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De Novo Transcriptome Assembly of the Chinese Swamp Buffalo by RNA Sequencing and SSR Marker Discovery

  • Tingxian Deng ,

    Contributed equally to this work with: Tingxian Deng, Chunying Pang

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Chunying Pang ,

    Contributed equally to this work with: Tingxian Deng, Chunying Pang

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Xingrong Lu,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Peng Zhu,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Anqin Duan,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Zhengzhun Tan,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Jian Huang,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Hui Li,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Mingtan Chen,

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

  • Xianwei Liang

    Affiliation Key Laboratory of Buffalo Genetics, Breeding and Reproduction technology, Ministry of Agriculture, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, Guangxi, P. R. China

De Novo Transcriptome Assembly of the Chinese Swamp Buffalo by RNA Sequencing and SSR Marker Discovery

  • Tingxian Deng, 
  • Chunying Pang, 
  • Xingrong Lu, 
  • Peng Zhu, 
  • Anqin Duan, 
  • Zhengzhun Tan, 
  • Jian Huang, 
  • Hui Li, 
  • Mingtan Chen, 
  • Xianwei Liang


The Chinese swamp buffalo (Bubalis bubalis) is vital to the lives of small farmers and has tremendous economic importance. However, a lack of genomic information has hampered research on augmenting marker assisted breeding programs in this species. Thus, a high-throughput transcriptomic sequencing of B. bubalis was conducted to generate transcriptomic sequence dataset for gene discovery and molecular marker development. Illumina paired-end sequencing generated a total of 54,109,173 raw reads. After trimming, de novo assembly was performed, which yielded 86,017 unigenes, with an average length of 972.41 bp, an N50 of 1,505 bp, and an average GC content of 49.92%. A total of 62,337 unigenes were successfully annotated. Among the annotated unigenes, 27,025 (43.35%) and 23,232 (37.27%) unigenes showed significant similarity to known proteins in NCBI non-redundant protein and Swiss-Prot databases (E-value < 1.0E-5), respectively. Of these annotated unigenes, 14,439 and 15,813 unigenes were assigned to the Gene Ontology (GO) categories and EuKaryotic Ortholog Group (KOG) cluster, respectively. In addition, a total of 14,167 unigenes were assigned to 331 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, 17,401 simple sequence repeats (SSRs) were identified as potential molecular markers. One hundred and fifteen primer pairs were randomly selected for amplification to detect polymorphisms. The results revealed that 110 primer pairs (95.65%) yielded PCR amplicons and 69 primer pairs (60.00%) presented polymorphisms in 35 individual buffaloes. A phylogenetic analysis showed that the five swamp buffalo populations were clustered together, whereas two river buffalo breeds clustered separately. In the present study, the Illumina RNA-seq technology was utilized to perform transcriptome analysis and SSR marker discovery in the swamp buffalo without using a reference genome. Our findings will enrich the current SSR markers resources and help spearhead molecular genetic research studies on the swamp buffalo.


The water buffalo (Bubalus bubalis), which belongs to the Bubalus genus of the Bovidae family, is an economically significant livestock that has been used as dairy, meat, and source of draught power [1]. These animals are typically found in tropical and subtropical regions, wet grasslands, marshes and swamps. The domestic water buffalo in Asia is generally classified into two major subspecies based on body size, outward appearance, biological characteristics, and chromosome karyotype, namely, the river buffalo (2n = 50) and the swamp buffalo (2n = 48) [2]. In China, the native buffaloes are of the swamp type, and are mainly distributed in 18 provinces of central and southern China, and have been divided into 18 local breeds based on regional distribution [3, 4]. In the past, swamp buffaloes were mainly raised by small-scale farmers for draught power for agricultural production. However, considering its economic importance as the provider of milk, meat, horns and even skin, extensive efforts on the genetic improvement of the dairy buffalo were conducted for several decades in China using a crossbreeding system. The milk yield of crossbreeds Murrah F1 and F2 reached 1,240.5 kg and 1,423.3 kg respectively, which were 13.5% and 30.2% higher than that of selected local buffaloes (P < 0.01). The milk yield of crossbreeds Nili-Ravi F1 and F2 reached 2,041.2 kg and 2,351.3 kg respectively, which were 86.8% and 115.2% higher than that of selected buffaloes (P < 0.01) [5]. Although milk yield performance has markedly improved in crossbreeds compared to indigenous buffaloes, the average milk yield per lactation of crossbreeds is still far lower than that of purebred Murrah, Nili-Ravi, and Mediterranean buffaloes [6, 7]. One of the main long-term hindrances in the buffalo industry in China is the lack of breeds with high milk and reproductive performance.

With the purpose of increasing the size of the dairy buffalo herd and improving the production performance of dairy buffalo, previous studies have mainly focused on reproductive technologies [8, 9], the identification of genes and molecular markers that were associated to desirable traits [1012], genetic relationships, and genetic variations [13, 14]. To date, information on the technology for buffalo genetic breeding in China is limited, particularly relating to molecular breeding methods. One key impediment is lack of genomic information on the buffalo, which could be utilized in development of molecular markers for its selection and breeding. Several research groups have conducted genomic studies on the buffalo [15, 16], which has recently resulted in the release of the draft genome of the river buffalo [17], and is expected to play an important role in promoting the genetic improvement of the dairy buffalo. However, no published genome sequence is currently available for the swamp buffalo, which in turn may hinder molecular genetic studies on buffalo breeding.

Transcriptome studies have become an important method to obtain large amounts of sequence data that could enrich the genome resource for the non-model animals [18]. RNA sequencing (RNA-seq) is a high throughput technology that has been effectively utilized in transcriptional analysis, gene discovery, and development of molecular markers in various species such as human [19], cattle [20], sheep [21], goat [22] and pig [23]. The genetic relationship and diversity among different buffalo breeds have been mainly investigated using restriction fragment length polymorphism (RFLP) [24], random amplified polymorphic DNA (RAPD) [25], single nucleotide polymorphism (SNP) [26], and simple sequence repeat (SSR) [27] markers. SSR markers have been demonstrated to be an extremely useful tool for investigating population clustering, genetic divergence, parentage testing, and genetic resource conservation[2830]. Sarika et al. [31] developed the first microsatellite database of the water buffalo, BuffSatDb (, which is a web-based relational database of 910,529 microsatellite markers that was generated by in silico microsatellite mining and has helped in resolving the presence of degenerate bases in the current buffalo assembly. However, SSR markers that have been used in the analysis of genetic relationships and genetic variations of different buffaloes were mainly derived from other domesticated bovids [3234], and no SSR markers of the swamp buffalo have been developed and reported to date. Specifically, suitable SSR markers that could be used to improve the production performance of dairy buffalo are very scarce, and have yet to be developed. Therefore, a large-scale and low-cost approach is required to develop SSR markers for the swamp buffalo. In the present study, we performed Illumina paired-end sequencing of pooled tissues of the swamp buffalo to generate a set of unigenes that were used to develop SSR markers. Then, we identified novel SSR markers in the swamp buffalo which can be utilized for marker identification, parentage testing, genetic resource conservation, and molecular breeding.

Materials and Methods

1. Ethics statement

All animal procedures and study design were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (Ministry of Science and Technology of China, 2006) and were approved by the Animal Ethics Committee of the Buffalo Research Institute, Chinese Academy of Agricultural Sciences.

2. Animal materials and RNA extraction

Two swamp buffaloes (male and female) were obtained from the Buffalo Research Institute, Chinese Academy of Agricultural Sciences (Nanning, China) and slaughtered by exsanguination. Fresh tissue samples were collected, including the heart, brain, lung, kidney, fat, liver, spleen, uterus, testis, ovary, and gland, immediately frozen in liquid nitrogen and stored at -80°C until use.

Total RNA was extracted from each collected tissues sample using the TRIzol reagent following the manufacturer’s specifications (Invitrogen, Guangzhou, China). The quality and quantity of each RNA sample was measured by using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). A total of 16.5 μg of RNA was equally pooled from the collected tissues for cDNA library preparation.

3. Transcriptome sequencing

The mRNAs were isolated from total RNA using a Dynabeads mRNA DIRECT Kit (Invitrogen, Guangzhou, China) and fragmented into short fragments with a fragmentation buffer. By using these short fragments as templates, random primers, and SuperScript double-stranded cDNA synthesis kit (Invitrogen, Guangzhou, China), double-stranded cDNA was synthesized. The ligated fragments were then generated by a series of reaction processes that included purification of PCR products, end repair, dA-tailing, and ligation of Illumina adapters. After agarose gel electrophoresis, suitable fragments were selected for PCR amplification. An Illumina HiSeq 2000 sequencing platform was employed to sequence the cDNA library (BerryGenomics, Beijing, China).

4. Data filtering and de novo assembly

We filtered the raw data to generate clean data via a process that included the removal of adapter sequences, reads with ambiguous sequences “N”, and low-quality sequences (the percentage of low quality bases of quality value _5 was >50% in a read). After obtaining clean data, de novo transcriptome assembly was conducted with the short reads assembly program in the Trinity software using default parameters [35, 36]. Only assembled transcripts with lengths of >300 bp were included in subsequent analyses.

5. Functional annotation

To annotate the assembled unigenes, all unigenes were analyzed by using the EMBOSS software [37] package to generate putative protein sequences. For the putative protein sequences, we performed the BLASTx search against the NCBI Nr (, Swiss-Prot (, and KEGG pathway ( databases, with an E-value cut-off of 1E-5. To further analyze the annotation results, Blast2GO [38] was conducted to obtain the GO functional classification of the unigenes according to molecular function, biological process, and cellular component ontologies ( All assembled unigenes were also aligned to the KOG database ( to predict and classify possible functions. The KEGG annotation was performed using the software, KOBAS v2.0 [39].

6. SSR mining and primer design

The MIcroSAtellite (MISA, was utilized to identify SSR motifs. We screened for motifs with mono-six nucleotides in size and a minimum of 5 contiguous repeat units. Based on MISA results, primer pairs were designed using the software, Primer3 v2.23 [40] with default settings, and the size of the PCR products ranged from 100 bp to 300 bp.

7. Survey of SSR polymorphisms

Thirty-five individual buffaloes from 7 breeds in China (S2 Table) were selected for screening SSR polymorphisms. The genomic DNA was extracted from each buffalo blood tissue sample by using the TIANamp Blood DNA Kit (Tiangen Biotech (Beijing) Co., Ltd., Beijing, China), following the manufacturer’s specifications. The DNA concentration was calculated using standard protocols. PCR was performed in 20.0 μL reaction mixtures containing 1.0 μL of the DNA template (10 ng), 1.0 μL of the primer mix (10 μM of each), 10.0 μL of the premixed rTaq solution, and 8.0 μL of ddH2O. PCR was conducted in an ABI PCR machine using the following conditions: 3 min at 95°C, followed by 35 cycles of 30 s at 95°C, 30 s at 58°C–60°C, and 30 s at 72°C, and a final extension of 8 min at 72°C. After PCR amplification, the size of each amplified product with 10.0 μL volume was estimated using the LabChip GX instrument (PerkinElmer, USA).

Seven buffalo breeds were selected to validate the amplification and polymorphism of 115 random SSR markers. The values of the observed number of alleles (NA), expected heterozygosity (HE), observed heterozygosity (HO), and polymorphism information content (PIC) per SSR locus were calculated using the software, PowerMarker, version 3.25 [41]. An UPGMA hierarchical clustering was performed based on the matrix of genetic similarity estimates, following the procedures of the PowerMarker software.


1. Sequencing and de novo assembly of swamp buffalo transcriptome

To obtain a comprehensive overview of the swamp buffalo (B. bubalis) transcriptome, we performed transcriptome sequencing of pooled RNA samples from 11 different tissues on the Illumina Hiseq 2000 platform. The main steps and bioinformatics tools used for data analysis are shown in Fig 1. We obtained a total of 54,109,173 raw reads, which after removal of redundant reads, trimming of adaptors and filtering for low-quality sequences resulted in 52,979,055 high-quality clean reads with 10,595,811,000 bp of sequence data (Table 1). The results of FastQC v0.11.3 analysis showed that the Q20 percentage and GC percentage were 97.91% and 49.92%, respectively. Using the Trinity software (Release-20140717), de novo assembly was performed, which yielded 86,017 unigenes with a mean length of 972.41 bp and an N50 of 1,505 bp, representing a total of 83.65 Mb of genomic sequence. The present study was named the Transcriptome Shotgun Assembly Project, and the 86,017 unigenes identified in swamp buffalo were deposited in GenBank under accession number GDJS00000000.1 ($=activity). Of these deposited unigenes, 47,929 (55.72%) unigenes were >500 bp in size, 22,279 unigenes (25.90%) were >1,000 bp in size, and 9,969 (11.59%) unigenes were >2,000 bp long. According to a simple principle: the longest one was extracted when the unigenes had multiple open reading frames (ORFs), 76,703 (89.17%) unigenes with ORFs were generated using the software, ORF Finder (EMBOSS:6.3.1), which indicated that 8,740 unigenes had complete ORFs, with an average GC content of 44.5% (data not shown).

Fig 1. Flowchart of de novo assemble in swamp buffalo transcriptome and SSR discovery.

2. Functional annotation

The assembled unigenes were predicted by using the BLASTx [42] program against the NCBI non-redundant (Nr) and Swiss-Prot protein databases, with an E-value threshold of 1E-5. Among the 86,017 unigenes, 27,025 (31.41%) and 23,232 (27.00%) unigenes showed significant similarity to known proteins in the Nr and Swiss-Prot databases, respectively. Furthermore, 15,813 and 14,167 unigenes could be annotated according to the EuKaryotic Ortholog Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [43], respectively (Fig 2A). The E-value distribution of the hits showed that 70.20% of the unigenes had significant homology (< 1E-50) to entries in the Nr database, and nearly 87.69% of the sequences showed >70.00% similarity (Fig 2B and 2C). The 86,017 unigenes were annotated to 10 top-hit species, with Bos taurus and B. grunniens accounting for 66.52% of the annotated unigenes (Fig 2D). These results revealed that our transcriptome data on the swamp buffalo was successfully annotated.

Fig 2. Characteristics of the results of homology search for swamp buffalo unigenes.

(A) Venn diagram of BLAST hits for unigenes against protein databases (E-value ≤1.0e-05). Numbers in the circles indicate the number of unigenes annotated by single or multiple databases, (B) E-value distribution of BLAST hits for each unique sequence (E-value ≤1.0e-05). (C) Identity distribution of BLAST hits for each sequence. (D) Species distribution of the top BLAST hits for the assembled unigenes (E-value ≤1.0e-05).

Based on the results of Nr annotation, 14,439 unigenes were assigned to 64 functional groups in Gene Ontology (GO) [44]. Fig 3 shows that 112,386 (53.04%) unigenes comprised the largest category, namely, ‘biological process’, followed by ‘cellular component’ (73,975; 34.91%) and molecular function (25,535; 12.05%). The GO terms ‘cellular process’ (12,727; 11.32%) and ‘single-organism process’ (11,225; 9.99%), ‘cell’ (13,314; 18.00%), and ‘cell part’ (13,313; 18.00%), and ‘binding’ (11,917; 46.67%), and ‘catalytic activity’ (6,379; 24.98%) were the first and second largest groups among the three main categories (‘biological process’, ‘cellular component’, and ‘molecular function’), respectively. However, a few unigenes were assigned to ‘virion’ (GO: 0019012), ‘virion part’ (GO: 0044423), ‘morphogen activity’ (GO: 0016015), and ‘nutrient reservoir activity’ (GO: 0045735).

Fig 3. Gene ontology (GO) classification of assembled unigenes.

A total of 14,439 unigenes with significant similarity in NR protein databases were assigned to GO classifications.

In addition, all unigenes were subjected to a search against the KOG database for functional prediction and classification. A total of 15,813 unigenes showing Nr hits in the KOG database were functionally classified into 25 molecular families, including four orthology clusters (Fig 4). The orthology cluster described as ‘cellular processes and signaling’ predominated, which accounted for 41.75% of the annotations, followed by ‘metabolism’ (2,839; 15.92%) and ‘information storage and processing’ (2,742; 15.37%); whereas, another clustering was poorly characterized, which included ‘general prediction only’ and ‘function unknown’, which accounted for 26.96% of the annotations.

Fig 4. EuKaryotic orthologous group (KOG) classification.

Approximately 15,813 of the 86,017 unigenes with NR hits were grouped into 25 KOG classifications.

3. Functional classification using the KEGG pathway

All the assembled unigenes were subjected to KEGG pathway enrichment analysis. A total of 14,167 unigenes (16.47%) could be annotated and assigned to 5 main categories, which included 331 KEGG pathways (Fig 5, S1 Table). Among the five main categories, the largest category was ‘human diseases’, which contained 4,868 KEGG-annotated unigenes (26.52%), followed by ‘organismal systems’ (4,256; 23.19%), ‘environmental information processing’ (2,904; 15.82%), ‘metabolism’ (2,753; 15.00%), ‘cellular processes’ (1,962; 10.69%), and ‘genetic information processing’ (1,613; 8.79%). S1 Table shows that the KEGG human diseases contained 10 subcategories, which included Cancers: overview, Cancers: specific types, cardiovascular diseases, Endocrine and metabolic diseases, Immune diseases, Infectious diseases: Bacterial, Infectious diseases: Parasitic, Infectious diseases: Viral, Neurodegenerative diseases, and Substance dependence. Furthermore, 415 unigenes were assigned to the subcategory of Lipid metabolism. Among these, 34, 20, and 21 unigenes mapped to the subcategories of Linoleic acid metabolism, Alpha-linolenic acid metabolism, and Biosynthesis of unsaturated fatty acids, respectively.

Fig 5. Pathway assignment based on the Kyoto Encyclopedia of Genes and Genomes (KEGG).

(A) Classification based on cellular processes categories, (B) Classification based on environmental information processing categories, (C) Classification based on genetic information processing categories, (D) Classification based on human diseases categories, (E) Classification based on metabolism categories, and (F) Classification based on organismal systems categories.

4. Development and characterization of SSR markers

SSRs are useful molecular markers for genetic research and comparative genome analysis. To develop SSR markers in swamp buffalo, all assembled unigenes generated in the present study were used to mine potential microsatellites, which were defined as mono- to hexanucleotide SSRs that consisted of a minimum of five repeats. Table 2 presents the 18,446 SSRs that were detected in 17,401 unigenes, of which 2,939 unigenes contained more than one SSR, and 932 SSRs exhibited compound formation. The number of potential SSRs per unigene varied from 1 to 6, with an average of 1.06.

To further assess the mining quality of SSRs in swamp buffalo, we divided the SSRs into three groups based on the repeat motif classification criteria proposed by Weber [45] (Table 3). For the perfect repeat motifs (SSRs ≥15 bp in length), mono-, tri-, and dinucleotide motifs were placed as top three hits, with distribution frequencies of 38.53%, 36.08% and 22.56%, respectively, whereas the other motif types only accounted for 2.84% of the repeat motifs. Under the imperfect SSR category, 10,476 SSRs was detected, which included mono- (8,248; 78.73%) and dinucleotide (2,228; 21.27%) SSR units, and was ranked after the perfect repeat motifs. For the compound SSR category, all motifs belonged to the perfect type, including the mono-mono-, mono-di-, mono-tri-, mono-tetra-, di-mono-, di-di-, di-tri-, di-tetra-, tri-mono-, tri-di-, tri-tri-, tetra-tetra-, and hexa-trinucleotide types. The mono-mono-, di-di-, and tri-trinucleotide types were the most abundant, representing more than 77.04% of the 932 SSRs.

The frequency distribution of the perfect SSRs was also analyzed in the present study, with the mononucleotide type excluded. The most abundant motif detected in the SSRs was the AC/GT motif (29.85%), followed by the motifs AGC/CTG (19.26%), CCG/CGG (14.38%), and AGG/CCT (10.27%). The remaining types of motif accounted for 26.24% of the repeat motifs (Fig 6).

Fig 6. Frequency of classified repeat types of SSRs.

The most abundant dinucleotide and trinucleotide motifs were AC/GT and AGC/CTG.

5. Identification of polymorphic markers

One hundred and fifteen SSRs were randomly selected to estimate the genetic diversity of 7 buffalo breeds (S3 Table). We successfully amplified PCR products using 110 primer pairs, and 69 primer pairs exhibited polymorphisms among the 7 breeds. Of the 69 working primer pairs, 52 PCR products showed specific amplification with the expected sizes, whereas the other 17 PCR products were larger than the expected sizes, suggesting that the amplified regions likely contained introns. Table 4 shows the average values of the NA, HE, and HO in the 69 SSRs, which were 9.00, 0.74, and 0.18, respectively. The PIC values ranged from 0.33 to 0.91, with an average value of 0.70, suggesting that those highly polymorphic markers could be used to investigate genetic diversity in buffalo. The unweighted pair group method with arithmetic mean (UPGMA) cluster analysis divided 7 breeds into two groups, one representing the river buffalo types (Murrah and Nili-Ravi), whereas the other comprised all the indigenous breeds (5 swamp types) (Fig 7). For the swamp buffalo group, the DC swamp buffalo was closely related to the DH swamp buffalo.

Fig 7. UPGMA dendrogram of the genetic relationships among seven buffalo breeds.

The dendrogram was generated using the Nei similarity coefficient based on 69 polymorphic primer pairs.


The Chinese swamp buffaloes have been divided into 14 local types and many populations based mainly on regional distribution [46]. Not only are they draught animals, but they also have a tremendous economic importance as dairy and meat in many highly populated countries [47, 48]. Despite their utility, studies on genomics as a tool for marker assisted cross-breeding techniques are still lacking in this species because of strategies which were relatively costly, time consuming and labor intensive [49]. The high demand for low-cost sequencing has led to the development of high-throughput technologies such as next-generation sequencing [50]. Transcriptome sequencing is one such powerful and cost-effective tool in generating large-scale transcriptome data that may be used in developing molecular markers and in identifying novel genes in model [35, 51] and non-model [52, 53] organisms. To our knowledge, the present study is the first attempt to perform de novo assembly and to conduct a comprehensive characterization of the comprehensive transcripts of swamp buffalo. We obtained a total of 52,979,055 high-quality reads with 97.04% Q20 bases using Illumina paired-end sequencing, and de novo assembly yielded 86,017 unigenes, which might be useful for further research into functional genomics in the swamp buffalo. The average length of the assembled unigenes with an N50 of 1,505 bp was 972.41 bp, which was longer than the results of previous studies [5456], suggesting that our transcriptome sequencing data was successfully assembled. The 49.92% GC-content of the swamp buffalo transcriptome was higher than the genome-wide average GC-content of the river buffalo draft genome (42.20%) and those of other animals (41.80%–42.30%) [5760], which might be attributable to the unique tissue-specific transcripts and experimental designs [61, 62]. These results are indicative that the transcripts generated from the swamp buffalo were of high quality and may thus be utilized in future studies on gene cloning, molecular genetics, and transgenesis of the swamp buffalo.

To predict and analyze the biological function of assembled transcripts at the whole-transcriptome level, a sequence similarity search was performed against various protein databases, which included Nr, Swiss-Prot, GO, KOG, and KEGG. Most of the assembled unigenes (62,337; 72.47%) showed matches with known proteins in public databases, indicating that 27.53% of the unigenes may represent novel genes whose function has not yet been identified. In particular, most of unigenes were annotated to the B. taurus and B. grunniens (first and second hits) against the Nr database, probably because: (1) it confirmed that the swamp buffalo is closely related to B. taurus and B. grunniens; (2) The genomes of both B. taurus and B. grunniens have earlier been completely sequenced [63, 64]. We mapped 23.16% of the annotated unigenes to the KOG database and 25.37% to the GO terms, which indicated that our transcriptome data represented a broad diversity of transcripts in swamp buffalo. Similar results were also reported in other species, such as sheep [65], fish [66], horse [67], rubber tree bark [68], the Tibetan leguminous shrub Sophora moorcroftiana [69], and the Jerusalem artichoke [70]. On the other hand, around 26.96% of the annotated unigenes were poorly characterized to orthologous clusters and thus were described as ‘general prediction only’ and ‘function unknown’; this occurrence may be due to the absence of a reference genome for the swamp buffalo. In addition, we also predicted a total of 14,167 unigenes that mapped to 331 KEGG pathways. Moreover, 70.00% of the top 10 hit pathways were involved in signal transduction, whereas the others were related to pathways involving cancer, proteoglycans in cancer, and HTLV-I infection (S1 Table). Notably, some unigenes predicted by KEGG pathways were associated with linoleic acid metabolism, alpha-linolenic acid metabolism, and biosynthesis of unsaturated fatty acids, implying that swamp buffalo milk is very rich in unsaturated fatty acids and has important economic value and health benefits. These results indicated that the predicated pathways, together with gene annotation, may be utilized in future investigations on gene function, which in turn also confirms that de novo transcriptome sequencing is an efficient method for transcriptome characterization and gene discovery in the swamp buffalo.

SSRs that are widely distributed in a genome are important tools for assessing genetic diversity, genetic map construction, comparative genomics, and marker-assisted selection breeding. To our knowledge, no previous study has identified SSR markers in the swamp buffalo. The transcriptome data is an excellent source for SSR mining and has been utilized in various species [7174]. In the present study, we identified a total of 17,401 SSRs based on the unigene data of swamp buffalo and approximately 39.80% of identified SSRs were the perfect repeat motif type. When mononucleotide repeats were excluded, 48.61% of the 4,616 SSRs were determined to be trinucleotide repeats, followed by dinucleotide repeats (35.88%) and tetranucleotide repeats (3.25%), as well as pentanucleotide repeats and hexanucleotide repeats, which accounted for 0.48% of the motifs. The most abundant dinucleotide and trinucleotide motifs were AC/GT and AGC/CTG (Fig 5), which was in agreement with the findings of previous reports on other animal species [55, 75, 76], but different from those of plants [68, 77]. Of the 115 primer pairs randomly selected for PCR validation, 110 (95.65%) produced clear bands, and 69 (60.00%) exhibited polymorphisms. The high PCR rate of SSR markers in the swamp buffalo was similar to that obtained in other species [71, 78], but higher than that reported in a study conducted by Yan [75]. UPGMA dendrogram analysis revealed that the two river buffalo populations clustered together whereas the five swamp buffalo populations were clustered separately, which correlated with the geographic origin of the genotypes. The findings of UPGMA analysis was similar to that observed in previous studies [13, 79, 80]. In sum, the 17,401 potential SSRs identified in the present study provide a useful resource for future marker assisted breeding programs in the swamp buffalo.


In the present study, Illumina paired-end sequencing was performed, followed by de novo assembly and characterization of the transcriptome of the swamp buffalo. Our study generated a total of 54,109,173 raw reads, which consisted of 86,017 unigenes, of which 62,337 unigenes were annotated to the four public databases (Nr, Swiss-Prot, KOG, and KEGG), which in turn identified 17,401 SSRs as putative molecular markers. These findings may serve as a valuable resource for genetic and genomic studies on the buffalo.

Supporting Information

S1 Table. Summary of KEGG classification of assembled unigenes,


S2 Table. Characteristics of seven buffalo breeds for SSR validation,


S3 Table. Primers information for SSRs in swamp buffalo,


Author Contributions

Conceived and designed the experiments: TXD XWL CYP. Performed the experiments: TXD CYP PZ XRL AQD MTC JH ZZT HL. Analyzed the data: TXD. Contributed reagents/materials/analysis tools: TXD. Wrote the paper: TXD.


  1. 1. NANDA AS, NAKAO T. Role of buffalo in the socioeconomic development of rural Asia, current status and future prospectus. Animal Science Jounal. 2003;74(6):443–55.
  2. 2. Michelizzi VN, Dodson MV, Pan Z, Amaral ME, Michal JJ, McLean DJ, et al. Water buffalo genome science comes of age. International journal of biological sciences. 2010;6(4):333–49. pmid:20582226; PubMed Central PMCID: PMC2892297.
  3. 3. Zhang CX. Science and Technology in Chinese Buffaloes. Nanning: Guangxi Scientific and Technical Publishing House; 2000.
  4. 4. Borghese A. Buffalo livestock and products. Italy: Roma; 2013.
  5. 5. Yang BZ, Liang XW, Qin J, Yang CJ, Shang JH. Brief introduction to the development of Chinese Dairy buffalo industry. The 10th World buffalo congress and the 7th Asian buffalo congress; Phuke, Thailand2013. p. 111–20.
  6. 6. Thiruvenkadan AK, Rajendran R, Muralidhran J. Buffalo genetic resources of India and their conservation. The 10th World buffalo congress and the 7th Asian buffalo congress; Phuke, Thailand2013. p. 227–35.
  7. 7. Borghese A. Buffalo livestock and products in Europe. The 10th World buffalo congress and the 7th Asian buffalo congress; Phuke, Thailand.2013. p. 50–74.
  8. 8. Warriach HM, McGill DM, Bush RD, Wynn PC, Chohan KR. A review of recent developments in buffalo reproduction—a review. Asian-Australasian journal of animal sciences. 2015;28(3):451–5. pmid:25656203; PubMed Central PMCID: PMC4341092.
  9. 9. Drost M. Advanced reproductive technology in the water buffalo. Theriogenology. 2007;68(3):450–3. pmid:17481722.
  10. 10. El-Magd MA, Abo-Al-Ela HG, El-Nahas A, Saleh AA, Mansour AA. Effects of a novel SNP of IGF2R gene on growth traits and expression rate of IGF2R and IGF2 genes in gluteus medius muscle of Egyptian buffalo. Gene. 2014;540(2):133–9. pmid:24613755.
  11. 11. Bonfatti V, Giantin M, Gervaso M, Coletta A, Dacasto M, Carnier P. Effect of CSN1S1-CSN3 (alpha(S1)-kappa-casein) composite genotype on milk production traits and milk coagulation properties in Mediterranean water buffalo. Journal of dairy science. 2012;95(6):3435–43. pmid:22612978.
  12. 12. Pauciullo A, Cosenza G, Steri R, Coletta A, La Battaglia A, Di Berardino D, et al. A single nucleotide polymorphism in the promoter region of river buffalo stearoyl CoA desaturase gene (SCD) is associated with milk yield. The Journal of dairy research. 2012;79(4):429–35. pmid:22994977.
  13. 13. Lei CZ, Zhang CM, Weining S, Campana MG, Bower MA, Zhang XM, et al. Genetic diversity of mitochondrial cytochrome b gene in Chinese native buffalo. Animal genetics. 2011;42(4):432–6. pmid:21749426.
  14. 14. Kumar S, Gupta J, Kumar N, Dikshit K, Navani N, Jain P, et al. Genetic variation and relationships among eight Indian riverine buffalo breeds. Molecular ecology. 2006;15(3):593–600. pmid:16499687.
  15. 15. Iamartino D, Williams JL, Sonstegrad T. The buffalo genome and the application of genomics in animal mangement and improvement. The 10th World buffalo congress and the 7th Asian buffalo congress; Phuke, Thailand.2013. p. 151–8.
  16. 16. BGI. Lal Teer Livestock Limited and BGI Jointly Announced the Complete Sequence of Water Buffalo: A Key to Achieve Food (Meat and Milk) Security BGI; 2014. Available from:
  17. 17. Zimin AV, Williams J. Bubalus bubalis, whole genome shotgun sequencing project USA: NCBI; 2015. Available from:
  18. 18. Finseth FR, Harrison RG. A comparison of next generation sequencing technologies for transcriptome assembly and utility for RNA-Seq in a non-model bird. PLoS One. 2014;9(10):e108550. pmid:25279728; PubMed Central PMCID: PMC4184788.
  19. 19. Savage SR, Bretz CA, Penn JS. RNA-Seq reveals a role for NFAT-signaling in human retinal microvascular endothelial cells treated with TNFalpha. PloS one. 2015;10(1):e0116941. pmid:25617622; PubMed Central PMCID: PMC4305319.
  20. 20. Canovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano JF. SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mammalian genome: official journal of the International Mammalian Genome Society. 2010;21(11–12):592–8. pmid:21057797; PubMed Central PMCID: PMC3002166.
  21. 21. Zhang C, Wang G, Wang J, Ji Z, Liu Z, Pi X, et al. Characterization and comparative analyses of muscle transcriptomes in Dorper and small-tailed Han sheep using RNA-Seq technique. PloS one. 2013;8(8):e72686. pmid:24023632; PubMed Central PMCID: PMC3758325.
  22. 22. Geng R, Yuan C, Chen Y. Exploring differentially expressed genes by RNA-Seq in cashmere goat (Capra hircus) skin during hair follicle development and cycling. PloS one. 2013;8(4):e62704. pmid:23638136; PubMed Central PMCID: PMC3640091.
  23. 23. Jung WY, Kwon SG, Son M, Cho ES, Lee Y, Kim JH, et al. RNA-Seq approach for genetic improvement of meat quality in pig and evolutionary insight into the substrate specificity of animal carbonyl reductases. PloS one. 2012;7(9):e42198. pmid:22962580; PubMed Central PMCID: PMC3433470.
  24. 24. El Nahas SM, Mossallam AA. AcuI identifies water buffalo CSN3 genotypes by RFLP analysis. Journal of genetics. 2014;93(3):e94–6. pmid:25572073.
  25. 25. Paraguison RC, Faylon MP, Flores EB, Cruz LC. Improved RAPD-PCR for discriminating breeds of water buffalo. Biochemical genetics. 2012;50(7–8):579–84. pmid:22403004.
  26. 26. 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(11):e48792. pmid:23144973; PubMed Central PMCID: PMC3492240.
  27. 27. Gargani M, Pariset L, Soysal MI, Ozkan E, Valentini A. Genetic variation and relationships among Turkish water buffalo populations. Animal genetics. 2010;41(1):93–6. pmid:19799598.
  28. 28. Patzak J, Paprstein F, Henychova A, Sedlak J. Comparison of genetic diversity structure analyses of SSR molecular marker data within apple (Malusxdomestica) genetic resources. Genome / National Research Council Canada = Genome / Conseil national de recherches Canada. 2012;55(9):647–65. pmid:22954156.
  29. 29. Vidal NM, Grazziotin AL, Ramos HC, Pereira MG, Venancio TM. Development of a gene-centered ssr atlas as a resource for papaya (Carica papaya) marker-assisted selection and population genetic studies. PLoS One. 2014;9(11):e112654. pmid:25393538; PubMed Central PMCID: PMC4231050.
  30. 30. De Lorenzis G, Imazio S, Biagini B, Failla O, Scienza A. Pedigree reconstruction of the Italian grapevine Aglianico (Vitis vinifera L.) from Campania. Molecular biotechnology. 2013;54(2):634–42. pmid:23054630.
  31. 31. Sarika , Arora V, Iquebal MA, Rai A, Kumar D. In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB. BMC genomics. 2013;14:43. pmid:23336431; PubMed Central PMCID: PMC3563513.
  32. 32. Barker JS, Moore SS, Hetzel DJ, Evans D, Tan SG, Byrne K. Genetic diversity of Asian water buffalo (Bubalus bubalis): microsatellite variation and a comparison with protein-coding loci. Animal genetics. 1997;28(2):103–15. pmid:9172308.
  33. 33. Zhang Y, Vankan D, Zhang Y, Barker JS. Genetic differentiation of water buffalo (Bubalus bubalis) populations in China, Nepal and south-east Asia: inferences on the region of domestication of the swamp buffalo. Animal genetics. 2011;42(4):366–77. pmid:21749419.
  34. 34. Sraphet S, Moolmuang B, Na-Chiangmai A, Panyim S, Smith DR, Triwitayakorn K. Use of Cattle Microsatellite Markers to Assess Genetic Diversity of Thai Swamp Buffalo (Bubalus bubalis). Asian-Aust J Anim Sci. 2008;21(2):177–80.
  35. 35. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature biotechnology. 2011;29(7):644–52. pmid:21572440; PubMed Central PMCID: PMC3571712.
  36. 36. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature protocols. 2013;8(8):1494–512. pmid:23845962; PubMed Central PMCID: PMC3875132.
  37. 37. Olson SA. EMBOSS opens up sequence analysis. European Molecular Biology Open Software Suite. Briefings in bioinformatics. 2002;3(1):87–91. pmid:12002227.
  38. 38. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6. pmid:16081474.
  39. 39. Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39(Web Server issue):W316–22. pmid:21715386; PubMed Central PMCID: PMC3125809.
  40. 40. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115. pmid:22730293; PubMed Central PMCID: PMC3424584.
  41. 41. Liu K, Muse SV. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005;21(9):2128–9. pmid:15705655.
  42. 42. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of molecular biology. 1990;215(3):403–10. pmid:2231712.
  43. 43. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32(Database issue):D277–80. pmid:14681412; PubMed Central PMCID: PMC308797.
  44. 44. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics. 2000;25(1):25–9. pmid:10802651; PubMed Central PMCID: PMC3037419.
  45. 45. Weber JL. Informativeness of human (dC-dA)n.(dG-dT)n polymorphisms. Genomics. 1990;7(4):524–30. pmid:1974878.
  46. 46. Qiu H. Bovine breeds in China. Shanghai: Shanghai Scientific and Technical Publishing House; 1986.
  47. 47. FAO. Water Buffalo: an asset undervalued, F.R.O.f.A.a. Bangkok: Thailand: Pacific Editor; 2000.
  48. 48. Roth J, Myers P. Bubalus bubalis 2004. Available from:
  49. 49. Metzker ML. Emerging technologies in DNA sequencing. Genome research. 2005;15(12):1767–76. pmid:16339375.
  50. 50. Jiang Z, Rokhsar DS, Harland RM. Old can be new again: HAPPY whole genome sequencing, mapping and assembly. International journal of biological sciences. 2009;5(4):298–303. pmid:19381348; PubMed Central PMCID: PMC2669597.
  51. 51. Wang Z, Fan Y, Xu J, Li L, Heng D, Han S, et al. Transcriptome analysis of the hippocampus in novel rat model of febrile seizures. PloS one. 2014;9(4):e95237. pmid:24736375; PubMed Central PMCID: PMC3988142.
  52. 52. Koringa PG, Jakhesara SJ, Bhatt VD, Patel AB, Dash D, Joshi CG. Transcriptome analysis and SNP identification in SCC of horn in (Bos indicus) Indian cattle. Gene. 2013;530(1):119–26. pmid:23978612.
  53. 53. Ropka-Molik K, Zukowski K, Eckert R, Gurgul A, Piorkowska K, Oczkowicz M. Comprehensive analysis of the whole transcriptomes from two different pig breeds using RNA-Seq method. Animal genetics. 2014;45(5):674–84. pmid:24961663.
  54. 54. Nie Q, Fang M, Jia X, Zhang W, Zhou X, He X, et al. Analysis of muscle and ovary transcriptome of Sus scrofa: assembly, annotation and marker discovery. DNA research: an international journal for rapid publication of reports on genes and genomes. 2011;18(5):343–51. pmid:21729922; PubMed Central PMCID: PMC3190955.
  55. 55. Liu H, Wang T, Wang J, Quan F, Zhang Y. Characterization of Liaoning cashmere goat transcriptome: sequencing, de novo assembly, functional annotation and comparative analysis. PloS one. 2013;8(10):e77062. pmid:24130835; PubMed Central PMCID: PMC3793953.
  56. 56. Salem M, Paneru B, Al-Tobasei R, Abdouni F, Thorgaard GH, Rexroad CE, et al. Transcriptome assembly, gene annotation and tissue gene expression atlas of the rainbow trout. PloS one. 2015;10(3):e0121778. pmid:25793877; PubMed Central PMCID: PMC4368115.
  57. 57. Bovine Genome S, Analysis C, Elsik CG, Tellam RL, Worley KC, Gibbs RA, et al. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science. 2009;324(5926):522–8. pmid:19390049; PubMed Central PMCID: PMC2943200.
  58. 58. Dong Y, Xie M, Jiang Y, Xiao N, Du X, Zhang W, et al. Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nature biotechnology. 2013;31(2):135–41. pmid:23263233.
  59. 59. International Sheep Genomics C, Archibald AL, Cockett NE, Dalrymple BP, Faraut T, Kijas JW, et al. The sheep genome reference sequence: a work in progress. Animal genetics. 2010;41(5):449–53. pmid:20809919.
  60. 60. Groenen MA, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF, et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature. 2012;491(7424):393–8. pmid:23151582; PubMed Central PMCID: PMC3566564.
  61. 61. Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-Seq data. BMC bioinformatics. 2011;12:480. pmid:22177264; PubMed Central PMCID: PMC3315510.
  62. 62. Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13(2):204–16. pmid:22285995; PubMed Central PMCID: PMC3297825.
  63. 63. Zimin AV, Delcher AL, Florea L, Kelley DR, Schatz MC, Puiu D, et al. A whole-genome assembly of the domestic cow, Bos taurus. Genome biology. 2009;10(4):R42. pmid:19393038; PubMed Central PMCID: PMC2688933.
  64. 64. Qiu Q, Zhang G, Ma T, Qian W, Wang J, Ye Z, et al. The yak genome and adaptation to life at high altitude. Nature genetics. 2012;44(8):946–9. pmid:22751099.
  65. 65. Chen HY, Shen H, Jia B, Zhang YS, Wang XH, Zeng XC. Differential gene expression in ovaries of Qira black sheep and Hetian sheep using RNA-Seq technique. PloS one. 2015;10(3):e0120170. pmid:25790350; PubMed Central PMCID: PMC4366253.
  66. 66. LBaLBB Steve Smith. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC genomics. 2013;14:375. pmid:23738713
  67. 67. Kyung-Do Park JP, Junsu Ko, Byung Chul Kim, Heui-Soo Kim, Kung Ahn, Kyoung-Tag Do, Hansol Choi H-MK, Sanghoon Song, Sunghoon Lee, Sungwoong Jho, Hong-Sik Kong, Young Mok Yang B-HJ, Chulhong Kim, Tae-Hyung Kim, Seungwoo Hwang, Jong Bhak, Cho H-KLaB-W. Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq. BMC genomics. 2012;13:473. pmid:22971240
  68. 68. Li D, Deng Z, Qin B, Liu X, Men Z. De novo assembly and characterization of bark transcriptome using Illumina sequencing and development of EST-SSR markers in rubber tree (Hevea brasiliensis Muell. Arg.). BMC genomics. 2012;13:192. pmid:22607098; PubMed Central PMCID: PMC3431226.
  69. 69. Li H, Yao W, Fu Y, Li S, Guo Q. De novo assembly and discovery of genes that are involved in drought tolerance in Tibetan Sophora moorcroftiana. PloS one. 2015;10(1):e111054. pmid:25559297; PubMed Central PMCID: PMC4283959.
  70. 70. Jung WY, Lee SS, Kim CW, Kim HS, Min SR, Moon JS, et al. RNA-seq analysis and de novo transcriptome assembly of Jerusalem artichoke (Helianthus tuberosus Linne). PloS one. 2014;9(11):e111982. pmid:25375764; PubMed Central PMCID: PMC4222968.
  71. 71. Long Y, Wang Y, Wu S, Wang J, Tian X, Pei X. De novo assembly of transcriptome sequencing in Caragana korshinskii Kom. and characterization of EST-SSR markers. PloS one. 2015;10(1):e0115805. pmid:25629164; PubMed Central PMCID: PMC4309406.
  72. 72. Jiang B, Xie D, Liu W, Peng Q, He X. De novo assembly and characterization of the transcriptome, and development of SSR markers in wax gourd (Benicasa hispida). PloS one. 2013;8(8):e71054. pmid:23951078; PubMed Central PMCID: PMC3738631.
  73. 73. Cardoso-Silva CB, Costa EA, Mancini MC, Balsalobre TW, Canesin LE, Pinto LR, et al. De novo assembly and transcriptome analysis of contrasting sugarcane varieties. PloS one. 2014;9(2):e88462. pmid:24523899; PubMed Central PMCID: PMC3921171.
  74. 74. Farrell JD, Byrne S, Paina C, Asp T. De novo assembly of the perennial ryegrass transcriptome using an RNA-Seq strategy. PloS one. 2014;9(8):e103567. pmid:25126744; PubMed Central PMCID: PMC4134189.
  75. 75. Yan Q, Zhang Y, Li H, Wei C, Niu L, Guan S, et al. Identification of microsatellites in cattle unigenes. Journal of genetics and genomics = Yi chuan xue bao. 2008;35(5):261–6. pmid:18499068.
  76. 76. Lagercrantz U E H, and Andersson L. The abundance of various polymorphic microsatellite motifs differs between plants and vertebrates. Nucleic Acids Res. 1993;21(5):1111–5. pmid:8464696
  77. 77. Zhang H, Wei L, Miao H, Zhang T, Wang C. Development and validation of genic-SSR markers in sesame by RNA-seq. BMC genomics. 2012;13:316. pmid:22800194; PubMed Central PMCID: PMC3428654.
  78. 78. Long Y, Zhang J, Tian X, Wu S, Zhang Q, Zhang J, et al. De novo assembly of the desert tree Haloxylon ammodendron (C. A. Mey.) based on RNA-Seq data provides insight into drought response, gene discovery and marker identification. BMC genomics. 2014;15:1111. pmid:25511667; PubMed Central PMCID: PMC4377846.
  79. 79. Yue XP, Li R, Xie WM, Xu P, Chang TC, Liu L, et al. Phylogeography and domestication of Chinese swamp buffalo. PloS one. 2013;8(2):e56552. pmid:23437167; PubMed Central PMCID: PMC3577850.
  80. 80. Zhang Y, Sun DX, Yu Y, Zhang Y. Genetic Variation and Divergence among Swamp Buffalo, River Buffalo and Cattle: A Microsatellite Survey on Five Populations in China. Asian-Aust J Anim Sci. 2008;21(9):1238–43.