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SCIENCE CHINA Life Sciences
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SCIENCE CHINA Life Sciences, Volume 63 , Issue 5 : 750-763(2020) https://doi.org/10.1007/s11427-019-9551-7

Building a sequence map of the pig pan-genome from multiple de novo assemblies and Hi-C data

Xiaomeng Tian 1,†, Ran Li 1,†, Weiwei Fu 1,†, Yan Li 2,†, Xihong Wang 1, Ming Li 1, Duo Du 1, Qianzi Tang 2, Yudong Cai 1, Yiming Long 1, Yue Zhao 1, Mingzhou Li 2,*, Yu Jiang 1,*
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  • ReceivedJan 7, 2019
  • AcceptedApr 3, 2019
  • PublishedJul 8, 2019
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Abstract


Funded by

the National Natural Science Foundation of China(31822052,31572381)


Acknowledgment

This work was supported by the National Natural Science Foundation of China (31822052 and 31572381) to Y.J and the Science & Technology Support Program of Sichuan (2016NYZ0042 and 2017NZDZX0002) to M.Z.L. We thank the High Performance Computing platform of Northwest A&F University for their assistance with the computing.


Interest statement

The author(s) declare that they have no conflict of interest.


Supplement

SUPPORTING INFORMATION

Figure S1 Comparison of contig N50 among pig, human and other animal reference genomes.

Figure S2 Geographic distributions of the original pig breeds collected in this study.

Figure S3 The expression of TIG3 in subcutaneous adipose tissue (light red background) and other tissues (light blue background) of pigs harboring this gene.

Figure S4 Protein alignment of TIG3 in mammals (pig, dog, panda and human), chicken, alligator and zebrafish.

Figure S5 Selection test for TIG3 in pig and other species.

Figure S6 One pan-sequence covers partial genic regions of ZNF622, representing a new splicing event.

Figure S7 SNP density in TAD boundary (blue) and TAD internal (red) region in five samples digested by MboI enzyme.

Figure S8 Schematic diagram showing our strategy in identifying potential putative enhancer in pan-sequences.

Figure S9 An IGV view of Illumina reads at two example regions in chr2 and chr1.

Figure S10 Comparison of RNA-seq read mapping quality using the pan-genome versus Sscrofa11.1.

Figure S11 Comparison of RNA-seq read mapping rate using the pan-genome versus Sscrofa11.1.

Figure S12 Transcriptional potential of the pan-sequences.

Figure S13 Correlation coefficient of Hi-C data from different samples.

Table S1 Sample information of Hi-C data

Table S2 Detailed statistics of assemblies used in pig-pangenome construction

Table S3 Enriched KEGG functional classes among genes that annotated in pan-sequences

Table S4 Summary statistics of whole genome resequencing data in this study

Table S5 The presence and absence of pan-sequences in 87 resequencing samples

Table S6 The frequency distribution of population-specific pan-sequences in Chinese pigs and European pigs

Table S7 Summary statistics of the Hi-C data used in our experiment

Table S8 The anchored location of pan-sequences to Sscrofa11.1 by flanking sequences based and Hi-C based methods

Table S9 List of pan-sequences which shown interaction with a known promoter identified using Hi-C analysis

Table S10 Enriched KEGG functional classes among genes that might be regulated by the putative enhancers of pan-sequences

Table S11 Summary of adjusted SNPs after addition of pan-sequences

Supplementary Dataset 1 The sequences of pig pan-genome

Supplementary Dataset 2 The male-specific pan-sequences

Supplementary Dataset 3 The annotation of pan-sequences

Supplementary Dataset 4 The copy number variation dataset of pig pan-genome

The supporting information is available online at http://life.scichina.com and https://link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.


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    Figure 1

    Construction of the pig pan-genome and the characterization of pan-sequences. A, Maximum likelihood phylogenetic tree, sequence length, GC content and repeat composition of missing sequences identified in each individual assembly of 11 breeds (left to right). B, The total sequence length and breed-specific sequence length of each breed for non-redundant pan-sequences. C, Length distribution of all pan-sequences. (Wuzhishan pigs had the largest number of sequences because this individual is the only male among all the 11 assemblies and the sequencing platform of this individual differed from that used for other samples.)

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    Figure 2

    Pan-sequences validation and population-specific pattern. A, Homologue identification of pan-sequences in 10 mammalian genomes. Only the best hit was retained for each pan-sequence. B, An 87×87 matrix showing the number of shared pan-sequences among all the individuals by pairs. Each cell represents the number of shared pan-sequences by two individuals. See Table S3 in Supporting Information for the classification of each group. C, Genes contained in the pan-sequences. One pan-sequence of 14.3 kb harbours the complete genic region of TIG3. The four tracks at the bottom represent the reads mapping of whole-genome resequencing data of two samples (labelled “1” and “2”) and the inferred exons as well as their splicing isoforms based on RNA-seq (labelled “3” and “4”). D, The expression of TIG3 in 92 RNA-seq samples from 10 animals from China (light red background) and Europe (light blue background). The 10 animals corresponded to 10 of our 11 assemblies used in this study excluding the Wuzhishan assembly. E, The normalized read depth (NRD) of male-specific pan-sequences in each male. See Table S3 in Supporting Information for the classification of each group. (Only the sequences with the frequency ranging from 0.5 to 0.9 are shown.)

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    Figure 3

    The 3D spatial structure of the pan-genome. A, The distributions of the A/B compartment, TAD and anchored pan-sequences. B, The relative length-proportion of the A compartment over the B compartment in the pig genome (left) and the relative length-proportion of pan-sequences located in the A compartment over those located in the B compartment (right). C, The relative length-proportion of TAD boundary regions over TAD interior regions in Sscrofa11.1 (left) and the relative length-proportion of pan-sequences located in TAD boundary regions over TAD interior regions (right). D, An example of improving a 3D spatial structure after replacing the weakly interacting sequences with the non-reference pan-sequences. The interaction of pan-sequences with flanking sequences was supported by more read contacts than the original interaction of the counterparts in the genome with the flanking sequences.

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    Figure 4

    Improvements of genomic analyses by using the pan-genome. A, Comparison of the mapping ratio of resequencing data using the pan-genome versus Sscrofa11.1. B, Comparison of read-mapping quality using the pan-genome versus Sscrofa11.1. C, Comparison of corrected read-mapping depth using the pan-genome versus Sscrofa11.1. D, Improved read mapping using the pan-genome versus Sscrofa11.1 as viewed with IGV.

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    Figure 5

    The processing pipeline used to construct the PIGPAN database. PIGPAN integrated genomics, transcriptomics and regulatory data. Users can search for a gene symbol or a genomic region to obtain results in the form of an interactive table and graph. A, The system diagram of PIGPAN. B, The 17 tracks released against the pig pan-genome in our local UCSC Genome Browser server. C, One case showing the copy number difference of the KIT gene between European and Chinese pigs by using PIGPAN.