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The program consists of courses and either research (MSc and Ph. D) or practicum (MSc. AC), both of which are conducted under the supervision of a faculty member. Graduate faculty in the Department of Computer Science are interested in a wide range of subjects related to computing, including programming languages and methodology, software engineering, operating systems, compilers, distributed computation, networks, numerical analysis and scientific computing, financial computation, data structures, algorithm design and analysis, computational complexity, cryptography, combinatorics, graph theory, artificial intelligence, neural networks, knowledge representation, computational linguistics, computer vision, robotics, database systems, graphics, animation, interactive computing, and human- computer interaction. For further details, consult the graduate student handbook prepared by the department and available online. Contact and Address. Web. www. cs. toronto. Email. gradadmissions@cs. Telephone: (4. 16) 9. Fax: (4. 16) 9. 46- 1. Department of Computer Science Graduate Office. University of Toronto. Room 4. 24. 2, Bahen Centre for Information Technology. Mathematics Medical, health, and. Programming languages and software engineering. Computer systems and networking. Carnegie Mellon University's School of Computer Science will launch a new bachelor's degree program in computational biology next fall, complementing the. MATD represents a new direction in developmental math. With the help of the software. Login about us news careers contact us support feedback. The Department of Computer Science offers a graduate program leading to three. Topics in Applied Discrete Mathematics. Computer Vision for Advanced Digital. St. George Street. Toronto, Ontario M5. S 2. E4. Canada. Degree Programs Applied Computing. Mathematics About Us MIT Mathematics Department Home Page. Computer Help; Community. Diversity; Women in Math; Sponsors; Giving; Visiting Committee; News & Events. The Science, Mathematics And Research for Transformation (SMART) Scholarship for Service Program has been established by the Department of Defense (DoD). Applicants must also satisfy the Department of Computer Science's additional admission requirements stated below. An appropriate bachelor's degree in computer science. A minimum average grade of B+ over the final two years of undergraduate studies. Applicants whose primary language is not English and who have graduated from a university where the primary language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 5. Test of Written English (TWE); 9. Internet- based test and 2. Three letters of support from faculty and/or employers. A statement of purpose. Program Requirements. This is a 1. 6- month professional master's program comprising 3. FCEs) of coursework and an eight- month industrial internship. The internship (3. FCEs) is coordinated by the department, and evaluated on a pass/fail basis. There is no thesis requirement. Program Length. 4 sessions full- time (typical registration sequence: F/W/S/F)Time Limit. Applicants must also satisfy the Department of Computer Science's additional admission requirements stated below. An appropriate bachelor's degree with a standing equivalent to at least a University of Toronto B+. Preference given to applicants who have studied computer science or a closely related discipline. Applicants whose primary language is not English and who graduated from a university where the language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 5. Test of Written English (TWE); 9. Internet- based test and 2. Program Requirements. FCEs) in computer science. The courses must satisfy a breadth requirement to ensure a broad and well- balanced knowledge of computer science. A major research paper (CSC 4. Y . The standard for this paper is that it could reasonably be submitted for peer- reviewed publication. This degree is offered on either a full- time or part- time basis. Program Length. 4 sessions full- time (typical registration sequence: F/W/S/F); 8 sessions part- time. Time Limit. 3 years full- time. Doctor of Philosophy. Applicants must also satisfy the Department of Computer Science's additional admission requirements stated below. Successful completion of an appropriate master's degree with a standing equivalent to at least a University of Toronto B+. In exceptional circumstances, applicants may be admitted to this program directly from a bachelor's degree with a standing equivalent to at least a University of Toronto A- . Preference is given to applicants who have studied computer science or a closely related discipline. Applicants whose primary language is not English and who graduated from a university where the language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 5. Test of Written English (TWE); 9. Internet- . Students admitted to the Ph. D directly from a bachelor's degree will require 4. FCEs and a thesis. The courses must satisfy a breadth requirement to ensure a broad and well- balanced knowledge of computer science. The most important part of doctoral work is original research conducted under the direction of a faculty member. This research must constitute a significant and original contribution to computer science. The results must be presented in a thesis and defended at the departmental oral examination and the Doctoral Final Oral Examination. A meeting of the Ph. D supervisory committee meeting must be held by the 1. Ph. D program. This is typically the initial meeting with the supervisory committee and is referred to as the qualifying oral examination. After the qualifying oral, the student's Ph. D supervisory committee must meet at least once annually. The student must have their thesis topic approved at a Ph. D supervisory committee meeting within the time frame for achieving candidacy. The departmental thesis defense must be passed before the SGS Final Oral Examination can be scheduled. Program Length. 4 years full- time; 5 years direct- entry. Time Limit. 6 years full- time; 7 years direct- entry. Please consult the department for course offerings. Programming Languages and Methodology. Christopher - BSc, MSc, Ph. DBonner, Anthony - BSc, MSc, Ph. DBorodin, Allan - BS, SM, Ph. D, FAAASBoutilier, Craig - BS, SM, Ph. D, CRCBrudno, Michael (Mikhail) - AB, SM, Ph. DChechik, Marsha - BS, SM, Ph. D, Bell University Labs Chair in Software Engineering. Chignell, Mark - BSc, Ph. DChristara, Christina - BS, SM, Ph. DDe Lara, Eyal - BS, MS, Ph. DDemke Brown, Angela - BS, SM, Ph. DDickinson, Sven Josef - BASc, MS, Ph. DEasterbrook, Stephen Michael - BSc, Ph. DEllen, Faith - BM, MMath, Ph. DFairgrieve, Thomas - BMath, MSc, Ph. DFarzan, Azadeh - BS, Ph. DFiume, Eugene - BM, MSc, Ph. DFleet, David James - BS, MS, Ph. DFox, Mark - BSc, Ph. DFrey, Brendan - BSc, MSc, Ph. DGanjali, Yashar - BSc, MSc, Ph. DGoel, Ashvin - BTech, MS, Ph. DGoldenberg, Anna - Ph. DHadzilacos, Vassos - BSE, Ph. DHertzmann, Aaron - BA, MS, Ph. DHirst, Graeme - BA, BSc, MSc, Ph. DJackson, Kenneth - BSc, MSc, Ph. DJacobsen, Hans- Arno - MCS, Ph. DJepson, Allan - BSc, Ph. D. (Coordinator of Graduate Studies)Johnson, F. Ryan - BSc, MSEE, Ph. DKim, Philip - BS, Ph. DKoudas, Nick - BS, MS, Ph. DKutulakos, Kiriakos - BS, MSc, Ph. DLi, Baochun - BEng, MSc, DPhil. Lie, David - BASc, MS, Ph. DLiebeherr, Jorg - Dip. Ing, Ph. DMarbach, Peter Josef - Dip. Ing, MS, Ph. DMc. Ilraith, Sheila - BSc, MSc, Ph. DMihailidis, Alex - BASc, MASc, Ph. DMiller, Renee - BS, BM, MS, Ph. D, Bell University Labs Chair in Information Systems. Molloy, Michael - BMath, MMath, Ph. DMorris, Quaid - BS, Ph. DMoses, Alan - BA, Ph. DNeal, Radford - BSc, MSc, Ph. DNikolov, Aleksandar - Ph. DPenn, Gerald - BS, MSc, Ph. DPitassi, Toniann - BS, SM, Ph. DRoth, Frederick - Ph. DSchroeder, Bianca - MSc, Ph. DSingh, Karan - BS, MS, Ph. DStevenson, Suzanne Ava - MS, Ph. DStumm, Michael - MS, Ph. DToueg, Sam - BS, MA, MSEE, Ph. DTruong, Khai Nhut - BSc, Ph. DTsotsos, John - BASc, MSc, Ph. D, CRCUrtasun, Raquel - Ph. DVeneris, Andreas - BSc, MSc, Ph. DYu, Eric - BSc, MMath, Ph. DZemel, Richard - BA, SM, Ph. DZhang, Zhaolei - BS, Ph. RAGA: RNA sequence alignment by genetic algorithm. PRALINE - is a multiple sequence alignment program with many options to optimize. PROMALS - constructs multiple protein sequence alignments using information from. CARNA is a tool for multiple alignment of RNA. Sequence alignment - Wikipedia. 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. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non- biological sequences, such as calculating the edit distance cost between strings in a natural language or in financial data. Interpretation. In sequence alignments of proteins, the degree of similarity between amino acids occupying a particular position in the sequence can be interpreted as a rough measure of how conserved a particular region or sequence motif is among lineages. The absence of substitutions, or the presence of only very conservative substitutions (that is, the substitution of amino acids whose side chains have similar biochemical properties) in a particular region of the sequence, suggest . Systematic evaluation of spliced alignment programs for RNA. Future developments in RNA-seq alignment methods would benefit from improved. Protocols are grouped by the underlying alignment program. Although DNA and RNA nucleotide bases are more similar to each other than are amino acids, the conservation of base pairs can indicate a similar functional or structural role. Alignment methods. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high- quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that . By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large- scale database search, that do not guarantee to find best matches. Representations. In almost all sequence alignment representations, sequences are written in rows arranged so that aligned residues appear in successive columns. In text formats, aligned columns containing identical or similar characters are indicated with a system of conservation symbols. As in the image above, an asterisk or pipe symbol is used to show identity between two columns; other less common symbols include a colon for conservative substitutions and a period for semiconservative substitutions. Many sequence visualization programs also use color to display information about the properties of the individual sequence elements; in DNA and RNA sequences, this equates to assigning each nucleotide its own color. In protein alignments, such as the one in the image above, color is often used to indicate amino acid properties to aid in judging the conservation of a given amino acid substitution. For multiple sequences the last row in each column is often the consensus sequence determined by the alignment; the consensus sequence is also often represented in graphical format with a sequence logo in which the size of each nucleotide or amino acid letter corresponds to its degree of conservation. Most web- based tools allow a limited number of input and output formats, such as FASTA format and Gen. Bank format and the output is not easily editable. Several conversion programs that provide graphical and/or command line interfaces are available. There are also several programming packages which provide this conversion functionality, such as Bio. Python, Bio. Ruby and Bio. Perl. Global and local alignments. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. This can be especially useful when the downstream part of one sequence overlaps with the upstream part of the other sequence. In this case, neither global nor local alignment is entirely appropriate: a global alignment would attempt to force the alignment to extend beyond the region of overlap, while a local alignment might not fully cover the region of overlap. In that case, the short sequence should be globally aligned but only a local alignment is desired for the long sequence. Pairwise alignment. Pairwise alignments can only be used between two sequences at a time, but they are efficient to calculate and are often used for methods that do not require extreme precision (such as searching a database for sequences with high similarity to a query). The three primary methods of producing pairwise alignments are dot- matrix methods, dynamic programming, and word methods. To find the sequence alignment program that performs best under all non-trivial situations. Eddy S: A memory efficient dynamic programming algorithm for optimal structural alignment of a sequence to an RNA secondary structure. List of sequence alignment software This list of sequence alignment. DNA sequence alignment of unrestricted size in single or multiple GPUs. BLAST's nucleotide alignment program. 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. ClustalW2 is a general purpose multiple sequence alignment program for DNA or proteins. It attempts to calculate the best match for the selected sequences, and lines them up so that the identities. This research software is designed for biologists and not computer scientists. It combines all the major DNA and protein sequence analysis tools into one software solution. A structural RNA sequence alignment program on the basis of MOGA. Cofolga2mo is designed for global pairwise sequence alignment. By performing a BRAliBase 2.1 benchmark test, we found that the approximate set for RNA sequence. Although each method has its individual strengths and weaknesses, all three pairwise methods have difficulty with highly repetitive sequences of low information content - especially where the number of repetitions differ in the two sequences to be aligned. One way of quantifying the utility of a given pairwise alignment is the 'maximum unique match' (MUM), or the longest subsequence that occurs in both query sequences. Longer MUM sequences typically reflect closer relatedness. Dot- matrix methods. The dot- plot shows a patchwork of lines, demonstrating duplicated segments of DNA. The dot- matrix approach, which implicitly produces a family of alignments for individual sequence regions, is qualitative and conceptually simple, though time- consuming to analyze on a large scale. In the absence of noise, it can be easy to visually identify certain sequence features. To construct a dot- matrix plot, the two sequences are written along the top row and leftmost column of a two- dimensional matrix and a dot is placed at any point where the characters in the appropriate columns match. Some implementations vary the size or intensity of the dot depending on the degree of similarity of the two characters, to accommodate conservative substitutions. The dot plots of very closely related sequences will appear as a single line along the matrix's main diagonal. Problems with dot plots as an information display technique include: noise, lack of clarity, non- intuitiveness, difficulty extracting match summary statistics and match positions on the two sequences. There is also much wasted space where the match data is inherently duplicated across the diagonal and most of the actual area of the plot is taken up by either empty space or noise, and, finally, dot- plots are limited to two sequences. None of these limitations apply to Miropeats alignment diagrams but they have their own particular flaws. Dot plots can also be used to assess repetitiveness in a single sequence. A sequence can be plotted against itself and regions that share significant similarities will appear as lines off the main diagonal. This effect can occur when a protein consists of multiple similar structural domains. Dynamic programming. In typical usage, protein alignments use a substitution matrix to assign scores to amino- acid matches or mismatches, and a gap penalty for matching an amino acid in one sequence to a gap in the other. DNA and RNA alignments may use a scoring matrix, but in practice often simply assign a positive match score, a negative mismatch score, and a negative gap penalty. However, it is possible to account for such effects by modifying the algorithm.) A common extension to standard linear gap costs, is the usage of two different gap penalties for opening a gap and for extending a gap. Typically the former is much larger than the latter, e. Thus, the number of gaps in an alignment is usually reduced and residues and gaps are kept together, which typically makes more biological sense. The Gotoh algorithm implements affine gap costs by using three matrices. Dynamic programming can be useful in aligning nucleotide to protein sequences, a task complicated by the need to take into account frameshift mutations (usually insertions or deletions). The framesearch method produces a series of global or local pairwise alignments between a query nucleotide sequence and a search set of protein sequences, or vice versa. Its ability to evaluate frameshifts offset by an arbitrary number of nucleotides makes the method useful for sequences containing large numbers of indels, which can be very difficult to align with more efficient heuristic methods. In practice, the method requires large amounts of computing power or a system whose architecture is specialized for dynamic programming. The BLAST and EMBOSS suites provide basic tools for creating translated alignments (though some of these approaches take advantage of side- effects of sequence searching capabilities of the tools). More general methods are available from both commercial sources, such as Frame. Search, distributed as part of the Accelrys. GCG package, and Open Source software such as Genewise. The dynamic programming method is guaranteed to find an optimal alignment given a particular scoring function; however, identifying a good scoring function is often an empirical rather than a theoretical matter. Although dynamic programming is extensible to more than two sequences, it is prohibitively slow for large numbers of sequences or extremely long sequences. Word methods. These methods are especially useful in large- scale database searches where it is understood that a large proportion of the candidate sequences will have essentially no significant match with the query sequence. Word methods are best known for their implementation in the database search tools FASTA and the BLAST family. An enhanced RNA alignment benchmark for sequence alignment programs. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone - the similarity range where alignment quality drops drastically - starts at 6. RNAs in comparison to 2. In this study we enhance the previous benchmark. RESULTS: The RNA sequence sets in the benchmark database are taken from an increased number of RNA families to avoid unintended impact by using only a few families. The size of sets varies from 2 to 1. Alignment quality is scored by two measures: one takes into account only nucleotide matches, the other measures structural conservation. The performance order of parameters- -like nucleotide substitution matrices and gap- costs- -as well as of programs is rated by rank tests. CONCLUSION: Most sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 7. Parameters for gap- open and gap- extension have a large influence on alignment quality lower than APSI < or = 7. The use of different 4 x 4 substitution matrices improved program performance only in some cases. The performance of iterative programs drastically increases with increasing sequence numbers and/or decreasing sequence identity, which makes them clearly superior to programs using a purely non- iterative, progressive approach. The best sequence alignment programs produce alignments of high quality down to APSI > 5. APSI the use of sequence+structure alignment programs is recommended. |
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January 2017
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