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Database of molecules

- Two models (of vocabularies)
- Proteins / Nucleic Acids
- Residues (+ modifications)
- Genbank / Swissprot

- Compounds
- Atoms & covalent bonds (SMILE/SMARTS language)
- Pubchem / ACS

- Proteins / Nucleic Acids
- Glycans
- Residues: monosaccahrides (+ many modifications)
- Branching nonlinear structure

Simplified molecular input line entry specification (SMILE)

- Glucose
- OC[[email protected]@H](O1)[[email protected]@H](O)[[email protected]](O)[[email protected]@H](O)[[email protected]@H](O)1

Representation of glycans

- Vocabulary
- monosaccharides rather than atoms

- Two challenges
- Controlled vocabulary of monosaccharides
- GlycoCT

- From residues to molecules: glycan exchange format
- GLYDE-II

- Controlled vocabulary of monosaccharides

Searching the glycan database: comparison

- Glycan representation
- tree vs. sequences

- Glycan matching
- exact vs. non-exact
- Graph theoretic algorithm

- alignment? Mutations are natural events.
- Multiple glycan matching

- exact vs. non-exact
- Glycan pattern searching
- Significance estimation

GLYDE standard

- An XML based representation format for glycan structures
- Inter-convertible with existing data represented using IUPAC or LINUCS.
- GLYDE II: Incorporation of Probability based representation
- Visualization: structures using GLYDE (XML) files

GLYDE - An expressive XML standard for the representation of glycan structure. Carbohydrate Research, 340 (18), Dec 30, 2005.

Collaborative GlycoInformatics

- Enable querying and export of query results in GLYDE format
- Using GLYDE representation for disambiguation, mapping and matching

GLYDE

MonosaccharideDB

<glyde>

<residue>

.

.

</residue>

</glyde>

SweetDB

QUERY

<glyde>

<residue>

.

.

</residue>

</glyde>

KEGG

RESULT

Semantic GlcyoInformatics - Ontologies

- GlycO: A domain ontology for glycan structures, glycan functions and enzymes (embodying knowledge of the structure and metabolisms of glycans)
- Contains 600+ classes and 100+ properties – describe structural features of glycans; unique population strategy
- URL: http://lsdis.cs.uga.edu/projects/glycomics/glyco

- ProPreO: a comprehensive process Ontology modeling experimental proteomics
- Contains 330 classes, 6 million+ instances
- Models three phases of experimental proteomicsURL: http://lsdis.cs.uga.edu/projects/glycomics/propreo

GlycO taxonomy

The first levels of the GlycO taxonomy

Most relationships and attributes in GlycO

GlycO exploits the expressiveness of OWL-DL.

Cardinality constraints, value constraints, Existential and Universal restrictions on Range and Domain of properties allow the classification of unknown entities as well as the deduction of implicit relationships.

<aglycon name="Asn"/>

<residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc">

<residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc">

<residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="Man" >

<residue link="3" anomeric_carbon="1" anomer="a" chirality="D" monosaccharide="Man" >

<residue link="2" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc" >

</residue>

<residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc" >

</residue>

</residue>

<residue link="6" anomeric_carbon="1" anomer="a" chirality="D" monosaccharide="Man" >

<residue link="2" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc">

</residue>

</residue>

</residue>

</residue>

</residue>

</Glycan>

ProPreO

- ProPreO: A process ontology to capture proteomics experimental lifecycle:
- Separation
- Mass spectrometry
- Analysis
- 330 classes
- 110 properties
- 6 million+ instances

Usage: Mass spectrometry analysis

Manual annotation of mouse kidney spectrum by a human expert. For clarity, only 19 of the major peaks have been annotated.

Goldberg, et al, Automatic annotation of matrix-assisted laser desorption/ionization N-glycan spectra, Proteomics 2005, 5, 865–875

- Semantic Annotation of Experimental Data
- Enables Ontology-mediated Disambiguation
- Allows correlation between disparate entities using Semantic Relations

P(S | M = 3461.57) = 0.6

P(T | M = 3461.57) = 0.4

Goldberg, et al, Automatic annotation of matrix-assisted laser desorption/ionization N-glycan spectra, Proteomics 2005, 5, 865–875

Graph Theoretic Basics

- tree: an acyclic connected graph, whose vertices we refer to as nodes;
- rooted tree: a tree having a specific node called the root, from which the rest of the tree extends.
- children: nodes that extend from a node x by one edge are called the children of x; and conversely, x would be called the parent of these children;
- Leaf: a node with no children;
- Subtree: subtree of a tree T is a tree whose nodes and edges are subsets of those of T;
- ordered tree: the rooted tree in which the children of each node are ordered;
- labeled tree: a tree in which a label is attached to each node;
- Forest: a set of trees
- Oligosaccarides can be represented as labeled (monosaccahrides), ordered (if linkages are specified) and rooted trees.

Maximum Common Subtree Problem (MCST)

- Input: Two labeled rooted trees T1 and T2.
- Output: A tree which is a subtree of both tree T1 and T2 and whose number of edges is the maximum among all such possible subtrees.
- Variants: Each of T1 and T2 can be ordered or unordered.

Aoki, et. al. Efficient Tree-Matching Methods for Accurate Carbohydrate Database Queries. Genome Informatics 14: 134-143 (2003).

A bottom-up dynamic programming algorithm

- Let {u1, …,un} and {v1, …,vm} are the sets of nodes in T1 and T2, respectively;
- R[ui, vj] – the size of the maximum subtree of T1(ui) and T2(vj), the subtrees of T1 and T2 with ui and vj as roots, respectively;
- Computed from leaves to roots (bottom-up)
- MCST of T1 and T2 R[root(T1), root(T2)]

- R[ui, ] = R[vj, ] = 0;
- M(u, v) is a matching in a bipartite graph between the children of u and children of v; if both T1 and T2 are ordered trees, M(u, v) = 1.

Aoki, et. al. Efficient Tree-Matching Methods for Accurate Carbohydrate Database Queries. Genome Informatics 14: 134-143 (2003). Implemented in KEGG glycan matching and many other services.

Alignment algorithm?

- Complexity: unordered tree ~O(4!mn) ~ O(24mn); ordered tree ~ O(mn). Typically m, n < 25.
- Extended to MCST problem in multiple trees
- Is the MCST of T1, T2 and T2 is the MCST between MCST(T1, T2) and T3, where MCST(T1, T2) is the maximum subtree of T1 and T2?
- Multi-MCST problem is NP-hard (Akutsu, 2002)
- Reduciable from Longest Common Substring problem (LCS)

- Finding substructures, motif finding problem profile models

- Should we consider indels as DNA/protein alignments?
- Indels is not a natural changes; but mutation might be.
- Profile HMM may not be appropriate

Maximum Common Approximate Subtree Problem (MCAST)

- Input: Two labeled rooted trees T1 and T2.
- Output: A tree which is a k-appximatesubtreeof both tree T1 and T2 and whose number of edges is the maximum among all such possible subtrees.
- T is a k-appximatesubtree of U if one of U’s subtree can be transformed to T by replacing at most k labels.

Subtree finding problem (pattern matching problem)

- Input: a labeled rooted tree P and a set (database) S of labeled rooted trees.
- Output: all trees in S which each has a subtree matching P.
- Variants: (1) P can be ordered or unordered; (2) P must be on the root; (3) P must be on the leaves
- A bottom-up DP algorithm modified from MCST algorithm; complexity O(|P|*|T|) for each T in the database.

A bottom-up dynamic programming algorithm

- Let {u1, …,un} and {v1, …,vm} are the sets of nodes in P and T.
- R[ui, vj] – indicator if the tree with the root of ui is a subtree of the tree with the root of vj, which is rooted by vj
- Output subtree with the root of vjwhich has R[root(P), vj] = 1;

- R[x, ] = R[, y] = 0.
- R[x, y] = 1, if x = y and x or y is the leave of P and T, respectively.
- For ordered tree, matching edges rather than nodes.
- Variants: (1) leaves: R[x, y] = 1, if x = y and x and y are both leaves; (2) root: Output tree T which has R[root(P), root(T)] = 1;

Significance of matching glycans

- MCST of T1 and T2 has k nodes (monosaccharides)
- N(T, k): # of subtrees of T with k nodes
- Can be counted by a DP algorithm (how?)

- P = a-k N(T1, k) N(T2, k)

Motif retrieval from glycans

- PSTMM (Probabilistic Sibling-dependent Tree Markov Model)
- Learns patterns from glycan structures

- Profile PSTMM
- Extracts patterns (as profiles) from glycan structures

- Kernel methods
- Classification of glycans
- Extraction of “features” to predict glycan biomarkers

Kernel method

- Extracted glycan structures from CarbBank
- Pre-analysis showed that the trisaccharide structure was most effective for classification
- Furthermore, since the non-reducing end is usually the portion being recognized, this information was included in the kernel model

Other kernels

- Q-gram distribution kernel:
- Wanted to be able to analyze any data regardless of marker structure or size
- Definition of q-gram: A sub-tree containing q nodes
- All of the q-grams for a particular glycan were included in the kernel

- Multiple kernel:
- A kernel of kernels

Using a gram distribution, potential biomarkers of the appropriate size can be extracted from the data

Data mining for glycobiology appropriate size can be extracted from the data

- Kernels can be utilized in many ways
- Feature retrieval methods for detecting putative biomarkers
- Cell-specific glycan structures can be extracted
- Sequences of glycan binding proteins can be included in a new kernel to predict binding domains
- Many more possibilities, depending on the data

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