Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation [0.03%]
基于深度生成模型的学习固有遗传模式和特征关联性的离散基因型模拟方法
Sihan Xie,Thierry Tribout,Didier Boichard et al.
Sihan Xie et al.
Background: Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have primarily focused on generating...
Georgios K Georgakilas,Brett Metcalfe,Ariane Bize et al.
Georgios K Georgakilas et al.
Background: As the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data driven innovation, the requirement for trustworthy data management, annotation a...
Community-curated Galaxy interfaces with the Galaxy Labs Engine [0.03%]
基于Galaxy Labs Engine的社区精选Galaxy界面
Cameron J Hyde,Anna Syme,Bérénice Batut et al.
Cameron J Hyde et al.
The Galaxy platform is a globally distributed environment for data-intensive research, providing thousands of analysis tools across major public servers. However, this decentralised ecosystem presents usability challenges for both users and...
GEfetch2R: fetching single-cell/bulk RNA-seq data from public repositories to R and benchmarking the subsequent format conversion tools [0.03%]
GEfetch2R：从公共存储库获取单细胞/批量RNA测序数据到R，并对后续格式转换工具进行基准测试
Yabing Song,Jianbin Wang,Jiaxin Gao
Yabing Song
Background: Downloading and reanalyzing the existing single-cell RNA sequencing (scRNA-seq) data provides an efficient choice to gain clues and new insights. However, no tool can fetch the diverse scRNA-seq data types (ra...
HVRLocator: A Computationally Efficient Tool for Identifying Hypervariable Regions in Large 16S rRNA Datasets [0.03%]
高效识别大型16S rRNA数据集中高可变区的计算工具HVRLocator
Clara Arboleda-Baena,Felipe Borim Correa,Joao Pedro Saraiva et al.
Clara Arboleda-Baena et al.
Background: Metabarcoding of the 16S rRNA gene is widely used to assess microbial diversity due to its cost-effectiveness and efficiency. However, publicly available 16S rRNA metabarcoding datasets often lack standardized...
Comparative analysis of 163 ant genomes reveals recurrent horizontal gene transfer from bacteria to ants [0.03%]
对163个蚂蚁基因组的比较分析揭示了细菌到蚂蚁的重复水平基因转移现象
Janina L Rinke,Lukas Franke,Ding He et al.
Janina L Rinke et al.
Background: Horizontal gene transfer (HGT) from bacteria can drive phenotypic innovation and adaptation in eukaryotes. Ants are likely carriers of HGT-derived genes, as they have repeatedly established mutualistic associa...
NEXT-scASV: A Nextflow Pipeline for Allele-Specific Variants Calling from single cell RNA-seq data [0.03%]
NEXT-scASV：一种用于从单细胞RNA测序数据中进行等位基因特异性变异检测的Nextflow管道
Andrey Shevtsov,Andrey Buyan,Vladimir Nozdrin et al.
Andrey Shevtsov et al.
The rapid accumulation of single-cell sequencing data presents major computational challenges in reproducibility, scaling, and handling data characteristics like sparsity and technical variations, which complicate even basic analyses. The n...
MicroFinder: conserved gene-set mapping and assembly ordering for manual curation of bird dot microchromosomes [0.03%]
MicroFinder：鸟类点微染色体保守基因集映射和手动注释组装排序
Thomas C Mathers,Michael Paulini,Cibele G Sotero-Caio et al.
Thomas C Mathers et al.
Background: Obtaining chromosomally complete genome assemblies across the tree of life is an important goal of biodiversity genomics. However, some lineages remain recalcitrant to assembly. Birds present a substantial ass...
Cancer genome standards for long-read sequencing using cancer cell line mixtures [0.03%]
基于癌细胞系混合样本的长读长测序癌症基因组标准
Jia Zhang,Ho Yi Wong,Lingchen Liu et al.
Jia Zhang et al.
Long-read sequencing (LRS) improves genome alignment and facilitates resolving variants in genomic regions of low complexity, making it a promising approach for cancer variant detection and biomarker discovery. Here, we evaluate the perform...
scDenorm: a denormalisation tool for integrating single-cell transcriptomics data [0.03%]
scDenorm：一种整合单细胞转录组学数据的去规范化工具
Yin Huang,Anna Vathrakokili Pournara,Ying Ao et al.
Yin Huang et al.
Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalisation methods can lead to biases, such as unexpected batch...