Pan-genome Graphs biodata14
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1) The document discusses de Bruijn graphs and how they can be constructed and used to represent relationships between multiple genomes without the use of a reference genome. 2) De Bruijn graphs are built by using every unique k-mer as a node and connecting nodes that share a k-1 overlap. This implicitly captures overlaps between sequences. 3) The graphs can be used to analyze similarities and differences between many genomes at once and investigate phylogenetic relationships.
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Community detection aims to identify groups of nodes in a network that are more densely connected internally than to the rest of the network. It can reveal properties of networks without privacy risks. While similar to clustering, community detection methods consider graph properties directly due to challenges from network data. Two recent methods are discussed - one based on shortest path betweenness to iteratively remove inter-community edges, and another based on optimizing modularity, a measure of community structure quality. Modularity can be computed using the eigenvectors of the modularity matrix.
Spectral clustering with motifs and higher-order structures
I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject.
Gravitational Billion Body Project
The document summarizes research on running cosmological N-body simulations across multiple supercomputers located in different countries. Key points:
1) Researchers were able to run simulations on up to 750 cores distributed across 4 supercomputers in different locations, achieving up to 92% performance of a single supercomputer.
2) They developed software called SUSHI (Simulating Universe Structure formation on Heterogeneous Infrastructures) that uses tree and particle-mesh algorithms to simulate structure formation across supercomputers over long and short distances.
3) Timing results showed simulations spent about 90% of time on calculations when run across 2-3 supercomputer sites separated by up to 30,000 km of network cable.
[241]large scale search with polysemous codes
This document discusses using polysemous codes to perform large-scale search over visual signatures. Polysemous codes allow product quantization codes to be interpreted as both compact binary codes for efficient Hamming distance search and codes that preserve distance information for accurate nearest neighbor search. The key ideas are to learn an index assignment that maps similar product quantization codes to binary codes with smaller Hamming distance, and to directly optimize this assignment to match the distances between codebook centroids. This allows using a single code representation for both fast Hamming search and precise distance search, without increasing memory requirements. The document provides examples of applying polysemous codes to build a large graph connecting images based on visual similarity.
Phylogenomic Supertrees. ORP Bininda-Emond
Supertree construction combines smaller phylogenetic trees into a single larger tree to infer relationships across taxa. While traditionally used when molecular data was limited, supertrees are now threatened by phylogenomic analyses of entire genomes. However, divide-and-conquer approaches that analyze subsets of taxa in parallel and combine results could make supertree methods scalable again by improving both speed and accuracy over analyzing all taxa at once. The key is optimizing the methodology to provide an accurate initial supertree for global refinement.
Similar to Pan-genome Graphs biodata14 (20)
Networks in Space: Granular Force Networks and Beyond
Networks in Space: Granular Force Networks and Beyond
Interactive High-Dimensional Visualization of Social Graphs
Interactive High-Dimensional Visualization of Social Graphs
Machine Learning Foundations for Professional Managers
Machine Learning Foundations for Professional Managers
Interpretation of the biological knowledge using networks approach
Interpretation of the biological knowledge using networks approach
Spectral clustering with motifs and higher-order structures
Spectral clustering with motifs and higher-order structures
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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2:同时对留学生所学专业登记给予评定
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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Pan-genome Graphs biodata14
- 3. Background • “Pan Genome” - way to think about, compute on, visualize the differences and similarities of many genomes at once • Reference free structure • Many, many genomes
- 4. de Bruijn Graph Construction • Dk = (V,E) • V = All length-k subfragments • E = Directed edges between consecutive subfragments • Nodes overlap by k-1 words It was the best of It was the best was the best of • Locally constructed graph reveals the global sequence structure • Overlaps between sequences implicitly computed Slide: http://cbcb.umd.edu/confcour/CMSC828H-materials/ Lecture12-MSchatz-DeBruijnAssembly.pptx Original Fragment Directed Edge de Bruijn, 1946 Idury and Waterman, 1995 Pevzner, Tang, Waterman, 2001
- 5. Strategy: find all k-mers, build graph • Every k-mer becomes a node • Two nodes are linked with an edge if they share a k-1 mer GACTGGGACTCC GACTGG ACTGGG CTGGGA TGGGAC GGACTC GGGACT GACTCC
- 6. Strategy: k-mers from feature families, build graph • Every k-mer becomes a node – If it is present in m genomes • Two nodes are linked with an edge if they share a k-1 mer • d# = a feature family d1d2d3d4d5d6d7d8 d9 d1d2d3d4d5d 6 d1d2d3d4d5d6d7d8 d2d3d4d5d6d 7 d3d4d5d6d7d8 d4d5d6d7d8d9 d9
- 7. rf-graph de Bruijn “like”
- 9. Similarities and Differences 10 groups of 10 Organism Sum Pairwise Distances (Phylogenetic) E. coli 0.07 Coxiella 10.42 Mycobacterium 2.70 Brucella 0.08 Rickettsia 8.62 Burkholderia 7.21 Clostridium 9.05 Bacillus 4.48 Staph. 2.08 Strep. 4.79
- 10. Similarities and Differences 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Node Increase vs. MUMi 0.850 0.900 0.950 1.000 Node Increase MUMi Node Increase vs. Diversity Score 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 Node Increase Diversity Score Node Increase = (Nodes – Max(Families)) / Nodes Diversity Score= Sum of maximum pairwise distances in Order level tree MUMi= Maximum of all pairwise MUMi in a group
- 11. Layout Gephi Toolkit Yifan Hu’s Multilevel Force Atlas 2
- 12. Colors and Lines
- 13. Dealing with many Genomes N=2 K=5 M=2 B. Abortus N=40, K=5, M=2, B. Suis N=20 K=5 M=2 Brucella N=400, K=5, M=2, All Brucella N=1000, K=10, M=100, E. coli
- 15. For the Web • GEXF – NetworkX, Gephi, – Cytoscape, Gexf-JS, D3-Gexf • BGZF GFF – Backing store – Byte range loading
- 16. Other Uses • “Rearrangement” detection
- 17. Other Uses • “Scaffolding” – e.g. 86 contigs • Closing – Predicted primers
- 18. Other Uses • Rearrangements – Insertions/Deletions – Islands – Inversions
- 19. Other Uses • Synthetic BAM
- 20. Takeaways • A new way to leverage protein family databases • “Reference free” structure for many bacterial genomes using feature families • Quickly investigate whole genome relationships and speed up potentially expensive calculations
- 21. Acknowledgements • Eric Nordberg • Lenny Heath • CID at VBI (PATRIC) • RAST – Argonne (PATRIC) https://github.com/aswarren https://twitter.com/aswarren