UltraPse
UltraPse generates fixed-length numerical vector representations of biological sequences to enable prediction of biological functions and cellular attributes.
Key Features:
- Fixed-length conversion: Converts variable-length biological sequences into fixed-length numerical vectors suitable for downstream analysis.
- Multiple representation modes: Implements multiple existing sequence representation modes for diverse analytical needs.
- User-defined modes: Allows definition of custom representation modes, physicochemical properties, and sequence types.
- Extensibility: Provides a modular framework to integrate new representation modes without rewriting existing programs.
- Support for diverse sequence types: Accommodates various types of biological sequences through customizable representations.
- Computational efficiency: Optimized for high computational speed in generating sequence representations.
Scientific Applications:
- Function prediction: Producing input features for predicting biological functions from sequence data.
- Cellular attribute inference: Enabling prediction of cellular attributes based on sequence-derived vectors.
- Representation development and evaluation: Facilitating development, customization, and benchmarking of novel sequence representation modes for sequence analysis and downstream predictive tasks.
Methodology:
Generates fixed-length numerical vectors from variable-length sequences by applying multiple sequence representation modes and permitting user-defined representation modes, physicochemical properties, and sequence types.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Programming Languages:
- C++
- Added:
- 7/30/2018
- Last Updated:
- 12/10/2018
Operations
Publications
Du P, Zhao W, Miao Y, Wei L, Wang L. UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. International Journal of Molecular Sciences. 2017;18(11):2400. doi:10.3390/ijms18112400. PMID:29135934. PMCID:PMC5713368.
DOI: 10.3390/ijms18112400
PMID: 29135934
PMCID: PMC5713368
Funding: - National Natural Science Foundation of China: 31401132, 61005041
- Tianjin Natural Science Foundation: 12JCQNJC02300