Limits...
Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures.

Nithin C, Patwa N, Thomas A, Bahadur RP, Basak J - BMC Plant Biol. (2015)

Bottom Line: Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method.Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris.This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

View Article: PubMed Central - PubMed

Affiliation: Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. nithin_aneesh@iitkgp.ac.in.

ABSTRACT

Background: MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data.

Results: We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20.

Conclusions: There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

No MeSH data available.


Hybridized structure of mature miRNA with its targets. The mature miRNA forms the 5′ end and the target is at the 3′ end separated by 6 nucleotides. The pvu-miR166d with its two targets: (a) EST 312062389 coding for UDP-N-acetylglucosamine pyrophosphorylase protein regulated by cleavage, (b) EST 312035414 coding for SNF1-related protein kinase regulatory subunit inhibited by translational regulation
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4464996&req=5

Fig8: Hybridized structure of mature miRNA with its targets. The mature miRNA forms the 5′ end and the target is at the 3′ end separated by 6 nucleotides. The pvu-miR166d with its two targets: (a) EST 312062389 coding for UDP-N-acetylglucosamine pyrophosphorylase protein regulated by cleavage, (b) EST 312035414 coding for SNF1-related protein kinase regulatory subunit inhibited by translational regulation

Mentions: The psRNATarget server was used to predict the miRNA targets. The default sequences of the target candidates in the server are of old version, hence the updated EST sequences of P. vulgaris from NCBI GenBank were used as target candidates. For 130 miRNAs that belong to 69 families, 1303 target sequences were predicted. In order to characterise the targets, BLASTX was used with the predicted target sequences as query and the entire protein sequences of Viridiplantae as subject. Using 80 % sequence identity cut-off, 318 targets for 95 miRNAs were characterised (Additional file 6 Table S6). For additional 339 targets for 80 miRNAs, the BLASTX predicted uncharacterised and hypothetical proteins. The hybridized structures of mature pvu-miR166d with its two targets, EST 312062389 coding for UDP-N-acetyl glucosamine pyrophosphorylase protein and EST 312035414 coding for SNF1-related protein kinase regulatory subunit are shown in Fig. 8.Fig. 8


Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures.

Nithin C, Patwa N, Thomas A, Bahadur RP, Basak J - BMC Plant Biol. (2015)

Hybridized structure of mature miRNA with its targets. The mature miRNA forms the 5′ end and the target is at the 3′ end separated by 6 nucleotides. The pvu-miR166d with its two targets: (a) EST 312062389 coding for UDP-N-acetylglucosamine pyrophosphorylase protein regulated by cleavage, (b) EST 312035414 coding for SNF1-related protein kinase regulatory subunit inhibited by translational regulation
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4464996&req=5

Fig8: Hybridized structure of mature miRNA with its targets. The mature miRNA forms the 5′ end and the target is at the 3′ end separated by 6 nucleotides. The pvu-miR166d with its two targets: (a) EST 312062389 coding for UDP-N-acetylglucosamine pyrophosphorylase protein regulated by cleavage, (b) EST 312035414 coding for SNF1-related protein kinase regulatory subunit inhibited by translational regulation
Mentions: The psRNATarget server was used to predict the miRNA targets. The default sequences of the target candidates in the server are of old version, hence the updated EST sequences of P. vulgaris from NCBI GenBank were used as target candidates. For 130 miRNAs that belong to 69 families, 1303 target sequences were predicted. In order to characterise the targets, BLASTX was used with the predicted target sequences as query and the entire protein sequences of Viridiplantae as subject. Using 80 % sequence identity cut-off, 318 targets for 95 miRNAs were characterised (Additional file 6 Table S6). For additional 339 targets for 80 miRNAs, the BLASTX predicted uncharacterised and hypothetical proteins. The hybridized structures of mature pvu-miR166d with its two targets, EST 312062389 coding for UDP-N-acetyl glucosamine pyrophosphorylase protein and EST 312035414 coding for SNF1-related protein kinase regulatory subunit are shown in Fig. 8.Fig. 8

Bottom Line: Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method.Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris.This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

View Article: PubMed Central - PubMed

Affiliation: Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. nithin_aneesh@iitkgp.ac.in.

ABSTRACT

Background: MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data.

Results: We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20.

Conclusions: There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

No MeSH data available.