if done improperly, can lead to overestimated results and incorrect interpretations. Here, we created julearn, an open-source Python library allowing researchers to design and evaluate complex ML pipelines without encountering common pitfalls. We present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects. Julearn simplifies the access to ML providing an easy-to-use environment. With its design, unique features, simple interface, and practical documentation, it poses as a useful Python-based library for research projects.

This work is an update and extension of the previously published article "Ultralong Oxford Nanopore Reads Enable the Development of a Reference-Grade Perennial Ryegrass Genome Assembly" by Frei et al. The published genome assembly of the doubled haploid perennial ryegrass (Lolium perenne L.) genotype Kyuss (Kyuss v1.0) marked a milestone for forage grass research and breeding. However, order and orientation errors may exist in the pseudo-chromosomes of Kyuss, since barley (Hordeum vulgare L.), which diverged 30 million years ago from perennial ryegrass, was used as the reference to scaffold Kyuss. To correct for structural errors possibly present in the published Kyuss assembly, we de novo assembled the genome again and generated 50-fold coverage high-throughput chromosome conformation capture (Hi-C) data to assist pseudo-chromosome construction. The resulting new chromosome-level assembly Kyuss v2.0 showed improved quality with high contiguity (contig N50 = 120 Mb), high completeness (total BUSCO score = 99%), high base-level accuracy (QV = 50), and correct pseudo-chromosome structure (validated by Hi-C contact map). This new assembly will serve as a better reference genome for Lolium spp. and greatly benefit the forage and turf grass research community.

This paper presents two key data sets derived from the Pomar Urbano project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced to Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study includes data from the capitals of all 27 federative units of Brazil, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. We expect the data sets presented in this paper to serve as resources for further studies in urban foraging, food security, cultural ecosystem services, and environmental sustainability.

This paper introduces a new approach to cell clustering using the Variable Neighborhood Search (VNS) metaheuristic. The purpose of this method is to cluster cells based on both gene expression and spatial coordinates. Initially, we confronted this clustering challenge as an Integer Linear Programming minimization problem. Our approach introduced a novel model based on the VNS technique, demonstrating the efficacy in navigating the complexities of cell clustering. Notably, our method extends beyond conventional cell-type clustering to spatial domain clustering. This adaptability enables our algorithm to orchestrate clusters based on information gleaned from gene expression matrices and spatial coordinates. Our validation showed the superior performance of our method when compared to existing techniques. Our approach advances current clustering methodologies and can potentially be applied to several fields, from biomedical research to spatial data analysis.

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results.

Availability & implementation: The source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology.

Availability & implementation: The source code of STCellbin is available at https://github.com/STOmics/STCellbin.

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow.

Trimeresurus albolabris, also known as the white-lipped pit viper or white-lipped tree viper, is a highly venomous snake distributed across Southeast Asia and the cause of many snakebite cases. In this study, we report the first whole genome assembly of T. albolabris obtained with next-generation sequencing from a specimen collected in Mengzi, Yunnan, China. After genome sequencing and assembly, the genome of this male T. albolabris individual was 1.51 Gb in length and included 38.42% repeat-element content. Using this genome, 21,695 genes were identified, and 99.17% of genes could be annotated using gene functional databases. Our genome assembly and annotation process was validated using a phylogenetic tree, which included six species and focused on single-copy genes of nuclear genomes. This research will contribute to future studies on Trimeresurus biology and the genetic basis of snake venom.

The snake pipefish, Entelurus aequoreus (Linnaeus, 1758), is a northern Atlantic fish inhabiting open seagrass environments that recently expanded its distribution range. Here, we present a highly contiguous, near chromosome-scale genome of E. aequoreus. The final assembly spans 1.6 Gbp in 7,391 scaffolds, with a scaffold N50 of 62.3 Mbp and L50 of 12. The 28 largest scaffolds (>21 Mbp) span 89.7% of the assembly length. A BUSCO completeness score of 94.1% and a mapping rate above 98% suggest a high assembly completeness. Repetitive elements cover 74.93% of the genome, one of the highest proportions identified in vertebrates. Our demographic modeling identified a peak in population size during the last interglacial period, suggesting the species might benefit from warmer water conditions. Our updated snake pipefish assembly is essential for future analyses of the morphological and molecular changes unique to the Syngnathidae.