Computational analysis of the Caenorhabditis elegans genome sequence.

Jones, Steven John Mathias (1999). Computational analysis of the Caenorhabditis elegans genome sequence. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.0000ff67

Abstract

The genomic sequencing of the model genetic organism, the nematode Caenorhabditis elegans is now essentially complete, representing the first genome sequence to be derived for a multicellular organism. This thesis describes the strategies and software tools that have been utilized in the analysis of the genomic sequence: Preliminary analysis of genomic organisation is also presented.
C. elegans chromosomes do not store genetic information in a uniform manner. Gene density varies between different chromosomal regions and between chromosomes. The highly recombinagenic autosomal arms possess more repetitive elements and generally have a lower gene density than the recombinationally suppressed central regions. Although, the gene density within autosomal arms is higher than had been previously expected. A positive correlation is observed between the number of genetically defined loci from a chromosomal region and the expression rate of a region as estimated by the abundance of Expressed Sequence Tags (ESTs). A similar positive correlation is observed with the proportion of genes possessing similarity to rion-nematoda proteins. Chromosomal regions with a high density of gene clusters have fewer genetically derived loci. Demonstrating that redundancy reduces the genetic accessibility of a region towards classical genetic approaches.
Introns are larger on the autosomal arms than the central clusters. Exon length shows no correlation with chromosomal position but increases with expression rate. Stop codon preference is also influenced by expression rate.
Clusters of similar genes are also found on the C. elegans chromosomes although their distribution is not random. The majority of gene clusters have been determined to lie on chromosome V and the left arm of II. The orientation of the genes within gene clusters suggests that inversion events are common and provide a selective advantage. Alternative splicing has also been studied and the results suggest that many alternative transcripts can be attributed to errors in splice acceptor processing.