Shallow shotgun sequencing is an accurate microbiome profiling method, but its use for functions is limited. BioSIFTR extrapolates shallow shotgun read mapping to precalculated pangenome functions in 15 MGnify biome-specific catalogues, which span host-associated and environmental microbiomes. We benchmarked BioSIFTR on synthetic communities and on three gut microbiomes — red junglefowl, mouse, and human — using matched deep shotgun and 16S rRNA metabarcoding data, outperforming similar approaches. Further validation will require matched benchmark datasets and is a priority for future work. BioSIFTR is thus a practical method for functional inference from cost-effective shallow shotgun sequencing, currently validated in gut microbiomes.
The rapid growth of genomic sequencing is driving the implementation of graph pangenomes in evolutionary studies, yet the computational costs and complex interpretations at higher taxonomic levels hinder their broad use. Here, we present an evolutionary graph pangenome (evograph) pipeline to streamline diversity analyses across species and families. Applying this framework to 709 Poales chloroplast genomes indicates that genomic diversity is driven primarily by species-specific variation. We resolve a 77.7 Kbp consensus genome and apply graph theory metrics to highlight distinct genus-level evolutionary signatures. Evographs provide a powerful, nuanced framework for resolving evolutionary complexity across high taxonomic levels.
Accurate detection of antimicrobial resistance (AMR) genes is essential for AMR surveillance and genotypic prediction. Current approaches typically either depend on a correct genome assembly or use k-mers directly from the reads. However AMR genes are often multicopy and are enriched at repeat loci where genome assemblers tend to fail, and k-mer methods struggle to distinguish different copies. We present Amira, which detects AMR genes directly from long reads by building gene-space de Bruijn graphs, allowing precise association of reads with distinct AMR gene copies, enabling accurate assembly. Across simulations, curated samples, and public datasets, Amira improves copy-number recall and nucleotide accuracy compared with existing methods.
Genome-wide protein–DNA mapping studies have produced thousands of enriched sequence motifs, many of which are redundant because transcription factors (TFs) bind cooperatively as distinct complexes to shared sites. Here, we present Genomic CLustered Alignment of Motif Profiles (GenoCLAMP), an algorithm that unifies motif-clustering and alignment to collapse redundant motifs into high-quality merged matrix representations. We demonstrate that GenoCLAMP reliably recovers known TF complexes that bind common motifs and uncovers evidence for previously uncharacterized motifs. These results establish GenoCLAMP as a robust and efficient framework for motif consolidation, enhancing computational analyses and biological interpretation of large-scale TF–DNA binding datasets.
Germline structural variants (SVs), genomic variants involving larger segments of the human genome, are understudied in the context of cancer. The impact of germline variants on the regulation of specific genes is highly dependent on tissue and cellular context, necessitating focused, multi-omics studies by cancer tissue of origin. Here, we combine patient germline SV data with tumor multi-omics profiling across 2006 breast and gynecologic cancers, including breast, cervical, ovarian, uterine endometrial, and uterine carcinosarcoma tumors, to determine the extent to which germline SV patterns influence the tumor molecular landscape. Rare germline SV events predicted to disrupt specific cancer-relevant genes cumulatively affect approximately 29
