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In the present manuscript, an adaptive evolutionary multiple sequence alignment algorithm is proposed that uses a combination of consensus and SP-score methods. The algorithm searches intermediate pairwise consensus strings that are used to identify the final consensus string for a given set of DNA/RNA/protein sequences. The proposed algorithm is an extension of MPSAGA algorithm that uses positional matrix representation of sequences. An empirical study was performed in the present work to compare the proposed algorithm with the other three contemporary ClustalW, TCOFFEE, and MUSCLE algorithms on the four datasets. The overall observations from the various experiments revealed that the proposed algorithm outperforms than the other algorithms tested in aligning multiple sequences with an average increase of 0.03% in alignment length by inserting 0.02% increased number of gaps.

The increase in world population is stressing the need for increased production of food supplies from plants. At the same time, plant pathogens are also developing resistance to several anti-pathogen compounds. The present situation may become worse, in the near future, if not controlled. One of the solutions to the present situation is to develop disease-resistant varieties of plants. The disease resistance in plants is controlled by the products of disease-resistance genes. The plant genomes contain many disease-resistance candidate genes, activation of which can confer the natural resistance against various diseases in plants. The major step in the development of disease-resistant plant varieties is to search for the disease-resistance candidate genes in the plant genome and prioritize them. The experiments pertaining to identify disease-resistance candidate genes can be accomplished using wet lab studies but are usually time-consuming. The present chapter is a survey of the available in silico approaches to identify the candidate genes conferring disease resistance in plants. After providing a brief overview of the multilayered defense mechanism, the present article discusses different approaches for the stepwise identification of disease-resistant candidate genes in plants.

Microbes are exploited as a synthetic platform in the system biology where biochemistry of various biosynthetic pathways can be redesigned. De novo pathway biosynthesis is used to produce extremely valuable, high molecular weight compounds from renewable sources. The tools of bioinformatics and various other analytical approaches have played a significant role in in silico identification and characterization of novel chemical scaffolds of microbial origin by genome mining. However, the majority of the computational tools employ forward approach to link “genes” to their corresponding “metabolites,” while the automated computational tools based on the retrosynthetic approach, to connect chemical structures of “metabolites” to their biosynthetic “genes,” are still in their infancy. The retrobiosynthesis approach is an ingenious pathway design concept that has gained interest in the recent days because of its potential to assist in the redesigning of novel metabolic routes in a given biosynthetic pathway to produce the optimum levels of the targeted secondary metabolite. The approach employs a stepwise, backward to forward search to identify the most advantageous reactions to formulate and optimize the custom-made pathway evolution, whereas the retrosynthetic approaches are the computer-assisted framework to analyze various enzymatic reactions of the natural product biosynthetic pathways to connect and predict their corresponding genetic cluster. The main aim of the present chapter is to provide a brief overview of the retrobiosynthesis methodology and summarize about the available genome mining tools utilized in the forward and retrosynthetic approaches to envisage secondary metabolite biosynthetic gene clusters and vice versa as well as pathway design analysis and evolution. The perspectives on the further development of the genome-scale metabolic models and various other tools and databases in the field are also discussed.