Normalized Feature Vectors: A Novel Alignment-Free Sequence Comparison Method Based on the Numbers of Adjacent Amino Acids

2013 ◽  
Vol 10 (2) ◽  
pp. 457-467 ◽  
Author(s):  
De-Shuang Huang ◽  
Hong-Jie Yu
Keyword(s):  
Amino Acids ◽  
Sequence Comparison ◽  
Comparison Method ◽  
Feature Vectors ◽  
Alignment Free

Protein map: An alignment-free sequence comparison method based on various properties of amino acids

Gene ◽  
2011 ◽  
Vol 486 (1-2) ◽  
pp. 110-118 ◽  
Author(s):  
Chenglong Yu ◽  
Shiu-Yuen Cheng ◽  
Rong L. He ◽  
Stephen S.-T. Yau
Keyword(s):  
Amino Acids ◽  
Sequence Comparison ◽  
Comparison Method ◽  
Alignment Free ◽  
Protein Map

Statistical considerations underpinning an alignment-free sequence comparison method

2010 ◽  
Vol 39 (3) ◽  
pp. 325-335
Author(s):  
Junmei Jing ◽  
Conrad J. Burden ◽  
Sylvain Forêt ◽  
Susan R. Wilson
Keyword(s):  
Sequence Comparison ◽  
Comparison Method ◽  
Alignment Free

scholarly journals GUTSS: An Alignment-Free Sequence Comparison Method for Use in Human Intestinal Microbiome and Fecal Microbiota Transplantation Analysis

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0158897 ◽  
Author(s):  
Mitchell J. Brittnacher ◽  
Sonya L. Heltshe ◽  
Hillary S. Hayden ◽  
Matthew C. Radey ◽  
Eli J. Weiss ◽  
...  
Keyword(s):  
Sequence Comparison ◽  
Fecal Microbiota Transplantation ◽  
Comparison Method ◽  
Fecal Microbiota ◽  
Intestinal Microbiome ◽  
Alignment Free

scholarly journals SAINT: automatic taxonomy embedding and categorization by Siamese triplet network

2021 ◽  
Author(s):  
Yang Young Lu ◽  
Yiwen Wang ◽  
Fang Zhang ◽  
Jiaxing Bai ◽  
Ying Wang
Keyword(s):  
Sequence Analysis ◽  
Sequence Comparison ◽  
Large Scale ◽  
Sequence Data ◽  
Comparison Method ◽  
Supplementary Information ◽  
Data Alignment ◽  
Alignment Free ◽  
Comparison Methods ◽  
Real World Datasets

AbstractMotivationUnderstanding the phylogenetic relationship among organisms is the key in contemporary evolutionary study and sequence analysis is the workhorse towards this goal. Conventional approaches to sequence analysis are based on sequence alignment, which is neither scalable to large-scale datasets due to computational inefficiency nor adaptive to next-generation sequencing (NGS) data. Alignment-free approaches are typically used as computationally effective alternatives yet still suffering the high demand of memory consumption. One desirable sequence comparison method at large-scale requires succinctly-organized sequence data management, as well as prompt sequence retrieval given a never-before-seen sequence as query.ResultsIn this paper, we proposed a novel approach, referred to as SAINT, for efficient and accurate alignment-free sequence comparison. Compared to existing alignment-free sequence comparison methods, SAINT offers advantages in two aspects: (1) SAINT is a weakly-supervised learning method where the embedding function is learned automatically from the easily-acquired data; (2) SAINT utilizes the non-linear deep learning-based model which potentially better captures the complicated relationship among genome sequences. We have applied SAINT to real-world datasets to demonstrate its empirical utility, both qualitatively and quantitatively. Considering the extensive applicability of alignment-free sequence comparison methods, we expect SAINT to motivate a more extensive set of applications in sequence comparison at large scale.AvailabilityThe open source, Apache licensed, python-implemented code will be available upon acceptance.Supplementary informationSupplementary data are available at Bioinformatics online.

Extraction of high quality k-words for alignment-free sequence comparison

2014 ◽  
Vol 358 ◽  
pp. 31-51 ◽  
Author(s):  
Upuli Gunasinghe ◽  
Damminda Alahakoon ◽  
Susan Bedingfield
Keyword(s):  
Sequence Comparison ◽  
High Quality ◽  
Alignment Free
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