UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences

Abstract

With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

“…[ 93 ], and UltraPse 11 https://github.com/pufengdu/UltraPse . software [ 94 ] which allows all possible sequence representation modes for user-defined sequence types. Here, UltraPse source code has been downloaded and run locally on our ubuntu platform to extract pseudo amino acid composition (Type I General PseAAC) for each protein sequence in the benchmark datasets.…”

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

“…In this section, we describe two lncRNA features and two protein features, based on lncRNA sequences, protein sequences and known lncRNA-protein interactions. On one hand, a great number of features [30–36] can be extracted from lncRNAs sequences and proteins sequences, and feature-extraction tools such as Pse-in-One[37], BioSeq-Analysis[38], repRNA[39] [40], iMiRNA-PseDPC [41] and UltraPse [42] have been available. One the other hand, known lncRNA-protein interactions can bring features to describe lncRNAs and proteins.…”

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

“…However, complicated strategies are usually needed to integrate knowledge into the models. Now that a huge amount of data from multiple omics, such as transcriptomics, metabonomics, have been accumulated and there are many feature extracting methods (Iqbal et al, 2014;Liu et al, 2015;Du et al, 2017;Liu et al, 2017;Gao and Wu 2018;Wang et al, 2020), some researchers regarded the identification of enzyme candidates as the catalytic and non-catalytic classification problem and built models to classify protein sequences or encoding genes into either catalytic or non-catalytic by using machine learning algorithms such as support vector machine (SVM), K-nearest neighbors (KNN), Bayesian, and RF (Teng et al, 2010;Halperin et al, 2008;Ferrari and Mitchell 2014;Nagao et al, 2014;Amidi et al, 2017). The workflow for classifying protein sequences as catalytic and non-catalytic protein sequences is illustrated in Figure 1.…”