Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes.

Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.

RAfilter: an algorithm for detecting and filtering false-positive alignments in repetitive genomic regions.

Telomere to telomere (T2T) assembly relies on the correctness of sequence alignments. However, the existing aligners tend to generate a high proportion of false-positive alignments in repetitive genomic regions which impedes the generation of T2T-level reference genomes for more important species. In this paper, we present an automatic algorithm called RAfilter for removing the false-positives in the outputs of existing aligners. RAfilter takes advantage of rare k-mers representing the copy-specific features to differentiate false-positive alignments from the correct ones. Considering the huge numbers of rare k-mers in large eukaryotic genomes, a series of high-performance computing techniques such as multi-threading and bit operation are used to improve the time and space efficiencies. The experimental results on tandem repeats and interspersed repeats show that RAfilter was able to filter 60%-90% false-positive HiFi alignments with almost no correct ones removed, while the sensitivities and precisions on ONT datasets were about 80% and 50% respectively.

A chromosome-scale and haplotype-resolved genome assembly of carnation (Dianthus caryophyllus) based on high-fidelity sequencing.

Dianthus caryophyllus is an economic species often considered excellent cut flowers and is suitable for bouquets and gardens. Here, we assembled the haplotype-resolved genome of D. caryophyllus 'Aili' at the chromosome level for the first time. The total lengths of the two assembled haplotypes of carnation were 584.88 Mb for haplotype genome 1 (hap1) and 578.78 Mb for haplotype genome 2 (hap2), respectively. We predicted a total of 44,098 and 42,425 protein-coding genes, respectively. The remarkable structure variation was identified between two haplotypes. Moreover, we identified 403.80 Mb of transposable elements (TEs) in hap1, which accounted for 69.34% of the genome. In contrast, hap2 had 402.70 Mb of TEs, representing 69.61% of the genome. Long terminal repeats were the predominant transposable elements. Phylogenetic analysis showed that the species differentiation time between carnation and gypsophila was estimated to be ~54.43 MYA. The unique gene families of carnation genomes were identified in 'Aili' and previously published 'Francesco' and 'Scarlet Queen'. The assembled and annotated haplotype-resolved D. caryophyllus genome not only promises to facilitate molecular biology studies but also contributes to genome-level evolutionary studies.

A Novel Method to Efficiently Highlight Nonlinearly Expressed Genes.

For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and environmental factors. Maximal information coefficient (MIC) was proposed to capture a wide range of associations of two variables in both linear and nonlinear relationships. However, with MIC it is difficult to highlight genes with nonlinear expression patterns as the genes giving the most strongly supported hits are linearly expressed, especially for noisy data. It is thus important to also efficiently identify nonlinearly expressed genes in order to unravel the molecular basis of disease and to reveal new therapeutic targets. We propose a novel nonlinearity measure called normalized differential correlation (NDC) to efficiently highlight nonlinearly expressed genes in transcriptome datasets. Validation using six real-world cancer datasets revealed that the NDC method could highlight nonlinearly expressed genes that could not be highlighted by t-test, MIC, edgeR, and DEseq2, although MIC could capture nonlinear correlations. The classification accuracy indicated that analysis of these genes could adequately distinguish cancer and paracarcinoma tissue samples. Furthermore, the results of biological interpretation of the identified genes suggested that some of them were involved in key functional pathways associated with cancer progression and metastasis. All of this evidence suggests that these nonlinearly expressed genes may play a central role in regulating cancer progression.