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1.7 READING LIST use visual .net qr code jis x 0510 development tobuild qr code 2d barcode with .net Microsoft Office Official Website (2) Compare st .net framework QR Code atistical properties of human and chimp complete mitochondrial DNA (respectively NC 001807 and NC 001643). (3) Find unusual dimers in rat mitochondrial DNA (NC 001665).

. 1.7 Reading list The molecular QR Code JIS X 0510 for .NET structure of DNA was elucidated by Watson and Crick and published in 1953 (Watson and Crick, 1953). The rst wave of genome sequences appeared in the early 1980s and included small phages and viruses, mostly based on Fred Sanger s work (Sanger et al.

, 1982), (Sanger et al., 1978), as well as mitochondrial genomes (Anderson et al., 1981; Bibb et al.

, 1981). The second wave of genomes included prokaryotes and small eukaryotes, the rst free-living organisms ever sequenced, and started in the mid 1990s.The paper (Fleischmann et al.

, 1995) reports the sequencing and basic statistical analysis of H. in uenzae s genome, and is a recommended reading for this course, as is the paper (Fraser et al., 1995) presenting the sequence of M.

genitalium. Many of the genomic properties discussed in this chapter are addressed in those papers. The article of Blattner et al.

(1997) reports the sequencing and analysis of E. coli s genome, and that of Goffeau et al. (1996) the rst eukaryote, the fungus S.

cerevisiae. The third wave of genome sequences, starting in the year 1998, includes multicellular organisms. The complete sequence of C.

elegans was the rst to be completed, followed by others including the human genome, published by two competing groups in papers which appeared simultaneously in Science and Nature (Consortium, 2001; Venter, 2001). The genomes of mouse, rat, chicken, dog, cow, chimp followed at ever-increasing pace. A general discussion of statistical properties of genomic sequences can be found in Karlin et al.

(1998), including many of the concepts presented in this chapter. A description of GenBank can be found in the article Benson et al. (2004).

A discussion of the biological facts needed to understand this chapter can be found in Brown (1999) and Gibson and Muse (2004), including horizontal gene transfer, DNA structure, and general cell biology. Links to these and many more papers, as well as to data and software for the exercises and all the examples, can be found on the book s website: www.computational-genomics.

net. 2 . All the sequence s men Gene nding r Genes and proteins r Gene nding and sequences r Statistical hypothesis testing 2.1 The human genome sweepstakes In May of 2003 it was announced that Lee Rowen of the Institute for Systems Biology in Seattle, Washington was the winner of GeneSweep, an informal betting pool on the number of genes contained in the human genome. Rowen s guess of 25 947 won her half of the $1200 pool and a signed copy of James Watson s book, The Double Helix. GeneSweep had been created in 2000 by Ewan Birney of the European Bioinformatics Institute just as large pieces of the genome were being completed; because of the increasing amount of sequence becoming available, the cost of bets rose from $1 in 2000, to $5 in 2001, to $20 in 2002.

One of the most surprising things about Rowen s winning guess was that it was almost certainly 3 000 genes off the mark above the true number of genes! Researchers had placed wagers on gures as high as 300 000 genes, with only three sub-30 000 guesses. This number of genes put humans below the two plants that have been sequenced and barely above the worm, C. elegans.

Though the draft sequence of the human genome was published in 2001, nailing down exactly how many genes it contained turned out to be a tricky proposition. Genes are what make proteins and other biological molecules that are necessary for life, but they are not marked in any physical way in the genome. Rather, they are relatively short stretches of DNA that the cellular machinery must nd and read.

Because of this lack of obvious signposts marking off genes ( obvious to us the cell nds genes very easily), computational methods are needed to nd and identify them. Without these methods researchers would be stuck sorting through 3.5 Gb (3.

5 billion base pairs) of As, Cs, Gs, and Ts in order to settle the winner of GeneSweep. But even the best gene- nding algorithms have many problems identifying genes in the human genome. This chapter addresses the computational challenge of nding genes in a genome, a key step in annotation; for simplicity we focus on the much easier problem of nding genes in prokaryotes.

Sequenced genomes contain as few as 500 genes in the bacterium Mycoplasma genitalium to upwards of 30 000 genes in a number of different plant and animal species. Knowing what genes.
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