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 Vicia sativa 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.).

This study is focus on the de novo assembly and sequence annotation of Vicia sativa L. of SRR403901 from NCBI database. Raw data downloaded from NCBI SRA (http://trace.ncbi.nlm.nih.gov/Traces/sra/?run= SRR403901) which is from Illumina HiSeq 2000 platform and the sample is single ended with 12.4 M spots and 42.4% GC content. Raw sequence was converted in to fastq file format for further an notation with the use of SRA TOOL KIT from NCBI. (http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software)

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 http://www.nipgr.res.in/ngsqctoolkit.html. 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 R K, et al).

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 (http://www.clcbio.com/products/clc-genomics-workbench/).

The assembled file was further considered for annotation in which first step was to identify translated protein sequences from contigs. BLASTX at NCBI (http://www.ncbi.nlm.nih.gov/blast/Blast.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.

Blast2GO is an ALL in ONE tool for functional annotation of (novel) sequences and the analysis of annotation data (http://www.blast2go.com/b2ghome). 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.genomics.org.cn) 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 (http://www.genome.jp/kegg/pathway.html) was used to annotate the pathway of these unigenes.

We employed MIcroSAtellite (MISA) (http://pgrc.ipk-gatersleben.de/misa/) for microsatellite mining which gives various statistical outputs of transcripts with useful information.

PlantTFcat: An Online Plant Transcription Factor and Transcriptional Regulator Categorization and Analysis Tool used for identifying plant transcription factor in sequences (http://plantgrn.noble.org/PlantTFcat/).

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).

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 22748 contigs generated with average value of 503 by this software and other details are shown in Table 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 22748 transcript contigs, 13482 were having BLAST hits to known proteins with high significant similarity and 1114 had no BLAST hits (Table 3). Out of total transcripts contigs, Table 4 and Figure 1 shows that species distribution in which 9819 sequences showed significant similarity with Medicago truncatula and least similarity was found with Prunus mume (24).

3.2 Enzyme Code (EC) Classification

Enzyme classified with total of 2336 sequences which is further classified into six classes which are of Oxidoreductases (429), Transferases (927), Hydrolases (718), Lyases (80), Isomerases (82) and Ligases (100) which is shown in Figure 2.

3.3 Gene Ontology (GO) Classification

To functionally categorize Vicia sativa L. transcript contigs, Gene Ontology (GO) terms were assigned to each assembled transcript contigs. Out of 22748 transcript contigs, 18761 unigenes were grouped into GO functional categories (http://www.geneontology.org), which are distributed under the three main categories of Molecular Function (7026), Biological Process (5815) and Cellular Components (5920) (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.

A total of 500 unigenes were annotated with 122 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.