Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

The complete genome sequence of the African buffalo (Syncerus caffer)

BMC Genomics volume 17, Article number: 1001 (2016) Cite this article

Abstract

Background

The African buffalo (Syncerus caffer) is an important role player in the savannah ecosystem. It has become a species of relevance because of its role as a wildlife maintenance host for an array of infectious and zoonotic diseases some of which include corridor disease, foot-and-mouth disease and bovine tuberculosis. To date, no complete genome sequence for S. caffer had been available for study and the genomes of other species such as the domestic cow (Bos taurus) had been used as a proxy for any genetics analysis conducted on this species. Here, the high coverage genome sequence of the African buffalo (S. caffer) is presented.

Results

A total of 19,765 genes were predicted and 19,296 genes could be successfully annotated to S. caffer while 469 genes remained unannotated. Moreover, in order to extend a detailed annotation of S. caffer, gene clusters were constructed using twelve additional mammalian genomes. The S. caffer genome contains 10,988 gene clusters, of which 62 are shared exclusively between B. taurus and S. caffer.

Conclusions

This study provides a unique genomic perspective for the S. caffer, allowing for the identification of novel variants that may play a role in the natural history and physiological adaptations.

Background

The African buffalo (Syncerus caffer) is the largest bovid species in the African savannah ecosystem. Buffalo are of great ecological importance because of their role as bulk feeders in the grazing hierarchy. Due to their size they are able to process taller and coarser grasses than most other species [1], playing an important facilitative role for the smaller grazers [2]. They inhabit zones with almost all vegetation types, provided a permanent water source is present. In addition, they are an important prey species and have high economic value in the ecotourism and hunting industries [3]. The African buffalo also hosts a vast array of nematodes, pathogens and infectious diseases and plays an important role in the maintenance and transmission of economically important livestock diseases such as foot-and-mouth disease (FMD), bovine brucellosis, corridor disease and bovine tuberculosis (BTB) [4, 5]. For numerous other diseases, buffalo may act as amplifier or incidental hosts, as is the case with ehrlichiosis, Rift Valley fever and anthrax [6]. It is assumed that the African buffalo, unlike some domestic bovids (e.g. cattle), may exhibit partial resistance to some of these diseases, highlighting the importance of understanding the genetic mechanisms at work.

Advances in methods for characterizing the genetic variation in individuals, populations and species have revolutionized ecological research. Population genetic diversity, inbreeding, hybridization, species designations, dispersal patterns and evolutionary processes are just a few of the applications of genetic data in the conservation and management of wildlife [7, 8]. One of the greatest challenges of working with non-model species is the lack of availability of genome variation data with which to design these studies [9]. The assembly of an accurate genome for important non-model study species provides an invaluable resource for research. Complete and accurate reference genome information prevents the erroneous identification of polymorphisms, and misalignments [10]. In the absence of a complete reference genome, a related species can be used as a proxy reference to facilitate the identification of various single nucleotide variants (SNVs), but the amount of sequence that can be accurately mapped may be insignificant [11].

The identification of novel genetic variants in an important species such as the African buffalo may help to provide answers to numerous genetic and other research questions, including disease susceptibility, that have heretofore been unattainable [12]. Previously, we aligned buffalo short reads obtained from high-throughput sequencing to the B. taurus genome assembly to facilitate SNV discovery [13]. Here, we present the first complete de novo assembled full-length genome for the African buffalo with an assembled N50 contig size reaching 43 kilobase pairs (kbp) and an N50 scaffold length of 2.4 Mb, which represents the first fully sequenced and de novo assembled sequence of the African buffalo. We carried out a number of additional analyses, including evolutionary analyses and genetic content. Our results will significantly aid in understanding the genetics of the African buffalo and contribute to the fields of molecular ecology, population genetics and disease susceptibility, ultimately supporting conservation and management efforts.

Results

Twelve mate-pair libraries with different insert sizes were prepared using DNA from a 2-year old male buffalo and sequenced to a high (60-fold) coverage on an Illumina Hi-Seq 2000 and assembled de novo. Following data filtration and the removal of low quality reads, a total of 242.39 Gbp of usable sequence (equating to 89.78-fold coverage of the whole genome) and an average read length of 78.67 bp was obtained (Additional file 1: Table S1). The total length of the genome assembly equated to 2.68 Gbp and a total of 1235 scaffold contigs made up 90% of the genome assembly and 97.9% of the estimated length (Additional file 2: Table S2). It was estimated that the genome size of S. caffer is 2732 Mbp (Additional file 3: Figure S1). A total of 19,765 genes were predicted and of these a total of 19,296 genes were annotated to S. caffer while a total of 469 genes could not be annotated (Table 1). Data analysis and annotation of the non-coding RNA identified a large number of miRNA and tRNA (31,940 and 36,163 respectively). The estimated heterozygosity ratio of the sequenced buffalo was 0.6 × 10−3 (Additional file 4: Figure S2) and we estimated 1,639,766 heterozygous SNVs.

Table 1 Assembly and annotation of the S. caffer genome
Full size table

To extend a detailed annotation, the S. caffer genome, gene clusters were constructed using twelve additional mammalian genomes (Felis catus, Rattus norvegicus, Pan troglodytes, Canis familiaris, Equus caballus, H. sapiens, S. scrofa, O. aries, T. truncatus, B. taurus, Copelatus ferus and M. musculus). The S. caffer genome contains 10,988 orthologous gene clusters and a total of 7321 are shared among four species (Table 2; Fig. 1a). A total of 62 predicted gene clusters are shared exclusively between S. caffer and B. taurus and 179 are unique to S. caffer (Fig. 1a). It was determined that the divergence time between S. caffer and B. taurus is 5.7–9.3 million years ago (MYA) (Additional file 5: Figure S5).

Table 2 Summary of gene families of S. caffer and twelve other mammalian genomes
Full size table
Fig. 1
figure 1

Analysis of orthologous gene families. a. Unique and shared gene families between the S. caffer genome and other species. b. Evolution of ortholog gene clusters. The estimated numbers of ortholog groups in the common ancestral species are shown in the internal nodes. The numbers of orthologous groups that have expanded or contracted in each lineage are shown on the corresponding branch, with + referring to expansion and – referring to contraction. The S. caffer genome has 10,988 orthologous gene families and a total of 7321 are shared among four species. A total of 62 are shared exclusively between S. caffer and B. taurus and 179 predicted genes that are unique to S. caffer. Both figures were based on the comparison of orthologous gene families among thirteen mammalian species

Full size image

To investigate signatures of selection we obtained a total of 2236 1:1 orthologous gene sets in S. caffer, B. taurus, C. ferus, O. aries, S. scrofa and T. truncates and this was based on the gene family results. Finally, we inferred 120 genes which contain positive selected sites in buffalo. These include ubiquitin carboxyl-terminal hydrolase 26 (UCHL26), Interleukin 19 (IL19) and Cyclin B (CCNB). Based on the comparison of orthologous gene families among the 12 mammalian species, the S. caffer genome has 538 expanded and 2251 contracted gene families when compared to B. taurus as a common ancestor (Fig. 1b). The expanded genes were coupled to a large variety of GO terms, including G-coupled protein and olfactory receptors.

Discussion

The African buffalo has become a species of great interest in recent years because it serves as maintenance host for numerous infectious and zoonotic diseases such as FMD, corridor disease and bovine tuberculosis [5]. In addition, its high economic value in the ecotourism and trophy hunting industries make this species invaluable to game ranchers and breeders [4, 5, 14]. The African buffalo is one of only three main species of buffalo found in the world. The other two include the American Bison (Bison bison) and the domesticated Asiatic buffalo (Bubalus bubalis). Reference genomes of 2.82 Gbp and 2.77 Gbp have been assemble d for these respectively, but are not publically available [15, 16]. The Bubalus bubalis genome was found to encode 21,550 protein coding genes, which is comparable to the African buffalo genome we report here, which is 2.73 Gbp in size with a total of 19,765 predicted protein coding genes and of these 19,296 were annotated. Moreover, the annotation of non-coding RNA identified unexpectedly large numbers of miRNA and tRNA [17, 18]. The use of standard tools namely tRNAscanner SE (version 1.23) as well as alignment using BLAST and INFERNAL for de novo non-coding RNA annotation consistently identified 31,940 miRNA genes. The large number is implausible (data not shown), suggesting the need for improved prediction tools in some species including S. caffer.

Although big game species such as the African lion, African elephant and the white rhino have been sequenced [19–21], the only suitable reference genome publically available for the alignment of African buffalo sequences was that of B. taurus [13]. S. caffer has 52 chromosomes [22] compared to the 30 chromosomes found in B. taurus [23]. Our analyses here estimated 1,639,766 heterozygous SNVs in the sequenced buffalo, compared to the 3,833,249 heterozygous variants identified in the Holstein B. taurus genome [24]. The African buffalo is not an antecedent of the cow and their most recent common ancestor is estimated to have existed approximately 5 - 10 MYA, at the time of divergence of the sub-tribes Bubalina, which is composed of the Syncerus and Bubalus genera and Bovina, comprising the Bos and Bison genera [25, 26]. In previous work, we determined that only 19 to 23% of the low coverage buffalo short reads mapped to the cow reference genome using BWA and Bowtie, illustrating the need for a species-specific reference [13].

We anticipate that the annotated African buffalo genome will facilitate our future genetic association studies of susceptibility to BTB, which is a threat to conservation areas in South Africa [27]. Previously, we identified novel SNVs by Sanger sequencing conserved regions across species, a process that was time-consuming and often resulted in non-specific amplification [28]. Alternatively, we had to rely on short reads mapped to an unsuitable reference genome [13]. It will now be possible to design species-specific primers for susceptibility genes of interest, based on the gene annotation completed here. Low coverage genome sequencing of additional buffalo will allow us to establish a SNV database, which would also be a resource for future population genetic and disease association studies [29].

Several of the genes that were substantially expanded and contracted in the African buffalo compared to other mammals are involved in immunity. This includes the contracted genes Chemokine (C-X-C motif) ligand 2 (CXCL2) and complement component 8 alpha subunit (C8A) as well as the expanded genes T cell receptor gamma variable 3 (TRGV3) and Killer immunoglobulin-like receptor KIR3DL splice variant 3 (KIR3DL). In total it is estimated that there are 175 genes involved in immune responses in the African buffalo and these are possible candidate genes to investigate in disease susceptibility studies.

Conclusions

In summary, the African buffalo genome offers unique insight into the phylogenetic history and adaptation of an ecologically important species. Additionally, the availability of a complete reference genome allows for improved mapping of short reads, thereby aiding in novel SNV discovery and future genetic studies.

Methods

Buffalo blood was obtained from a number of S. caffer individuals from the southern section of the Kruger National Park, South Africa for other projects. Based on the quantity and quality of the DNA available and to meet the sequencing requirements, the DNA from a 2-year old male was chosen for DNA sequencing. DNA was extracted from whole blood using the salt-chloroform extraction method [30]. No ethics approval was required as the DNA was collected and extracted for a previous study, under the directive of South African National Parks (SANParks), and its use in the present study is incidental.

Genome assembly for S. caffer

All libraries were sequenced using an Illumina Hi-Seq 2000 instrument. The sequencing libraries were constructed with insert sizes of 170 bp, 500 bp, 800 bp, 2 kbp, 5 kbp and 10 kbp respectively. S. caffer genome scaffolds (1235 scaffolds; N50 contig: 43 kbp, N50 scaffold: 2.4 Mb) were aligned to the reference B. taurus genome assembly. The protein coding genes in S. caffer were determined through the integration of annotations from homology-based methods as well as de novo gene assembly. For homology-based prediction, proteins from B. taurus, Homo sapiens, Mus musculus, Ovis aries, Sus scrofa, Tursiops truncatus were mapped to the S. caffer genome using TblastN [31] and were then submitted to GeneWise version 2.2.0 [32] in order to obtain gene models. For de novo prediction, two software programs were used: SNAP [33] and AUGUSTUS [34].

Genome size evaluation

The genome size of an individual can be estimated from the K-mer frequency of the read data. Importantly, the K should be large enough that most of the genome can be distinguished. For most eukaryotic genomes, a K-mer value of 17 is used (K = 17). For the present study, a total of 74 Gb (approximately 30X) of the data could be retained for 17-mer analyses. Simulations were done to estimate the heterozygosity ratio.

Genome assembly

The short reads were assembled using the latest version of SOAPdenovo (http://soap.genomics.org.cn/soapdenovo.html), a genome assembler that has been developed for specific use with next generation short read sequences [35]. SOAPdenovo uses the de Bruijn graph algorithm, which is sensitive to sequencing errors and for this reason, only high-quality, filtered data were used for the de novo assembly. Gaps in the initial assembly were filled using Gapcloser [35]. Short reads from fragmented small insert-size libraries (<500 bp) were assembled into contigs using sequence overlap information. Contigs were not extended into regions in which repeat sequences created ambiguous associations. The resulting assembly contained a small contig N50 with a length of 43 kbp and a scaffold N50 length of 2.4 Mbp.

GC content and sequencing depth analysis

The completeness of the genome assembly was evaluated by aligning sequence reads to the newly generated genome assembly and subsequently determining the percentage of total aligned reads. High quality reads i.e. reads with a percentage of high quality bases that satisfies a user-specific cutoff, with an average coverage of 38-fold were aligned to the S. caffer genome using BWA-MEM [36]. A total of 99.93% reads were aligned to the assembly.

Variable GC content difference is a primary determinant for the non-random distribution of sequencing depth [37]. Moreover, the distribution of GC content vs. sequencing depth is a means of ascertaining sequencing bias or contamination (Additional file 6: Figure S3 and Additional file 7: Figure S4). Regions with very low GC contents (<20%) or very high GC contents (>80%) will have a low sequencing depth. Should specific regions of the genome have a GC content that is significantly different from that predicted, it is reasonable to assume that there may be bacterial, viral or fungal contamination in the sample and reads should be eliminated by additional alignment. For the purposes of this study, two sequencing depth distribution blocks were obtained; one with an average sequencing depth of 38X and the other with a sequencing depth of 19X. The sample that was sequenced was a male, and it is anticipated that hemizygous regions will have lower coverage depth because there is only one allelic counterpart on the Y chromosome. A total of 2825 sequences are found at low coverage (19X) (Additional file 7: Figure S4) and were subsequently aligned to the X and Y chromosomes of B. taurus. It was found that 99.85% of the sequences align directly to the X and Y chromosome of B. taurus while 0.6 and 0.64% align to the fungi and bacterial databases respectively. It was therefore concluded that this block of low coverage region forms part of the sex chromosomes and would thus not adversely affect the de novo assembly.

Gene annotation

  1. 1.

    Repetitive element annotation

    Tandem Repeats Finder (TRF) was used to identify non-interspersed repetitive elements. Transposable elements (TEs) were predicted in the homology searches found in Repbase TE libraries using Repeat ProteinMask and RepeatMasker (Additional file 8: Table S3, Additional file 9: Table S4 and Additional file 10: Table S5).

  2. 2.

    Non-coding RNA annotation

    A total of four non-coding RNA (ncRNA) types were annotated.

    • micro RNA (miRNA)

    • transfer RNA (tRNA)

    • ribosomal RNA (rRNA)

    • small nuclear RNA (sn-RNA)

    All four of these RNA types were found in S. caffer genome using the complete genome sequence. Scanning for tRNA was performed using tRNAscan-SE [38] by using a short interspersed elements (SINEs) premasked genome to search for reliable tRNA positions. The snRNAs and miRNAs were identified by aligning with BLAST and INFERNAL to search for putative sequences in the Rfam database [39]. The rRNA fragments were identified by aligning the rRNA template sequences from the human genome using BlastN.

  3. 3.

    Gene prediction

    The protein coding genes in S. caffer were determined through the integration of annotations from homology-based methods as well as de novo gene assembly. For homology-based prediction, proteins from B. taurus, H. sapiens, M. musculus, O. aries, S. scrofa, T. truncates and mapped to the buffalo genomes using TblastN [31] and were then submitted to GeneWise [32] in order to obtain gene models. For de novo prediction, two software programs were used: SNAP [33] and Augustus [34] with gene model parameters trained from H. sapiens, and filtered partial genes and small genes that had less than 150 bp coding length. This followed with the alignment of the predictions to a TE protein database using BlastP [31] with an E-value ≤ 1e-5 and filtered TE-derived genes that had more than 50% alignment rate (Additional file 11: Table S6).

Gene family construction

To determine the genetic evolution in S. caffer, gene cluster analysis included the genomes of 13 mammals. Proteins of all genes for each of the species chosen were analyzed using Treefam [40]. All proteins sequences were aligned to themselves using BlastP [31] with E-value cut-off of 1e-7 (Table 2).

Phylogeny and divergence

A total of 1745 single copy gene clusters were identified and were used to construct a phylogenetic tree of the 13 mammal species. MUSCLE [3] was used for alignment and gaps removed by Gblocks [41]. A total of 2,265,138 (55.45%) remained and were used to construct the phylogeny. Moreover, the species divergence time was estimated based on 195,689 fourfold degenerate sites via Bayesian estimation approach using PAML [42]. The phylogenetic relationship of S. caffer and twelve additional mammals was predicted. The data can be accessed using the following link: http://purl.org/phylo/treebase/phylows/study/TB2:S20207?x-access-code=f3bdb11f9f55ac0609abf60ab1c01255&format=html.

Gene family expansion and contraction

The evolutionary changes in the protein family size (expansion or contraction) were analyzed using the CAFÉ program [43]. This package assesses the protein family expansion or contraction based on the topology of the phylogenetic tree (Fig. 1).

Branch site positive selection

Orthologues were aligned using the PRANK alignment algorithm available the GUIDANCE software program [44], which can improve the performance of positive selection inference by filtering out unreliable alignment regions.

Ka/Ks of S. caffer and B. taurus

The ratio of the total number of non-synonymous substitutions per non-synonymous site (Ka) to the number of synonymous substitutions per synonymous site (Ks), referred to as Ka/Ks, can be used as an indicator of selective pressure acting on a protein-coding gene. In total, 19,994 orthologues from both S. caffer and B. taurus were chosen using Reciprocal Best Hits (RBH) methodology [45] based on the BLAST alignment. Subsequently, the coding regions of the two species were aligned using webPRANK [46] and unreliable regions were removed. Ka/Ks scores were calculated using the KaKs Calculator [47].

Abbreviations

BTB:

Bovine tuberculosis

FMD:

Foot and mouth disease

kbp:

kilobase pairs

MYA:

Million years ago

RBH:

Reciprocal best hits

SANParks:

South African National Parks

SINE:

Short interspersed elements

SNVs:

Single nucleotide variants

TE:

Transposable elements

TRF:

Tandem repeats finder.

References

  1. Michel AL, Bengis RG. The African buffalo: a villain for inter-species spread of infectious diseases in Southern Africa. Onderstepoort J Vet Res. 2012;79:453.

    Article  PubMed  Google Scholar 

  2. Jolles AE. Population biology of African buffalo (Syncerus caffer) at Hluhluwe-iMfolozi Park. South Africa Afr J Ecol. 2007;45:398–406.

    Article  Google Scholar 

  3. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113.

    Article  PubMed  PubMed Central  Google Scholar 

  4. le Roex N, Koets AP, van Helden PD, Hoal EG. Gene polymorphisms in African buffalo associated with susceptibility to bovine tuberculosis infection. PLoS One. 2013;8:e64494.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Grobler JP, Van Der Bank FH. Genetic diversity and isolation in African Buffalo (Syncerus caffer). Biochem Syst Ecol. 1996;24:757–61.

    Article  CAS  Google Scholar 

  6. Beechler BR, Bengis R, Swanepoel R, Paweska JT, Kemp A, van Vuren PJ, et al. Rift valley Fever in Kruger national park: do buffalo play a role in the inter-epidemic circulation of virus? Transbound Emerg Dis. 2015;62:24–32.

    Article  CAS  PubMed  Google Scholar 

  7. Kirk H, Freeland JR. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci. 2011;12:3966–88.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shafer ABA, Wolf JBW, Alves PC, Bergström L, Bruford MW, Brännström I, et al. Genomics and the challenging translation into conservation practice. Trends Ecol Evol. 2015;30:78–87.

    Article  PubMed  Google Scholar 

  9. Garvin MR, Saitoh K, Gharrett AJ. Application of single nucleotide polymorphisms to non‐model species: a technical review. Mol Ecol Resour. 2010;10:915–34.

    Article  CAS  PubMed  Google Scholar 

  10. Baker M. De novo genome assembly: what every biologist should know. Nat Methods. 2012;9:333–7.

    Article  CAS  Google Scholar 

  11. Everett MV, Grau ED, Seeb JE. Short reads and nonmodel species: exploring the complexities of next‐generation sequence assembly and SNP discovery in the absence of a reference genome. Mol Ecol Resour. 2011;11:93–108.

    Article  PubMed  Google Scholar 

  12. le Roex N, van Helden PD, Koets AP, Hoal EG. Bovine TB in livestock and wildlife: what’s in the genes? Physiol Genomics. 2013;45:631–7.

    Article  PubMed  Google Scholar 

  13. le Roex N, Noyes H, Brass A, Bradley DG, Kemp SJ, Kay S, et al. Novel SNP Discovery in African Buffalo, Syncerus caffer, using high-throughput Sequencing. PLoS One. 2012;7:e48792.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Booth VR. A comparison of the prices of hunting tourism in Southern and Eastern Africa. Budapest: International Council for Game and Wildlife Conservation and Food and Agriculture Organisation of the United Nations; 2009.

  15. Dobson LK. Sequencing the Genome of the North American Bison [Internet] [Thesis]. 2015 [cited 2016 Jul 13]. Available from: http://oaktrust.library.tamu.edu/handle/1969.1/155759

  16. Water buffalo genome sequenced [Internet]. [cited 2016 Jul 14]. Available from: http://www.cabi.org/animalscience/news/23623

  17. Iben JR, Maraia RJ. tRNAomics: tRNA gene copy number variation and codon use provide bioinformatic evidence of a new anticodon:codon wobble pair in a eukaryote. RNA. 2012;18:1358–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ghorai A, Ghosh U. miRNA gene counts in chromosomes vary widely in a species and biogenesis of miRNA largely depends on transcription or post-transcriptional processing of coding genes. Front. Genet. [Internet]. 2014 [cited 2016 Jul 13];5. Available from: http://journal.frontiersin.org/article/10.3389/fgene.2014.00100/abstract

  19. Elephant (Loxodonta africana) Genome Browser Gateway [Internet]. [cited 2016 Feb 19]. Available from: http://genome.ucsc.edu/cgi-bin/hgGateway?org=Elephant&db=loxAfr3&hgsid=154866831

  20. Pre Ensembl 78: Ceratotherium simum simum - Description [Internet]. [cited 2016 Feb 19]. Available from: http://pre.ensembl.org/Ceratotherium_simum_simum/Info/Index

  21. Cho YS, Hu L, Hou H, Lee H, Xu J, Kwon S, et al. The tiger genome and comparative analysis with lion and snow leopard genomes. Nat Commun. 2013;4:2433.

    PubMed  PubMed Central  Google Scholar 

  22. Smitz N, Berthouly C, Cornélis D, Heller R, Van Hooft P, Chardonnet P, et al. Pan-African Genetic Structure in the African Buffalo (Syncerus caffer): Investigating Intraspecific Divergence. PLoS ONE [Internet]. 2013 [cited 2016 Jul 14];8. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578844/

  23. Qiu Q, Zhang G, Ma T, Qian W, Wang J, Ye Z, et al. The yak genome and adaptation to life at high altitude. Nat Genet. 2012;44:946–9.

    Article  CAS  PubMed  Google Scholar 

  24. Kõks S, Lilleoja R, Reimann E, Salumets A, Reemann P, Jaakma Ü. Sequencing and annotated analysis of the Holstein cow genome. Mamm Genome Off J Int Mamm Genome Soc. 2013;24:309–21.

    Article  Google Scholar 

  25. Bovine Genome Sequencing and Analysis Consortium, Elsik CG, Tellam RL, Worley KC, Gibbs RA, Muzny DM, et al. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science. 2009;324:522–8.

    Article  Google Scholar 

  26. MacEachern S, McEwan J, Goddard M. Phylogenetic reconstruction and the identification of ancient polymorphism in the Bovini tribe (Bovidae, Bovinae). BMC Genomics. 2009;10:177.

    Article  PubMed  PubMed Central  Google Scholar 

  27. le Roex N, Cooper D, van Helden PD, Hoal EG, Jolles AE. Disease Control in Wildlife: Evaluating a Test and Cull Programme for Bovine Tuberculosis in African Buffalo. Transbound Emerg Dis. 2015;63:647–57.

  28. Müller A, Möller M, Adams LA, Warren RM, Hoal EG, van Helden PD. Comparative analysis of a putative tuberculosis-susceptibility gene, MC3R, and pseudogene sequences in cattle, African buffalo, hyena, rhinoceros and other African bovids and ruminants. Cytogenet Genome Res. 2012;136:117–22.

    Article  PubMed  Google Scholar 

  29. le Roex N, Berrington CM, Hoal EG, van Helden PD. Selective breeding: the future of TB management in African buffalo? Acta Trop. 2015;149:38–44.

    Article  PubMed  Google Scholar 

  30. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mount DW. Using the Basic Local Alignment Search Tool (BLAST). CSH Protoc. 2007;2007:pdb.top17.

  32. Birney E, Clamp M, Durbin R. GeneWise and Genomewise. Genome Res. 2004;14:988–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003;19:ii215–25.

    Article  PubMed  Google Scholar 

  34. Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5:59.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008;24:713–4.

    Article  CAS  PubMed  Google Scholar 

  36. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma Oxf Engl. 2009;25:1754–60.

    Article  CAS  Google Scholar 

  37. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008;456:53–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lowe TM, Eddy SR. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucleic Acids Res. 1997;25:0955–64.

    Article  CAS  Google Scholar 

  39. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res. 2003;31:439–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Li H, Coghlan A, Ruan J, Coin LJ, Hériché J-K, Osmotherly L, et al. TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 2006;34:D572–80.

    Article  CAS  PubMed  Google Scholar 

  41. Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56:564–77.

    Article  CAS  PubMed  Google Scholar 

  42. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91.

    Article  CAS  PubMed  Google Scholar 

  43. De Bie T, Cristianini N, Demuth JP, Hahn MW. CAFE: a computational tool for the study of gene family evolution. Bioinforma Oxf Engl. 2006;22:1269–71.

    Article  Google Scholar 

  44. Penn O, Privman E, Ashkenazy H, Landan G, Graur D, Pupko T. GUIDANCE: a web server for assessing alignment confidence scores. Nucleic Acids Res. 2010;38:W23–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ward N, Moreno-Hagelsieb G. Quickly Finding Orthologs as Reciprocal Best Hits with BLAT, LAST, and UBLAST: How Much Do We Miss? PLoS ONE. 2014;9:e101850.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Löytynoja A, Goldman N. webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinformatics. 2010;11:579.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhang Z, Li J, Zhao X-Q, Wang J, Wong GK-S, Yu J. KaKs_Calculator: calculating Ka and Ks through model selection and model averaging. Genomics Proteomics Bioinformatics. 2006;4:259–63.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the Kruger National Park and SANParks for providing the sample used in this study. The South African Medical Research Council, the South African National Research Foundation and Stellenbosch University supported this work financially.

Funding

This project was funded by the National Research Foundation of South Africa, the South African Medical Research Council and Stellenbosch University.

Availability of data and materials

The complete genome sequence has been submitted to the National Centre for Biotechnology Information with accession number PRJNA341313 and can be accessed through the website.

Authors’ contributions

NLR, EH and PVH conceived the project. NLR and PVH were responsible for sample collection and DNA extraction was carried out by NLR. GT and BG were responsible for the bioinformatics analysis of the data. BG and MM wrote the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval

Authorization was provided by Stellenbosch University Animal Care and Use (SU ACU) and conducted in accordance with the policies and procedures as stipulated by South African National Parks (SANParks).

Author information

Authors and Affiliations

  1. SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Brigitte Glanzmann, Marlo Möller, Nikki le Roex, Gerard Tromp, Eileen G. Hoal & Paul D. van Helden

Authors
  1. Brigitte Glanzmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marlo Möller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikki le Roex
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerard Tromp
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eileen G. Hoal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paul D. van Helden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brigitte Glanzmann.

Additional files

Additional file 1: Table S1.

Data statistics for de novo assembly of the S. caffer genome. (PDF 63 kb)

Additional file 2: Table S2.

Genome assembly results. (PDF 62 kb)

Additional file 3: Figure S1.

17-mer coverage distribution. (PDF 69 kb)

Additional file 4: Figure S2.

Simulations to estimate heterozygosity ratio. (PDF 72 kb)

Additional file 5: Figure S5.

Phylogeny and divergence of 13 mammals. (PDF 352 kb)

Additional file 6: Figure S3.

Distribution of the read depth for the de novo assembled S. caffer genome. (PDF 86 kb)

Additional file 7: Figure S4.

GC content and average sequencing depth values for the S. caffer genome. (PDF 361 kb)

Additional file 8: Table S3.

Summary of repetitive elements obtained using 3 pipelines. (PDF 46 kb)

Additional file 9: Table S4.

Summary of TE classification. (PDF 49 kb)

Additional file 10: Table S5.

Top 10 copy number variations TE subfamily in African buffalo genome. (PDF 50 kb)

Additional file 11: Table S6.

Summary of function annotation for African buffalo. (PDF 45 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Glanzmann, B., Möller, M., le Roex, N. et al. The complete genome sequence of the African buffalo (Syncerus caffer). BMC Genomics 17, 1001 (2016). https://doi.org/10.1186/s12864-016-3364-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-016-3364-0

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us