WebMOTIFS: Web-based integrated motif discovery
... a single representative motif for each cluster. WebMOTIFS also provides the option of Bayesian motif discovery with THEME. The THEME algorithm searches for motifs consistent with proteins from specified DNA-binding domain families. The significance of each discovered motif is determined using cross- ...
... a single representative motif for each cluster. WebMOTIFS also provides the option of Bayesian motif discovery with THEME. The THEME algorithm searches for motifs consistent with proteins from specified DNA-binding domain families. The significance of each discovered motif is determined using cross- ...
Protocol S1.
... regression approach allows cycles, of which there are several in RegulonDB. Both methods perform significantly better than random. The second group of algorithms we tested takes a pairwise approach where each potential interaction between a regulator and a target is considered independently. These ...
... regression approach allows cycles, of which there are several in RegulonDB. Both methods perform significantly better than random. The second group of algorithms we tested takes a pairwise approach where each potential interaction between a regulator and a target is considered independently. These ...
Motif Finding Problem
... 2 x l alignment matrix with Score(s,2,DNA) • At each of the following t-2 iterations CONSENSUS finds a “best” l-mer in sequence i from the perspective of the already constructed (i-1) x l alignment matrix for the first (i-1) sequences • In other words, it finds an l-mer in sequence i maximizing Scor ...
... 2 x l alignment matrix with Score(s,2,DNA) • At each of the following t-2 iterations CONSENSUS finds a “best” l-mer in sequence i from the perspective of the already constructed (i-1) x l alignment matrix for the first (i-1) sequences • In other words, it finds an l-mer in sequence i maximizing Scor ...
Transcriptional regulatory networks in Saccharomyces cerevisiae
... ● Identified network motifs that provide specific regulatory capacities for yeast ● These motifs can be used as building blocks to construct large network structures through an automated approach that combines genome-wide location and expression data (without prior knowledge) ● Future research will ...
... ● Identified network motifs that provide specific regulatory capacities for yeast ● These motifs can be used as building blocks to construct large network structures through an automated approach that combines genome-wide location and expression data (without prior knowledge) ● Future research will ...
Probabilistic Segmentation - Department of Zoology, UBC
... • The presence of an ADL motif in about half of the promoters in the srh and sri chemoreceptor gene subfamilies might reflect the use of ADL to sense a particular class of ligands. • Probabilistic segmentation can be used to identify functional regulatory elements with no previous knowledge of gene ...
... • The presence of an ADL motif in about half of the promoters in the srh and sri chemoreceptor gene subfamilies might reflect the use of ADL to sense a particular class of ligands. • Probabilistic segmentation can be used to identify functional regulatory elements with no previous knowledge of gene ...
lecture07_13
... - Start from a random PWM – Move from one PWM to another so as to improve the score which fits the sequence to the motif – Keep doing this until no more improvement is obtained : Convergence to local optima ...
... - Start from a random PWM – Move from one PWM to another so as to improve the score which fits the sequence to the motif – Keep doing this until no more improvement is obtained : Convergence to local optima ...
Network properties and models
... Large network comparison is computationally hard due to NPcompleteness of the underlying subgraph isomorphism problem: • Given 2 graphs G and H as input, determine whether G contains a subgraph that is isomorphic to H. ...
... Large network comparison is computationally hard due to NPcompleteness of the underlying subgraph isomorphism problem: • Given 2 graphs G and H as input, determine whether G contains a subgraph that is isomorphic to H. ...
Multiple Sequence Alignment
... Dynamic Programming • The maximum match can be determined by representing in a two-dimensional array, all possible pair combinations that can be constructed from the amino acid sequences of the proteins, A and B, being compared. ...
... Dynamic Programming • The maximum match can be determined by representing in a two-dimensional array, all possible pair combinations that can be constructed from the amino acid sequences of the proteins, A and B, being compared. ...
Slide 1
... Our goal is to understand how the combinations of various Transcription Factor Binding Sites (TFBS) on a gene affect it’s expression in different experimental conditions. ...
... Our goal is to understand how the combinations of various Transcription Factor Binding Sites (TFBS) on a gene affect it’s expression in different experimental conditions. ...
Network models - Department of Computing
... Large network comparison is computationally hard due to NPcompleteness of the underlying subgraph isomorphism problem: • Given 2 graphs G and H as input, determine whether G contains a subgraph that is isomorphic to H. ...
... Large network comparison is computationally hard due to NPcompleteness of the underlying subgraph isomorphism problem: • Given 2 graphs G and H as input, determine whether G contains a subgraph that is isomorphic to H. ...
- Cal State LA - Instructional Web Server
... Uses a recurrence algorithm that takes into account different taxonomic levels as well as the specific branch constraints Cuts down on run time by checking the number of leaves in the pattern and the target tree Allows users to search for orthologs/paralogs ...
... Uses a recurrence algorithm that takes into account different taxonomic levels as well as the specific branch constraints Cuts down on run time by checking the number of leaves in the pattern and the target tree Allows users to search for orthologs/paralogs ...
CSCE590/822 Data Mining Principles and Applications
... their protein sequences are more conserved than average between species (shown in yeast vs. worm) ...
... their protein sequences are more conserved than average between species (shown in yeast vs. worm) ...
How to visually interpret biological data using networks
... seen more easily. Most networks can be visualized by using a ‘spring-embedded’ or ‘force-directed’ layout algorithm, based on the idea of edges ‘pulling together’ nodes that ‘repel’ each other. Other, more specific, layout algorithms are available, such as ‘hierarchical’ algorithms, which are useful ...
... seen more easily. Most networks can be visualized by using a ‘spring-embedded’ or ‘force-directed’ layout algorithm, based on the idea of edges ‘pulling together’ nodes that ‘repel’ each other. Other, more specific, layout algorithms are available, such as ‘hierarchical’ algorithms, which are useful ...
Protocol S4 – Clustering to define complexes, functional
... Clusters of interacting proteins were generated from three networks: PI (Protocol S3), GC (Protocols S5) and function prediction (Protocols S9) using the Markov Clustering (MCL) algorithm [1], following both biological and structural optimization. These networks are weighted networks, were edge’s we ...
... Clusters of interacting proteins were generated from three networks: PI (Protocol S3), GC (Protocols S5) and function prediction (Protocols S9) using the Markov Clustering (MCL) algorithm [1], following both biological and structural optimization. These networks are weighted networks, were edge’s we ...
Abstract - Logic Systems
... Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate” patterns that are contiguous. The few existing algorithms that can be applied to find such c ...
... Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate” patterns that are contiguous. The few existing algorithms that can be applied to find such c ...
duplicativenetworks
... The model should capture the underlying mechanisms that generate the network while satisfying known mathematical properties: Ohno’s model of genome growth by duplication Duplication based graphs [Kleinberg et al.], [Kumar et al] [Pastor-Satorras et al], [Chung et al.]: each iteration duplicates a ra ...
... The model should capture the underlying mechanisms that generate the network while satisfying known mathematical properties: Ohno’s model of genome growth by duplication Duplication based graphs [Kleinberg et al.], [Kumar et al] [Pastor-Satorras et al], [Chung et al.]: each iteration duplicates a ra ...
Graph preprocessing
... assessment methods examine how the CNS measures evaluated here perform for other types of network data, such as genetic interaction networks which have their own characteristics, such as the presence of both positively and negatively weighted edges it is possible to develop hybrid CNS measures t ...
... assessment methods examine how the CNS measures evaluated here perform for other types of network data, such as genetic interaction networks which have their own characteristics, such as the presence of both positively and negatively weighted edges it is possible to develop hybrid CNS measures t ...
A Statistical Method for Finding Transcriptional Factor Binding Sites
... Allows variations in the binding site instances of a given transcription factor Allows for motifs to include “spacers” ...
... Allows variations in the binding site instances of a given transcription factor Allows for motifs to include “spacers” ...
Random Walks on Graphs: An Overview
... Random walks and Markov chains • A Markov chain describes a stochastic process over a set of states according to a transition probability matrix • Markov chains are memoryless • Random walks correspond to Markov chains: • The set of states is the set of nodes in the graph • The elements of the tran ...
... Random walks and Markov chains • A Markov chain describes a stochastic process over a set of states according to a transition probability matrix • Markov chains are memoryless • Random walks correspond to Markov chains: • The set of states is the set of nodes in the graph • The elements of the tran ...
PPT - Department of Computer Science
... • The sequences that match the pattern are called the supporting sequences of a pattern. It is possible that a pattern matches a sequence at more than one position. • The Hit/Seq ratio of a pattern is the average number of occurrences of a pattern among its supporting sequences. ...
... • The sequences that match the pattern are called the supporting sequences of a pattern. It is possible that a pattern matches a sequence at more than one position. • The Hit/Seq ratio of a pattern is the average number of occurrences of a pattern among its supporting sequences. ...
CSC 2417 Algorithms in Molecular Biology PS3: Due December 8
... a) The Gibbs Sampling algorithm we described in class (and many other related approaches) assume independence of adjacent positions. This is not always a valid assumption; for example the bias against CpG di-nucleotides makes adjacent nucleotides non-independent. Develop an HMM representation of a p ...
... a) The Gibbs Sampling algorithm we described in class (and many other related approaches) assume independence of adjacent positions. This is not always a valid assumption; for example the bias against CpG di-nucleotides makes adjacent nucleotides non-independent. Develop an HMM representation of a p ...
Network motif
All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as graphs, which include a wide variety of subgraphs. One important local property of networks are so-called network motifs, which are defined as recurrent and statistically significant sub-graphs or patterns.Network motifs are sub-graphs that repeat themselves in a specific network or even among various networks. Each of these sub-graphs, defined by a particular pattern of interactions between vertices, may reflect a framework in which particular functions are achieved efficiently. Indeed, motifs are of notable importance largely because they may reflect functional properties. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Although network motifs may provide a deep insight into the network’s functional abilities, their detection is computationally challenging.