MS-BioGraphs: Sequence Similarity Graph Datasets

Citation Author(s):
Mohsen
Koohi Esfahani
Queen's University Belfast, University of Sistan
Sebastiano
Vigna
Università degli Studi di Milano
Paolo
Boldi
Università degli Studi di Milano
Hans
Vandierendonck
Queen's University Belfast
Peter
Kilpatrick
Queen's University Belfast
Submitted by:
Mohsen Koohi Es...
Last updated:
Thu, 04/11/2024 - 09:08
DOI:
10.21227/gmd9-1534
Data Format:
WebGraph
Research Article Link:
On Overcoming HPC Challenges of Trillion-Scale Real-World Graph Datasets
Links:
MS-BioGraphs Web Page
MS-BioGraphs Sample and Validation Code
MS-BioGraphs: Sequence Similarity Graph Datasets
Dataset Announcement: MS-BioGraphs, Trillion-Scale Public Real-World Sequence Similarity Graphs
WebGraph Framework
ParaGrapher, Loading Large-Scale Compressed Graphs
License:
Creative Commons Attribution-NonCommercial-ShareAlike
172 Views
Categories:
Other
Biomedical and Health Sciences
Keywords:
High-Performance Graph Processing, Graph Algorithms, Real-world graph dataset, High Performance Graph Processing, Sequence Similarity Graphs
0
0 ratings - Please login to submit your rating.
CITE SHARE/EMBED

Abstract 

MS-BioGraphs are a family of sequence similarity graph datasets with up to 2.5 trillion edges. The graphs are weighted edges and presented in compressed WebGraph format. The dataset include symmetric and asymmetric graphs. The largest graph has been created by matching sequences in Metaclust dataset with 1.7 billion sequences. These real-world graph dataset are useful for measuring contributions in High-Performance Computing and High-Performance Graph Processing. Moreover, they  provide  a representation of the data   acts as a new source for extracting domain-specific information and knowledge by deploying graph algorithms.  Sequence similarity graphs have several usages in biology including sequence clustering,  predicting pseudo-gene functions, effective selection of conotoxins, predicting evolution  and gene transfer.

Please note that the dataset has not yet been uploaded completely and for now it can be accessed from https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ .

Instructions: 

The graphs are presented in WebGraph format, https://webgraph.di.unimi.it/ and as arc-labelled graphs.

For sample code and validation, please refer to https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-Validation .

Dataset Files

LOGIN TO ACCESS DATASET FILES
Open Access dataset files are accessible to all logged in  users. Don't have a login?  Create a free IEEE account.  IEEE Membership is not required.

QUESTIONS?

Login to Send Author a Private Message

Report a problem with this Dataset