The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

/pdf/bioconductor-open-software-development-for-computational-3fbxl4vnkq.pdf

Bioconductor: open software development for computational biology and bioinformatics

物件導向軟體之架構(Object-Oriented Software Construction)探討

Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.

/pdf/pregel-a-system-for-large-scale-graph-processing-30lgjenxxg.pdf

Pregel: a system for large-scale graph processing

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (
 https://github.com/marbl/mash
 ).

/pdf/mash-fast-genome-and-metagenome-distance-estimation-using-3b84vfnedh.pdf

Mash: fast genome and metagenome distance estimation using MinHash.

SUMMARY We describe a package of practical tools and libraries for manipulating graphs and their drawings. Our design, which aimed at facilitating the combination of the package components with other tools, includes stream and event interfaces for graph operations, high-quality static and dynamic layout algorithms, and the ability to handle sizable graphs. We conclude with a description of the applications of this package to a variety of software engineering tools. Copyright c 1999 John Wiley & Sons, Ltd.

An open graph visualization system and its applications to software engineering

Foreword. Preface. I User Guide. 1. Introduction. Some Graph Terminology. Graph Concepts. Vertex and Edge Descriptors. Property Maps. Graph Traversa. Graph Construction and Modification Algorithm Visitors. Graph Classes and Adaptors. Graph Classes. Graph Adaptors. Generic Graph Algorithms. The Topological Sort Generic Algorithm. The Depth-First Search Generic Algorithm. 2.Generic Programming in C++. Introduction. Polymorphism in Object-Oriented Programming. Polymorphism in Generic Programming. Comparison of GP and OOP. Generic Programming and the STL. Concepts and Models. Sets of Requirements. Example: InputIterator. Associated Types and Traits Classes. Associated Types Needed in Function Template. Typedefs Nested in Classes. Definition of a Traits Class. Partial Specialization. Tag Dispatching. Concept Checking. Concept-Checking Classes. Concept Archetypes. The Boost Namespace. Classes. Koenig Lookup. Named Function Parameters. 3. A BGL Tutorial. File Dependencies. Graph Setup. Compilation Order. Topological Sort via DFS. Marking Vertices Using External Properties. Accessing Adjacent Vertices. Traversing All the Vertices. Cyclic Dependencies. Toward a Generic DFS: Visitors. Graph Setup: Internal Properties. Compilation Time. A Generic Topological Sort and DFS. Parallel Compilation Time. Summary. 4. Basic Graph Algorithms. Breadth-First Search. Definitions. Six Degrees of Kevin Bacon. Depth-First Search. Definitions. Finding Loops in Program-Control-Flow Graphs. 5. Shortest-Paths Problems. Definitions. Internet Routing. Bellman-Ford and Distance Vector Routing. Dijkstra and Link-State Routing. 6. Minimum-Spanning-Tree Problem. Definitions. Telephone Network Planning. Kruskal's Algorithm. Prim's Algorithm. 7. Connected Components. Definitions. Connected Components and Internet Connectivity. Strongly Connected Components and Web Page Links. 8. Maximum Flow. Definitions. Edge Connectivity. 9. Implicit Graphs: A Knight's Tour. Knight's Jumps as a Graph. Backtracking Graph Search. Warnsdorff's Heuristic. 10. Interfacing with Other Graph Libraries. Using BGL Topological Sort with a LEDA Graph. Using BGL Topological Sort with a SGB Graph. Implementing Graph Adaptors. 11. Performance Guidelines. Graph Class Comparisons. The Results and Discussion. Conclusion. II Reference Manual. 12. BGL Concepts. Graph Traversal Concepts. Undirected Graphs. Graph. IncidenceGraph. BidirectionalGraph. AdjacencyGraph. VertexListGraph. EdgeListGraph. AdjacencyMatrix. Graph Modification Concepts. VertexMutableGraph. EdgeMutableGraph. MutableIncidenceGraph. MutableBidirectionalGraph. MutableEdgeListGraph. PropertyGraph. VertexMutablePropertyGraph. EdgeMutablePropertyGraph. Visitor Concepts. BFSVisitor. DFSVisitor. DijkstraVisitor. BellmanFordVisitor. 13. BGL Algorithms. Overview. Basic Algorithms. breadth_first_search. breadth_first_visit. depth_first_search. depth_first_visit. topological_sort. Shortest-Path Algorithms. dijkstra_shortest_paths. bellman_ford_shortest_paths. johnson_all_pairs_shortest_paths. Minimum-Spanning-Tree Algorithms. kruskal_minimum_spanning_tree. prim_minimum_spanning_tree. Static Connected Components. connected_components. strong_components. Incremental Connected Components. initialize_incremental_components. incremental_components. same_component. component_index. Maximum-Flow Algorithms. edmunds_karp_max_flow. push_relabel_max_flow. 14. BGL Classes. Graph Classes. adjacency_list. adjacency_matrix. Auxiliary Classes. graph_traits. adjacency_list_traits. adjacency_matrix_traits. property_map. property. Graph Adaptors. edge_list. reverse_graph. filtered_graph. SGB GraphPointer. LEDA GRAPH . std::vector . 15. Property Map Library. Property Map Concepts. ReadablePropertyMap. WritablePropertyMap. ReadWritePropertyMap. LvaluePropertyMap. Property Map Classes. property_traits. iterator_property_map. Property Tags. Creating Your Own Property Maps. Property Maps for Stanford GraphBase. A Property Map Implemented with std::map. 16 Auxiliary Concepts, Classes, and Functions. Buffer. ColorValue. MultiPassInputIterator. Monoid. mutable queue. Disjoint Sets. disjoint_sets. find_with_path_halving. find_with_full_path_compression. tie. graph_property_iter_range. Bibliography. Index. 0201729148T12172001

The Boost graph library : user guide and reference manual

In this paper we present the Generic Graph Component Library (GGCL), a generic programming framework for graph data structures and graph algorithms. Following the theme of the Standard Template Library (STL), the graph algorithms in GGCL do not depend on the particular data structures upon which they operate, meaning a single algorithm can operate on arbitrary concrete representations of graphs. To attain this type of flexibility for graph data structures, which are more complicated than the containers in STL, we introduce several concepts to form the generic interface between the algorithms and the data structures, namely, Vertex, Edge, Visitor, and Decorator. We describe the principal abstractions comprising the GGCL, the algorithms and data structures that it provides, and provide examples that demonstrate the use of GGCL to implement some common graph algorithms. Performance results are presented which demonstrate that the use of novel lightweight implementation techniques and static polymorphism in GGCL results in code which is significantly more efficient than similar libraries written using the object-oriented paradigm.

/pdf/the-generic-graph-component-library-319wz2j4pu.pdf

The generic graph component library

Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described Approaches for fast neural network training for library-based CD metrology are also described

Accurate and fast neural network training for library-based critical dimension (CD) metrology

Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of models having varying combinations of floating and fixed critical parameters and corresponding simulated spectra is generated. Each model is generated to determine one or more critical parameters for unknown structures based on spectra collected from such unknown structures. It is determined which one of the models best correlates with each critical parameter based on reference data that includes a plurality of known values for each of a plurality of critical parameters and corresponding known spectra. For spectra obtained from an unknown structure using a metrology tool, different ones of the models are selected and used to determine different ones of the critical parameters of the unknown structure based on determining which one of the models best correlates with each critical parameter based on the reference data.

Multi-model metrology

Fill-reducing sparse matrix orderings have been a topic of active research for many years. Although most such algorithms are developed and analyzed within a graph-theoretical framework, for reasons of performance the corresponding implementations are typically realized with programming languages devoid of language features necessary to explicitly represent graph abstractions. Recently, generic programming has emerged as a programming paradigm capable of providing high levels of performance in the presence of programming abstractions. In this paper we present an implementation of the Minimum Degree ordering algorithm using the newly-developed Generic Graph Component Library. Experimental comparisons show that, despite our heavy use of abstractions, our implementation has performance indistinguishable from that of a widely used Fortran implementation.

/pdf/generic-graph-algorithms-for-sparse-matrix-ordering-1jerdpau70.pdf

Lie-Quan Lee

Papers

The Boost graph library : user guide and reference manual

The generic graph component library

Accurate and fast neural network training for library-based critical dimension (CD) metrology

Multi-model metrology

Generic Graph Algorithms for Sparse Matrix Ordering