Research Report

De Novo RNA Seq Assembly and Annotation of Phaseolus vulgaris L. (SRR1283084)  

Sagar S. Patel1 , Dipti B. Shah1 , Hetalkumar J. Panchal2
1 G. H. Patel Post Graduate Department of Computer Science and Technology, Sardar Patel University, Vallabh Vidyanagar, Gujarat-388120, India
2 Gujarat Agricultural Biotechnology Institute, Navsari Agricultural University, Surat, Gujarat- 395007, India
Author    Correspondence author
Genomics and Applied Biology, 2014, Vol. 5, No. 5   doi: 10.5376/gab.2014.05.0005
Received: 21 Aug., 2014    Accepted: 24 Sep., 2014    Published: 22 Oct., 2014
© 2014 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Patel et al., 2014, De Novo RNA Seq Assembly and Annotation of Phaseolus vulgaris L. (SRR1283084), Genomics and Applied Biology, Vol.5, No.5, 1-6 (doi: 10.5376/gab.2014.05.0005)


Phaseolus vulgaris L. which is also known as Common bean; is produced in the tropics on small-scale farms where unfavorable factors limit the yield potential. Recently, next-generation sequencing technology, termed RNA-seq, has provided a powerful approach for analyzing the Transcriptome. This study is focus on RNA-seq of Phaseolus vulgaris L. of SRR1283084 from NCBI database for de novo Transcriptome analysis. A total of 20.4 million single reads were generated with N50 of 293 bp. Sequence assembly contained total 6999 contigs which is further search with known proteins, a total of 1679 genes were identified. Among these, only 629 unigenes were annotated with 3724 gene ontology (GO) functional categories and sequences mapped to 89 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). These data will be useful for gene discovery and functional studies and the large number of transcripts reported in the current study will serve as a valuable genetic resource of the Phaseolus vulgaris L..

Transcriptome; Bioinformatics; Phaseolus vulgaris L..

Next generation sequencing methods for high throughput RNA sequencing (transcriptome) is becoming increasingly utilized as the technology of choice to detect and quantify known and novel transcripts in plants. This Transcriptome analysis method is fast and simple because it does not require cloning of the cDNAs. Direct sequencing of these cDNAs can generate short reads at an extraordinary depth. After sequencing, the resulting reads can be assembled into a genome-scale transcription profile. It is a more comprehensive and efficient way to measure Transcriptome composition, obtain RNA expression patterns, and discovers new exons and genes (Mortazavi et al., 2008; Wang et al.,2009); sequencing data of Transcriptome was assembled using various assembly tools, functional annotation of genes and pathway analysis carried with various Bioinformatics tools. The large number of transcripts reported in the current study will serve as a valuable genetic resource for Phaseolus vulgaris L..

High-throughput short-read sequencing is one of the latest sequencing technologies to be released to the genomics community. For example, on average a single run on the Illumina Genome Analyser can result in over 30 to 40 million single-end (~35 nt) sequences. However, the resulting output can easily overwhelm genomic analysis systems designed for the length of traditional Sanger sequencing, or even the smaller volumes of data resulting from 454 (Roche) sequencing technology. Typically, the initial use of short-read sequencing was confined to matching data from genomes that were nearly identical to the reference genome. Transcriptome analysis on a global gene expression level is an ideal application of short-read sequencing. Traditionally such analysis involved complementary DNA (cDNA) library construction, Sanger sequencing of ESTs, and microarray analysis. Next generation sequencing has become a feasible method for increasing sequencing depth and coverage while reducing time and cost compared to the traditional Sanger method (L J Collins et al.).

1 Methods
1.1 Sequence Retrieval
This study is focus on the de novo assembly and sequence annotation of Phaseolus vulgaris L. of SRR1283084 from NCBI database. Raw data downloaded from NCBI SRA (http://trace.ncbi.nlm. SRR1283084) which is from Illumina HiSeq 2000 platform and the sample is single ended with 20.4 M spots and 46.4% GC content. Raw sequence was converted in to fastq file format for further annotation with the use of SRA TOOL KIT from NCBI. ( sra/sra.cgi?view=software).
1.2 NGS QC Toolkit
NGS QC Toolkit, it is an application for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis (Patel RK, et al).
1.3 De novo sequence assembly by CLC GENOMICS WORKBENCH 7
A comprehensive and user-friendly analysis package for analyzing, comparing, and visualizing next generation sequencing data. This package was used for de novo sequence assembly of sequence with by default parameters of de novo assembly tool (
The assembled file was further considered for annotation in which first step was to identify translated protein sequences from contigs. BLASTX at NCBI ( cgi?PROGRAM=blastx&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) performed with changing few parameters like non redundant protein database (nr) selected as Database; Eudicots selected in organism option and in Algorithm parameters Max target Sequences set to 10 and Expect threshold set to 6.
1.5 Blast2GO
Blast2GO is an ALL in ONE tool for functional annotation of (novel) sequences and the analysis of annotation data ( Based on the results of the protein database annotation, Blast2GO was employed to obtain the functional classification of the unigenes based on GO terms. The transcript contigs were classified under three GO terms such as molecular function, cellular process and biological process (Ness et al., 2011; Shi et al., 2011; Wang et al., 2010). WEGO (http://www.wego. tool was used to perform the GO functional classification for all of the unigenes and to understand the distribution of the gene functions of this species at the macro level. The KEGG database ( was used to annotate the pathway of these unigenes.
1.6 SSR mining
We employed MIcroSAtellite (MISA) (http://pgrc.ipk- for microsatellite mining which gives various statistical outputs of transcripts with useful information.
1.7 Plant transcription factor
PlantTFcat: An Online Plant Transcription Factor and Transcriptional Regulator Categorization and Analysis Tool used for identifying plant transcription factor in sequences (
2 Result and Discussions
2.1 NGS QC Toolkit
Sequence was filtered with this tool by removing adaptors and other contaminated materials then quality of sequence also checked with this tool and finally high quality filter sequence file considered for de novo sequence assembly (Table 1).


Table 1 NGS QC Toolkit Result

2.2 De novo Sequence Assembly
CLC GENOMICS WORKBENCH 7 considered for de novo sequence assembly with by default parameters like Mismatch Cost = 2, Insertion Cost = 3, Deletion Cost = 3, Length Fraction = 0.5, Similarity Fraction = 0.8, Word size = 21 and finally 6999 contigs generated with average value of 302 by this software and other details are shown in Table 2.


Table 2 Contig measurement

2.3 Functional annotation with BLASTX and blast2GO
2.3.1 BLASTX
BLASTX was performed to align the contigs against non-redundant sequences database using an E value threshold of 10-6. Out of 6999 transcript contigs, 1988 were having BLAST hits to known proteins with high significant similarity and 102 had no BLAST hits (Table 3). Out of total transcripts contigs, Table 4 and Figure 1 shows that species distribution in which 2378 sequences showed significant similarity with Phaseolus vulgaris itself and least similarity was found with Nicotiana tabacum (5).


Table 3 Blast Result


Table 4 Blast Result of SpeciesDistribution


Figure 1 Blast Result of Species Distribution

2.3.2 Enzyme Code (EC) Classification
Enzyme classified with total of 563 sequences which is further classified into six classes which are of Oxidoreductases (110), Transferases (232), Hydrolases (147), Lyases (23), Isomerases (20) and Ligases (31) which is shown in Figure 2.


Figure 2 Enzyme Code (EC) Classification

2.3.3 Gene Ontology (GO) Classification
To functionally categorize Phaseolus vulgaris L. transcript contigs, Gene Ontology (GO) terms were assigned to each assembled transcript contigs. Out of 6999 transcript contigs, 3724 unigenes were grouped into GO functional categories (, which are distributed under the three main categories of Molecular Function (1727), Biological Process (1168) and Cellular Components (829) (Figure 3). Figure 4 which is output of WEGO tool;it shows that, Within the Molecular Function category, genes encoding binding proteins and proteins related to catalytic activity were the most enriched. Proteins related to metabolic processes and cellular processes were enriched in the Biological Process category. With regard to the Cellular Components category, the cell and cell part were the most highly represented categories.


Figure 3 Gene Ontology Result


Figure 4 WEGO Tool Result

A total of 629 unigenes were annotated with 89 pathways in the KEGG database (http://www.genome. jp/kegg/pathway.html). Many transcripts include various pathways like metabolic pathways, plant-pathogen interaction pathways, fatty acid metabolism pathway and fatty acid biosynthesis.
2.4 SSR mining
Microsatellite markers (SSR markers) are some of the most successful molecular markers in the construction of a Phaseolus vulgaris L. genetic map and in diversity analysis (Zhang et al). For identification of SSRs, all transcripts were searched with perl script MISA. We identified a total of 1405 SSRs in 1304 transcripts (Table 5). The mono-nucleotide SSRs represented the largest fraction of SSRs identified followed by tri-nucleotide and di-nucleotide SSRs. Although only a small fraction of tetra-, penta- and hexa-nucleotide SSRs were identified in transcripts, the number is quite significant.


Table 5 Statistics of SSRs identified in transcripts

2.5 Plant Transcription Factor
Further, transcription factor encoding transcripts were identified by sequence comparison to known transcription factor gene families. Result shows that transcription factor genes distributed with at least 32 families were identified (Figure 5). The overall distribution of transcription factor encoding transcripts among the various known protein families is very similar with that of other legumes as predicted earlier (Libault et al., 2009).


Figure 5 Plant Transcription Factor Result

3 Conclusion
This study is focus on Phaseolus vulgaris L. species (SRR1283084) from NCBI database for de novo Transcriptome analysis by RNA-seq using next-generation Illumina sequencing. The transcriptome sequencing enables various functional genomics studies for an organism. Although several high throughput technologies have been developed for rapid sequencing and characterization of transcriptomes, expressed sequence data are still not available for many organisms, including many crop plants. In this study, we performed de novo functional annotation of the Phaseolus vulgaris L. transcriptome without considering any reference species with significant non-redundant set of 6999 transcripts. The detailed analyses of the data set has provided several important features of Phaseolus vulgaris L. transcriptome such as GC content, conserved genes across legumes and other plant species, assignment of functional categories by GO terms and identification of SSRs by MISA tool. It is noted that this study ofPhaseolus vulgaris L. will be useful for further functional genomics studies as it includes useful information of each transcript.
We are heartily thankful to Prof. (Dr.) P.V. Virparia, Director, GDCST, Sardar Patel University, Vallabh Vidyanagar, for providing us facilities for the research work.
Collins J. L., Biggs J. P., Voelckel C., and Joly S., 2008, An approach to transcriptome analysis of non-model organisms using short-read sequences, Genome Informatics 21:3-14
Jianan Zhang, Shan Liang, Jialei Duan, Jin Wang, Silong Chen, Zengshu Cheng, Qiang Zhang, Xuanqiang Liang and Yurong Li, 2012, De novo assembly and Characterisation of the Transcriptome during seed development, and generation of genic-SSR markers in Peanut (Arachis hypogaea L.), BMC Genomics 2012 13:90
Libault, M., Joshi, T., Benedito, V.A., Xu, D., Udvardi, M.K., and Stacey, G., 2009, Legume Transcription Factor Genes: What makes legumes so special?. Plant Physiology 151: 991-1001
Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., and Wold, B., 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 5(7): 621-8
Ness, R.W., Siol, M., and Barrett S.C.H., 2011, De novo sequence assembly and characterization of the floral transcriptome in cross and self-fertilizing plants, BMC Genomics 12: 298
Patel RK, Jain M, 2012, NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data, PLoS ONE 7(2): e30619. doi:10.1371/journal.pone.0030619
Rohini Garg, Ravi K. Patel, Akhilesh K. Tyagi, and Mukesh Jain., 2011, De Novo Assembly of Chickpea Transcriptome Using Short Reads for Gene Discovery and Marker Identification, DNA RESEARCH 18, 53–63; doi:10.1093/dnares/dsq028
Shi, C.Y., Yang, H., and Wei, C.L., 2011, Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds, BMC Genomics 12 : 131
Vaidya K., Ghosh A., Kumar V, Chaudhary S, Srivastava N, Katudia K, Tiwari T and Chikara K., 2012, De novo transcriptome sequencing in Trigonella foenum-graecum to identify genes involved in the biosynthesis of diosgenin. The Plant Genome:doi: 10.3835/plantgenome2012.08.0021
Wang, X.W., Luan, J.B., Li, J.M., Bao, Y.Y., Zhang, C.X., and Liu, S.S., 2010, De novo characterization of a whitefly transcriptome and analysis of its gene expression during development, BMC Genomics 11: 400
Wang, Z., Gerstein, M., and Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics, Nat Rev Genet. 10(1): 57-63
Genomics and Applied Biology
• Volume 5
View Options
. PDF(596KB)
. Online fPDF
Associated material
. Readers' comments
Other articles by authors
. Sagar S. Patel
. Dipti B. Shah
. Hetalkumar J. Panchal
Related articles
. Transcriptome
. Bioinformatics
. Phaseolus vulgaris L..
. Email to a friend
. Post a comment