Comparative study of five Legume species based on De Novo Sequence Assembly and Annotation  

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
Computational Molecular Biology, 2014, Vol. 4, No. 9   doi: 10.5376/cmb.2014.04.0009
Received: 03 Sep., 2014    Accepted: 25 Sep., 2014    Published: 23 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.
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Patel et al., 2014, Comparative study of five Legume species based on De Novo Sequence Assembly and Annotation, Computational Molecular Biology, Vol.4, No.9, 1-6 (doi: 10.5376/cmb.2014.04.0009)


Legume species are an important oilseed crop in tropical and subtropical regions of the world. Recently, next-generation sequencing technology, termed RNA-seq, has provided a powerful approach for analysing the Transcriptome. This study is focus on RNA-seq of five legume species which are Arachis hypogaea L. (The peanut) of SRR1212866, Cicer arietinum L. of SRR627764, Phaseolus vulgaris L. of SRR1283084, Trigonella foenum-graecum L. of SRR066197 and Vicia sativa L. of SRR403901 from NCBI database. Comparative study focuses on various important features like; reads were generated with N50, sequence assembly contigs which is further searched with known proteins and genes; among these, how many genes were annotated with gene ontology (GO) functional categories and sequences mapped to 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 these five legume species.

De Novo assembly; Bioinformatics; Legume species; Sequence Assembly and Annotation

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 described five legume species.

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 five legume species which are Arachis hypogaea L. (The peanut) of SRR1212866, Cicer arietinum L. of SRR627764, Phaseolus vulgaris L. of SRR1283084, Trigonella foenum-graecum L. of SRR066197 and Vicia sativa L. of SRR403901 from NCBI database for de novo Transcriptome analysis. Raw data downloaded from NCBI SRA (http://trace. which are from Illumina HiSeq 2000 platform and LS454 platform- 454 GS FLX. Raw sequence was converted into fastq file format for further annotation with the use of SRA TOOL KIT from NCBI (http://trace.ncbi.nlm.nih. gov/Traces/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 ( 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 Sequence Comparison
(Tbale 1).
2.2 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 2).


Table 1 Species comparison based on sequence


Table 2 NGS QC Toolkit Result

3 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 contigs generated with average values by this software and other details are shown in Table 3.


Table 3 Contig measurement in Length

4 Functional annotation with BLASTX and blast2GO
2.4.1 BLASTX
BLASTX was performed to align the contigs against non-redundant sequences database using an E value threshold of 10-6. Various statistical information of BLAST result is given in Table 4.


Table 4 Blast Result comparison

4.2 Enzyme Code (EC) Classification
Enzyme classified with sequences which are further classified into six classes which are of Oxidoreductases, Transferases, Hydrolases, Lyases, Isomerases and Ligases which is shown in Table 5.


Table 5 Enzyme Code (EC) Classification

4.3 Gene Ontology (GO) Classification
To functionally categorize various legume transcript contigs, Gene Ontology (GO) terms were assigned to each assembled transcript contigs. Transcript contigs were grouped into GO functional categories (, which are distributed under the three main categories of Molecular Function, Biological Process and Cellular Components (Table 6).


Table 6 Gene Ontology (GO) Classification

Figure 1 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. We found same in all other legume species so we have considered only this one figure for illustration of WEGO tool.


Figure 1 WEGO Tool Result of Arachis hypogaea L.

Many genes were annotated with different pathways in the KEGG database ( pathway.html). Further comparative result is shown in Table 7. Many transcripts include various pathways like metabolic pathways, plant-pathogen interaction pathways, fatty acid metabolism pathway and fatty acid biosynthesis.


Table 7 KEGG Result

5 SSR mining
Microsatellite markers (SSR markers) are some of the most successful molecular markers in the construction of a peanut genetic map and in diversity analysis (Zhang et al). For identification of SSRs, all transcripts were searched with perl script MISA. SSR mining result is described in Table 8 which shows detailed information of each species’ SSR result. 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 in most of species.


Table 8 Statistics of SSRs identified in transcripts

6 Plant Transcription Factor
Further, transcription factor encoding transcripts were identified by sequence comparison to known transcription factor gene families. Result in Table 9 shows that transcription factor genes distributed with families were identified and which is described in Table 9 and Figure 2 which is Plant Transcription Factor Result of Trigonella foenum-graecum L..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).


Table 9 Plant Transcription Factor Result


Figure 2 Plant Transcription Factor Result of Trigonella foenum-graecum L.

This study is focus on five different legume species from NCBI database for de novo sequence assembly and analysis by RNA-seq using next-generation Illumina and 454 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 five different legume species without considering any reference species with significant non-redundant set transcripts. The detailed analyses of the data set has provided several important features of five species 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 comparative study of five different legume species which are Arachis hypogaea L., Cicer arietinum L., Phaseolus vulgaris L., Trigonella foenum-graecum L.and Vicia sativa L.will be useful for further functional genomics studies as it includes useful information of each species with full annotation.
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.
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