BIF501 Current Solved Paper

BIF501 - Bioinformatics II

Q1. Define Pseudogenes: (2 marks)

These are non-functional genes (sometimes there are mutations in the genes and if those mutations are present in some important regions then the genes’ functions gets knocked out – known as psudogenes). There is one category of them and is known as processed pseudogenes that lack introns and promoters.

A DNA sequence that resembles a gene but has been mutated into an inactive form over the course of evolution. It often lacks introns and other essential DNA sequences necessary for function. Though genetically similar to the original functional gene, pseudogenes do not result in functional proteins, although some may have regulatory effects.

2. Mitochondrial DNA and Chloroplasts DNA .2

Mitochondrial DNA (both in animals and plants), Chloroplasts DNA (in plants), these are membranebound organelles and they may be present in hundreds to thousands of copies (so there is also multiple copies of these genomes). Mitochondrion is the site for respiration whereas chloroplast is the site for photosynthesis. Their DNA’s can be labeled as mtDNA or cpDNA respectively. Plasmids, yeast, Transposons, Viral genomes and retroviruses are other examples of organellar DNA.

Q3. Write detail on sequence LOGOS?

A sequence logo is a graphical representation of the sequence conservation of nucleotides (in a strand of DNA/RNA) or amino acids (in protein sequences) . A sequence logo is created from a collection of aligned sequences and depicts the consensus sequence and diversity of the sequences. (Internet)

Sequence LOGOS:

─ A visual representation of a position-specific distribution ─ Easy for nucleotides, but we need colour to depict up to 20 amino acid proportions.

Overall height at position is proportional to the information content

• Proportions of each nucleotide/amino acid are in relation to their observed frequency, with most frequent on top, next most frequent below

4. Biological Databases: 3

Biological databases in general store biological data and their main goals are

1. Data storage
2. Information retrieval
3. Knowledge discovery

Classification:

Biological databases can be classified as ➢ Primary databases (that stores the Primary Sequences) ➢ Secondary databases (the primary sequences are annotated and kept in Secondary Databases) ➢ Specialized Databases (they are dedicated towards some specific organism or can have some disease data)

Biological databases can also be classified on the bases of types of data which they contain, such as: ➢ Nucleotide databases ➢ Protein databases ➢ RNA databases ➢ Genome databases ➢ Expression databases (Gene Expression Databases)

Q5. KMP Matcher?

Knuth Morris Pratt (KMP) is an algorithm, which checks the characters from left to right. When a pattern has a subpattern appears more than one in the sub-pattern, it uses that property to improve the time complexity, also for in the worst case. The time complexity of KMP is O(n).J (Internet) Drug Development, Molecular Medicine, Crop Improvement, Personalized Medicine, Microbial Genome. Preventive Medicine, Gene Therapy. Biotechnology, Comparative Study.

Introduction:

A linear time algorithm for string matching

• Does not involve backtracking on string s i.e., repetitive comparison of nucleotide residues

Components:

• The Prefix Function
• The KMP Matcher

The Prefix Function Π:

Encapsulates knowledge about how the pattern matches against shifts of itself

• This information can be used to avoid useless shifts of the pattern ‘p’
• This enables avoiding backtracking on the string ‘S’

The KMP Matcher:

Given: string ‘S’, pattern ‘p’ and prefix function ‘Π’ Find: the occurrence of ‘p’ in ‘S’ Return: the number of shifts of ‘p’ after which occurrence is found

6. Tandem repeats interspersed repeats

Tandem repeats occur in DNA when a pattern of one or more nucleotides is repeated and the repetitions are directly adjacent to each other. Several protein domains also form tandem repeats within their amino acid primary structure, such as armadillo repeats. Interspersed repeats are repeated DNA sequences located at dispersed regions in a genome. They are also known as mobile elements or transposable elements. As discussed in Chapter 8 Section D, a stretch of DNA sequence may be copied to a different location through DNA recombination they can be Tandem repeats (array of repeats together), interspersed repeats (those repeats which are separated by normal genome followed by repeats i.e. normal genome is between those repeats).

Short Tandem repeats

Analyzing STRs is more accurate than the RFLP technique because their small size makes them easier to separate. If you want to create a fingerprint, you might look at 20 different STRs at different places in order to create a profile.

7. Genome and Genome informatics? 2

Genome: Genome sequencing provides the sequences of all the genes of an organism • A major application of Bioinformatics is analysis of full genomes that have been sequenced. Challenge is to identify those genes that are predicte d to have a particular biological function Genome Information:

Genome informatics is the field in which computational and statistical techniques are applied to derive biological information from genome sequences. Genome informatics includes methods to analyze DNA sequence information and to predict protein sequence and structure.

Q8. Generalized algorithm for pattern finding?

Goal: Find all ocuracne of a pattern in text.

Input:

Pattern p=[p1…pn] of length n Text t=[t1…tm] of length m Output:

An indication that pattern P exist in T, or it does not exist in T.

Q9. MS2?

Infects E.Coli and Enterobacteriaceae • Single stranded sense RNA • Encodes MS2 coat protein. MS2 sequencing

• The entire nucleotide sequence was established by nuclease digestion and characterization

10. What is Donor site and Acceptor Splice Junctions:

Donor site

• Coding region | GT (introns starts)

Acceptor

• (introns ends)YAG | coding region -Y can be any pyrimidine.

11. Partial Digest Problem:

Given all pairwise distances between points on a line, reconstruct the positions of those points. Input: The multiset of pairwise distances L, containing (𝑛) integers.

Output: A set X, of n integers, such that ΔX = L

ΔA is equal to Δ(A ⊕ {v}), where A⊕ {v} is defined to be

{a + v : a ϵ A},

Also ΔA = Δ(−A),where−A = {−a : a ϵ A}

A = {0, 2, 4, 7, 10}, Δ(A ⊕ {100}) =

{100, 102, 104, 107, 110}, and −A = {−10,−7,−4,−2, 0}

{0, 1, 3, 8, 9, 11, 12, 13, 15} and {0, 1, 3, 4, 5, 7, 12, 13, 15}

12. Difference local alignment input and output? 5

Local Alignment Problem : Find the best local alignment between two strings

Input : Strings v and w and a scoring matrix δ

Output: Substring of v and w whose global alignment, as defined by δ, is maximal amoung all global alignment of all substrings of v and w.

Q13. Four cases of pattern finding?

Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning,[1] together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms. However, these are distinguished: machine learning is one approach to pattern recognition, while other approaches include handcrafted (not learned) rules or heuristics; and pattern recognition is one approach to artificial intelligence, while other approaches include symbolic artificial intelligence

Q14. Bypass Algorithm?

Branch and Bound Algorithm

NEXTVERTEX Algorithm BYPASS Algorithm Skip the subtree rooted at vertex (a, i) Increment ai (unless ai = k

A tree that has uninteresting subtrees. The numbers next to a leaf represent the “score” for that L-mer. Scores at internal vertices represent the maximum score in the subtree rooted at that vertex. To improve the brute force algorithm, we can “prune” subtrees that do not contain highscoring leaves. For example, since the score