EUPAN enables pan-genome studies of a large number of eukaryotic genomes

Hu, Zhiqiang; Sun, Chen; Lu, Kuang-chen; Chu, Xixia; Zhao, Yue; Lu, Jinyuan; Shi, Jiannong; Wei, Chaochun

doi:10.1093/bioinformatics/btx170

Cited by 51 publications

(49 citation statements)

References 13 publications

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“…For bacterial data, several tools for pan-genome analysis have been developed (Vinuesa and Contreras-Moreira 2015; Chaudhari et al 2016; Xiao et al 2015) but solutions that cope well with the data richness of eukaryotic species are often tailored to defined taxonomic groups (Song et al 2015) or expect closely related taxa. EUPAN is a pipeline for pan-genome analysis of closely related eukaryotic genomes developed within the scope of the 3000 Rice Genomes Project (Hu et al 2017). The approach parts from mapping of raw reads to reference genomes followed by coordinated assembly and lift-over of gene annotations for inferring presence/absence of gene models.…”

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confidence: 99%

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences

Laetsch

Blaxter

2017

G3 Genes|Genomes|Genetics

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The field of comparative genomics is concerned with the study of similarities and differences between the information encoded in the genomes of organisms. A common approach is to define gene families by clustering protein sequences based on sequence similarity, and analyze protein cluster presence and absence in different species groups as a guide to biology. Due to the high dimensionality of these data, downstream analysis of protein clusters inferred from large numbers of species, or species with many genes, is nontrivial, and few solutions exist for transparent, reproducible, and customizable analyses. We present KinFin, a streamlined software solution capable of integrating data from common file formats and delivering aggregative annotation of protein clusters. KinFin delivers analyses based on systematic taxonomy of the species analyzed, or on user-defined, groupings of taxa, for example, sets based on attributes such as life history traits, organismal phenotypes, or competing phylogenetic hypotheses. Results are reported through graphical and detailed text output files. We illustrate the utility of the KinFin pipeline by addressing questions regarding the biology of filarial nematodes, which include parasites of veterinary and medical importance. We resolve the phylogenetic relationships between the species and explore functional annotation of proteins in clusters in key lineages and between custom taxon sets, identifying gene families of interest. KinFin can easily be integrated into existing comparative genomic workflows, and promotes transparent and reproducible analysis of clustered protein data.

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confidence: 99%

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences

Laetsch

Blaxter

2017

G3 Genes|Genomes|Genetics

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“…We utilized a “map-to-pan” strategy 8 to determine gene presence/absence based on the observation that more than 98% of the genome can be covered by short read mapping at sequencing depth ≥20. The presence/absence of each gene was determined by the read coverage of the gene body and its coding region.…”

Section: Methodsmentioning

confidence: 99%

“…All codes for detection of the novel sequences and determination of gene PAVs were implemented in EUPAN package 8 available at http://cgm.sjtu.edu.cn/eupan/ . Software and their used versions were described as above.…”

Section: Methodsmentioning

confidence: 99%

“…This SV data set contains a total number of 93,683 SVs (deletions, inversions and duplications of >100 bps and <1 Mbps, and translocations), or an average of 12,178 SVs per genome detected in the 453 high-quality genomes with sequencing depths ≥20× and mapping depths ≥15×. We also present the PAV data sets of 48,098 full-length protein-coding genes (35,633 Nipponbare reference genes and 12,465 novel genes) and 23,876 gene families in the 453 rice accessions, which were obtained from a “map-to-pan” pipeline 8 . These data not only provide a more comprehensive understanding of the genomic diversity within O. sativa , but also provide additional genomic markers for genome-wide association studies of rice.…”

Section: Background and Summarymentioning

confidence: 99%

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Novel sequences, structural variations and gene presence variations of Asian cultivated rice

Wang

et al. 2018

Sci Data

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Genomic diversity within a species genome is the genetic basis of its phenotypic diversity essential for its adaptation to environments. The big picture of the total genetic diversity within Asian cultivated rice has been uncovered since the sequencing of 3,000 rice genomes, including the SNP data publicly available in the SNP-Seek database. Here we report other aspects of the genetic diversity, including rice sequences assembled from over 3,000 accessions but absent in the Nipponbare reference genome, structural variations (SVs) and gene presence/absence variations (PAVs) in 453 accessions with sequencing depth over 20x. Using either SVs or gene PAVs, we were able to reconstruct the population structure of O. sativa, which was consistent with previous result based on SNPs. Moreover, we demonstrated the usefulness of the new data sets by successfully detecting the strong association of the “Green Revolution gene”, sd1, with plant height. Our data provide a more comprehensive view of the genetic diversity within rice, as well as additional genomic resources for research in rice breeding and plant biology.

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“…Moreover, we refer to traditional single sequence reference assemblies as linear reference genomes. Reference pangenomes are already available for viral and prokaryotic strains and populations (Tettelin et al 2005;Hogg et al 2007;Lefebure and Stanhope 2007;Jacobsen et al 2011;Zhou et al 2018), but are less common for eukaryotes (Li et al 2010;Hu et al 2017;Peter et al 2018;Tian et al 2018;McCarthy and Fitzpatrick 2019). Common pangenome representations includes multiple sequence alignments, kmer-based, or graphs.…”

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The Collaborative Cross Graphical Genome

Chen

Rao

et al. 2019

Preprint

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4 These authors contributed equally to this work A b s t r a c tThe mouse reference is one of the most widely used and accurately assembled mammalian genomes, and is the foundation for a wide range of bioinformatics and genetics tools. However, it represents the genomic organization of a single inbred mouse strain. Recently, inexpensive and fast genome sequencing has enabled the assembly of other common mouse strains at a quality approaching that of the reference. However, using these alternative assemblies in standard genomics analysis pipelines presents significant challenges. It has been suggested that a pangenome reference assembly, which incorporates multiple genomes into a single representation, are the path forward, but there are few standards for, or instances of practical pangenome representations suitable for large eukaryotic genomes. We present a pragmatic graph-based pangenome representation as a genomic resource for the widely-used recombinantinbred mouse genetic reference population known as the Collaborative Cross (CC) and its eight founder genomes. Our pangenome representation leverages existing standards for genomic sequence representations with backward-compatible extensions to describe graph topology and

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EUPAN enables pan-genome studies of a large number of eukaryotic genomes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 51 publications

References 13 publications

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences

Novel sequences, structural variations and gene presence variations of Asian cultivated rice

The Collaborative Cross Graphical Genome

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