GeneMiner2 : Accurate and Automated Recovery of Genes From Genome Skimming Data

ABSTRACT With the growing accessibility of low cost genome skimming, large‐scale recovery of target genes in non‐model species has become feasible, providing strong data for phylogenomic and evolutionary studies. However, existing tools for data assembly including Read2Tree, HybPiper, and GeneMiner still suffer from computational difficulty and assembly errors when processing genome skimming data, especially for low‐level taxa that lack closely related genome references. Here, we present GeneMiner2, an updated version of GeneMiner, as an efficient and automated gene assembly and phylogenetic tool to overcome these limitations. Compared with the previous version, GeneMiner2 introduces three key optimizations by deploying a two‐level hash table that speeds up k ‐mer filtering, applying fine‐grained read selection with strand‐orientation and structural‐anomaly detection to handle complex heterozygous regions, and incorporating adaptive k ‐mer selection in the de Bruijn assembler. Together enabling accurate assembly of target genes without the need for closely related references. Using simulated datasets with low coverage and divergent references, we validated the robustness and improved accuracy of GeneMiner2 in the assembly of single‐copy genes. Moreover, using genome skimming data from the subfamily Apioideae (Apiaceae), GeneMiner2 outperformed Read2Tree and HybPiper in accuracy, completeness, and speed. Equipped with a user‐friendly graphical interface, GeneMiner2 integrates functions including assembly quality control, paralog detection, tree reconstruction, and divergence time calibration, offering a robust and efficient solution for phylogenetic inference using genomic level data. GeneMiner2 supports cross‐platform operation. GeneMiner2 provides a user‐friendly graphical interface for desktop users, and its high‐performance command‐line interface is specifically optimised for high‐throughput analyses on Linux servers and computing clusters. Installation instructions, detailed documentation, and source code are available on GitHub ( https://github.com/sculab/GeneMiner2 ).