eChapters

Most banana producers in Africa, Latin America and Asia are small-scale farmers who grow bananas either for home consumption or local markets. Because bananas and plantains produce fruit year-round, they provide an extremely valuable food source during the hunger season (when the food from one annual/semi-annual harvest has been consumed, and the next is still to come). Bananas and plantains are therefore critical to global food security. However, the export trade of banana depends mainly on commercial plantations in Latin America, East Asia and Africa. These plantations are owned by multinational corporations.

4.1    Importance of Banana in International Trade
    
4.2    Major Exporting Countries
    
4.3    Major Importing Countries
    
4.4    World Market Structure for Fresh Bananas
    
4.5    Domestic Trade
    
4.6    Banana Trade Wars : A Glimpse
    
4.7    Fair Trade Bananas

Edible bananas are classified into several main groups and subgroups. In the first place is the diploid M. acuminata group which originated in Malaya, Indonesia, the Philippines, Southern India, East Africa, Burma, Thailand, the West Indies, Colombia and Brazil. This group is characterized by dark-brown sheaths of pseudostem,  and the leaves are nearly free from wax. The bunches are small and the fruits small, thin-skinned and sweet. Cultivars of this group are more important in New Guinea than any where else. Lady Finger which was popular in Queensland and New South Wales belonged to this group.

In second place, there was a group represented by the prominent and widely cultivated ‘Gros Michel’ originally from Burma, Thailand, Malaya, Indonesia and Ceylon. It was introduced into Martinique early in the 19th Century by a French naval officer and, a few years later, was taken to Jamaica; from there it was carried to Fiji, Nicaragua, Hawaii and Australia, in that sequence. It is a large, tall plant bearing long bunches of large, yellow fruits, and it was formerly the leading commercial cultivar in Central Africa, Latin America and the Caribbean, but has been phased out because of its great susceptibility to Panama disease. It has given rise to several named sports or mutants (Mortan, 1987). 

2.1    Origin
    
2.2    Production
    
2.3    Distribution

2.4    World Scenario

Bananas and plantains constitute the fourth most important global food commodity (after rice, wheat and maize). They are cultivated in over 120 countries in the tropical and subtropical regions of the world in an area of  approximately 100 million hectares, where they constitute a major staple food crop for millions of people, as well as providing a valued source of income through local and international trade.
 
Banana is one of the oldest fruit crops of India, finding its reference even in epics like Ramayana. India is  believed to be one of centres of  origin, besides southeast Asian countries like Burma, Thailand, Malaysia and Indonesia. It is a crop having multifarious uses as every part of the plant is utilized and hence aptly  called as ‘Kalpatharu’ the plant of virtues. It is important internationally from the point of view of  trade as well as livelihood security.

Introduction
At the beginning of the 21^st century, with a population of 6.1 billion in 2000 and expected to be headed for 9.1 billion by 2050 (UN News Centre, 2005), the challenge of yet again increase food production by many folds only in 50 years has become a daunting task in itself. The situation is further worsen because now, we need to increase food production in sustainable way by 2050 on approximately the same area of arable land using less resources, particularly, fossil fuel, water and nitrogen, at a time when we must also mitigate some of the enormous challenges associated with climate change. Furthermore, there is the critical and urgent humanitarian need to alleviate poverty, hunger and malnutrition which is afflicting more than 1 billion people

For most of the past 50 years world food production has outpaced rising demand. World population has doubled since World War II, whereas food production has tripled. In the developing world the calories available per person per day increased from an average of 1,925 calories in 1961 to 2,540 in 1992. World food production has expanded due to the Green Revolution—adoption of crop rotation, the mass production and use of petroleum-based fertilizers and chemical pesticides, expanded irrigation, and the introduction of genetically superior, disease-resistant cultivars. The trend may now be changing for the worse, however. Since about 1990 global grain production has risen only slightly and, despite slower rates of population growth, grain supplies per capita have fallen. In the worst case, Africa now produces nearly 30% less food per person than it did in 1967. As per FAO estimates, world food production would have to double to provide food security for the 8 billion people projected for 2025. By 2050, when world population is projected to be over 9 billion, the situation would be even more challenging.

Data mining is becoming an increasingly important tool to transform the data into information. In recent years, data mining has been widely used in area of Bioinformatics for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics. Data mining is used to understand gene expression analysis, searching and understanding of protein mass spectroscopy data, 3D structural and functional analysis and mining of DNA and protein sequences for structural and functional motifs. For the first several hundred years of research in biology, the main bottleneck to scientific progress was data collection. Our new found data-richness, however, has shifted this bottleneck from collection to analysis. Biomedical literatures have been increased at the exponential rate. To find the useful and needed information from such a huge data set is a daunting task for users. Text mining is a powerful tool to solve this problem and it is used for biological knowledge discovery.

A DNA microarray is a multiplex technology used in molecular biology. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, each containing picomoles (10-12 moles) of a specific DNA sequence, known as probes (or reporters).

Abstract
This paper presents a lucid and application-oriented delineation of the theory of probability which has proved itself as a basic tool in the theoretical and applied researches in almost all disciplines of science, specially, in the problems encompassing the real-life issues. Bio-informatics has recently emerged as a potential discipline of science and, needless to mention that the application of the probability theory has delved deep to elucidate the laws under its ambit. The inference procedures based on biochemical systems require the formulation of stochastic models (owing to the prevalence of low to moderate levels of stochasticity and noise in their dynamics) which, in turn, are developed using the probability laws in abundance. In Systems Biology, the estimation of parameters poses a prime task in biokinetic models developed on experimental data, and such estimation procedures have been possible only by applying appropriate tools derived using probability. Likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty is an off-shoot of the celebrated, Bayes' theorem of probability.

In recent years, a new branch of biological science, called as bio-informatics receives more attention among its users and calls for application of computational statistical algorithms to a greater extent and needs employment of more sophisticated mathematical/statistical tools. Though biological science and information technology forms a major part in bio-informatics research as applied to agriculture, statistical tools act as their backbone. Its utility is more felt starting from the stage of framing of hypothesis till analyzing/interpreting results of a scientific experiment.  It can also be viewed as a three way multi-disciplinary approach with biotechnology, information technology and statistical technology as its three faces, with the principles of statistics applied to biological science through an interface information technology.

Introduction
Science and its development have had a great impact on the biological research. The main problem arises to understand and interpret the data which are the results of the higher dimensional research in the above mentioned field, and it is extremely difficult some times to give answers for complex and tedious biological questions generated through the large datasets. Parallel developments of statistical methods provide relaxation over these issues. The statistical method by bioinformatics presents many new and logical problems for researchers, scientists, and scholars related to these communities. Thus the knowledge of statistical concepts and techniques are prime important in this chain. The effort is made to cover stochastic theory, finite Markov chains in learning models to diffusion processes in population genetics, advanced statistical methods with Hidden Markov model and provide some literature on Bayesian computation like MCMC and their resources. Fortunately the applied aspect of probability theory and theory of stochastic processes seem to have been extensively used in the research and development level of biological field but the lacking of required material is a very problematic aspect in the system with proper and systematic ways.

Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays that require increasingly complex analysis tools. The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Kowledge on software  and web-based interfaces for tools all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience.However, making tools accessible does not necessarily address the crucial problem of reproducibility.

Biotechnology in the food processing sector targets the selection and improvement of microorganisms, including bacteria, yeasts and moulds with the objectives of improving process control, yields and efficiency as well as the quality, safety and consistency of bioprocessed products. They perform a series of processes such as fermentation, hydrolysis, etc. and even spoilage of food, depending upon the species of organisms, nature of raw material (food), environmental factors (temperature, pH, salt concentration, etc.), duration, etc.

Fermentation
Is the process of bioconversion of organic substances by microorganisms and/or enzymes (complex proteins) of microbial, plant or animal origin. It is one of the oldest forms of food preservation which is applied globally.

Abstract

Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. The aim of the study is to investigate the ability of neural network for yield prediction of rice in Andaman and Nicobar Islands. Field experiments were conducted at the various regions of Andaman and Nicobar Islands during 1980 to 2002 and data collected was subjected to Artificial Neural Networks (ANN) approach for developing a meaningful yield prediction for four varieties of rice C14-8, Quing Livan No.1, Taichung , Sen Yu, Jaya under island conditions with the objective to estimate the crop production a few days or months before harvesting of crop. The observations of all experiments viz., Varietal spectrum, Weather parameters , Yield components, Soil, Abiotic stressors, Biotic stressors for the period of twenty years from 1980 to 2002 were collected from the basic records of experimental field. All the data were used for training data set. Prediction of crop yield, mainly strategic plant of rice has been an interesting research area to agronomists since long for its importance in national and international economic programming. Application of ANN was found to be significant tool for prediction of yield in rice.

Introduction 
Plant disease has been highly impacted by the development of new technology. The degree of the impact depends on how much the technology affects the component steps of the system. Many of the steps in plant disease diagnosis and management depend heavily on accurate and timely information and so can greatly benefit from the development of improved methods of accessing and disseminating information. Computers provide a wide range of tools which are extensively used in plant disease diagnosis and management. In fact, they will be a key to improve farm practices in the coming decades as it is scientifically pertinent to organize farm information in spatial databases because agricultural systems are inherently spatial. Biological and physical aspects of agricultural system create spatial heterogeneity, and as a result, patchiness is the rule in the occurrence and distribution of plant pathogens and disease (Campbell and Madden, 1990). In the area of plant diseases computers are useful

Introduction

Spatial planning tools brings together multiple users of the earth to make informed and coordinated decisions about how to use of resources  and helps to organize, analyze and present information from a number of different sources.  Thus, viewing from single interest or multiple use viewpoints is enhanced and can make a very important contribution to environment. Use of Remote Sensing (RS) and Geographical Information Systems (GIS) tools has the capacity to bring together experts from a variety of disciplines to address complex spatial problems.

Introduction
DNA barcoding is a molecular technique that uses a short section of DNA (genetic marker) extracted from a standardized region of tissue to identify it as belonging to a particular species. A desirable locus for DNA barcoding should be standardized and that should present in most of the taxa of interest and sequencable without species-specific PCR primers. A large database of sequences for the desirable locus needs to be developed. The desirable locus should be short enough to be easily sequenced with current technology, and provide a large variation between species yet a relatively small amount of variation within a species. Although several loci have been suggested, but the mitochondrial Cox1 became a gene of choice for animals and many other eukaryotes. However, the DNA regions that are being used are chosen to optimize the resolution for their specific experimental purposes.

Introduction

India is an agriculture based country with 60% of the manpower employed in agriculture (World Bank, 2010). Few of the main environmental issues are soil erosion, water pollution from raw sewage and runoff of agricultural pesticides. There is a need to maintain a sustainable environment and a sustainable agricultural system.  Sustainable Agricultural system is a self sustaining system, is dependent up the ability to bring together and manage simultaneously the soil, water, plant and animal resources with the limitation of climatic and environment in such a way that the agricultural production/profitability can be maintained in the future too. So initiatives need to be taken to achieve a sustainable agricultural system. Tools have been developed to aid agriculturist, extension personnel and policy makers. The World Wide Web has made the tools easy accessible.

Modern research on life science is progressing at the nanoscale not only with the objective to reduce time and cost but also to ease the handling and working conditions. Application of nanotechnology in various domains of science has led to the enormous innovations. This field facilitates better understanding as well as treatment of living system, activate biotechnological processes, synthesizing new drugs for targeted delivery and biocompatible materials for sustainable environment. Throughout the world nano science is getting wide propaganda. Its application to medicine is increasing rapidly in order to develop nano-drugs with prolonged availability and efficient delivery through rearrangement of molecules at the nanoscale using molecular interactions for example van der Waals forces, H bonds, electrostatic dipoles and various surface forces.

Abstract
Proteins are comprised of domains, folds and motifs, which form its basic building blocks. Most efforts to understand protein evolution and its function have focused on domains as these can fold into a stable three-dimensional structure independent of the rest of the protein chain and generally perform a unique function conserved over the evolution. Genetic recombinant techniques allow reorganization of domains, called domain shuffling, resulting in different combinations of domains in different proteins. This along with swapping and insertion of domains results in complex architectures and are responsible for new protein functions in evolution. Protein domains are very useful in analyzing the mechanisms of protein folding and their stability and structural transformations in various conditions. This can be done reliably if a multi-domain target is considered only in terms of its constituent domains. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently.

A gene microarray is a (mxn) matrix, containing n-experimental conditions, also called attributes for m-genes. Gene clustering refers to partitioning a microarray of genes based on the commonality of attributes. Genes belonging to a cluster often do not coexpress or coregulate. This calls for biclustering that segregates the (mxn) matrix into small windows. In other words biclustering refers to simultaneous clustering of rows and columns of the microarray. This chapter reviews some of the well known biclustering algorithms, and proposes an alternative solution to the problem using evolutionary multi-objective optimization technique.

Introduction

Clustering is process to automatically partition a set of p-dimensional data points (p³1) into classes, where the data in each class have similarity with respect to a given distance metric. Each class usually has an ideal member that possesses the necessary characteristics of the class.

Introduction
Machine learning is a branch of artificial intelligence which is concerned with the development of algorithms and techniques that allow computers to learn. These techniques are tolerant of imprecision, uncertainty, partial truth, and approximation. There have emerged many methods based on machine learning techniques, such as Artificial Neural Networks (NNs), Support Vector Machines (SVMs), Decision Tree (DT), Bayesian Networks (BNs), Hidden Markov Models (HMMs), etc. These techniques attempt to study, model, and analyze very complex phenomena  for which more conventional methods have not yielded low cost, analytic, and complete solutions.

Bioinformatics is the application of information technology to the area of molecular biology. The increase in the number and complexity of biological databases has raised the need for powerful data analysis tools and techniques. To fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories (Inza et al., 2010). Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in approximation.

Since the dawn of the Industrial Revolution, mankind has been introducing numerous hazardous compounds into the environment at an exponential rate.  Environmental pollutants have become a major global concern. The modern growth of industrialization, urbanization, modern agricultural development, energy generation, have resulted in indiscriminate exploitation of natural resources for fulfilling the human desires and need, which have contributed in disturbing the ecological balance on which the quality of our environment depends.
 
Technological revolution has brought new changes in products and processes in industry. The waste generated from the development of products and processes are of concern to the environmental list. A variety of pollutants such as heavy metals, xenobiotics, polycyclic aromatic hydrocarbons (PAHs), chlorinated and nitro-aromatic compounds are found to be highly toxic, mutagenic and carcinogenic for living organisms (Zhang and Bennett. 2005; Samanta et al., 2002).

Introduction and Importance of active site

Proteins are polymers of amino acids coupled by peptide bonds. The constituency of a protein that interacts with a ligand is habitually referred to as the “active site.” Ligands can be proteins, DNA or smaller molecules, such as pharmaceutical compounds [1]. The active site generally lies on the surface of the protein. In some cases, the active site is buried within the protein. Residues with reactive groups (ASP, GLU, SER, CYS, HIS, LYS, ARG) tend to be profuse in protein active sites [2]. The SER-HIS-ASP (sometimes SER-HIS-GLU) named as “catalytic triad” which is a motif, generally found in enzyme active site. Enzymes chip in a significant role in scheming the flow of metabolites within a cell, they catalyze virtually all of the reactions that make and modify the molecules mandatory for biological pathways [3]. The identification of catalytic residues is a key step in recognizing the function of enzymes. The site on the surface of an enzyme molecule that binds and acts on the substrate molecule are called as active site.

Abstract
This chapter describes how modern quantum mechanical methods can be applied in a systematic manner to understand pharmacophore and mechanistic aspects of drugs. This chapter also explains why accurate computation of drug-receptor interactions is important for in silico drug designing and testing.

WHY in silico DESIGNING?
Drug designing is the inventive art of finding a new lead compound based on knowledge of its biological target and then its slow conversion to a compound of therapeutic benefit to patient termed as “drug”. This whole process comprises of long sequels of synthesizing compounds followed by their testing and activity evaluation procedures; often a seemingly endless task until desired potency level is achieved.

Abstract
Combined advancement in computer sciences and hardware with uniformly significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. Apart from that, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population.  Although inappropriate pharmacokinetic properties are a major cause of abrasion in the safety issues are recognized as today’s single largest cause of drug candidate failure. It is expected that the right balance of in vivo, in vitro and computational toxicology predictions applied as early as possible in the discovery process will help to reduce the number of safety issues.

Epigenetics is the study of reversible heritable changes in gene function that occur without a change in the sequence of nuclear DNA and Gene-regulatory information that is not expressed in DNA sequences is transmitted from one generation (of cells or organisms) to the next. Epigenetics has become an integral part of the study of evolution and development during the past 50 years. It was originally defined by Conrad Hal Waddington in 1942 (Waddington, 1942), but it is currently referred to any mechanism that affects genes without changing the nucleotide sequence. These mechanisms are involved in deploying the genetic program for the many processes operating during the lifespan of a cell: development, differentiation, stress response, and pathological conditions, and by the environment to adapt to those situations (Rakyan and Beck, 2006). Epigenetic mechanisms include covalent chemical modification of DNA (methylation) and chromatin (covalent histone modifications), non-coding RNAs, and polycomb group (PcG) genes, and are ultimately related to the regulation of gene expression and chromatin structure.

Introduction
Genetic research has been transformed by technological developments, and has by necessity become extremely quantitative, as massive quantities of varying complex data types can now be generated very rapidly. High-throughput data are being used in a variety of basic science investigations that have implications in the diagnosis and treatment of human disease: identification and characterization of genetic variants associated with a particular disease within and across populations; discovery of gene expression signatures associated with disease phenotypes; identification and testing of potential disease biomarkers. Methodological advances in the statistical treatment and interpretation of these data are needed in order to meet the pressing need for powerful, efficient and robust analysis.

Introduction

Zymomonas mobilis, a gram negative, anaerobic, micro aerotolerant, ethanologenic bacterium metabolizes glucose, fructose, and sucrose. One of the major factor that limits the usage of Z. mobilis is the narrow substrate range. Moreover, there are no obvious genes reported for using lactose, maltose or cellobiose. Our preliminary sequence analysis of the Zymomonas mobilis ZM4 genome indicated the presence of a putative beta-galactosidase (bga) gene. However, the mechanism of beta-galactosidase (Bga) function is still unknown. Hence, Bioinformatics analysis was made to predict the physicochemical, biochemical and biophysical properties, domains, secondary and tertiary structures of the putative beta-galactosidase. These studies suggested that the putative beta-galactosidase gene was poorly expressed from its native promoter in Z. mobilis. The absence of beta-galactoside transacetylase and permease genes in Z. mobilis led to its inability to metabolize lactose. Hence, function predictions of Bga are performed because of the fact that it has a homologous partner.

Biology is in the middle of a major paradigm shift driven by computing technology. Due to the impact of information technology, biological sciences have been rapidly becoming much more computational and analytical. Rapid progress in research in the field of genetics and related field combined with the tools provided by modern biotechnology has generated massive volumes of genetic and protein sequence data over last two decades. Compilation, storage, analysis for extraction of information and knowledge from this data becomes a challenging task as usual analytical procedures are not directly applicable to these data sets.
 
Bioinformatics has been defined as a means for analysing, comparing, graphically displaying, modeling, storing, systemising, searching, and ultimately distributing biological information, which includes sequences, structures, functions, and phylogeny. Thus, bioinformatics may be defined as a discipline that generates computational tools, databases, and methods to support genomic and post-genomic research. It comprises the study of DNA structure and function, gene and protein expression, protein production, structure and function, genetic regulatory systems, and clinical applications. Bioinformatics applications need knowledge from computer science, mathematics, statistics, medicine, chemistry and biology.

Natural Gene Flow in Plants

More than 70% of plant species, for example potato, oat, rape-seed, wheat, and kiwifruit have come from spontaneous interspecific or intergeneric hybridization although the extent of natural hybridization differs among the plant genera and families. It creates genetic variation and produce recombinant genotypes that may exhibit heterosis.The stabilization of these hybrids often through polyploidy (autopolyploidy or allopolyploidy) can result in the formation of new biological species (Masterson, 1994), thereby, having impact on both ecological and evolutionary processes (Martinsen et al., 2001). In addition, species genomes are often differentially permeable to introgression if they are under simple genetic control (unlike many genes contribute to hybrid fitness with linkages) and certain portion of the genome can readily incorporate alien alleles. Introgression is a selective process of gene flow that filters deleterious genes and takes only beneficial genes (incorporating the alleles from one taxon into other).

Introduction

The branch of science that deals with resolving the evolutionary relationships among organisms is phylogenetics. Phylogeny is the description of biological relationships, usually expressed as a tree. A statement of phylogeny among objects assumes homology and depends on classification. Phylogeny states a topology of the relationships based on classification according to similarity of one or more sets of characters, or on a model of evolutionary processes. In many cases, phylogenetic relationships based on different characters are consistent, and support one another. If different characters induce inconsistent phylogenetic relationships, they all are dubious. Conversely, note that the same similarity data may be consistent with different possible topologies or trees.
 
Molecular approaches to phylogeny developed against a background of traditional taxonomy, based on a variety of morphological characters, embryology, and from fossils, information about the geological context (stratigraphy).

Introduction

Microbes (bacteria, archea and eukarya) are single celled organisms that rule the planet. Microbes occupy every possible niche, from the high atmosphere to the deep ocean, from hot springs to artic ice, from hot deserts to deep forests including many regions that cannot yet be accessed for study. The biosphere contains between 1030 and 1031 microbial genomes, which is about 2-3 times more than the number of plant and animal cells combined (67). In fact, studies have shown that microbes associated with the human body are about ten times more than the number of human cells (5).  Microbes play a critical role in the life cycle of all living organism and environment in general.  Some microbes cause disease, but the most of them are either innocuous or play a role in physiology and cellular response including immune response, digestion and vitamin production of various organisms.

Abstract

DNA barcoding, an additional global standard taxonomic tool for identifying plant species helps the non-taxonomists also to identify the plant species in a more reliable way with the help of devices including nanotechnology. Researchers proposed various genes or gene portions as potential barcodes either singly or in combination, yet there is no standard/ universal barcode DNA region for plants. The more accurate plant DNA barcode region would have to be from highly variable markers in the nuclear genome preferably from multiple loci and in combination with variable DNA regions of chloroplast genome, to resolve the problems that arise due to hybridization, allopolyploidization and introgression. Nonetheless, the phylogenetically informative sites might not be the potential DNA barcode regions as the former requires synapomorphies unlike autapomorphies with high levels of informative characters in the latter DNA taxonomy, but could provide insights into genetic distances between species.

Abstract

Biological Pathways represent system level sophistication of organisms. An exclusive peek into the extent of sophistication of an organism may provide proof of its past evolutionary struggle and position in the developmental trend from pathway point of view. This knowledge may not align with the traditional universally acknowledged standard 16s rRNA trend. Analyzing the reasons behind such discrepancy can constitute an important research work. To map sophistication of one organism with respect to other organisms, phylogeny is required. Here, we are mapping the level of sophistication in terms of enzymes among a set of TCA cycles belonging to different species, arbitrarily chosen from the KEGG/PATHWAY database. For this purpose, we converted the metabolic pathway information into simplified enzyme-enzyme relational (E-E-R) graphs. Similarity scores obtained from inter-genus, intra-genus as well as inter-species comparison of these graphs are used for phylogenetic tree construction. These phylogenetic trees throw light on the trend of development of metabolic pathways among different species. But peculiarly, these trees have less similarity with the conventional evolutionary phylogenetic trees constructed from NCBI taxonomy data. In this paper, we have tried to find some justifications for this dissimilarity among phylogenetic trees, by considering their habitats.

Evolution is a self-occurring natural phenomenon by which new organisms develop gradually as a result of change in genetic pool of a population. It is a very widely and historically studied phenomenon which still surprises scientists due to its underlying complexity. The three processes which contribute most to evolution are genetic drift (also known as random drift or allelic drift), natural selection and mutations. Genetic drift and natural selection act at a population level, whereas mutations act at individual level. For evolution to occur, the changes these processes bring about must be heritable, which they are.
 
Genes are present on chromosomes inside nucleus of the cell and the collection of all the genes in a cell is known as the genome of that cell. Genome size and complexity varies from species to species. Simple organisms, such as bacteria and protozoans have a very simple genome, with very few genes, whereas complex higher organisms such as humans have a large genome, with thousands of genes. The position of a gene on a chromosome is known as a locus. Organisms may have more than one copy of a chromosome in the cell.

Chromosome number and nuclear DNA amount are important biodiversity characters together provide a very powerful method for determining evolutionary relationships. Furthermore provide an estimate of relatedness between species that in turn suggest reasons underlying the success or failure of crosses, even for interploidy hybridization. It is expected that species that are cytogenetically most similar will be easiest to hybridize and produce viable interspecific offspring (Thomas, 1993). Hence, it is important to study karyotypes and other characters of the genome of the species involved in a breeding programme.

According to Jackson (1971) and Stebbins (1950) the karyotype is the phenotypic appearance of the somatic chromosomes in contrast to their genic content.

Significant advancement in high throughput technologies (microarrays, automated sequencing and mass spectrometry), transcriptomics, the global study of transcription, together with genomics and proteomics, has undoubtedly contributed to a systems biology approach. These technologies have generated vast amount of sequence data from plants, animals and microorganisms. Fortunately, efficient computational tools (intelligent data networks, query, retrieval, analysis and visualization tools) have now optimized data mining, accelerating the process of discovery. The analysis of sequence variation is of major importance in genetic studies. In this context, molecular markers represent one of the most powerful tools for the analysis of variations in the genome and the association of heritable traits with underlying genetic variation. The development of high-throughput methods for the detection of simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) has led to a revolution in their use as molecular markers. The availability of large sequence data sets permits mining for these molecular markers, which may then be used for numerous applications such as genetic mapping, association mapping, evolutionary and phylogenic studies.

Introduction

The proteome is often described as the set of all expressed proteins in a cell, tissue or organism. It is the systematic analysis of cellular proteins, that is, their identity, quantity and function. One advantage of proteomics over transcriptome is that mRNA represents an early stage of gene expression whereas the study at the proteome level describes the state of protein which is the final functional molecule of the cell. Thus, it gives a clear picture of the cell function that may not be concluded from mRNA study. In fact we can say that proteins are more than just information carriers so it gives more information with respect to only nucleic acid based studies.  Proteomics covers two-dimensional gel electrophoresis (2-DE), image analysis, mass spectrometry, amino acid sequencing, and bio-informatics to resolve comprehensively, to quantify, and to characterize proteins. Biomarker discovery as an important aspect of proteomics technology is highly appreciable when it comes to diseases. Today,  a better understanding of the disease mechanisms leading to discovery of new disease specific biomarkers are very much possible due to the sequencing of the human genome. It has accelerated the proteomic technologies for accepting the challenges so as to meet the diseased related investigations.

1. Introduction
The era of genome sequencing was exploded by the upcoming of Human Genome Project (1989), coordinated by the U.S. Department of Energy and the National Institute of Health to identify all the approximately 20,000-25,000 genes, 3 billion chemical base pairs, store this information in databases, improve the tools for data analysis, transfer related technologies to the private sector, and address the ethical, legal, and social issues (ELSI) that may arise from the project. The dawn of genome sequencing began with Fredric Sanger’s sequencing almost twenty-five years ago. The time consuming and labor intensive gel preparation and their running, as well as the cost of such experiments replaced the conventional sequencing by “shotgun” sequencing.

Abstract
In-silico analysis of nucleic acids and proteins from fishes has enriched our understanding to several fundamental problems of classical fish biology and genetics. Mining of data which is analogous to information discovery is the process of automating knowledge discovery. Analysis of data in all possible manner and recognition of a pattern which has a scientific basis by an efficient algorithm is the fundamental principle of Bioinformatics. Ever increasing number of Bioinformatics tools as computational packages are now a days used in tandem to analyze sequence of a loci and to model a novel gene product. High efficiency algorithms with excellent homology searching functions have lead to this advancement. Cat fishes, a group of diverse bony fishes have been studied with respect to genetic analysis in several species and several peptides. Tracking of the evolutionary foot prints using genomic libraries and between expressed sequences has been generating much interest.

Introduction

Saffron is a naturally derived plant product from the dried stigma of the Crocus sativus flower  (Gresta et al., 2009).The saffron (Crocus sativus L.) is an Iridaceae family member, herbal perennial, sterile (2n=3x=24) geophyte, traditionally propagated by vegetative corms. Its cultivation dates back to late Bronze Age in the Mediterranean region (Negbi, 1999) in Anatolia since the ancient times (Arslan et al., 2007) and for at least 3500 years in Egypt and Middle East. Saffron contains more than 150 volatile and aroma yielding compounds. The chemical components mainly include carotenoids, crocin, picrocrocin, safranal etc (Abdullaev, 1993, Tarantalis et al., 1995, Karaoglu 
et al., 2007). Saffron is mostly used as spice, food colorant, dye or perfume. The dried stigmas introduce unique color, taste and fragrance and thus making it the world’s most expensive spice (Baghlian et al., 2010). Saffron has been used in folklore herbal medicines for centuries (Basker and Negbi, 1983).

The analysis of chromosome behaviour at meiosis can provide valuable information about the genomic composition of both species and hybrids. The basis for this analysis is the idea that chromosomes that pair and undergo chiasma formation are homologous. Where two genomes are present, as in diploids, the expectation is that only bivalents will be observed and where there are multiple genomes, as in polyploids, two general patterns of pairing are possible. In autopolyploids, configurations of multiple chromosomes are to be expected since all the component genomes are homologous and have the possibility of pairing with each other, whereas in allopolyploids, with different genomes, bivalent formation is the expectation. Thus, meiotic analysis can provide an oversight for genome analysis to investigate genetic relationships between the species. Further, chromosomal rearrangements are correlated with pollen fertility in species and F₁ hybrids.  Observations on pollen formation, size and viability can provide useful information, as low pollen viability is often indicative of meiotic irregularities and large sized pollen can indicate the formation of unreduced gametes. In addition, the occurrence of nucleolar dominance has been recorded in many interspecific hybrids.

Introduction

The recent revolution in genomics and bioinformatics has caught the world by storm. The issues surrounding the human genome, including the analysis of genetic variation, access to genetic information and the privacy of the individual have fuelled public debate and extended way beyond the scientific and technical literature. During the past few years, bioinformatics, defined as the computational handling and processing of genetic information, has become one of the most highly visible fields of modern science. A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with bio-molecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this chapter we provide an introduction and overview of the current state of the field.

Introduction

Functional genomics is a field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects (such as genome sequencing projects) to describe gene (and protein) functions and interactions. Unlike genomics and proteomics, functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein-protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism (such as SNP) analysis, as well as measurement of molecular activities. The latter comprise a number of “-omics” such as transcriptomics (gene expression), proteomics (protein expression), phosphoproteomics and metabolomics. Together these measurement modalities quantifies the various biological processes and powers the understanding of gene and protein functions and interactions. Functional genomics uses mostly high-throughput techniques to characterize the abundance gene products such as mRNA and proteins.

The equipments used in artificial insemination should be clean and sterilized. The use of unsterilized equipments in AI causes the spread of infection in the herd and infertility. Rinse the equipments with tap water immediately after use and then lukewarm detergent solution. The detergent used for washing the AI equipments should be non corrosive. Use of corrosive detergents will reduce half-life of the plastic and rubber wares. Wash thoroughly with tap water till the equipments become grease free and perfectly clean. Then rinse in distilled water at least thrice. Use of distilled water does not leave any salt residues on articles after drying.

21.1 Liquid Nitrogen Container/ Cryocan

To store liquid nitrogen a special type of container is used and is known as a LIQUID NITROGEN CONTAI NER OR CRYOCAN. Frozen semen doses are always preserved in liquid nitrogen which is an inert liquid having a boiling point of -1960C and a freezing point of -212°C. Liquid Nitrogen Containers are doubled walled metal (Stainless Steel/Aluminium) vacuum vessels with a very efficient insulation system. Due to highly efficient insulation system the temperature inside the container can remain -196 oC as long as atleast two inches of Liquid Nitrogen is present in the container. It has got an inner chamber which is suspended in outer chamber through neck tube which is a non metal and bad conductor of heat. Walls of the inner chamber are coated with a high quality of insulation material. The space between the inner and outer chamber is also filled with high quality insulation material. Vacuum is created in between inner and outer chamber. Loss of vacuum will lead to the boiling of the liquid nitrogen gas and hence rapid loss of this gas from the inner chamber.

Artificial insemination (AI) is the technique of placing sperm into a female’s uterus (intrauterine), or cervix (intracervical) using artificial means rather than by natural copulation. Modern techniques for artificial insemination were first developed for the dairy cattle industry to allow many cows to be impregnated with the sperm of a bull with traits for improved milk production. It is the first generation reproductive biotechnology which has revolutionized the animal productivity. It has now been established as cheapest and fasted mode of genetic improvement of livestock. AI can be done using fresh semen, semen preserved at room temperature, refrigerated (4ºC) and cryopreserved (-196ºC) semen. The use of AI with frozen semen has become very popular and is being successfully used throughout the country.

The process of converting the frozen semen to liquid state while retaining maximum semen quality is called thawing. The process of thawing is as important as freezing as there is equal probability of loss of semen quality in freezing and thawing. Therefore, great care should be taken while thawing to insure that it is done with minimum loss of sperm quality. There are two methods of thawing:

1.    Rapid thawing

2.    Slow thawing

The effectiveness of each method of thawing depends upon the extender composition and freezing procedure. With any thawing procedure, the recommendations of the semen supplier should be followed. The thumb rule is that rapid freezing rates require rapid thawing.