# Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics

## Xuhua Xia

Language: English

Pages: 363

ISBN: 1441943919

Format: PDF / Kindle (mobi) / ePub

The many books that have been published on bioinformatics tend toward either of two extremes: those that feature computational details with a great deal of mathematics, for computational scientists and mathematicians; and those that treat bioinformatics as a giant black box, for biologists. This is the first book using comprehensive numerical illustration of mathematical techniques and computational algorithms used in bioinformatics that converts molecular data into organized biological knowledge.

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genome..........................................64 2.2 Variation in viral genome size can be explained by variation in mutation rate.....................................................65 2.3 A representative virus: Phage λ...........................................66 3. Fundamentals of bacterial species................................................67 4. genomic AT% of bacterial species is indicative of cellular AT

pT 13 LNo = p ( S | θ No ) = p 3A pC4 pG3 pT3 (5.17) In statistical inference, you will often encounter notations equivalent to p(S|θYes) or p(S|θNo). You should instantly recognize it as a likelihood function. You may note that, for P(S|θNo), the order of sites is irrelevant. Chapter 5 86 P(S|θNo) remains the same if you rearrange the nucleotides in sequence S in any order. For P(S|θYes), the order is important and rearrangement of the nucleotides in S will change P(S|θYes). If P(S|θYes)

for a 1st-order Markov model which is also known as a Markov chain. The four corresponding elements for human Chapter 6 112 chromosome 22 are also included in Table 6-1. We note that a purine is more likely to be followed by a purine than by a pyrimidine and that a pyrimidine is more likely followed by a pyrimidine than by a purine, and that this pattern, obvious enough for S, is also true for human chromosome 22 (Table 6-1). This helps us to predict the probability of Xi+1 given Xi. Take S

............................................................ 25 2.1.2 Local alignment .............................................................. 29 2.1.3 The simple scoring scheme needs extension .................. 30 2.2 Pairwise alignment with a similarity matrix........................30 2.2.1 DNA matrices ................................................................. 30 2.2.2 Protein matrix ................................................................. 32 2.3 Pairwise alignment with

left single-stranded for an extended period of time during mtDNA duplication 8. Bioinformatics and vertebrate mitochondria 151 Mitochondrial DNA is prone to damage from free oxygen radicals that occur during the production of ATP through the electron transport chain. Spontaneous deamination (Figure 8-2) of both A and C (Lindahl, 1993; Sancar and Sancar, 1988) occurs frequently in human mitochondrial DNA (Tanaka and Ozawa, 1994). Deamination of A leads to hypoxanthine that forms stronger base