NDLI: Efficient detection of viral transmission with threshold-based methods

Content Provider	IEEE Xplore Digital Library
Author	Rytsareva, I. Campo, D.S. Zheng, Y. Sims, S. Tetik, C. Chirag, J. Chockalingam, S.P. Thankachan, S.V. Sue, A. Aluru, S. Khudyakov, Y.
Copyright Year	2015
Description	Author affiliation: Mol. Epidemiology & Bioinf. Lab., Centers for Disease Control & Prevention, Atlanta, GA, USA (Rytsareva, I.; Campo, D.S.; Tetik, C.; Thankachan, S.V.; Khudyakov, Y.) \|\| Dept. of Comput. Sci. & Eng., IIT Bombay, Mumbai, India (Zheng, Y.) \|\| Sch. of Comput. Sci. & Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Sims, S.; Chirag, J.; Chockalingam, S.P.; Aluru, S.) \|\| Centers for Disease Control & Prevention, Nat. Center for Emerging & Zoonotic Infectious Diseases, Atlanta, GA, USA (Sue, A.)
Abstract	Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed. The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples. For instance, our relatively small dataset of 401 samples, a total of 80200 pairwise sample comparisons must be performed, which account for 4.56 x 1010 pairwise sequence comparisons. We present a fast and efficient three-step filtering strategy that removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
Starting Page	1
Ending Page	6
File Size	367084
Page Count	6
File Format	PDF
e-ISBN	9781467396639
DOI	10.1109/ICCABS.2015.7344723
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Sequential analysis Filtering Scientific computing Outbreak detection Genetics Bioinformatics Public health Next-Generation Sequencing Diseases
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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