Notes

1)
* Genome assembly refers to the process of putting nucleotide sequence into the correct order
* Chaos theory addresses the unpredictable ways in which initial conditions of a system can affect subsequent system behavior. Chaos theory is concerned with unpredictable courses of
events. The irregular and unpredictable time evolution of many nonlinear and complex linear systems has been named chaos
* In the human genome, genes occur in the same physical location on the chromosome, but there can be different numbers of copies and variable numbers of repeated sequence that
complicate assembly.
* Although bacterial genomes are much smaller, genes are not necessarily in the same location and multiple copies of the same gene may appear in different locations on the genome. * Therefore even with the availability of commercial software and ever growing reference databases, the process of genome assembly can take considerably longer than the time to
obtain actual sequence
* Genome chaos, a process of complex, rapid genome re-organization, results in the formation of chaotic genomes, which is followed by the potential to establish stable genomes
* Cancer genomes can undergo major restructurings involving many chromosomal locations at key stages in tumor development. This restructuring process has been designated
“genome chaos”. In order to examine how chaotic cancer genome restructuring may be, the cell and molecular processes for DNA restructuring are reviewed.
* Examination of the action of these processes in various cancers reveals a degree of specificity that indicates genome restructuring may be sufficiently reproducible to enable possible
therapies that interrupt tumor progression to more lethal forms.


2)
* One application of Gibbs sampling useful in computational molecular biology is the detection and alignment of locally conserved regions (motifs) in sequences of amino acids or
nucleic acids assuming no prior information in the patterns or motifs
* Gibbs sampling strategies claim to be fast and sensitive, avoiding the problem that EM algorithms fall into as far as getting trapped by local optima
* Gibbs sampling uses Monte Carlo sampling from the various prior, model, and predictive distributions indicated previously. The sampling is dependent (not pseudorandom) because
the sampling at any iteration depends on the values in the previous iteration; however, the sampling procedure is known to converge on the desired posterior distribution.
* Gibbs sampling (or more descriptively, successive substitution sampling) is a respected Markov-chain Monte Carlo procedure for discovering sequence motifs. As a theoretical
framework, however, it encounters several practical problems when searching for regulatory elements in DNA.
* First, it tends to find DNA repeat elements, regardless of their biological interest.
* Second, it often requires prohibitive computational time to find multiple instances of a regulatory element in a single sequence.
* The algorithm takes a set of sequences as input. The Gibbs sampler step in A-GLAM uses simulated annealing to maximize an "overall score", a figure of merit corresponding to a
Bayesian marginal log-odds score
* The Gibbs sampling step produces an alignment whose overall score s is given.
* Gibbs sampling is used to find multiple occurrences of functional elements of the genome


3)
* In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional,
structural, or evolutionary relationships between the sequences.
* Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix.
* In bioinformatics pairwise sequence alignment method is used to match two sequences. These sequences can be of DNA RNA or proteins. Biological sequences that are similar but not exactly matched provide useful information between the two sequences. This information specifies the structural function or evolutionary relationship between two sequences.
* This is the most common way of finding similarity between two sequences by comparing them to one another
* By comparing them we can conclude if they are similar or not

There are two different ways for pairwise sequential alignment

* Local Alignment --> In this alignment, it takes small stretches of sequences and then progress. This method is useful when we have to align domains (functional unit of proteins). This fails when we have to align a sequence or larger database
* Global Alignment --> In this alignment, it takes long stretch of residues that attempts to align each residue in each sequence
* P-VALUE - Also called as similarity value or score 'S' is the probability that a score of at least 'S' should be obtained in a match between any two unrelated protein sequences of
similar composition and length. If P<0.01 then we assume the target sequence is homologue of retrieved sequence.
* E-VALUE - Also called as Expected value is associated with P-VALUE and is the expected frequency of similarity scores of at least 'S' would occur by chance.
* If E value is less than 1^-10 to 1^-80 then the two sequences are homologues and very similar even E value is considered as Zero.
* If E value lies between 1^-50 to 1^-20 then there somewhat similarity
* If E value lies between 1^-10 to 1^-2 then there is no similarity and alignment is mismatched or different from predicted


4)
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