Browsing by Author "Munyua, J.K."

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    Additive main effects and multiplicative interaction analysis of genotype x environmental interaction among sweetpotato genotypes
    (2009-04-08) Mwololo, J.K.; Muturi, Phyllis W.; Mburu, M.K.; Njeru, R.W.; Kiarie, N.; Munyua, J.K.; Ateka, E.M.; Muinga, R.W.; Kapinga, R.E.; Lemaga, B.
    Sweetpotato is an important food, feed and cash crop in Eastern Africa. Highly stable and adaptable genotypes are important in sweetpotato productivity and evaluation across sites would form a basis for breeding varieties that are stable. Seventeen sweetpotato genotypes were evaluated for two seasons in three sites which have differentials in sweetpotato virus disease prevalence and climatic conditions in the coastal region of Kenya to determine their stability and adaptability in the region. The experimental design was randomized complete block design. Harvesting was done at four and half months after planting and tuber yield was determined. Data was analysed using the additive main effects and multiplicative interaction model (AMMI) to establish the genotype x environmental interactions (GEI). There was wide variation across the environments in the two seasons. Stability and adaptability was identified among sweetpotato genotypes. Varities Jonathan, Exshimba, SPK 004 and Kemb 10 were highly adapted across all the environments whereas Ejumula, Jewel, Jubilee, Bungoma, and sponge were stable. The highly adapted genotypes can be used as a basis for further improvement through breeding by crossing with the stable genotypes.
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    An overview of advances in bioinformatics and its application in functional genomics
    (2010-04-28) Mwololo, J.K.; Munyua, J.K.; Muturi, Phyllis W.; Munyiri, S.W.
    Bioinformatics is the scientific discipline that is concerned with the efficient management and useful interpretation of large scale biological information. Functional genomics aims at mapping DNA sequences and the components they encode for, to the function they perform. Initial efforts in bioinformatics were focused on the analysis of DNA sequence data. Presently, the scope and objectives of bioinformatics research and development have been broadened owing to the accelerating generation of data from various sources and for various cellular processes, the continuously evolving analytical technologies and the increasing computational capability. Bioinformatics offers an indispensable technology for function assignment and it has been used widely for gene annotation based on protein function predictions. However, as the sequence information is growing exponentially, the number of genes of unknown function is also growing, creating a challenge in the current computational approaches applied in bioinformatics. These limitations are being overcome through advances combining experimental and computational approaches, e.g. nanofabrication techniques. Despite the progress attained, analysis frameworks that could be used to analyze large data arising from signal transduction and biotransformation to provide quantitative predictions are inadequate. Trancriptome profiling is important because it provides information on the number of genes and their abundance in a tissue or given an induced condition e.g. diseased plants. Microarrays are hybridization experiments involving comparison of relative amounts of cellular mRNA from two tissue samples. Most of microarrays used in biological sciences can be divided into complementary DNA (cDNA) and oligonucleotide microarrays. The exploitation of hybridization in microarray analyses has sharply accelerated the search for defective genes of interest in both plants and animals. Microarrays provide the means to repeatedly measure the expression levels of a large number of genes at a time. Major limitations of this technology include decreased sensitivity of the arrays to the detection of genes with low expression levels and difficulties in data exchange due to the lack of standardization in platform fabrication, assay protocols and analysis methods.
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    Utilization of proteins and nucleic acids in the study of gene function: a comparative review
    (2010) Mwololo, J.K.; Karaya, H.G.; Munyua, J.K.; Muturi, Phyllis W.; Munyiri, S.W.
    Proteomics is one of the fastest growing areas in areas of research, largely because the global-scale analysis of proteins is expected to yield more direct understanding of function and regulation than analysis of genes. Protein structure characterizes its function and a protein sequence that relates to a known structure forms a basis for identifying gene function. Proteins are encoded by the genome (genes), and the set of proteins encoded by the genome, including the added variation of post-translational modification, constitute the proteome. The proteins are involved in nearly all metabolic activities, hence are part of the tools that make living machines work. The proteome is neither as uniform nor as static as the genome. However challenges encountered in identifying the biochemical and cellular functions of the many gene products which are currently not yet characterized has necessitated the use of the proteome. Gel electrophoresis techniques allow the separation of cellular proteins on a polymer according to their molecular weight and isoelectric point. The development of automated methods for the annotation of predicted gene products (proteins) with functional categories is becoming increasingly important. Compared to the study of the genetic code, proteomics may allow greater understanding of the complexity of life and the process of evolution due to the large number of proteins that can be produced by an individual organism. The measurable changes in protein profiles are also being used in diagnosis of emerging diseases. A major challenge to proteomics is that proteins are dynamic and interacting molecules, and their variability can complicate detailed studies on gene function. Nevertheless, measuring the intermediate step between genes and proteins i.e. the messenger RNA (mRNA) or the transcriptome bridges the gap between the genetic code and the functional molecules that regulate cell functions. This review examines protein amenability to prediction of gene function and the potential of proteomics in biological research.