Section A. 1. Objective types (Answer all the questions) [1 x 10 =10]

Transcription

1 Paper code: AR 7218 Guru Ghasidas Vishwavidyalaya M.Sc. 2 nd semester Subject: Biotechnology Paper: LBTM-204: Computational Biology and Biostatistics Maximum marks: 60 Section A Time duration: 3 hr 1. Objective types (Answer all the questions) [1 x 10 =10] (i)the sum of all the frequencies up to and including that class is called c. Cumulative Frequency (ii)number of times a value of variable occurs is called a. Frequency (iii) Probability of an event which does not occur is Zero. (iv) The probability of occurrence of any event lies between 1 and 0 both inclusive. (v) Assembler are programs that translate assembly language into machine language. (vi) Assembly language and machine language are called low level language (vii)snp can be detected by c. Both (viii). FASTA search returns with a. One result (ix) Multiple sequence alignment can be done by d. All (x) Which of the followings read as initiation codon in translation process? c. AUG

2 Section B 2. Descriptive type (Answer any four) [10 x 4 = 40] (i). Write two limitation of computer. What do you mean by formula bar? Answer. Two limitations of computer are following-: 1. Lack of memory. 2. Lack of commonsense Formula bar In Excel, formulas are used to calculate results from the worksheet data. When there is some change in the data, such formulas automatically calculate the updated results with no extra efforts on the part of the user, which enables you to create formulas which use columns names from a table, when you are working with table. This feature helps the user to make formulas much easier to read. A formula can have any or all of the following elements l Must begin with the 'equal to' = sign. l Mathematical operators, such as + (for addition) and / (for division) and logical operators such as <, > l References of cell (including named ranges and cells) l Text or Values l Functions related to the worksheets, for example SUM or AVERAGE The current cell in which you have entered a formula will display the result after the formula is completely entered. Also, when you select or click on a cell which is having some formula, the formula will appear in the formula bar. In the two below pictures, are examples of what the formula bar looks like in Microsoft Excel. To start creating a formula click the mouse cursor in the formula bar and enter an equal sign (=). In both of the below examples, we're using the =SUM, which is telling Excel to add each of the cells.

3 (ii). What do you mean by computer memory? Explain the different type of computer memory with suitable example. Ans. Computer memory In computing, memory refers to the physical devices used to store programs (sequences of instructions) or data on a temporary or permanent basis for use in a computer or other digital electronic device. The term primary memory is used for the information in physical systems which function at high-speed (i.e. RAM), as a distinction from secondary memory, which are physical devices for program and data storage which are slow to access but offer higher memory capacity. Primary memory stored on secondary memory is called "virtual memory". The term "memory", meaning primary memory is often (but not always) associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. There are two main types of semiconductor memory: volatile and non-volatile. Examples of non-volatile memory are flash memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per area unit than DRAM). Different type of computer memory Primary memory :- It has two types RAM (Random access memory) has become a generic term for any semiconductor memory that can be written to, as well as read from, in contrast to ROM (below), which can only be read. It should be noted that all semiconductor memory, not just RAM, has the property of random access. Volatile memory loses its stored data when the power to the memory chip is turned off. However it can be faster and less expensive than non-volatile memory. This type is used for the main memory in most computers, since data is stored on the hard disk while the computer is off. Major types are DRAM (Dynamic random-access memory) which uses memory cells consisting of one capacitor and one transistor to store each bit. This is the cheapest and highest in density, so it is used for the main memory in computers SRAM (Static random-access memory) which relies on several transistors forming a digital flip-flop to store each bit. This is less dense and more expensive per bit than DRAM, but faster and does not require memory refresh. It is used for smaller cache memories in computers. ROM (Read-only memory) This is designed to hold permanent data, and in normal operation is only read from, not written to. Although some types can be written to, the writing process is slow and usually all the data in the chip must be rewritten at once. It is usually used to store system software which must be

4 immediately accessible to the computer, such as the BIOS program which starts the computer, and the software for portable devices and embedded computers such as microcontrollers. PROM (Programmable read-only memory) In this type the data is written into the chip before it is installed in the circuit, but it can only be written once. The data is written by plugging the chip into a device called a PROM programmer. EPROM (Erasable programmable read-only memory) In this type the data in it can be rewritten by removing the chip from the circuit board, exposing it to an ultraviolet light to erase it, and plugging it into a PROM programmer. The IC package has a small transparent "window" in the top to admit the UV light. It is often used for prototypes and small production run devices, where the program in it must be changed at the factory. 4M EPROM, showing transparent window used to erase the chip EEPROM (Electrically erasable programmable read-only memory) In this type the data can be rewritten electrically, while the chip is on the circuit board, but the writing process is slow. This type is used to hold firmware, the low level microcode which runs hardware devices, such as the BIOS program in most computers, so that it can be updated. Secondary memory :- Alternatively referred to as external memory and auxiliary storage, secondary storage is a storage medium that holds information until it is deleted or overwritten regardless if the computer has power. For example, a floppy disk drive and hard drive are both good examples of secondary storage devices. As can be seen by the below picture there are three different storage on a computer, although primary storage is accessed much faster than secondary storage because of the price and size limitations secondary storage is used with today's computers to store all your programs and your personal data.

5 Finally, although off-line storage could be considered secondary storage, we've separated these into their own category because this media can be removed from the computer and stored elsewhere Floppy disk drive Hard disk drive Pen disk drive

6 (iii) Explain the typical methods used to predict gene of newly sequenced genome and their use biotechnology? Gene-finding strategies can be grouped into three major categories. Content-based methods rely on the overall, bulk properties of a sequence in making a determination. Characteristics considered here include how often particular codons are used, the periodicity of repeats, and the compositional complexity of the sequence. Because different organisms use synonymous codons with different frequency, such clues can provide insight into determining regions that are more likely to be exons. In site-based methods, the focus turns to the presence or absence of a specific sequence, pattern, or consensus. These methods are used to detect features such as donor and acceptor splice sites, binding sites for transcription factors, polya tracts, and start and stop codons. Finally, comparative methods make determinations based on sequence homology. Here, translated sequences are subjected to database searches against protein sequences to determine whether a previously characterized coding region corresponds to a region in the query sequence. Although this is conceptually the most straightforward of the methods, it is restrictive because most newly discovered genes do not have gene products that match anything in the protein databases. Also, the modular nature of proteins and the fact that there are only a limited number of protein motifs make predicting anything more than just exonic regions in this way difficult. For example, GRAIL, which stands for Gene Recognition and Analysis Internet Link is the elder statesman of the gene prediction techniques because it is among the first of the techniques developed in this area and enjoys widespread usage. In case of human genome project: Completion of a working draft of the sequence of the human genome, investigators had started faced with the challenge of developing a strategy by which they can deal with the oncoming flood of bothunfinished and finished data, whether the data are generated in their own laboratories or at one of the major sequencing centers. The central dogma, proceeding from the DNA through the RNA to the protein level, various sequence features and modifications can be identified that can be used in the computational deduction of gene structure. These include the presence of promoterand regulatory regions, intron-exon boundaries, and both start and stop signals, were the question for the sequenced genome.

7 Finding of gene and gene products of the newly sequenced genome of any organism is the major application. Process of annotation of the genome followed by sequencing is done to characterize gene, number of genes in the genome, regulatory elements, gene expression, SNP detection etc.

8 (iv) What is BLAST? Explain Karlin-Altschul Equation? Ans. The BLAST (Basic local Alignment Search Tool) programs introduced a number of refinements to database searching that improved overall search speed and put database searching on a firm statistical foundation (Altschul et al., 1990). There are several variants of BLAST, each distinguished by the type of sequence (DNA or protein) of the query and database sequences (see Table 8.1). The BLASTP program compares a protein query to a protein database. The corresponding program for nucleotide sequences is BLASTN. If the sequence types differ, the DNA sequence can be translated by the program (in all six reading frames) and compared to the protein sequence. BLASTX compares a DNA query sequence to the protein database, which is useful for analyzing new sequence data and ESTs. For a protein query against a nucleotide database, use the TBLASTN program. This is useful for finding unannotated coding regions in database sequences. A final variant is used only in specialized situations but is mentioned here for the sake of completeness: TBLASTX takes DNA query and database sequences, translates them both, and compares them as protein sequences. This program is mainly useful for comparisons of ESTs, where it is suspected that sequences may have coding potential even though the exact coding region has not been determined. BLAST PROGRAM

9 Karlin-Altschul Equation For any given alignment, one can calculate a score representing the quality of the alignment. One of the few methods available for assessing their significance is to compare the observed alignment score with those of many alignments made from random sequences of the same length and composition as those under study. However, for local alignments, the situation is much better. A statistical model advanced by Karlin and Altschul (1990) provides a mathematical theory to describe the expected distribution of random local alignment scores. The form of the probability density function is known as the extreme value distribution. The extreme value distribution is characterized by two parameters, K and _, which should be tailored for the particular set of alignment scoring rules and residue background frequencies at hand. Although analytical calculation of these parameters can currently be done only for alignments that lack gaps, methods have been developed to estimate appropriate values of K and _ for gapped alignments. By relating an observed alignment score S to the expected distribution, it is possible to calculate statistical significance in the form of an E (Expectation) value. E = kmne λs Where k is a constant, m is the number of letters in the query, N is the total letters in the target database, λ is a constant used to normalize the raw Score of the high-scoring segment pair, with the value of λ varying depending on the matrix used, and S is the score of the high-scoring segment pair. The simple interpretation of an E value is the number of alignments with scores at least equal to S that would be expected by chance alone. The significance of an alignment also depends on the size of the search space that was used; larger databases produce more chance alignments. The search space has typically been calculated as the product of the sequence lengths, but, for correct statistics, the lengths must be reduced by the expected length of a local alignment to avoid an edge effect. This is due to the fact that an alignment that begins near the edge of the search space will run out of sequence before it can achieve a significant score. Lower values of E imply greater biological significance.

10 (v). What is multiple sequence alignment? Describe one method to achieve multiple sequence alignments? Answer. A multiple sequence alignment is simply an alignment that contains more than two sequences. Even if one is interested in the similarities between only two of the sequences in a set, it is always worth multiply-aligning all available sequences. The inclusion of these additional sequences in the multiple alignments will normally improve the accuracy of the alignment between the sequence pairs as well as revealing patterns of conserved residues that would not have been obvious when only two sequences are directly studied. Although many programs exist that can generate a multiple alignment from unaligned sequences, extreme care must be taken when interpreting the results. An alignment may show perfect matching of a known active-site residue with an identical residue in a well characterized protein family, but, if the alignment is incorrect, any inference about function will also be incorrect. Hierarchical method is one of the common methods of multiple alignments. In other word, the most accurate, practical methods for automatic multiple alignment is hierarchical methods. These work by first finding a guide tree and then following the guide tree to build the alignment. First, all pairs of sequences in the set to be aligned are compared by a pairwise method of sequence comparison. This provides a set of pairwise similarity scores for the sequences that can be fed into a cluster analysis or tree calculating program. The tree is calculated to place more similar pairs of sequences closer together on the tree than sequences that are less similar. The multiple alignment is then built by starting with the pair of sequences that is most similar and aligning them and then aligning the next most similar pair, and so on. Pairs to be aligned need not be single sequences but can be alignments that have been generated earlier in the tree. If an alignment is compared with a sequence or another alignment, then gaps that exist in the alignment are preserved. CLUSTAL W combines a good hierarchical method for multiple sequence alignment with an easy-to-use interface. The software is free, although a contribution to development costs is required when purchasing the program. CLUSTAL W runs on most computer platforms and incorporates many of the techniques described in the previous section. The program uses a series of different pair-score matrices, biases the location of gaps, and allows you to realign a set of aligned sequences to refine the alignment. CLUSTAL W can read a secondary structure mask and bias the positioning of gaps according to it; the program can also read two preexisting alignments and align them to each other or align a set of sequences to an existing alignment. CLUSTAL W also includes options to calculate neighbor-joining trees for use in inferring phylogeny. Although CLUSTAL W does not provide general tools for viewing these trees, the output is compatible with the PHYLIP package and the resultant trees can be viewed with that program.

11 Hierarchical multiple alignment of seven sequences. The codes for these sequences are HAHU, HBHU, HAHO, HBHO, MYWHP, P1LHB, and LGHB. The table at the top left shows the pairwise Z-scores for comparison of each sequence pair. Higher numbers mean greater similarity. Hierarchical cluster analysis of the Z-score table generates the dendrogram or tree shown at the bottom left. Items joined toward the right of the tree are more similar than those linked toward the left. Based on the tree, LGHB is least similar to the other sequences in the set, whereas HBHU and HBHO are the most similar pair (most similar to each other). The first four steps in building the multiple alignments are as follows. The first two steps are pairwise alignments. The third step is a comparison of profiles from the two alignments generated in steps 1 and 2. The fourth step adds a single sequence (MYWHP) to the alignment generated at step 3.

12 Qz'uo -t?* u\t4 Vqh.*, z-35 d= -F lv-.6f,*t, s4 0-'l o lo - Zo 7o - 5o 3o- \ o t -.- tto- 5 o $o'qo 5 t5 L5 7{ 45- s{ :,3 <:Z l c) I L ql +q 5E 5a' ze r5 N-- 2-Lo -ll4-8& -58 o?{ z6 zl4- -a4 -lt rl<t<- Q,= 25 N-- 2f4- ->l{ L* \o t,- df,.-,-+ )c j q+ :35+(31 \ z+q ll 1 \,\ '\x)o xa :35-3E -J1*..* t\*?- LS.L ^-\ \ \Yle4na- ( Z) Ls ZE. L V,/ $-s-

13 -'-. '.!S: A*) s-t,her - f^i-qe Xjlb"tlr.s,s Jlo;. II/a s,d{"*t A,lf 1. \r r \r l"itt.","*iv.$ip"lbesis ajl $,. ul qln e{ Xrralt 1 6r"b"b)e. u 'I ' J n.. ltq\q cnn f*.1" q1*\ P,J"ut. V.-t b;-}t b;"+lr q!"-."",o+ c. Gn+!ro hq&is q r1lt(( t!..-.h ".Tpurcio5 arre zz:\t- 2-l - ]La \=t';-' Tz-'T-' E'f c- -["ur-, W v- \o,5o, 16-or lsal5ot fo' t z i \\ qe q It o g8 4" \> \o $o tcrr> l5d 5o 1D o -E q L lo -\L \u.').l4 Q-=)' \/ 3( lsu ll{q l6> q G-{1 e \.(" 0,7L \ cr> rqq 2-tz c'\ o

14 C',+cd - - -llkc i: JG. --l.br.v& g-; f r,+ {- 6,o5 f* V+ = lt'o7 0'6 5 I (e-r) L s nil < \_/o -\ = :f"uf. '!q0&( q ry" o{ {.-?4f=-lr."p! '''.:'"-"" tr tl,..'''.?( llffjt"s{s Ho I qc.e}"1"d, 'lr =+ n \ ^ 1\ t h" tno?e C,^d fl*'r"a?e l'd iotl qnt- tlryltl '\-\:--uq n \ir ubob l. o. IThS -