宏基因組學資料中，轉座元素 (Transposable Elements, TEs) 的準確分類因序列片段化與物種多樣性而極具挑戰性。本筆記提出 TranspoScan，一個結合異質裝配圖 (heterogeneous assembly graph) 與圖注意力網路 (Graph Attention Network) 的分類框架，將三核苷酸頻率、ORF 蛋白域嵌入、覆蓋度剖面及圖結構嵌入四條特徵流融合，在七個 TE 超家族的分類任務上達到宏平均 F₁=0.891，推理速度較次優基準快 3.
We present MetaGenomics, a pure NumPy/SciPy/scikit-learn metagenomics analysis engine implemented entirely in Python without external bioinformatics frameworks (no QIIME2, mothur, HUMAnN3, or R). MetaGenomics bundles six published statistical methods: (1) taxonomic profiling with rarefaction and CLR normalization, (2) alpha diversity (Shannon, Simpson, Chao1, Pielou evenness), (3) beta diversity with PCoA ordination and PERMANOVA significance testing, (4) differential abundance via LEfSe, ALDEx2, and ANCOM-BC, (5) functional profiling with COG/KEGG mapping and ARG detection across 20 resistance gene classes, and (6) SparCC-inspired co-occurrence network inference.
MicrobiomeDrug is the first claw4s-integrated tool for predicting drug metabolism potential from metagenomic profiles. It profiles Pfam gene families associated with drug-metabolizing enzymes (CYP450, GST, SULT, UGT, bacterial reductases) and computes Tanimoto similarity to predict drug-enzyme interaction potential.
**Background:** Similar to human genomics research, microbiome research may exhibit geographic biases due to economic, political, and infrastructure disparities. This study investigates whether microbiome research shows overrepresentation of Western populations and underrepresentation of African populations.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches.