Literature DB >> 29048483

Draft genome of the gayal, Bos frontalis.

Ming-Shan Wang1,2, Yan Zeng1,2, Xiao Wang1,2, Wen-Hui Nie1, Jin-Huan Wang1, Wei-Ting Su1, Newton O Otecko1,2, Zi-Jun Xiong1,3, Sheng Wang4, Kai-Xing Qu5, Shou-Qing Yan6, Min-Min Yang1,2, Wen Wang1,2, Yang Dong7,8, Dong-Dong Wu1,2, Ya-Ping Zhang1,2,9.   

Abstract

Gayal (Bos frontalis), also known as mithan or mithun, is a large endangered semi-domesticated bovine that has a limited geographical distribution in the hill-forests of China, Northeast India, Bangladesh, Myanmar, and Bhutan. Many questions about the gayal such as its origin, population history, and genetic basis of local adaptation remain largely unresolved. De novo sequencing and assembly of the whole gayal genome provides an opportunity to address these issues. We report a high-depth sequencing, de novo assembly, and annotation of a female Chinese gayal genome. Based on the Illumina genomic sequencing platform, we have generated 350.38 Gb of raw data from 16 different insert-size libraries. A total of 276.86 Gb of clean data is retained after quality control. The assembled genome is about 2.85 Gb with scaffold and contig N50 sizes of 2.74 Mb and 14.41 kb, respectively. Repetitive elements account for 48.13% of the genome. Gene annotation has yielded 26 667 protein-coding genes, of which 97.18% have been functionally annotated. BUSCO assessment shows that our assembly captures 93% (3183 of 4104) of the core eukaryotic genes and 83.1% of vertebrate universal single-copy orthologs. We provide the first comprehensive de novo genome of the gayal. This genetic resource is integral for investigating the origin of the gayal and performing comparative genomic studies to improve understanding of the speciation and divergence of bovine species. The assembled genome could be used as reference in future population genetic studies of gayal.
© The Author 2017. Published by Oxford University Press.

Entities:  

Keywords:  Bos frontalis; annotation; genome assembly; phylogeny

Mesh:

Year:  2017        PMID: 29048483      PMCID: PMC5710521          DOI: 10.1093/gigascience/gix094

Source DB:  PubMed          Journal:  Gigascience        ISSN: 2047-217X            Impact factor:   6.524


Data Description

Background

The gayal is a large-sized endangered semi-domesticated bovine species belonging to the family Bovidae, tribe Bovini, group Bovina, genus Bos, and species Bos frontalis (NCBI Taxon ID: 30 520). It is also called the mithan or mithun. Its distribution spans eastern Bhutan through the Arunachal Pradesh in India to the Naga and Chin hills in the Arakan Yomarange region that defines the borders between India, Bangladesh, Myanmar, and China [1, 2]. The gayal has unique characters and appearances compared to gaur, cattle, and other bovine species [3]. These features include a bony dorsal ridge on the shoulder and white stockings on all 4 legs (Figure 1). It has been previously held that gayal was domesticated from gaur and/or from a hybrid descendant from crossing domestic cattle (B. indicus or B. taurus) and wild gaur [2, 4, 5]. Karyotype analysis indicates that the Indian gayal has a 2n = 58 karyotype, same as the local gaur (2n = 58) [6, 7], but different from Chinese and Malaysian gaurs (B. gaurus, 2n = 56) as well as domesticated cattle (B. indicus and B. taurus, 2n = 60) [2, 6–10]. Phylogenetic analyses in multiple studies based on mtDNA or Y-chromosomal DNA place gayal in conflicting clustering positions with respect to cattle, zebu, and wild gaur. For example, Chinese gayal, or Dulong cattle, are known to harbor zebu or taurine mtDNA footprints, suggesting hybrid origin [5, 11], and more studies have shown a high mtDNA and Y-chromosomal DNA sequences similarity between gayal and guar [12-15]. One study has even placed the gayal as a distinct and separate species/subspecies [16]. In contrast, phylogenetic analyses based on single nucleotide polymorphisms (SNPs) from 20 randomly selected single-copy gene orthologs of B. taurus, B. mutus (wild yak), and Bubalus bubalis placed Chinese gayal off the B. mutus and B. taurus clade, indicating that gayal is distinct from the modern domestic cattle, B. taurus [5]. These authors further demonstrated from mtDNA analysis that the gayal is the most proximal to domesticated cattle (B. taurus and B. indicus), suggesting that the gayal could be a hybrid emanating from crossing of male wild gaur and female domestic cattle [5]. These differences illustrate the existence of unresolved uncertainties regarding the origin of gayal.
Figure 1:

A picture showing a female gayal (Bos frontalis, provided by Kai-Xing Qu).

A picture showing a female gayal (Bos frontalis, provided by Kai-Xing Qu). Research has revealed a high genomic divergence among bovine species [17, 18]. Consequently, mapping of resequencing data from 1 bovine species onto the reference genome of different species (for instance, gayal vs cattle) creates avenues for biases and/or errors in sequence alignment and SNP calling procedures. This challenge extends to species of great research interest like gayal, which so far have no de novo assembled reference genome. For instance, Mei et al. recently reported a whole-genome sequencing (resequencing) of Chinese gayal [5]. In their analysis, they retrieved variants based on mapping gayal sequencing reads (×13.06) to the cattle reference genome. Importantly, hydride gayals are hard to distinguish only through morphological characterization, yet Mei et al. did not examine the karyotype of the gayal they resequenced. In contrast to the gayal, de novo genome assembly has been accomplished for related species like cattle (Bos taurus) [19], yak (Bos grunniens) [17], wisent (Bison bonasus) [20], North American bison (Bison bison) [21], zebu (Bos indicus) [22], and water buffalo (Bubalus bubalis) [23]. This represents a critical resource toward mitigating the challenges inherent in resequencing approaches and provides great opportunities to refine the evolutionary history of bovine species. In this study, we for the first time report the draft genome assembly of the gayal with a high sequencing depth generated on the Illumina genome sequencing platform. This valuable resource is important to the research of the origin and evolution of this species, which has been classified as endangered by the International Union for Conservation of Nature (IUCN).

Sample collection and sequencing

The gayal (NCBI taxonomy ID: 30 520) used for genome sequencing came from a Dulong in Yunnan province, China (Figure 1). It was kept at Yunnan Academy of Grassland and Animal Science for breeding and research purposes. Karyotype examination showed that it has 2n = 58 chromosomes (Figure 2). We extracted total genomic DNA from skin fibroblast cell lines of the gayal using the Qiagen Blood and Tissue Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. The cells are maintained at the Cell Bank of Kunming Institute of Zoology (specimen ID: KCB201042). A total of 17 paired-end genomic sequence libraries were constructed with a gradient insert size ranging from 180 bp to 20 kb, and sequencing was carried out on the Illumina HiSeq 2000 platform according to the manufacturer's instructions. For short insert size libraries (180 bp, 250 bp, 450 bp, and 600 bp), sequencing was performed at the Central Laboratory of Kunming Institute of Zoology with read lengths of 100 bp. Sequencing of long insert size libraries (800 bp, 2, 5, 10 and 20 kb) was conducted at BGI-Shenzhen with read lengths of 49 bp, except for the 800-bp insert size library, which was sequenced with a read length of 85 bp. A total of 350.38 Gb of raw sequence data has been generated in our study (Additional file 1: Table S1). Before assembly, we performed strict quality control by removing poor-quality reads and/or bases using scripts from SOAPec (version 2.02) [24]. Reads were shortened by 2 bp at both the head and tail. We dropped any read plus its corresponding paired end if it contained more than 30 low-quality bases or more than 5% unknown base (usually denoted by N). Reads with duplications and adapters were also removed. We corrected for sequencing errors using the k-mer (13 used in this study) frequency method in SOAPec (version 2.02) [24]. After filtering and correction, we retained 276.86 Gb of high-quality sequences for genome assembly (Additional file 1: Table S2).
Figure 2:

Karyotype of the gayal used for genome sequencing (provided by Wen-Hui Nie).

Karyotype of the gayal used for genome sequencing (provided by Wen-Hui Nie).

De novo assembly of gayal genome

In order to have a basic knowledge about the genome size and attributes of the gayal genome, we performed a 17-mer analysis using clean and high-quality sequences from 180 and 450 bp insert size libraries. We extracted the 17-mer sequences using sliding windows with a size of 17 bp and calculated the frequency of each 17-mer. A clear peak at ×25 with 2 upward convex signals apart from it is evident, suggesting high heterozygosity. The genome size for gayal is estimated to be 3.15 Gb (Figure 3; Additional file 1: Table S3).
Figure 3:

17-mer frequency distribution of sequencing reads.

17-mer frequency distribution of sequencing reads. We then performed de novo assembly of the gayal genome using Platanus (version 2.0; Platanus, RRID:SCR_015531) [25] in 3 steps: contig construction, scaffolding, and gap filling. To construct contigs based on short insert size libraries (180, 250, 450, 600, and 800 bp), we used Platanus (version 2.0) [25], which includes a series of procedures such as constructing de Bruijn graphs, clipping tips, merging bubbles, and removing low coverage links. In the scaffolding step, reads from both small and large insert libraries were mapped to contig sequences to construct scaffolds using distance information from read pairs. An additional local assembly of reads, with 1 end of a read pair uniquely aligned to a contig and the other end located within the gap, was performed using GapCloser (version 1.12; GapCloser, RRID:SCR_015026) [24]. These processes yielded a final draft gayal genome assembly with a total length of 2.85 Gb, contig N50 of 14.4 kb, and scaffold N50 of 2.74 Mb (Table 1). The assembled genome size is similar to that reported for cattle [26] and yak [17]. To assess the completeness of the assembled gayal genome, we performed BUSCO analysis (BUSCO, RRID:SCR_015008) [27] by searching against the arthropod universal benchmarking single-copy orthologs (BUSCOs, version 2.0). Overall, 85.2% and 7.8% of the 4104 expected vertebrate genes are identified in the assembled genome as complete and partial, respectively. Approximately 291 genes could be considered missing in our assembly. Of the expected complete vertebrate genes, 3434 and 60 are identified as single-copy and duplicated BUSCOs, respectively (Table 2). Our newly assembled gayal genome has a slightly lower completeness rate compared to genomes of yak [17], wisent [20], bison [21], zebu [22], and buffalo [23] (Table 2).
Table 1:

Statistics of the completeness of the hybrid de novo assembly of Bos frontalis genome

TermsContigScaffold
SizeNumberSizeNumber
N902461211 577158 6101357
N805335140 2371 060 177800
N70810999 9301 668 147587
N6011 04471 7642 170 469437
N5014 40550 5852 737 757320
Max length208 09913 764 521
Total length2 669 378 3342 848 570 279
Total number583 373460 059
Average length45756191
Number ≥ 500 bp394 757116 481
Number ≥ 1000 bp300 17853 989
Number ≥ 2000 bp229 79619 915
Number ≥ 5000 bp146 4935387
Table 2:

Statistics of the completeness of the assembled genomes for Bos frontalis and close related species by BUSCO (version 2)

SpeciesTermsComplete (C)Complete and single-copy (S)Complete and duplicated (D)Fragmented (F)Missing (M)
GayalNumber3494343460319291
Proportion, %85.1483.671.467.777.09
ZebuNumber3698364454158248
Proportion, %90.1188.791.323.856.04
WisentNumber3794376331180130
Proportion, %92.4591.690.764.393.17
YakNumber3841380932138125
Proportion, %93.5992.810.783.363.05
BuffaloNumber3817378037142145
Proportion, %93.0192.110.903.463.53
BisonNumber3779373544165160
Proportion, %92.0891.011.074.023.90
Statistics of the completeness of the hybrid de novo assembly of Bos frontalis genome Statistics of the completeness of the assembled genomes for Bos frontalis and close related species by BUSCO (version 2)

Annotation of genomic repeat sequences in the gayal genome

To search for the repeated sequences in the gayal genome, including tandem repeats, interspersed repeats, and transposable elements (TE; e.g., LINE, SINE, LTR, DNA transposons), we leveraged both de novo and homolog-based methods as used in previous publications [28, 29]. For the homolog-based methods, we used RepeatMasker (RepeatMasker, RRID:SCR_012954) and RepeatProteinMask [30] to search against the known Repbase TE library (RepBase21.01) [31] and TE protein database, respectively. In the de novo method, Piler [32] and RepeatModeler (RepeatModeler, RRID:SCR_015027) [33] are used to generate a de novo gayal repeat library, which is subsequently used in Repeat-Masker to annotate repeats. TRF [34] is then employed to predict tandem repeats. The combined results show that a total of 1.37 Gb of non-redundant repetitive sequences are identified in the gayal genome, which account for 48.13% of the whole genome. The most predominant repeat is the long interspersed nuclear elements (LINEs), which account for 40.43% (1.15 Gb in total) of the genome (Table 3; Additional file 1: Table S4, Figure S1, Figure S2).
Table 3:

Statistics of repeats in Bos frontalis genome

TypeRepeat size, bp% of genome
Trf17 696 1750.62
Repeatmasker868 885 92630.50
Proteinmask265 003 1489.30
De novo 917 371 71032.20
Total1 371 023 31248.13
Statistics of repeats in Bos frontalis genome

Gayal genome gene structure prediction

For gene structure prediction, we combined both de novo and homolog-based approaches to predict protein-coding genes in the gayal genome. In homolog-based method, gene sets from Bos taurus [19], Canis familiaris [35], Homo sapiens (ENSEMBL 80), Sus scrofa [36], Rattus norvegicus (ENSEMBL 80), and Ovis aries [37] were used as queries to search against the gayal genome (Additional file 1: Table S5). For the de novo–based method, AUGUSTUS (Augustus: Gene Prediction, RRID:SCR_008417) [38], Genescan (GENSCAN, RRID:SCR_012902) [39], and GlimmerHMM (GlimmerHMM, RRID:SCR_002654) [40] were used as engines to predict gene models. We then merged the gene prediction results derived from both methods using GLEAN [41] to generate a consensus gene set. In total, we have identified 26 667 protein-coding genes with a mean of 3.27 exons per gene (Table 4; Additional file 1: Figure S3). The lengths of genes, coding sequence (CDS), introns, and exons in gayal are comparable to those of the genomes used for homolog-based predictions (Additional file 1: Figure S3). In addition, we predicted non-coding RNA genes in the gayal genome. We used blast to search rRNA against the Human rRNA database, and tRNAscan-SE (tRNAscan-SE, RRID:SCR_010835) [42] to search tRNA in the genome sequences. We also used blast to search miRNA and snRNA via the Rfam database (release 11.0; Rfam, RRID:SCR_007891) [43]. We reveal a total of 2357 ribosomal RNA (rRNA), 29 821 transfer RNA (tRNA), 16 305 microRNAs (miRNA), and 1380 snRNA genes in the gayal genome (Additional file 1: Table S5).
Table 4:

General statistics of predicted protein-coding genes

Gene setTotalExon numberCDS length, bpmRNA length, bpExons per geneExon length, bpIntron length, bp
Homolog Bos taurus 19 666141 323132520 6187.191843118
Canis familiaris 17 627121 986132320 8026.921913290
Homo sapiens 24 783146 172110817 5675.891873360
Sus scrofa 20 283121 282114216 2885.971913041
Rattus norvegicus 17 988117 965127719 4696.551943273
Ovis aries 20 947147 367128720 9737.031833261
De novo AUGUSTUS41 227180 664112722 7864.382576403
GlimmerHMM27 067104 29487454333.852261597
Genescan46 598297 828132136 8286.392066585
Glean (final)26 66787 392115649963.273521686
General statistics of predicted protein-coding genes

Functional annotation of protein-coding genes

Gene functional annotation refers to searching functional motifs, domains, and possible biological processes by aligning translated gene coding sequences to known databases such as SwissProt and TrEMBL [44], NT database (from NCBI), Gene Ontology (GO, RRID:SCR_002811), and Kyoto Encyclopedia of Genes and Genomes (KEGG, RRID:SCR_012773) [45]. We have annotated all the protein-coding genes identified in this study to retrieve functional terms according to InterPro, KEGG, and GO terms. Overall, 81.74% (21 798), 54.56% (14 550), and 66.39% (17 704) genes show enrichment in InterPro, KEGG, and GO, respectively. In total, 25 916 protein-coding genes (97.18%) were successfully annotated for conserved functional motifs and functional terms (Additional file 1: Table S6).

Phylogenetic analysis and divergence time estimation

To investigate the phylogenic position of gayal, we retrieved nucleotide and protein data for cattle (Bos taurus) [19], yak (Bos grunniens) [17], wisent (Bison bonasus) [20], bison (Bison bison) [21], zebu (Bos indicus) [22], and buffalo (Bubalus bubalis) [23] from the NCBI database. Gene ortholog relationships of gayal and other bovine species were identified by reciprocal blast searching with an e-value of 1e-7. Genes with alternative splicing variants are represented by the longest transcript. Multiple sequence alignment of the genes within 1 copy gene set were performed using the MUSCLE program (MUSCLE, RRID:SCR_011812) [46]. Aligned sequences were trimmed to remove potentially unreliably aligned regions and gaps using Gblocks [47]. Alignments with lengths shorter than 100 bp were also discarded. Four-fold degenerate sites were extracted and concatenated into a supergene. Modeltest [48] was used to select the best substitution model. MrBayes (MrBayes, RRID:SCR_012067) [49] and RaxML (RAxML, RRID:SCR_006086) [50] software were used to reconstruct the evolutionary relationships between species, and MEGA5 [51] was used to view the tree. From these analyses, gayal clusters with the common ancestor of cattle and zebu (Figure 4).
Figure 4:

Phylogenetic trees of gayal and other bovine species. (A) Tree constructed based on maximum likelihood method. (B) Tree constructed using Bayesian inference.

Phylogenetic trees of gayal and other bovine species. (A) Tree constructed based on maximum likelihood method. (B) Tree constructed using Bayesian inference. Additionally, we sequenced the complete mitochondrial DNA (mtDNA, the first complete mtDNA of the gayal submitted to GenBank: MF614103) using the Sanger sequencing method, due to the fact that next-generation sequencing methods have lower ability and accuracy in recovering repeat sequences [28, 52], particularly in regions with rich GC content like the D-loop. We then downloaded mtDNA sequences of gayal and other bovine species from GenBank for phylogenic analysis. As shown in Figure 5 and Figure S4, the gayal we sequenced clusters with gaur (Figure 5; Additional file 1: Figure S4). Our results from both whole-genome and mtDNA data differ from the conclusion made by Mei et al., who mapped gayal genome resequencing data to a bovine reference [5]. Furthermore, the MCMCTREE program, implemented using the PAML (PAML, RRID:SCR_014932) [53] package, was used to estimate divergence times. The JC69 model and correlated molecular clock rates (clock = 3) were used in the calculation. Calibration time for the common ancestor of buffalo and cattle obtained from the TimeTree database [54] was used to calibrate the divergence time. This analysis estimated the divergence time of gayal from cattle and zebu at approximately 5.1 million years ago (Figure 6).
Figure 5:

Maximum likelihood trees of gayal and other bovine species using whole complete mtDNA. IDs in parentheses are GenBank accession number.

Figure 6:

Divergence time estimated between gayal and other bovine species.

Maximum likelihood trees of gayal and other bovine species using whole complete mtDNA. IDs in parentheses are GenBank accession number. Divergence time estimated between gayal and other bovine species. In conclusion, we have constructed a de novo assembly of the gayal genome, and we describe its genetic attributes. To our knowledge, this is the first de novo assembled genome for this species. We also demonstrate that together with the genomes of other bovine species, the new gayal genome supports investigations concerning the origin, evolutionary history, and local adaptation of gayal. This resource is also important for the future conservation of this endangered species. In addition, the de novo gayal genome adds to the list of available bovine genomes and has advantages over resequenced genomes in allowing accurate whole-genome alignment and retrieving constraint and/or rapidly evolved elements. It also strengthens the capacity to better assess introgression, incomplete lineage sorting (ILS), and structural variation (SV) among bovine species, as well as inferring their effects on the species tree. The assembled genome could be used as a reference in population genomic studies [55] of the gayal. Furthermore, comprehensive comparative analyses of these genomes will improve understanding of the formation and speciation of bovine species.

Availability of supporting data

The genome sequencing raw reads were deposited in the NCBI SRA database, project ID: PRJNA387130. The assembly and annotation of the gayal genome are available in the GigaScience database, GigaDB [56]. The complete mtDNA for the gayal generated by Sanger sequencing is also available in GenBank under the ID: MF614103. All supplementary figures and tables are provided in Additional file 1.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Y.P.Z., D.D.W., and M.S.W. designed the study. W.W. and Y.D. supervised the analyses. W.H.N., W.T.S., and J.H.W. cultivated the cells. Y.Z. and X.W. performed genome assembly and annotation. M.S.W. extracted genomic DNA and wrote the manuscript with the other authors’ input. M.S.W. and S.Q.Y. sequenced the gayal complete mitochondrial DNA and submitted to GenBank. S.W., Z.J.X., K.X.Q., N.O.O., D.Y., D.D.W., and Y.P.Z. revised the manuscript. All authors read and approved the final manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.
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7.  The genome sequence of taurine cattle: a window to ruminant biology and evolution.

Authors:  Christine G Elsik; Ross L Tellam; Kim C Worley; Richard A Gibbs; Donna M Muzny; George M Weinstock; David L Adelson; Evan E Eichler; Laura Elnitski; Roderic Guigó; Debora L Hamernik; Steve M Kappes; Harris A Lewin; David J Lynn; Frank W Nicholas; Alexandre Reymond; Monique Rijnkels; Loren C Skow; Evgeny M Zdobnov; Lawrence Schook; James Womack; Tyler Alioto; Stylianos E Antonarakis; Alex Astashyn; Charles E Chapple; Hsiu-Chuan Chen; Jacqueline Chrast; Francisco Câmara; Olga Ermolaeva; Charlotte N Henrichsen; Wratko Hlavina; Yuri Kapustin; Boris Kiryutin; Paul Kitts; Felix Kokocinski; Melissa Landrum; Donna Maglott; Kim Pruitt; Victor Sapojnikov; Stephen M Searle; Victor Solovyev; Alexandre Souvorov; Catherine Ucla; Carine Wyss; Juan M Anzola; Daniel Gerlach; Eran Elhaik; Dan Graur; Justin T Reese; Robert C Edgar; John C McEwan; Gemma M Payne; Joy M Raison; Thomas Junier; Evgenia V Kriventseva; Eduardo Eyras; Mireya Plass; Ravikiran Donthu; Denis M Larkin; James Reecy; Mary Q Yang; Lin Chen; Ze Cheng; Carol G Chitko-McKown; George E Liu; Lakshmi K Matukumalli; Jiuzhou Song; Bin Zhu; Daniel G Bradley; Fiona S L Brinkman; Lilian P L Lau; Matthew D Whiteside; Angela Walker; Thomas T Wheeler; Theresa Casey; J Bruce German; Danielle G Lemay; Nauman J Maqbool; Adrian J Molenaar; Seongwon Seo; Paul Stothard; Cynthia L Baldwin; Rebecca Baxter; Candice L Brinkmeyer-Langford; Wendy C Brown; Christopher P Childers; Timothy Connelley; Shirley A Ellis; Krista Fritz; Elizabeth J Glass; Carolyn T A Herzig; Antti Iivanainen; Kevin K Lahmers; Anna K Bennett; C Michael Dickens; James G R Gilbert; Darren E Hagen; Hanni Salih; Jan Aerts; Alexandre R Caetano; Brian Dalrymple; Jose Fernando Garcia; Clare A Gill; Stefan G Hiendleder; Erdogan Memili; Diane Spurlock; John L Williams; Lee Alexander; Michael J Brownstein; Leluo Guan; Robert A Holt; Steven J M Jones; Marco A Marra; Richard Moore; Stephen S Moore; Andy Roberts; Masaaki Taniguchi; Richard C Waterman; Joseph Chacko; Mimi M Chandrabose; Andy Cree; Marvin Diep Dao; Huyen H Dinh; Ramatu Ayiesha Gabisi; Sandra Hines; Jennifer Hume; Shalini N Jhangiani; Vandita Joshi; Christie L Kovar; Lora R Lewis; Yih-Shin Liu; John Lopez; Margaret B Morgan; Ngoc Bich Nguyen; Geoffrey O Okwuonu; San Juana Ruiz; Jireh Santibanez; Rita A Wright; Christian Buhay; Yan Ding; Shannon Dugan-Rocha; Judith Herdandez; Michael Holder; Aniko Sabo; Amy Egan; Jason Goodell; Katarzyna Wilczek-Boney; Gerald R Fowler; Matthew Edward Hitchens; Ryan J Lozado; Charles Moen; David Steffen; James T Warren; Jingkun Zhang; Readman Chiu; Jacqueline E Schein; K James Durbin; Paul Havlak; Huaiyang Jiang; Yue Liu; Xiang Qin; Yanru Ren; Yufeng Shen; Henry Song; Stephanie Nicole Bell; Clay Davis; Angela Jolivet Johnson; Sandra Lee; Lynne V Nazareth; Bella Mayurkumar Patel; Ling-Ling Pu; Selina Vattathil; Rex Lee Williams; Stacey Curry; Cerissa Hamilton; Erica Sodergren; David A Wheeler; Wes Barris; Gary L Bennett; André Eggen; Ronnie D Green; Gregory P Harhay; Matthew Hobbs; Oliver Jann; John W Keele; Matthew P Kent; Sigbjørn Lien; Stephanie D McKay; Sean McWilliam; Abhirami Ratnakumar; Robert D Schnabel; Timothy Smith; Warren M Snelling; Tad S Sonstegard; Roger T Stone; Yoshikazu Sugimoto; Akiko Takasuga; Jeremy F Taylor; Curtis P Van Tassell; Michael D Macneil; Antonio R R Abatepaulo; Colette A Abbey; Virpi Ahola; Iassudara G Almeida; Ariel F Amadio; Elen Anatriello; Suria M Bahadue; Fernando H Biase; Clayton R Boldt; Jeffery A Carroll; Wanessa A Carvalho; Eliane P Cervelatti; Elsa Chacko; Jennifer E Chapin; Ye Cheng; Jungwoo Choi; Adam J Colley; Tatiana A de Campos; Marcos De Donato; Isabel K F de Miranda Santos; Carlo J F de Oliveira; Heather Deobald; Eve Devinoy; Kaitlin E Donohue; Peter Dovc; Annett Eberlein; Carolyn J Fitzsimmons; Alessandra M Franzin; Gustavo R Garcia; Sem Genini; Cody J Gladney; Jason R Grant; Marion L Greaser; Jonathan A Green; Darryl L Hadsell; Hatam A Hakimov; Rob Halgren; Jennifer L Harrow; Elizabeth A Hart; Nicola Hastings; Marta Hernandez; Zhi-Liang Hu; Aaron Ingham; Terhi Iso-Touru; Catherine Jamis; Kirsty Jensen; Dimos Kapetis; Tovah Kerr; Sari S Khalil; Hasan Khatib; Davood Kolbehdari; Charu G Kumar; Dinesh Kumar; Richard Leach; Justin C-M Lee; Changxi Li; Krystin M Logan; Roberto Malinverni; Elisa Marques; William F Martin; Natalia F Martins; Sandra R Maruyama; Raffaele Mazza; Kim L McLean; Juan F Medrano; Barbara T Moreno; Daniela D Moré; Carl T Muntean; Hari P Nandakumar; Marcelo F G Nogueira; Ingrid Olsaker; Sameer D Pant; Francesca Panzitta; Rosemeire C P Pastor; Mario A Poli; Nathan Poslusny; Satyanarayana Rachagani; Shoba Ranganathan; Andrej Razpet; Penny K Riggs; Gonzalo Rincon; Nelida Rodriguez-Osorio; Sandra L Rodriguez-Zas; Natasha E Romero; Anne Rosenwald; Lillian Sando; Sheila M Schmutz; Libing Shen; Laura Sherman; Bruce R Southey; Ylva Strandberg Lutzow; Jonathan V Sweedler; Imke Tammen; Bhanu Prakash V L Telugu; Jennifer M Urbanski; Yuri T Utsunomiya; Chris P Verschoor; Ashley J Waardenberg; Zhiquan Wang; Robert Ward; Rosemarie Weikard; Thomas H Welsh; Stephen N White; Laurens G Wilming; Kris R Wunderlich; Jianqi Yang; Feng-Qi Zhao
Journal:  Science       Date:  2009-04-24       Impact factor: 47.728

8.  Analyses of pig genomes provide insight into porcine demography and evolution.

Authors:  Martien A M Groenen; Alan L Archibald; Hirohide Uenishi; Christopher K Tuggle; Yasuhiro Takeuchi; Max F Rothschild; Claire Rogel-Gaillard; Chankyu Park; Denis Milan; Hendrik-Jan Megens; Shengting Li; Denis M Larkin; Heebal Kim; Laurent A F Frantz; Mario Caccamo; Hyeonju Ahn; Bronwen L Aken; Anna Anselmo; Christian Anthon; Loretta Auvil; Bouabid Badaoui; Craig W Beattie; Christian Bendixen; Daniel Berman; Frank Blecha; Jonas Blomberg; Lars Bolund; Mirte Bosse; Sara Botti; Zhan Bujie; Megan Bystrom; Boris Capitanu; Denise Carvalho-Silva; Patrick Chardon; Celine Chen; Ryan Cheng; Sang-Haeng Choi; William Chow; Richard C Clark; Christopher Clee; Richard P M A Crooijmans; Harry D Dawson; Patrice Dehais; Fioravante De Sapio; Bert Dibbits; Nizar Drou; Zhi-Qiang Du; Kellye Eversole; João Fadista; Susan Fairley; Thomas Faraut; Geoffrey J Faulkner; Katie E Fowler; Merete Fredholm; Eric Fritz; James G R Gilbert; Elisabetta Giuffra; Jan Gorodkin; Darren K Griffin; Jennifer L Harrow; Alexander Hayward; Kerstin Howe; Zhi-Liang Hu; Sean J Humphray; Toby Hunt; Henrik Hornshøj; Jin-Tae Jeon; Patric Jern; Matthew Jones; Jerzy Jurka; Hiroyuki Kanamori; Ronan Kapetanovic; Jaebum Kim; Jae-Hwan Kim; Kyu-Won Kim; Tae-Hun Kim; Greger Larson; Kyooyeol Lee; Kyung-Tai Lee; Richard Leggett; Harris A Lewin; Yingrui Li; Wansheng Liu; Jane E Loveland; Yao Lu; Joan K Lunney; Jian Ma; Ole Madsen; Katherine Mann; Lucy Matthews; Stuart McLaren; Takeya Morozumi; Michael P Murtaugh; Jitendra Narayan; Dinh Truong Nguyen; Peixiang Ni; Song-Jung Oh; Suneel Onteru; Frank Panitz; Eung-Woo Park; Hong-Seog Park; Geraldine Pascal; Yogesh Paudel; Miguel Perez-Enciso; Ricardo Ramirez-Gonzalez; James M Reecy; Sandra Rodriguez-Zas; Gary A Rohrer; Lauretta Rund; Yongming Sang; Kyle Schachtschneider; Joshua G Schraiber; John Schwartz; Linda Scobie; Carol Scott; Stephen Searle; Bertrand Servin; Bruce R Southey; Goran Sperber; Peter Stadler; Jonathan V Sweedler; Hakim Tafer; Bo Thomsen; Rashmi Wali; Jian Wang; Jun Wang; Simon White; Xun Xu; Martine Yerle; Guojie Zhang; Jianguo Zhang; Jie Zhang; Shuhong Zhao; Jane Rogers; Carol Churcher; Lawrence B Schook
Journal:  Nature       Date:  2012-11-15       Impact factor: 49.962

9.  Data, information, knowledge and principle: back to metabolism in KEGG.

Authors:  Minoru Kanehisa; Susumu Goto; Yoko Sato; Masayuki Kawashima; Miho Furumichi; Mao Tanabe
Journal:  Nucleic Acids Res       Date:  2013-11-07       Impact factor: 16.971

10.  Whole-genome sequencing of the endangered bovine species Gayal (Bos frontalis) provides new insights into its genetic features.

Authors:  Chugang Mei; Hongcheng Wang; Wenjuan Zhu; Hongbao Wang; Gong Cheng; Kaixing Qu; Xuanmin Guang; Anning Li; Chunping Zhao; Wucai Yang; Chongzhi Wang; Yaping Xin; Linsen Zan
Journal:  Sci Rep       Date:  2016-01-25       Impact factor: 4.379

View more
  10 in total

Review 1.  Cyanobacterial secondary metabolites towards improved commercial significance through multiomics approaches.

Authors:  Shaloo Verma; Shobit Thapa; Nahid Siddiqui; Hillol Chakdar
Journal:  World J Microbiol Biotechnol       Date:  2022-04-29       Impact factor: 3.312

2.  De novo transcriptome sequencing of the northern fowl mite, Ornithonyssus sylviarum, shed light on parasitiform poultry mites evolution and its chemoreceptor repertoires.

Authors:  Biswajit Bhowmick; Huaqing Chen; Jesus Lozano-Fernandez; Joel Vizueta; Rickard Ignell; Qian Han
Journal:  Parasitol Res       Date:  2022-01-15       Impact factor: 2.289

3.  Draft genome of the gayal, Bos frontalis.

Authors:  Ming-Shan Wang; Yan Zeng; Xiao Wang; Wen-Hui Nie; Jin-Huan Wang; Wei-Ting Su; Newton O Otecko; Zi-Jun Xiong; Sheng Wang; Kai-Xing Qu; Shou-Qing Yan; Min-Min Yang; Wen Wang; Yang Dong; Dong-Dong Wu; Ya-Ping Zhang
Journal:  Gigascience       Date:  2017-11-01       Impact factor: 6.524

4.  First Draft Genome of the Sable, Martes zibellina.

Authors:  Guangshuai Liu; Chao Zhao; Dongming Xu; Huanxin Zhang; Vladimir Monakhov; Shuai Shang; Xiaodong Gao; Weilai Sha; Jianzhang Ma; Wei Zhang; Xuexi Tang; Bo Li; Yan Hua; Xiaofang Cao; Zhen Liu; Honghai Zhang
Journal:  Genome Biol Evol       Date:  2020-03-01       Impact factor: 3.416

5.  ADAR1 mediated regulation of neural crest derived melanocytes and Schwann cell development.

Authors:  Nadjet Gacem; Anthula Kavo; Lisa Zerad; Laurence Richard; Stephane Mathis; Raj P Kapur; Melanie Parisot; Jeanne Amiel; Sylvie Dufour; Pierre de la Grange; Veronique Pingault; Jean Michel Vallat; Nadege Bondurand
Journal:  Nat Commun       Date:  2020-01-10       Impact factor: 14.919

Review 6.  Genomic insights into ruminant evolution: from past to future prospects.

Authors:  Bao Wang; Le Chen; Wen Wang
Journal:  Zool Res       Date:  2019-11-18

Review 7.  Advances in the pathogenesis of psoriasis: from keratinocyte perspective.

Authors:  Xue Zhou; Youdong Chen; Lian Cui; Yuling Shi; Chunyuan Guo
Journal:  Cell Death Dis       Date:  2022-01-24       Impact factor: 9.685

8.  Gaur genome reveals expansion of sperm odorant receptors in domesticated cattle.

Authors:  Wai Yee Low; Benjamin D Rosen; Yan Ren; Derek M Bickhart; Thu-Hien To; Fergal J Martin; Konstantinos Billis; Tad S Sonstegard; Shawn T Sullivan; Stefan Hiendleder; John L Williams; Michael P Heaton; Timothy P L Smith
Journal:  BMC Genomics       Date:  2022-05-04       Impact factor: 4.547

9.  A draft genome of Drung cattle reveals clues to its chromosomal fusion and environmental adaptation.

Authors:  Yan Chen; Tianliu Zhang; Ming Xian; Rui Zhang; Weifei Yang; Baqi Su; Guoqiang Yang; Limin Sun; Wenkun Xu; Shangzhong Xu; Huijiang Gao; Lingyang Xu; Xue Gao; Junya Li
Journal:  Commun Biol       Date:  2022-04-13

10.  Consequences of Domestication on Gut Microbiome: A Comparative Study Between Wild Gaur and Domestic Mithun.

Authors:  Vandana R Prabhu; Ranganathan Kamalakkannan; Moolamkudy Suresh Arjun; Muniyandi Nagarajan
Journal:  Front Microbiol       Date:  2020-02-25       Impact factor: 5.640

  10 in total

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