In 1990, the United States Human Genome Project was initiated as a fifteen-year endeavor to sequence the approximately three billion bases making up the human genome (Vaughan, 1996).As of December 31, 2001, the public sequencing efforts have sequenced a total of 2.01 billion finished bases representing 63.0% of the human genome (http://www.ncbi.nlm.nih.gov/genome/seq/page.cgi?F=HsProgress.shtml&&ORG=Hs) to a Bermuda quality error rate of 1/10000 (Smith and Carrano, 1996). In addition, 1.11 billion bases representing 34.8% of the human genome has been sequenced to a rough-draft level. Efforts such as UCSC\u27s GoldenPath (Kent and Haussler, 2001) and NCBI\u27s contig assembly (Jang et al., 1999) attempt to assemble the human genome by incorporating both finished and rough-draft sequence. The availability of the human genome data allows us to ask questions concerning the maintenance of specific regions of the human genome. We consider two hypotheses for maintenance of high G+C regions: the presence of specific repetitive elements and compositional mutation biases. Our results rule out the possibility of the G+C content of repetitive elements determining regions of high and low G+C regions in the human genome. We determine that there is a compositional bias for mutation rates. However, these biases are not responsible for the maintenance of high G+C regions. In addition, we show that regions of the human under less selective pressure will mutate towards a higher A+T composition, regardless of the surrounding G+C composition. We also analyze sequence organization and show that previous studies of isochore regions (Bernardi,1993) cannot be generalized within the human genome. In addition, we propose a method to assemble only those parts of the human genome that are finished into larger contigs. Analysis of the contigs can lead to the mining of meaningful biological data that can give insights into genetic variation and evolution. I suggest a method to help aid in single nucleotide polymorphism (SNP)detection, which can help to determine differences within a population. I also discuss a dynamic-programming based approach to sequence assembly validation and detection of large-scale polymorphisms within a population that is made possible through the availability of large human sequence contigs
