MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

Abstract

Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the order Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.

Fig. 1. Overview of the MetaCC framework for metagenomic Hi-C analyses.

a The input metaHi-C dataset consists of shotgun libraries and Hi-C libraries. Short/long reads in shotgun libraries are assembled into contigs, to which Hi-C paired-end reads are subsequently aligned. In this way, raw Hi-C contact matrix displaying the proximity similarity between contigs within cells can be constructed. The raw Hi-C contact matrix is normalized by the NormCC normalization module to correct the systematic biases and spurious inter-species contacts are subsequently removed. Assembled contigs are then binned into high-quality MAGs leveraging the normalized Hi-C contact matrix. Finally, downstream analyses are conducted. b Visualize the procedures of NormCC normalization and spurious contact removal by plotting heatmaps of the Hi-C contact matrix for contigs belonging to the species Kluyveromyces wickerhamii and Ashbya gossypii from a synthetic yeast dataset.

Fig. 2. Benchmarking the NormCC normalization module on the synthetic yeast metaHi-C dataset.

a Discard-retain curves for evaluating spurious contact removal based on the raw, HiCzin-normalized, or NormCC-normalized Hi-C contact matrices, respectively. NormCC achieved the highest AUDRC (i.e., area under discard-retain curve). b Performance of contig clustering based on the raw, HiCzin-normalized, or NormCC-normalized Hi-C contact matrices as well as NormCC-normalized Hi-C contact matrix with spurious contact removal, respectively. NormCC outperformed HiCzin on the contig clustering in terms of F-score, ARI, and NMI.

Fig. 3. Benchmarking the MetaCC binning module on short-read metaHi-C datasets.

MetaCC binning outperformed other binners on both the human gut and wastewater short-read metaHi-C datasets according to the CheckM criteria (Near-complete: completeness ≥ 90% and contamination ≤ 10%; Substantially complete: 70% ≤ completeness < 90% and contamination ≤ 10%; Moderately complete: 50% ≤ completeness < 70% and contamination ≤ 10%).

Fig. 4. Benchmarking the MetaCC binning module on long-read metaHi-C datasets.

a MetaCC binning outperformed other binners on both the cow rumen and sheep gut long-read metaHi-C datasets according to the CheckM criteria (Near-complete: completeness ≥ 90% and contamination ≤ 10%; Substantially complete: 70% ≤ completeness < 90% and contamination ≤ 10%; Moderately complete: 50% ≤ completeness <70% and contamination ≤ 10%). HiCBin failed to bin contigs on the cow rumen dataset due to the nonconvergence of its adopted normalization method HiCzin. b Comparison of near-complete bins identified by MetaCC binning and other Hi-C-based binners from the long-read metaHi-C datasets. The total length of each bar shows the total number of near-complete (NC) bins recovered by each binner. Each bar is then colored according to the number of NC bins that can be identified by both binners (NC in both), the number of NC bins that are substantially complete in the other bin set (SC in other), and the number of NC bins that are moderately complete or missing in the other bin set (MC or miss in other). c Comparison of the number of species recovered by different binners with high quality. MAGs retrieved by MetaCC binning represent the largest taxonomic diversity at the species level.