• Sandip Das

      Articles written in Journal of Biosciences

    • Assessment of genetic variation withinBrassica campestris cultivars using amplified fragment length polymorphism and random amplification of polymorphic DNA markers

      Sandip Das Jyothi Rajagopal Sabhyata Bhatia P S Srivastava Malathi Lakshmikumaran

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      Genetic relationships were evaluated among nine cultivars ofBrassica campestris by employing random amplification of polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) markers. RAPDs generated a total of 125 bands using 13 decamer primers (an average of 9.6 bands per assay) of which nearly 80% were polymorphic. The per cent polymorphism ranged from 60–100%. AFLP, on the other hand generated a total of 319 markers, an average of 64 bands per assay. Of these, 213 were polymorphic in nature (66.8%). AFLP methodology detected polymorphism more efficiently than RAPD approach due to a greater number of loci assayed per reaction. Cultivar-specific bands were identified, for some cultivars using RAPD, and for most cultivars with AFLP. Genetic similarity matrix, based on Jaccard’s index detected coefficients ranging from 0.42 to 0.73 for RAPD, and from 0.48 to 0.925 for AFLPs indicating a wide genetic base. Cluster analyses using data generated by both RAPD and AFLP markers, clearly separated the yellow seeded, self-compatible cultivars from the brown seeded, self-incompatible cultivars although AFLP markers were able to group the cultivars more accurately. The higher genetic variation detected by AFLP in comparison to RAPD was also reflected in the topography of the phenetic dendrograms obtained. These results have been discussed in light of other studies and the relative efficiency of the marker systems for germplasm evaluation.

    • SWORDS: A statistical tool for analysing large DNA sequences

      Probal Chaudhuri Sandip Das

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      In this article, we present some simple yet effective statistical techniques for analysing and comparing large DNA sequences. These techniques are based on frequency distributions of DNA words in a large sequence, and have been packaged into a software called SWORDS. Using sequences available in public domain databases housed in the Internet, we demonstrate how SWORDS can be conveniently used by molecular biologists and geneticists to unmask biologically important features hidden in large sequences and assess their statistical significance.

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