Large-Scale Sequence Stratigraphy of the Phanerozoic of the San Juan Mountain Region, Colorado

1991 ◽  
Author(s):  
Donald E. Owen
Keyword(s):  
Sequence Stratigraphy ◽  
Large Scale ◽  
Mountain Region ◽  
San Juan ◽  
Scale Sequence

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.

scholarly journals Plasmid Tagging for Efficient Large-Scale Sequence Completion of Entire Clone Inserts

BioTechniques ◽  
2003 ◽  
Vol 34 (3) ◽  
pp. 604-608
Author(s):  
Alan A. Jackson ◽  
Glenn K. Fu
Keyword(s):  
Large Scale ◽  
Scale Sequence
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