1 / 20

Approximate Similarity Search in Genomic Sequence Databases using Landmark-Guided Embedding

Approximate Similarity Search in Genomic Sequence Databases using Landmark-Guided Embedding. Ahmet Sacan and I. Hakki Toroslu email: [ ahmet,toroslu ]@ ceng.metu.edu.tr Computer Engineering Department, Middle East Technical University Ankara, TURKEY. Outline. Background Sequence Alignment

lew
Download Presentation

Approximate Similarity Search in Genomic Sequence Databases using Landmark-Guided Embedding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Approximate Similarity Search in Genomic Sequence Databases usingLandmark-Guided Embedding AhmetSacan and I. HakkiToroslu email: [ahmet,toroslu]@ceng.metu.edu.tr Computer Engineering Department,Middle East Technical UniversityAnkara, TURKEY

  2. Outline • Background • Sequence Alignment • Blast • Embedding Subsequences • Fastmap, LMDS • Analysis of parameters to achieve stable and accurate mapping • Indexing Subsequences

  3. Sequence Similarity Search • Sequence similarity search is at the heart of bioinformatics research • Similarity information allows: structural, functional, and evolutionary inferences

  4. Sequence Alignment • Goal: maximize “alignment score” • Score of aligning two residues: • Substitution matrix • Optimal solution: Dynamic Programming • Global: Needleman-Wunsch (1970) • Local: Smith-Waterman (1981)

  5. Blast (Basic Local Alignment Search Tool) • Popular tool for similarity search in sequence databases • Generate “k-tuples” (“k-mers”, “words”) from query • CDEFG  CDE, DEF, EFG • CDE  ADE,CDC,CCE, CDE, … • Find (exact) matching k-tuples in the database • For each candidate sequence, extend the k-tuple match in both directions.

  6. Time-accuracy trade-off Proteins (203 tuples) DNA (411 tuples) • Challenge: • Allow flexible matching for larger words at reasonable time 1 2 3 4 … 11 k: Too many k-tuple hits to process Slows down the extension phase • Few/none k-tuple hits • Fast execution • Exact k-tuple matching not sensitive • Too many false negatives

  7. Raising the bar for k • Map k-tuples to a vector space • Mapping cannot be perfect, thus “approximate results” • Use Spatial Access Methods (e.g. R-tree, X-tree) to index and retrieve k-tuples

  8. Mapping k-tuples • Requirements: • Need to support out of sample extension • Speed • Candidate methods: • Fastmap (Faloutsos, 1995) • Landmark MDS (de Silva, 2003)

  9. Fastmap • Select two pivots • Distant pivots heuristic • Obtain projection using cosine law • Project objects to new hyperplane • Repeat

  10. Fastmap • Fast! O(Nd) • N: number of data points • d is the target dimensionality • For query, need only to calculate distances to set of pivots • Unstable (esp. if original space is non-Euclidean)

  11. Landmark MDS • Select n landmarks (pivots) • Embed landmarks using classical MDS • For the remaining objects, apply distance-based triangulation based on distances to landmarks

  12. Landmark MDS • Provides stable results • Good selection of landmarks is critical. • LMDSrandom • LMDSmaxmin • Add new landmarks that maximizes the minimum distance to already selected landmarks • LMDSfastmap • Use the same landmarks as found by Fastmap

  13. Evaluation • Synthetic datasets • Randomly generate k-tuples for a given k and alphabet size σ • Real dataset • Yeast proteins benchmark (σ=20) • 6,341 proteins, 2.9 million residues • 103 query proteins, 38-884 residues • Weighted Hamming distance • CB-EUC substitution matrix (Sacan, 2007)

  14. Target dimensionality (d) • Sammon’s metric stress: • Breaking point dimensionality k=5, synthetic dataset, identity matrix

  15. Subsequence length (k)and alphabet size (σ)

  16. Number of landmarks k=5, d=7, synthetic dataset, identity matrix

  17. Approximate k-tuple search performance • Find all k-tuples within a specified radius from a query k-tuple k=6, d=8, real dataset, CB-EUC matrix

  18. Homology search k=6, d=8, real dataset, CB-EUC matrix

  19. Search time search radius=7 Database size=100,000

  20. Conclusion • Applied an embedding-based approach to approximate sequence similarity search for the first time • Significant time improvements with negligible degradation in accuracy • Achieved more stable embedding with combined pivot selection strategy • Defined intrinsic Euclidean dimensionality of the dataset

More Related