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Item Optimization of de novo short read assembly of seabuckthorn (Hippophae rhamnoides L.) transcriptome(PLOS, 2013) Ghangal, Rajesh; Chaudhary, Saurabh; Jain, Mukesh; Purty, Ram Singh; Sharma, Prakash ChandSeabuckthorn (Hippophaerhamnoides L.) is known for its medicinal, nutritional and environmental importance since ancient times. However, very limited efforts have been made to characterize the genome and transcriptome of this wonder plant. Here, we report the use of next generation massive parallel sequencing technology (Illumina platform) and de novo assembly to gain a comprehensive view of the seabuckthorn transcriptome. We assembled 86,253,874 high quality short reads using six assembly tools. At our hand, assembly of non-redundant short reads following a two-step procedure was found to be the best considering various assembly quality parameters. Initially, ABySS tool was used following an additive k-mer approach. The assembled transcripts were subsequently subjected to TGICL suite. Finally, de novo short read assembly yielded 88,297 transcripts (> 100 bp), representing about 53 Mb of seabuckthorn transcriptome. The average length of transcripts was 610 bp, N50 length 1198 BP and 91% of the short reads uniquely mapped back to seabuckthorn transcriptome. A total of 41,340 (46.8%) transcripts showed significant similarity with sequences present in nr protein databases of NCBI (E-value < 1E-06). We also screened the assembled transcripts for the presence of transcription factors and simple sequence repeats. Our strategy involving the use of short read assembler (ABySS) followed by TGICL will be useful for the researchers working with a non-model organism's transcriptome in terms of saving time and reducing complexity in data management. The seabuckthorn transcriptome data generated here provide a valuable resource for gene discovery and development of functional molecular markers.Item RNA-seq for transcriptome analysis in non-model plants(Springer, 2013) Garg, Rohini; Jain, MukeshSequencing of mRNA using next-generation sequencing (NGS) technologies (RNA-seq) has the potential to reveal unprecedented complexity of the transcriptomes. The transcriptome sequencing of an organism provides quick insights into the gene space, opportunity to isolate genes of interest, development of functional markers, quantitation of gene expression, and comparative genomic studies. Although becoming cheaper, transcriptome sequencing still remains an expensive endeavor. Further, the assembly of millions and billions of RNA-seq reads to construct the complete transcriptome poses great informatics challenges. Here, first we outline various important issues from experimental design to data analysis, including various strategies of transcriptome assembly, which need substantial consideration for a successful RNA-seq experiment. Further, we describe a method for using RNA-seq to characterize the transcriptome of a plant species, taking the example of a legume crop plant chickpea. Our aim is to provide a quick start guide to the nonexpert researchers for NGS-based transcriptome analysis.Item Next-generation sequencing technologies for gene expression profiling in plants(Oxford University Press, 2012) Jain, MukeshNext-generation sequencing (NGS) provides a better approach to gene expression profiling with several advantages. The power of NGS along with novel molecular techniques and computational tools allow the researchers to perform the gene expression profiling to reveal transcriptional complexity of an organism and answering several biological questions. Although many studies for gene expression profiling related to various aspects have been performed in animal systems revealing unprecedented levels of complexity of transcriptomes, their use is still limited in plant biology. This review describes the use of NGS technologies with respect to gene expression profiling, bioinformatics challenges associated with data analysis and advances made so far in the plant biology research. We anticipate many more studies in recent future, which will surely advance our understanding of the complexity of plant genomes.Item Comparative analysis of kabuli chickpea transcriptome with desi and wild chickpea provides a rich resource for development of functional markers(Public Library of Science, 2012) Agarwal, Gaurav; Jhanwar, Shalu; Priya, Pushp; Singh, Vikash K.; Saxena, Maneesha S.; Parida, Swarup K.; Garg, Rohini; Tyagi, Akhilesh K.; Jain, MukeshChickpea (Cicer arietinum L.) is an important crop legume plant with high nutritional value. The transcriptomes of desi and wild chickpea have already been sequenced. In this study, we sequenced the transcriptome of kabuli chickpea, C. arietinum (genotype ICCV2), having higher commercial value, using GS-FLX Roche 454 and Illumina technologies. The assemblies of both Roche 454 and Illumina datasets were optimized using various assembly programs and parameters. The final optimized hybrid assembly generated 43,389 transcripts with an average length of 1065 bp and N50 length of 1653 bp representing 46.2 Mb of kabuli chickpea transcriptome. We identified a total of 5409 simple sequence repeats (SSRs) in these transcript sequences. Among these, at least 130 and 493 SSRs were polymorphic with desi (ICC4958) and wild (PI489777) chickpea, respectively. In addition, a total of 1986 and 37,954 single nucleotide polymorphisms (SNPs) were predicted in kabuli/desi and kabuli/wild genotypes, respectively. The SNP frequency was 0.043 SNP per kb for kabuli/desi and 0.821 SNP per kb for kabuli/wild, reflecting very low genetic diversity in chickpea. Further, SSRs and SNPs present in tissue-specific and transcription factor encoding transcripts have been identified. The experimental validation of a selected set of polymorphic SSRs and SNPs exhibited high intra-specific polymorphism potential between desi and kabuli chickpea, suggesting their utility in large-scale genotyping applications. The kabuli chickpea gene index assembled, and SSRs and SNPs identified in this study will serve as useful genomic resource for genetic improvement of chickpea.