GigaByte (Hong Kong, China)最新文献

The vast majority of gecko species are capable of tail regeneration, but singular geckos of Correlophus, Uroplatus, and Nephrurus genera are unable to regrow lost tails. Of these non-regenerative geckos, the crested gecko (Correlophus ciliatus) is distinguished by ready availability, ease of care, high productivity, and hybridization potential. These features make C. ciliatus particularly suited as a model for studying the genetic, molecular, and cellular mechanisms underlying loss of tail regeneration capabilities. We report a contiguous genome of C. ciliatus with a total size of 1.65 Gb, 152 scaffolds, L50 of 6, and N50 of 109 Mb. Repetitive content consists of 40.41% of the genome, and a total of 30,780 genes were annotated. Our assembly of the crested gecko genome provides a valuable resource for future comparative genomic studies between non-regenerative and regenerative geckos and other squamate reptiles.

Findings: We report genome sequencing, assembly, and annotation for the crested gecko, Correlophus ciliatus.

The rapid advancements in sequencing length necessitate the adoption of increasingly efficient sequence alignment algorithms. The Needleman-Wunsch method introduces the foundational dynamic-programming matrix calculation for global alignment, which evaluates the overall alignment of sequences. However, this method is known to be highly time-consuming. The proposed TSTA algorithm leverages both vector-level and thread-level parallelism to accelerate pairwise and multiple sequence alignments.

Availability and implementation: Source codes are available at https://github.com/bxskdh/TSTA.

Underutilized sheep and goat breeds can adapt to challenging environments due to their genetics. Integrating publicly available genomic datasets with new data will facilitate genetic diversity analyses; however, this process is complicated by data discrepancies, such as outdated assembly versions or different data formats. Here, we present the SMARTER-database, a collection of tools and scripts to standardize genomic data and metadata, mainly from SNP chip arrays on global small ruminant populations, with a focus on reproducibility. SMARTER-database harmonizes genotypes for about 12,000 sheep and 6,000 goats to a uniform coding and assembly version. Users can access the genotype data via File Transfer Protocol and interact with the metadata through a web interface or using their custom scripts, enabling efficient filtering and selection of samples. These tools will empower researchers to focus on the crucial aspects of adaptation and contribute to livestock sustainability, leveraging the rich dataset provided by the SMARTER-database.

Availability and implementation: The code is available as open-source software under the MIT license at https://github.com/cnr-ibba/SMARTER-database.

Global changes in cannabis legislation after decades of stringent regulation and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, quantitative trait loci and genetic maps, gene and protein expression data, metabolic profiles and their sample attributes. Single nucleotide polymorphisms were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service Application Programming Interface (API), developed on top of existing Tripal modules, returns generic tables of samples, properties and values. Use cases demonstrate the API's utility for various omics analyses, enabling researchers to perform multi-omics analyses efficiently.

Availability and implementation: The web portal can be accessed at www.icgrc.info.

The extracellular matrix, composed of macromolecules like collagen fibres, provides structural support to cells and acts as a barrier that metastatic cells degrade to spread beyond the primary tumour. While agent-based frameworks, such as PhysiCell, can simulate the spatial dynamics of tumour evolution, they only implement cells as circles (2D) or spheres (3D). To model the extracellular matrix as a network of fibres, we require a new type of agent represented by line segments (2D) or cylinders (3D). Here, we present PhysiMeSS, an addon of PhysiCell, introducing a new agent type to describe fibres and their physical interactions with cells and other fibres. PhysiMeSS implementation is available at https://github.com/PhysiMeSS/PhysiMeSS and in the official PhysiCell repository. We provide examples describing the possibilities of this framework. This tool may help tackle important biological questions, such as diseases linked to dysregulation of the extracellular matrix or the processes leading to cancer metastasis.

Recent advancements in next-generation sequencing (NGS) technologies have brought to the forefront the necessity for versatile, cost-effective tools capable of adapting to a rapidly evolving landscape. The emergence of numerous new sequencing platforms, each with unique sample preparation and sequencing requirements, underscores the importance of efficient barcode balancing for successful pooling and accurate demultiplexing of samples. Recently launched new sequencing systems claiming better affordability comparable to more established platforms further exemplifies these challenges, especially when libraries originally prepared for one platform need conversion to another. In response to this dynamic environment, we introduce NucBalancer, a Shiny app developed for the optimal selection of barcode sequences. While initially tailored to meet the nucleotide, composition challenges specific to G400 and T7 series sequencers, NucBalancer's utility significantly broadens to accommodate the varied demands of these new sequencing technologies. Its application is particularly crucial in single-cell genomics, enabling the adaptation of libraries, such as those prepared for 10x technology, to various sequencers including G400 and T7 series sequencers. NucBalancer efficiently balances nucleotide composition and sample concentrations, reducing biases and enhancing the reliability of NGS data across platforms. Its adaptability makes it invaluable for addressing sequencing challenges, ensuring effective barcode balancing for sample pooling on any platform.

Availability and implementation: NucBalancer is implemented in R and is available at https://github.com/ersgupta/NucBalancer. Additionally, a shiny interface is available at https://ersgupta.shinyapps.io/NucBalancer/.

A growing interest in Cannabis sativa uses for food, fiber, and medicine, and recent changes in regulations have spurred numerous genomic studies of this once-prohibited plant. Cannabis research uses Next Generation Sequencing technologies for genomics and transcriptomics. While other crops have genome portals enabling access and analysis of numerous genotyping data from diverse accessions, leading to the discovery of alleles for important traits, this is absent for cannabis. The CannSeek web portal aims to address this gap. Single nucleotide polymorphism datasets were generated by identifying genome variants from public resequencing data and genome assemblies. Results and accompanying trait data are hosted in the CannSeek web application, built using the Rice SNP-Seek infrastructure with improvements to allow multiple reference genomes and provide a web-service Application Programming Interface. The tools built into the portal allow phylogenetic analyses, varietal grouping and identifications, and favorable haplotype discovery for cannabis accessions using public sequencing data.

Availability and implementation: The CannSeek portal is available at https://icgrc.info/cannseek, https://icgrc.info/genotype_viewer.

The time required for genome sequencing and de novo assembly depends on the interaction between laboratory work, sequencing capacity, and the bioinformatics workflow, often constrained by external sequencing services. Bringing together academic biodiversity institutes and a medical diagnostics company with extensive sequencing capabilities, we aimed at generating a high-quality mammalian de novo genome in minimal time. We present the first chromosome-level genome assembly of the Whippet, using PacBio long-read high-fidelity sequencing and reference-guided scaffolding. The final assembly has a contig N50 of 55 Mbp and a scaffold N50 of 65.7 Mbp. The total assembly length is 2.47 Gbp, of which 2.43 Gpb were scaffolded into 39 chromosome-length scaffolds. Annotation using mammalian genomes and transcriptome data yielded 28,383 transcripts, 90.9% complete BUSCO genes, and identified 36.5% repeat content. Sequencing, assembling, and scaffolding the chromosome-level genome of the Whippet took less than a week, adding another high-quality reference genome to the available sequences of domestic dog breeds.

Background: Next-generation sequencing for microbial communities has become a standard technique. However, the computational analysis remains resource-intensive. With declining costs and growing adoption of sequencing-based methods in many fields, validated, fully automated, reproducible and flexible pipelines are increasingly essential in various scientific fields.

Results: We present RiboSnake, a validated, automated, reproducible QIIME2-based pipeline implemented in Snakemake for analysing 16S rRNA gene amplicon sequencing data. RiboSnake includes pre-packaged validated parameter sets optimized for different sample types, from environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input.

Conclusion: RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and user-friendly pipeline for microbiome analyses with in vitro validated settings. By automating the analysis with validated parameters for diverse sample types, RiboSnake enhances existing methods significantly. The workflow repository can be found on GitHub (https://github.com/IKIM-Essen/RiboSnake).

Amazon Web Services (AWS) instances provide a convenient way to run training on complex 'omics data analysis workflows without requiring participants to install software packages or store large data volumes locally. However, efficiently managing dozens of instances is challenging for training providers. We present a set of Bash scripts that make it quick and easy to manage Linux AWS instances pre-configured with all the software analysis tools and data needed for a course, and accessible using encrypted login keys and optional domain names. Creating over 30 instances takes 10-15 minutes. A comprehensive online tutorial describes how to set up and use an AWS account and the scripts, and how to customise AWS instance templates with other software tools and data. We anticipate that others offering similar training may benefit from using the scripts regardless of the analyses being taught.