Figure 1.
Bioinformatic pipeline followed for annotation of unigenes.
Figure 2.
Bioinformatic pipeline followed for identification of SSR bearing pre-miRNAs and its possible targets.
Figure 3.
Summary of de novo transcriptome sequencing and assembly of black pepper.
(A) The length distribution of contigs (B) The length distribution of Unigenes (C) Histogram showing unigene classification based on clusters of orthologous groups (COG) (D) Gene Ontology classification of unigenes (E) KEGG functional classification of unigenes.
Figure 4.
Summary of microsatellite repeats identified in the generated transcriptome.
(A) Classification of microsatellites based on different types of motifs (B) Classification of microsatellites based on nucleotide string (C) Characterisation of dinucleotide repeats detected in transcripts (D) Characterisation of trinucleotide repeats detected in transcripts (E) Characterisation of tetranucleotide repeats detected in transcripts (F) Characterisation of pentanucleotide repeats detected in transcripts.
Table 1.
List of putative ‘miRNA candidates’ identified.
Table 2.
List of all the potential targets for the ‘miRNA candidates’.
Table 3.
A comparison of high throughput sequencing data from recently reported root transcriptome with our generated leaf transcriptome of black pepper.
Figure 5.
Heat map showing summary of changes in gene expression based on RPKM values.
Figure 6.
Relative position of microsatellite motifs with respect to potential ‘pre-miRNA candidates’.
Figure 7.
Pie chart showing the relative number of SSR bearing ‘pre-miRNAs’ among different taxa (Viridiplantae, Viruses, Arthropoda, Nematoda, Platyhelminthes, Urochordata, Vertebrata, Mycetozoa and Protistae).
Figure 8.
Pie chart showing the relative number of SSR bearing ‘pre-miRNAs’ among different species of Viridiplantae.
Figure 9.
A comparative study between A. thaliana and P. nigrum on SSRs occurring within the ‘pre-miRNAs’.