Comparative transcriptomics and comprehensive marker resource development in mulberry
© Saeed et al. 2016
Received: 10 July 2015
Accepted: 26 January 2016
Published: 4 February 2016
High potential of Morus laevigata and Morus serrata has been proposed in the breeding programs for Morus sp. However, due to the lack of dense molecular markers this goal is still in its nascent stage and not yet realized. We thus, sequenced the transcriptomes of these two wild Morus species and utilized the data for marker development.
We generated 87.0 and 80.3 Mb of transcriptome data from M. laevigata and M. serrata, respectively. The transcriptomes from M. laevigata and M. serrata, were assembled into 95,181 and 85,269 transcripts, respectively, and annotated. We identified around 24,049 Simple Sequence Repeats (SSRs), 1,201,326 Single Nucleotide Polymorphisms (SNPs) and 67,875 Insertion-Deletions (InDels). The variants having a higher impact were also identified and their effect was further investigated.
The transcriptome resource from the wildly growing mulberry species developed in this study can find wide applicability in gene identification and/or characterization. It can also contribute immensely in the existing mulberry improvement programs.
Mulberry plays a crucial role in driving the sericulture industry as it serves as the sole feed for silkworm. In India, four species of mulberry have been reported to occur naturally i.e. M. indica, M. alba, M. laevigata and M. serrata . M. indica and M. alba are cultivated for silkworm rearing whereas the other two grow naturally in the wild. Apart from its uses in sericulture, mulberry is also cultivated for fruit especially M. laevigata which produces long sweet fruits, firewood, fodder, and used in furniture, traditional medicine etc. M. laevigata grows across the Indian sub-continent and some collections harbor important traits such as disease and termite resistance . M. serrata on the other hand, is restricted to higher altitudes (upto 3000 m above sea level) particularly northwestern Himalayas and is known to be tolerant to frost and drought [2, 3]. M. serrata also possesses several other important traits such as thicker leaves and higher moisture content and higher moisture retention .
Since the ultimate commercial importance of mulberry lies as a feedant for the silkworm, leaf palatability is an important trait directly dependent on leaf water retention capacity, total biomass, and size and weight which are considered significant in the present day breeding programs . Nonetheless, these species possess several agronomically important traits and to utilize the vast genetic potential of these species, hybridization programs between the wild species and cultivated varieties of Morus sp. are promising [5, 6].
Additionally, owing to the medicinal and commercial importance of mulberry, a need for developing comprehensive genomic resource has also been felt. In this pursuit, our lab has contributed immensely by generating rich transcriptome-based resources of mature leaf, drought specific transcriptome  and root tissue  of M. indica. The complete chloroplast genome of mulberry was also sequenced . Additionally, these resources have been utilized for generation of Simple Sequence Repeat (SSR) markers for use in mulberry and related species [8, 10]. Recently, though the draft genome of haploid mulberry, M. notabilis has been sequenced , this is far from complete limiting its practical utility. The growing concern in mulberry is evident from the recent efforts in the expansion of genomic resources  and its subsequent utilization in marker development programs . With recent advancements in sequencing technologies, prediction of markers from transcribed regions of the genome has become a method of choice for genotyping particularly for non-model species with less commercial value.
With the above background, in the present study we describe sequencing and generation of large-scale transcriptome based resource for two wild species of mulberry, M. laevigata and M. serrata, integrated with available information on haploid mulberry M. notabilis for DNA based marker development.
Results and discussion
Even with the advent of next generation sequencing techniques, sequencing whole genomes of ‘less attractive’ or non-model plants/wild species remains impracticable. A fundamental need for introducing wild gene pool in the cultivated varieties of M. indica and M. alba has been long felt . Thus, two wild species of Morus growing in different geographical locations in India were selected for transcriptome sequencing to explore their novel genetic potential and to undertake a comparative analysis. Also, transcriptome sequencing data comprising different tissues (leaf, bark, winter bud, male flower and root) of M. notabilis was downloaded from Morus genome database (http://morus.swu.edu.cn/morusdb, ). Together, these transcriptomes were used for generating a key resource for gene identification, characterization and marker development in the genus Morus.
Transcriptome sequencing using illumina Hiseq2000
Previously, we have reported generation and sequencing of EST libraries of M. indica from mature leaves , subtractive suppressive hybridization under drought stress  and roots  of M. indica. In the present study, transcriptomes of M. laevigata and M. serrata were sequenced using Illumina HiSeq2000. A total of 202,465,156 and 195,110,548 reads 2x100 bp in length were generated in M. laevigata and M. serrata, respectively. Of this, a total of 193,934,604 (95.8 %) and 187,061,908 (95.9 %) paired reads of M. laevigata and M. serrata respectively were of high quality (Q ≥ 20) and used for further analysis.
Assembly and transcript clustering
Summary of de-novo assembly statistics of M. laevigata and M. serrata transcriptomes
Number of Transcripts
Transcriptome Length (bp)
Average Transcript Length (bp)
Number of Clustered Transcripts
Clustered Transcriptome Length (bp)
Minimum Transcript Length (bp)
Maximum Transcript Length (bp)
Average Transcript Length (bp)
Analysis of mulberry transcriptome
Gene ontology assignment and analysis
SSR identification in mulberry
SSRs are simple and cost effective markers that can play a key role in improvement of non-model plants such as mulberry. The importance of SSRs in mulberry breeding can be highlighted with the use of SSRs in the development of the first linkage map . Despite their potential advantages there have been a few reports on development of SSR markers in mulberry [8, 10, 22–24]. Recently a database of predicted SSRs from M. notabilis has been developed . With the development of high throughput transcriptome based resources, reports of SSR prediction in large datasets have started emerging . Earlier we reported SSR identification in mulberry root ESTs  and subsequently in the genic and the non-genic regions .
Transcriptome data is extensively used for identification for markers, which being derived from the expressed region of the genome have broad applicability for breeding purposes. Earlier we reported EST based SSRs from root derived transcriptome of M. indica . In the present study, a total of 117,052,910 paired reads were obtained from M. notabilis out of which 81.01 % reads were aligned with M. laevigata de novo assembled transcriptome using Bowtie2 version 2.2.4 . Almost similar alignment rate (80.75 %) was observed when M. notabilis was aligned to M. serrata transcriptome. Similarly, 97,555,274 paired reads from M. serrata were obtained, out of which 93.74 % were aligned to M. laevigata de novo assembled transcriptome. The variant calling was done using FreeBayes version 0.9.20-8-gfef284a  from the three alignments. Additional file 3: Table 1 summarizes the variants identified initially before filtration.
Read depth of least three reads is a reliable quality check for the probabilistic model of SNP prediction. In our dataset, however, the minimum read depth/SNP was in the range of 10–200 reads/SNP that represented the maximum number of SNPs identified in Ml/Ms (86.57 %), Ml/Mn (73.48 %) and Ms/Mn (76.18 %) indicating good quality and high confidence level (Fig. 6b). Correspondingly, around 39.57, 50.81 and 52.59 % transcripts in Ml/Ms, Ml/Mn and Ms/Mn respectively contained at least one SNP. Around 3837, 17,625 and 17,856 transcripts contained more than 10 SNPs in Ml/Ms, Ml/Mn and Ms/Mn, respectively (Fig. 6c). A higher read depth supporting a large number of SNPs when M. notabilis transcriptome was used for alignment (possibly because of the higher number of reads present in the initial dataset of M. notabilis transcriptome). High read depth per transcript indicates good quality and a higher confidence in predicted SNPs. An average of 4.62, 10.59 and 11.47 SNPs/transcript (in transcripts containing at least one SNP) were identified in Ml/Ms, Ml/Mn and Ms/Mn, respectively. Transitions (A/T to G/C) were found to represent 63.39, 62.66 and 62.60 % of all SNPs in Ml/Ms, Ml/Mn and Ms/Mn, respectively (Additional file 4: Figure 3A). Similarly, the remainder was represented by transversions i.e. 36.60, 37.33 and 37.39 % in Ml/Ms, Ml/Mn and Ms/Mn respectively (Additional file 4: Figure 3A). Nearly similar transition to transversion ratios of 1.73, 1.67 and 1.67 were observed in Ml/Ms, Ml/Mn and Ms/Mn, respectively. The rates of base changes have been represented in Additional file 4: Figure 3B. We also identified around 9318, 29,445 and 29,112 InDels in Ml/Ms, Ml/Mn and Ms/Mn, respectively (Additional file 5: Figure 4). The length of insertions ranged from 1 to 15 bp while the length of deletions ranged from 1 to 17 bp with a density of 10.70, 34.03 and 36.40 InDels/Mb in Ml/Ms, Ml/Mn and Ms/Mn, respectively. Maximum number of InDels was of smaller size ranging from−3 to +4.
Distribution and effect of variants
In M. laevigata/M. notabilis, of the 25 % variants found in the coding region, around 13 % were found to be non-synonymous changes (Fig. 7). Similarly, in M. serrata/M. notabilis, a higher number of non-synonymous changes (13 %) were observed (Fig. 7). More than 77,000 non-synonymous changes were identified between M. laevigata or M. serrata/M. notabilis indicating a significant evolutionary distance between the two Indian species with M. notabilis as expected. A significant number of changes were also observed in the 5’ and 3’ UTR regions suggesting possible changes in these crucial regulatory regions.
Gene ontology enrichment analysis of high impact variants
Interestingly, among the biological processes, an enrichment of SNPs was observed in the response to stimulus category and its children terms i.e. response to abiotic stimulus, biotic stimulus, stress, external and endogenous stimulus (Additional file 6: Figure 5). Enrichment in these categories indicates putative changes that might be responsible for their differential responses. Enrichment in the secondary metabolic processes and related categories such as generation of precursor metabolites and energy, carbohydrate metabolic process and lipid metabolic process was also observed. These changes might be critical as mulberry is a rich source of secondary metabolites with vast medicinal potential . Interestingly, enrichment in developmental process and its children terms was also observed. Given the morphological variation between the two species , these categories can serve as a good resource to isolate genes leading to those changes. Intriguingly, upon comparison of M. serrata and M. notabilis, a higher number of metabolic processes related categories were significantly enriched with variants. This might possibly reflect that these categories may give a substantial contribution to the differences in the two species. In general, significant enrichment was observed between Indian wild species when compared with M. notabilis suggesting that the two varieties are closer to each other when compared with M. notabilis. The response to biotic stimulus category also showed enrichment, as genes belonging to this category are under rapid evolution to match with the avirulence genes of the pathogens [34, 35]. This information can further be used for identifying/understanding stress responsive mechanisms and changes thereof in different varieties.
Genotype specific differential expression pattern of genes under various stress conditions
Modulation of expression and synthesis of a plethora of genes involved in stress signaling are among the most essential adaptive responses of plants to alteration in environmental cues. The expression of several stress related genes in the wild varieties was found to be considerably elevated in both the wild varieties analyzed in the present study. Enhancement in the expression of these genes under drought, salt and cold stresses in the two wild species (M. laevigata and M. serrata) as compared with the existing cultivar (M. indica cv. K2) indicates the potential of these two species in mulberry improvement.
In the present study, we report generation and functional annotation of a rich transcriptome-based resource for two wild species of mulberry, which have been previously shown to possess important desirable traits such as bigger leaf size, higher leaf moisture retention, and also greater adaptability to adverse conditions. We sequenced and assembled the transcriptome of two Morus species i.e. M. laevigata and M. serrata, and identified around 95,181 and 85,269 unigenes, respectively. The number of unigenes identified is slightly higher than M. atropurpurea  and M. multicaulis  possibly due to difference in stages and species selection. The de-novo assembled transcriptome was enriched in genes related to abiotic stresses such as salt stress, cold stress and defense responses including hypersensitive responses, signal transduction and plant microbe interactions. These findings corroborate the utility of these two species in mulberry breeding program where genes of desired traits can be introgressed.
In the present study, we also identified SSR and SNP markers. A total of 12,206 and 11,843 SSRs were identified in M. laevigata and M. serrata, respectively. SNP markers from the three transcriptomes i.e. M. laevigata, M. serrata and M. notabilis were also identified. A total of 174,368, 512,245 and 514,713 high quality SNPs were also identified between Ml/Ms, Ml/Mn and Ms/Mn, respectively. SNP density of 2.01, 5.92 and 6.43 SNPs/kb (with respect to the total transcriptome) was observed which is in range between chickpea  and rice . Thus, upon comparison amongst M. laevigata, M. serrata and M. notabilis transcriptomes, a rich resource for markers was developed for direct application in mulberry improvement programs. Furthermore, we have identified high impact variant containing transcripts and deduced their ontologies, which could serve as a resource for gene selection for further validation of their role in mediating different responses in these two species. These high impact variants are distributed across important GO terms including transcription, translation, response to stress, anatomical features, and reproductive structure development. This could be suggestive of the differential regulation of these genes amongst these species manifested in the form of their respective phenotypes.
For transcriptome sequencing mature leaves of M. serrata (Department of Plant Molecular Biology, South Campus, University of Delhi, New Delhi) and M. laevigata (Department of Botany, North Campus, University of Delhi, New Delhi) were harvested and flash frozen in liquid nitrogen and stored at−80 °C until further use. Leaves of similar developmental stage (second and third leaves from the apex) were harvested in the month of February.
RNA isolation and transcriptome sequencing
Total RNA was isolated from mature leaves (approximately 100 mg) of mulberry using modified GITC method . RNA was further purified and DNase treated using Plant mini kit (Qiagen, USA). RNA was quantified using NanoVue (GE Healthcare) and the integrity of the RNA preparation was checked by Agilent 2100 Bioanalyzer® (Agilent Technologies). The library generation was performed using TruSeq RNA Sample Preparation kit and sequenced using Illumina HiSeq2000 with 100 paired end chemistry by commercial service providers (Innovative Life Discoveries, Gurgaon, India) following recommended protocols. Briefly, mRNA enrichment was done using oligo (dT) beads and utilized for cDNA synthesis employing random hexamers followed by the second strand synthesis. The cDNA was fragmented and purified, ligated to adapters and used for sequencing. The two libraries were barcoded and run in a single lane.
Transcriptome assembly and functional annotation
De-novo assembly of mulberry transcriptome was performed using Trinity  software version r20140413p1 with default parameters. To generate non-redundant, full-length transcriptome, clustering was done merging contigs with >80 % identity and having a coverage of >80 % to form a single transcript using CD-HIT-EST version 4.6.1 . The high quality (HQ) reads were further mapped to the clustered transcriptome to evaluate the secondary assembly using Bowtie2 version 2.2.4 . In order to functionally annotate the transcriptome resources of mulberry, gene ontology terms were assigned. The sequences were subjected to similarity searches against NCBI nr database using Fast Annotator (Chang Gung University, Taiwan; ) using BLASTX program and assigned a putative function based on sequence similarity.
The identification of simple sequence repeats (SSRs) was done using Perl script obtained from Gramene . The number of repetitive units for different repeats was as follows: Di-9, Tri-6, Tetra-5, Penta-and Hexa-4, rest-3 repeats.
SNPs and other variants identification
The transcriptomes of M. notabilis (leaf, male flower, winter bud, bark and root) were downloaded from Morus genome database (http://morus.swu.edu.cn/morusdb, ). The downloaded reads from M. notabilis were subjected to quality analysis for various parameters including per base sequence quality, sequence length distribution, per base N content and adapter contamination using FastQC version 0.11.2 .
For variant identification, the assembled unigenes of M. laevigata were used as a reference for aligning reads from M. serrata (Ml/Ms) and M. notabilis (Ml/Mn) and the unigenes of M. serrata were used as a reference for aligning the M. notabilis transcriptome (Ms/Mn). The high quality reads were aligned using Bowtie2 version 2.2.4 . FreeBayes version 0.9.20-8-gfef284a  was used for calling variants from the three alignments notably Ml/Ms, Ml/Mn and Ms/Mn. The variants were then filtered by vcftools version 0.1.13 , using different stringency parameters such as, quality score of at least 30 and above, with a minimum read depth of 10 and variant frequency of 100 %. We also filtered more than two InDels in a 10 bp window and SNPs within three bp of an InDel.
Evaluation of variants and enrichment analysis
To further our understanding of polymorphism between diverse mulberry species, we evaluated the effect of variants using single nucleotide polymorphism effect predictor SnpEff, version 3.0j (build 2012-09-05) . The variants that could have high impact on transcript/protein (In/Dels, frame shift, stop gained, stop lost and non synonymous coding) were used for enrichment analysis using BiNGO plugin version 3.0.2  of Cytoscape software 3.0.2 .
For expression analysis, mature leaves of M. laevigata, M. serrata and M. indica cv. K2 plants maintained in University of Delhi South Campus (UDSC) were used. For various treatments, detached leaves were immersed in water/solution. Leaves of similar developmental stage (second and third leaves from the apex) were employed for various treatments. Aerial drying (AD): Detached leaves were air dried on a blotting sheet in a culture room maintained at 28 °C for five hours. Cold stress (CS): Detached leaves were placed in RO water in an incubator at a temperature of 6 ± 2 °C for five hours. Control (Con): Detached leaves were mock treated by immersing in Reverse Osmosis (RO) water at 28 °C for five hours. Salt stress (SS): Detached leaves were placed in 250 mM NaCl solution for five hours.
Quantitative real time PCR
Total RNA was extracted from mulberry leaves following a modified GITC procedure  followed by a DNase treatment using RNeasy plant mini kit (Qiagen) according to manufacturers instructions. First strand cDNA was synthesized using 1 μg total RNA in a reaction volume of 25 μl using high capacity cDNA archive kit (Applied Biosystems). The primers were designed by using Primer Express 2.0 software (PE Applied Biosystems) using default parameters and their uniqueness was checked by BLAST tool against nr database (NCBI) and melting curve analysis. Quantitative real time PCR was carried out in Stratagene Mx3005P (Agilent Technologies). The data was normalized using Elongation Factor 1α as an internal control and relative expression values were calculated using ΔΔCT method. The data represents average of two biological replicates with three technical replicates each. List of primers used in this study can be found in Additional file 7: Table 2.
The sequence data for the two transcriptomes have been submitted to the Sequence Read Archive (SRA) with the accession numbers SRP068061 for Morus laevigata and SRP067869 for Morus serrata.
BS acknowledges Council for Scientific and Industrial Research (CSIR) for Senior Research Fellowship. This work was supported by grants from Department of Biotechnology, Government of India. The authors thank the reviewers for constructive suggestions for the improvement of this manuscript.
Open AccessThis 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.
- Tikader A, Dandin SB. Biodiversity, geographical distribution, utilization and conservation of wild mulberry morus serrata roxb. Casp J Env Sci. 2005;3:179–86.Google Scholar
- Naik MVG, Thumilan BM, Roy B, Dandin SB: Assessment of genetic diversity and interrelationship among wild mulberry (Morus laevigata and M. Serrata) collections of India through DNA marker analysis. In Plant breeding in post genomics era. Proceedings of Second National Plant Breeding Congress, Coimbatore, India, 1–3 March, 2006. Indian Society of Plant Breeders; 2006, 302–308.Google Scholar
- Tikader A, Dandin SB. Pre-breeding efforts to utilize two wild Morus species. Curr Sci. 2007;92:1729–33.Google Scholar
- Khurana P, Checker VG. The advent of genomics in mulberry and perspectives for productivity enhancement. Plant Cell Rep. 2011;30:825–38.View ArticlePubMedGoogle Scholar
- Tikader A, Kamble CK. Mulberry wild species in India and their use in crop improvement a review. Aust J Crop Sci. 2008;2:64–72.Google Scholar
- Tikader A, Dandin SB. Pre-breeding efforts to utilize two wild morus species. Curr Sci. 2007;92:1729–33.Google Scholar
- Gulyani V, Khurana P. Identification and expression profiling of drought-regulated genes in mulberry (morus sp.) by suppression subtractive hybridization of susceptible and tolerant cultivars. Tree Genet Genomes. 2011;7:725–38.View ArticleGoogle Scholar
- Checker VG, Saeed B, Khurana P. Analysis of expressed sequence tags from mulberry (morus indica) roots and implications for comparative transcriptomics and marker identification. Tree Genet Genomes. 2012;8:1437–50.View ArticleGoogle Scholar
- Ravi V, Khurana JP, Tyagi AK, Khurana P. The chloroplast genome of mulberry: complete nucleotide sequence, gene organization and comparative analysis. Tree Genet Genomes. 2006;3:49–59.View ArticleGoogle Scholar
- Mathithumilan B, Kadam NN, Biradar J, Reddy SH, Ankaiah M, Narayanan MJ, et al. Development and characterization of microsatellite markers for morus spp. And assessment of their transferability to other closely related species. BMC Plant Biol. 2013;13:194.PubMed CentralView ArticlePubMedGoogle Scholar
- He N, Zhang C, Qi X, Zhao S, Tao Y, Yang G, et al. Draft genome sequence of the mulberry tree morus notabilis. Nat Commun. 2013;4.Google Scholar
- Wang H, Tong W, Feng L, Jiao Q, Long L, Fang R, et al. De novo transcriptome analysis of mulberry (morus L.) under drought stress using RNA-Seq technology. Russ J Bioorg Chem. 2014;40:423–32.View ArticleGoogle Scholar
- Dai F, Tang C, Wang Z, Luo G, He L, Yao L. De novo assembly, gene annotation, and marker development of mulberry (morus atropurpurea) transcriptome. Tree Genet Genomes. 2015;11:1–11.View ArticleGoogle Scholar
- Li T, Qi X, Zeng Q, Xiang Z, He N. MorusDB: a resource for mulberry genomics and genome biology. Database. 2014, doi:10.1093/database/bau054.
- Lal S, Ravi V, Khurana JP, Khurana P. Repertoire of leaf expressed sequence tags (ESTs) and partial characterization of stress-related and membrane transporter genes from mulberry (morus indica L.). Tree Genet Genomes. 2009;5:359–74.View ArticleGoogle Scholar
- 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. Nat Biotechnol. 2011;29:644–52.PubMed CentralView ArticlePubMedGoogle Scholar
- Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.View ArticlePubMedGoogle Scholar
- Garg R, Patel RK, Tyagi AK, Jain M. De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res. 2011;18:53–63.PubMed CentralView ArticlePubMedGoogle Scholar
- O’Neil ST, Dzurisin JD, Carmichael RD, Lobo NF, Emrich SJ, Hellmann JJ. Population-level transcriptome sequencing of nonmodel organisms erynnis Propertius and papilio zelicaon. BMC Genomics. 2010;11:310.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen T-W, Gan R-CR, Wu TH, Huang P-J, Lee C-Y, Chen Y-YM, et al. FastAnnotator-an efficient transcript annotation web tool. BMC Genomics. 2012;13 Suppl 7:S9.View ArticleGoogle Scholar
- Venkateswarlu M, Urs SR, Nath BS, Shashidhar HE, Maheswaran M, Veeraiah TM, et al. A first genetic linkage map of mulberry (morus spp.) using RAPD, ISSR, and SSR markers and pseudotestcross mapping strategy. Tree Genet Genomes. 2006;3:15–24.View ArticleGoogle Scholar
- Aggarwal RK, Udaykumar D, Hendre PS, Sarkar A, Singh LI. Isolation and characterization of six novel microsatellite markers for mulberry (morus indica). Mol Ecol Notes. 2004;4:477–9.View ArticleGoogle Scholar
- Zhao W, Miao X, Jia S, Pan Y, Huang Y. Isolation and characterization of microsatellite loci from the mulberry, morus L. Plant Sci. 2005;168:519–25.View ArticleGoogle Scholar
- Arora V, Ghosh MK, Gangopadhyay G. SSR markers for assessing the hybrid nature of Two high yielding mulberry varieties. IJGEB. 2014;5:191–6.Google Scholar
- Krishnan RR, Sumathy R, Bindroo BB, Naik VG. MulSatDB: a first online database for mulberry microsatellites. Trees. 2014;28:1793–9.View ArticleGoogle Scholar
- Metzgar D, Bytof J, Wills C. Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Res. 2000;10:72–80.PubMed CentralPubMedGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. ArXiv Prepr. 2012;ArXiv:1207–3907.Google Scholar
- Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.PubMed CentralView ArticlePubMedGoogle Scholar
- Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92.View ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMed CentralView ArticlePubMedGoogle Scholar
- Maere S, Heymans K, Kuiper M. BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21:3448–9.View ArticlePubMedGoogle Scholar
- Priya S. Medicinal values of mulberry—an overview. J Pharm Res. 2012;5:3588–96.Google Scholar
- Bergelson J, Kreitman M, Stahl EA, Tian D. Evolutionary dynamics of plant R-genes. Science. 2001;292:2281–5.View ArticlePubMedGoogle Scholar
- Fluhr R. Sentinels of disease. Plant resistance genes. Plant Physiol. 2001;127:1367–74.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng X, Chen B, Lu G, Han B. Overexpression of a NAC transcription factor enhances rice drought and salt tolerance. Biochem Biophys Res Commun. 2009;379:985–9.View ArticlePubMedGoogle Scholar
- He X-J, Mu R-L, Cao W-H, Zhang Z-G, Zhang J-S, Chen S-Y. AtNAC2, a transcription factor downstream of ethylene and auxin signaling pathways, is involved in salt stress response and lateral root development. Plant J Cell Mol Biol. 2005;44:903–16.View ArticleGoogle Scholar
- Mao X, Chen S, Li A, Zhai C, Jing R. Novel NAC transcription factor TaNAC67 confers enhanced multi-abiotic stress tolerances in Arabidopsis. PLoS One. 2014;9:e84359.PubMed CentralView ArticlePubMedGoogle Scholar
- Kumar K, Rao KP, Sharma P, Sinha AK. Differential regulation of rice mitogen activated protein kinase kinase (MKK) by abiotic stress. Plant Physiol Biochem PPB Société Fr Physiol Végétale. 2008;46:891–7.View ArticleGoogle Scholar
- Jang JY, Kim DG, Kim YO, Kim JS, Kang H. An expression analysis of a gene family encoding plasma membrane aquaporins in response to abiotic stresses in Arabidopsis thaliana. Plant Mol Biol. 2004;54:713–25.View ArticlePubMedGoogle Scholar
- Alexandersson E, Danielson JAH, Råde J, Moparthi VK, Fontes M, Kjellbom P, et al. Transcriptional regulation of aquaporins in accessions of Arabidopsis in response to drought stress. Plant J Cell Mol Biol. 2010;61:650–60.View ArticleGoogle Scholar
- Lal S, Gulyani V, Khurana P. Overexpression of HVA1 gene from barley generates tolerance to salinity and water stress in transgenic mulberry (morus indica). Transgenic Res. 2008;17:651–63.View ArticlePubMedGoogle Scholar
- Checker VG, Chhibbar AK, Khurana P. Stress-inducible expression of barley Hva1 gene in transgenic mulberry displays enhanced tolerance against drought, salinity and cold stress. Transgenic Res. 2012;21:939–57.View ArticlePubMedGoogle Scholar
- Luan S. The CBL-CIPK network in plant calcium signaling. Trends Plant Sci. 2009;14:37–42.View ArticlePubMedGoogle Scholar
- Hu H-C, Wang Y-Y, Tsay Y-F. AtCIPK8, a CBL-interacting protein kinase, regulates the low-affinity phase of the primary nitrate response. Plant J Cell Mol Biol. 2009;57:264–78.View ArticleGoogle Scholar
- Chauhan H, Khurana N, Nijhavan A, Khurana JP, Khurana P. The wheat chloroplastic small heat shock protein (sHSP26) is involved in seed maturation and germination and imparts tolerance to heat stress. Plant Cell Environ. 2012;35:1912–31.View ArticlePubMedGoogle Scholar
- Chauhan H, Khurana N, Agarwal P, Khurana P. Heat shock factors in rice (oryza sativa L.): genome-wide expression analysis during reproductive development and abiotic stress. Mol Genet Genomics MGG. 2011;286:171–87.View ArticlePubMedGoogle Scholar
- Agarwal G, Jhanwar S, Priya P, Singh VK, Saxena MS, Parida SK, et al. Comparative analysis of Kabuli chickpea transcriptome with desi and wild chickpea provides a rich resource for development of functional markers. PLoS One. 2012;7:e52443.PubMed CentralView ArticlePubMedGoogle Scholar
- Barker GLA, Edwards KJ. A genome-wide analysis of single nucleotide polymorphism diversity in the world’s major cereal crops. Plant Biotechnol J. 2009;7:318–25.View ArticlePubMedGoogle Scholar
- Chomczynski P, Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987;162:156–9.View ArticlePubMedGoogle Scholar
- Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. Computational and experimental analysis of microsatellites in rice (oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 2001;11:1441–52.PubMed CentralView ArticlePubMedGoogle Scholar
- Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. Available Online At http.www.bioinformaticsbabraham.ac.ukprojectsfastqc. Accessed Dec 2014.