The paper introduces IDBA-UD, a de novo assembler designed for single-cell and metagenomic sequencing data with highly uneven depth. The assembler addresses the challenges posed by uneven sequencing depths, which are common in single-cell and metagenomic studies. Unlike existing assemblers that assume uniform sequencing depths, IDBA-UD employs multiple dephreational thresholds to remove erroneous k-mers in both low-depth and high-depth regions. It also uses local assembly with paired-end information to resolve branch problems in low-depth short repeat regions. An error correction step is included to correct high-depth reads that align to confident contigs. Experimental results on simulated and real datasets show that IDBA-UD outperforms other assemblers in terms of contig length, coverage, and accuracy. The paper discusses the effectiveness of the proposed techniques and provides a detailed comparison with existing methods.The paper introduces IDBA-UD, a de novo assembler designed for single-cell and metagenomic sequencing data with highly uneven depth. The assembler addresses the challenges posed by uneven sequencing depths, which are common in single-cell and metagenomic studies. Unlike existing assemblers that assume uniform sequencing depths, IDBA-UD employs multiple dephreational thresholds to remove erroneous k-mers in both low-depth and high-depth regions. It also uses local assembly with paired-end information to resolve branch problems in low-depth short repeat regions. An error correction step is included to correct high-depth reads that align to confident contigs. Experimental results on simulated and real datasets show that IDBA-UD outperforms other assemblers in terms of contig length, coverage, and accuracy. The paper discusses the effectiveness of the proposed techniques and provides a detailed comparison with existing methods.