Title :
VGA: A method for viral quasispecies assembly from ultra-deep sequencing data
Author :
Mangul, Serghei ; Wu, Nicholas C. ; Mancuso, Nicholas ; Zelikovsky, Alexander ; Ren Sun ; Eskin, Eleazar
Author_Institution :
Comput. Sci. Dept., Univ. of California, Los Angeles, Los Angeles, CA, USA
Abstract :
We present VGA, an accurate method for viral quasispecies assembly from ultra-deep sequencing data. The proposed method consists of a high-fidelity sequencing protocol and an accurate method for viral quasispecies assembly, referred to as Viral Genome Assembler (VGA). The proposed protocol is able to eliminate sequencing errors by using individual barcodes attached to the sequencing fragments. Results on both synthetic and real datasets show that our method able to accurately assemble HIV viral quasispecies and detect rare quasispecies previously undetectable due to sequencing errors. VGA outperforms state-of-the-art methods for the viral assembly. Furthermore, our method is the first viral assembly method which scales to millions of sequencing reads. Our tool VGA is freely available at http://genetics.cs.ucla.edu/vga/
Keywords :
diseases; genomics; medical computing; microorganisms; HIV viral quasispecies; VGA; Viral Genome Assembler; high-fidelity sequencing protocol; individual barcodes; rare quasispecies; real datasets; sequencing errors; sequencing fragments; synthetic datasets; ultradeep sequencing data; viral assembly method; viral quasispecies assembly; Assembly; Computer science; Human immunodeficiency virus; Protocols; Sequential analysis; Sociology; Statistics; NGS; error-correction protocol; viral assembly; viral quasispecies;
Conference_Titel :
Computational Advances in Bio and Medical Sciences (ICCABS), 2014 IEEE 4th International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4799-5786-6
DOI :
10.1109/ICCABS.2014.6863932