Parallel Linear Space Algorithm for Large-Scale Sequence Alignment

2005 ◽  
pp. 1207-1216 ◽  
Author(s):  
Eric Li ◽  
Cheng Xu ◽  
Tao Wang ◽  
Li Jin ◽  
Yimin Zhang
Keyword(s):  
Linear Space ◽  
Sequence Alignment ◽  
Large Scale ◽  
Scale Sequence

scholarly journals Activity based Deubiquitinase profiling reveals OTUB1 from oryza sativa Hydrolyzes Linear Ubiquitin Chains by Bi-modal Activation

2021 ◽  
Author(s):  
Lining Lu ◽  
Jiawei Wang ◽  
Ziqing Mei ◽  
Feng Wang
Keyword(s):  
Innate Immunity ◽  
Oryza Sativa ◽  
Immune Responses ◽  
Sequence Alignment ◽  
Species Distribution ◽  
Large Scale ◽  
Human Cells ◽  
One Pot ◽  
Hydrolysis Of ◽  
Scale Sequence

Abstract Met1 type ubiquitination and deubiquitination are involved in the regulation of many fundamental processes such as inflammation and innate immunity, and their interference by pathogens can suppress immune responses in human cells. However, no plant-derived deubiquitinases (DUBs) against Met1 ubiquitin chains have been reported. Using a dehydroalanine (DHA)-bearing Met1 diubiquitin (Met1-diUb) suicide probe, synthesized in one-pot, we identified OTUB1 from Oryza sativa (OsOTUB1) and uncovered its preference for Met1 ubiquitin chains. Also, by resolving the apo structure of OsOTUB1 and its complex with Ub or Met1-diUb, we demonstrated that OsOTUB1 hydrolyses Met1 ubiquitin chains by activation of both the distal and proximal ubiquitin, which is different from OTULIN and expands our mechanistic understanding of the DUB-mediated hydrolysis of Met1 ubiquitin chains. Through large-scale sequence alignment and hydrolysis experiments, two sites in the S1' pocket of the OTUB subfamily (OTUBs) were found to determine the hydrolytic ability of OTUBs against Met1 ubiquitin chains, regardless of species. Furthermore, by analyzing the species distribution of OTUBs capable of hydrolyzing Met1 ubiquitin chains, we found that whereas this activity does not exist in metazoans, it is conserved in green plants (Viridiplantae). This discovery may inform studies of the differentiation between primitive plants and animals.

Response to Comment on "Large-Scale Sequence Analysis of Avian Influenza Isolates"

Science ◽  
2006 ◽  
Vol 313 (5793) ◽  
pp. 1573c-1573c ◽  
Author(s):  
J. C. Obenauer ◽  
Y. Fan ◽  
C. W. Naeve
Keyword(s):  
Sequence Analysis ◽  
Avian Influenza ◽  
Large Scale ◽  
Scale Sequence

Fast two-stage phasing of large-scale sequence data

2021 ◽  
Author(s):  
Brian L. Browning ◽  
Xiaowen Tian ◽  
Ying Zhou ◽  
Sharon R. Browning
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Two Stage ◽  
Scale Sequence

Information enhancement methods for large scale sequence analysis

1993 ◽  
Vol 17 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Jean-Michel Claverie ◽  
David J. States
Keyword(s):  
Sequence Analysis ◽  
Large Scale ◽  
Scale Sequence

scholarly journals A parallel computational framework for ultra-large-scale sequence clustering analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 380-388 ◽  
Author(s):  
Wei Zheng ◽  
Qi Mao ◽  
Robert J Genco ◽  
Jean Wactawski-Wende ◽  
Michael Buck ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Computational Framework ◽  
Speed Up ◽  
Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

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