BLAST - UCSD CSE
... • Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. • Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future • Practicality ...
... • Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. • Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future • Practicality ...
DNA sequencing: graph theory
... • Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. • Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future • Practicality ...
... • Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. • Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future • Practicality ...
Website
... Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future Practicality again ...
... Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology. Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future Practicality again ...
Updated slides on graph algorithms for DNA sequencing
... Euler Theorem • A graph is balanced if for every vertex the number of incoming edges equals to the number of outgoing edges: in(v)=out(v) • Theorem: A connected graph is Eulerian (i.e., contains a Eulerian cycle) if and only if each of its vertices is balanced. ...
... Euler Theorem • A graph is balanced if for every vertex the number of incoming edges equals to the number of outgoing edges: in(v)=out(v) • Theorem: A connected graph is Eulerian (i.e., contains a Eulerian cycle) if and only if each of its vertices is balanced. ...
Pseudomon-1 motif
... UTRs of the COG0520 family sequenced from in Actinobacteria at the time of our analysis. Thus, most input data in this sequence cluster did not contain the motif. In contrast, the sequence clustering method in our current pipeline will likely partition the three SAM-IV RNAs into a different cluster ...
... UTRs of the COG0520 family sequenced from in Actinobacteria at the time of our analysis. Thus, most input data in this sequence cluster did not contain the motif. In contrast, the sequence clustering method in our current pipeline will likely partition the three SAM-IV RNAs into a different cluster ...
Ch6_seq_alignment
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
Sequence Alignment
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
CSE 181 Project guidelines - UCSD CSE
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
... Local vs. Global Alignment • The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph. • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. ...
Sequence Alignment www.bioalgorithms.info An Introduction to Bioinformatics Algorithms
... • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. • In the edit graph with negatively-scored edges, Local Alignmet may score higher than Global Alignment ...
... • The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph. • In the edit graph with negatively-scored edges, Local Alignmet may score higher than Global Alignment ...
Discovery of substrate cycles in large scale metabolic networks
... could enable the cell to maintain an independent steadystate cycle flux, where the cycle flux can in theory fluctuate without directly altering other fluxes in the metabolic network, provided the cycle does not drastically disturb the cofactor pools. This feature could promote local robustness, whic ...
... could enable the cell to maintain an independent steadystate cycle flux, where the cycle flux can in theory fluctuate without directly altering other fluxes in the metabolic network, provided the cycle does not drastically disturb the cofactor pools. This feature could promote local robustness, whic ...
Lecture 10
... Approximate vs. Exact Pattern Matching • So far all we ve seen exact pattern matching algorithms • Usually, because of mutations, it makes much more biological sense to find approximate pattern matches • Biologists often use fast heuristic ...
... Approximate vs. Exact Pattern Matching • So far all we ve seen exact pattern matching algorithms • Usually, because of mutations, it makes much more biological sense to find approximate pattern matches • Biologists often use fast heuristic ...
Profile TildeCRF: a new tool for protein homology detection
... bioinformatics. Profile hidden Markov models have been successful in tackling this problem, but it remains a generative classifier, which is hypothetically less accurate than discriminative models like Conditional Random Fields. Efforts in combining profile HMM with CRF have been suggested in the li ...
... bioinformatics. Profile hidden Markov models have been successful in tackling this problem, but it remains a generative classifier, which is hypothetically less accurate than discriminative models like Conditional Random Fields. Efforts in combining profile HMM with CRF have been suggested in the li ...
Goal - Cytoscape Wiki
... • Query a network for topological matches • Input: query and target networks, optional node/edge labels • Output: Topological query matches as subgraphs of target network • Supports: subgraph matching, node/edge labels, label wildcards, approximate paths • http://alpha.dmi.unict.it/~ctnyu/netmatch.h ...
... • Query a network for topological matches • Input: query and target networks, optional node/edge labels • Output: Topological query matches as subgraphs of target network • Supports: subgraph matching, node/edge labels, label wildcards, approximate paths • http://alpha.dmi.unict.it/~ctnyu/netmatch.h ...
ELM Database Entry Data Collection - Eukaryotic Linear Motif
... belonging to this functional site work and includes appropriate references (literature, PDB structures, related motifs in ELM,…). Free form text of at least a paragraph but at max 8192 characters. Give general overview – for large domain families like SH2, there may be a number of different motifs t ...
... belonging to this functional site work and includes appropriate references (literature, PDB structures, related motifs in ELM,…). Free form text of at least a paragraph but at max 8192 characters. Give general overview – for large domain families like SH2, there may be a number of different motifs t ...
meme
... * -mod — The type of distribution to assume.
* oops — One Occurrence Per Sequence MEME assumes that each
sequence in the dataset contains exactly one occurrence of
each
motif. This option is the fastest and most sensitive but the
motifs returned by MEME may be "blurry" if any of the
sequenc ...
... * -mod
Lecture on BLAST
... • Bootstrapping results to find very related sequences • Megablast: • Search longer sequences with fewer differences • WU-BLAST: (Wash U BLAST) • Optimized, added features ...
... • Bootstrapping results to find very related sequences • Megablast: • Search longer sequences with fewer differences • WU-BLAST: (Wash U BLAST) • Optimized, added features ...
preprint - Human Genome Center
... We first deal with a problem called the ‘range RMSD query’ problem. Given an aligned pair of structures, the problem is to compute the RMSD between two aligned substructures of them without gaps. This problem has many applications in protein structure analysis. We propose a linear-time preprocessing ...
... We first deal with a problem called the ‘range RMSD query’ problem. Given an aligned pair of structures, the problem is to compute the RMSD between two aligned substructures of them without gaps. This problem has many applications in protein structure analysis. We propose a linear-time preprocessing ...
Analysis and simulation of metabolic networks: Application to HEPG2
... consists on the analysis of metabolic fluxes in HepG2 (Metabolic Flux Balance, MFB) under different toxicants concentrations by complex network analysis tools. In addition, to the understanding of the results provided by MFB, it is important to check which parameters can be useful to characterize me ...
... consists on the analysis of metabolic fluxes in HepG2 (Metabolic Flux Balance, MFB) under different toxicants concentrations by complex network analysis tools. In addition, to the understanding of the results provided by MFB, it is important to check which parameters can be useful to characterize me ...
Full Text
... subsume one another, more than one amino acid group may apply, for example, the groups [VLI], [ V L I M ], and [CVLIMFYW]. In that case, we identify the most specific amino acid group, that which is subsumed by all others (i.e., [VLI]). Note that there may be more than one specific group. If [CVL] w ...
... subsume one another, more than one amino acid group may apply, for example, the groups [VLI], [ V L I M ], and [CVLIMFYW]. In that case, we identify the most specific amino acid group, that which is subsumed by all others (i.e., [VLI]). Note that there may be more than one specific group. If [CVL] w ...
MicroRNA Regulatory Patterns on the Human Metabolic Network
... their associated enzymes, genes and metabolites within human metabolism and then constructed a reaction-centric network, in which we joined the reactions that share a common metabolite as either a product or reactant. In the network one node represents one or a set of genes that encode the enzymes t ...
... their associated enzymes, genes and metabolites within human metabolism and then constructed a reaction-centric network, in which we joined the reactions that share a common metabolite as either a product or reactant. In the network one node represents one or a set of genes that encode the enzymes t ...
Deriving phylogenetic trees from the similarity analysis of metabolic
... structure, we combine both these aspects in formulating a measure of the distance between pathways. The former aspect of the distance, i.e., the similarity between two enzymes, can be defined using the sequence similarity of the corresponding genes, or structure similarity of the corresponding prot ...
... structure, we combine both these aspects in formulating a measure of the distance between pathways. The former aspect of the distance, i.e., the similarity between two enzymes, can be defined using the sequence similarity of the corresponding genes, or structure similarity of the corresponding prot ...
Ben-Hur1 pdf
... While PSSMs capture more information than a given motif about the sequence variability in a block, it is much more time consuming to compute the PSSM composition vector, since each scoring matrix needs to be considered, whereas in the case of the discrete motifs, the computation time is sub-linear i ...
... While PSSMs capture more information than a given motif about the sequence variability in a block, it is much more time consuming to compute the PSSM composition vector, since each scoring matrix needs to be considered, whereas in the case of the discrete motifs, the computation time is sub-linear i ...
Global and Local Sequence Alignment
... that is too simple may allow efficient algorithms, but may not yield results of biological interest. However, a definition that includes most of the relevant biology may entail intractable algorithms and statistics. The most successful approaches find a balance between these considerations. ...
... that is too simple may allow efficient algorithms, but may not yield results of biological interest. However, a definition that includes most of the relevant biology may entail intractable algorithms and statistics. The most successful approaches find a balance between these considerations. ...
Motif PPT - Mark Goadrich
... CONSENSUS: Greedy Motif Search • Find two closest l-mers in sequences 1 and 2 and forms 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 ...
... CONSENSUS: Greedy Motif Search • Find two closest l-mers in sequences 1 and 2 and forms 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 ...
A perturbation-based method for calculating explicit likelihood of
... of over 0.07. For example, for the Pfam alignment 2Hacid_DH_C, we got a cutoff value of 31%. This is interpreted by the authors of the SCA package to mean that any subalignment with fewer than 31% of the sequences is not in ‘statistical equilibrium’ and will not generate meaningful results when anal ...
... of over 0.07. For example, for the Pfam alignment 2Hacid_DH_C, we got a cutoff value of 31%. This is interpreted by the authors of the SCA package to mean that any subalignment with fewer than 31% of the sequences is not in ‘statistical equilibrium’ and will not generate meaningful results when anal ...
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.