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Lecture 10 - University of New England
Lecture 10 - University of New England

Glossary - ChristopherKing.name
Glossary - ChristopherKing.name

... RefSeq is a database of sequences that is edited by NCBI and is NON-redundant, meaning that it contains what NCBI determines is the strongest sequence data for each gene. Finally, we will be learning to use ClustalW, which is a multiple sequence alignment program. It allows you to enter a series of ...
Introduction to BLAST ppt
Introduction to BLAST ppt

... S. Needleman and C. Wunsch (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins, J. Molecular Biology, 48:443-453. D. Hirschberg (1975). A linear space algorithm for computing maximal common subsequences. Communications of the ACM, 18(6):341-3 ...
LECTURE 1 INTRODUCTION Origin of word: Algorithm The word
LECTURE 1 INTRODUCTION Origin of word: Algorithm The word

... This model seems to go a good job of describing the computational power of most modern (nonparallel) machines. It does not model some elements, such as efficiency due to locality of reference, as described in the previous lecture. There are some “loop-holes” (or hid den ways of subverting the rules) ...
biopatt - Carnegie Mellon School of Computer Science
biopatt - Carnegie Mellon School of Computer Science

... hidden Markov models. Pfam is available on the World Wide Web in the UK,…, Sweden, …, France, …, US. The latest version (6.6) of Pfam contains 3071 families, which match 69% of proteins in SWISS-PROT 39 and TrEMBL 14. Structural data, where available, have been utilised to ensure that Pfam families ...
Document
Document

LimTiekYeeMFKE2013ABS
LimTiekYeeMFKE2013ABS

Summary Team members: Weiqian Yan, Kanchan Khurad, and Yi
Summary Team members: Weiqian Yan, Kanchan Khurad, and Yi

... The paper, A Monte Carlo Algorithm for Fast Projective Clustering, proposes 2 novel approaches to approximate optimal clusters in high dimensional data space. As research has proven, existing clustering methods that work well in low dimensional spaces don’t work well in high dimensional space due to ...
Introduction to BLAST
Introduction to BLAST

Introduce methods of analyzing a problem and developing a
Introduce methods of analyzing a problem and developing a

Supplementary Material (doc 28K)
Supplementary Material (doc 28K)

... The complete set of parameters of TEIRESIAS used in this analysis is as follows: amino acids in the pattern (-l), number of overlapping characters in the convolved pattern (-c), maximum length of an elementary pattern (-w), minimum number of appearances of the pattern (-k), maximum number of bracket ...
BlastLecture8
BlastLecture8

Chapter 2 SEQUENCE ALIGNMENT
Chapter 2 SEQUENCE ALIGNMENT

alignable - gobics.de: Department of Bioinformatics
alignable - gobics.de: Department of Bioinformatics

File
File

... 1. Temperature readings are taken at 20 weather stations throughout the UK. Readings are taken at each station 8 times in one day. a) Describe how a 2-D array could be used to store the temperatures for each station. b) Declare this array c) Write an algorithm that will count the number of occasions ...
Laboratory B - Filogeografía
Laboratory B - Filogeografía

Gap Pad® 1500 - Bergquist Company
Gap Pad® 1500 - Bergquist Company

OLD_s1a_alg_analysis..
OLD_s1a_alg_analysis..

...  Worst-case running time of an algorithm:  The longest running time for any input of size n  An upper bound on the running time for any input  guarantee that the algorithm will never take longer  Example: Sort a set of numbers in increasing order; and the input is in decreasing order  The wors ...
What is Sequence Alignment?
What is Sequence Alignment?

... homology and those that occur by chance • Define a scoring function that accounts for mismatches and gaps Scoring Function (F): ...
Bioinformatics
Bioinformatics

... two fields have similar aims but the major difference is in scale.  Bioinformatics deals with basic biological data and pays attention to details while Biological Computation is a subset of CS that builds large scale theoretical models of biological systems in an attempt to expand understanding of ...
Lab7
Lab7

Stacks progression table
Stacks progression table

Molecular Phylogenetic Analysis: Design and Implementation of
Molecular Phylogenetic Analysis: Design and Implementation of

... selected, the first step is to align them [1,2]. The difference in lengths can appear due to sequencing errors (digitalizing the biological sample), mutations (insertions or deletions of one or more sites along the sequence) or because the researcher also wants to include fragments of the same genet ...
Ribinik
Ribinik

Text S1. Predicted Functional RNAs Within Coding Regions
Text S1. Predicted Functional RNAs Within Coding Regions

< 1 ... 8 9 10 11 12 13 14 15 16 ... 21 >

Smith–Waterman algorithm

The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme). The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible. Backtracking starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment. One does not actually implement the algorithm as described because improved alternatives are now available that have better scaling (Gotoh, 1982) and are more accurate (Altschul and Erickson, 1986).
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