In the current set of experiment forty maize genotypes were assessed for drought associated traits. For evaluation of these traits cluster, PC and correlation analysis was employed to obtain suitable parents that can be further exploited in future breeding programmes. Correlation analysis revealed some important associations among the traits studied. FRL had positive and significant associations but LT had significant negative correlation with RD at both 40% and 100% moisture levels while RD had negative at 100% and positive correlation at 40% moisture level with CC. The positive correlation among these yield contributing traits suggested that these characters are important for direct selection of drought tolerant high yielding genotypes. Principal component (PC) analysis showed first 4 PCs having Eigen value >1 explaining 86.7% and 88.4% of the total variation at 40% and 100% moisture level respectively with different drought related traits. Cluster analysis classified 40 accessions into four divergent groups. The members of cluster 1 and 2 may be combined in future breeding programs to obtain genotypes /hybrids that can perform well under drought stress conditions. Members of cluster 3 may be selected on the bases of RD, LT, DRW and RSR/W and can be combined with members of cluster 4 due to higher leaf temperature and RSR/L. The results concluded that the germplasm had wide genetic diversity thus can be utilized for future breeding programmes to obtain drought tolerant maize genotypes/ hybrids for adaptation to water scarce areas.
It is demonstrated earlier that the exact Smith-Waterman algorithm yields more accurate results than the members of the heuristic BLAST family of algorithms. Unfortunately, the Smith-Waterman algorithm is much slower than the BLAST and its clones.\nHere we present a technique and a webserver that uses the exact Smith-Waterman algorithm, and it is approximately as fast as the BLAST algorithm. The technique unites earlier methods of extensive preprocessing of the target sequence database, and CPU-specific coding of the Smith-Waterman algorithm.\nThe SwissAlign webserver is available at the http://swissalign.pitgroup.org address.
OPTICS is a density-based clustering algorithm that performs well\nin a wide variety of applications. For a set of input objects,\nthe algorithm creates a so-called reachability plot that\ncan be either used to produce cluster membership assignments,\nor interpreted itself as an expressive two-dimensional representation\nof the density-based clustering structure of the input set, even if the input set is embedded in higher dimensions.\nThe main focus of this work is a visualization method that can be used to assign colors\nto all entries of the input database, based on hierarchically represented\na-priori knowledge available for each of these objects.\nBased on two different, bioinformatics-related applications we\nillustrate how the proposed method can be efficiently used\nto identify clusters with proven real-life relevance.