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Other literature type . 2019
License: CC BY
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Other literature type . 2019
License: CC BY
Data sources: Datacite
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Additional file 2: of Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs

Authors: Phelan, Jody E.; O’Sullivan, Denise; Machado, Diana; Ramos, Jorge; Yaa Oppong; Campino, Susana; O’Grady, Justin; +5 Authors

Additional file 2: of Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs

Abstract

Table S1. New features in TBProfiler. Table S2. Distribution of drug resistance types by lineage. Table S3. Number of tested isolates for each drug. Table S4. ENA project codes of isolates used in the study. Table S5. Raw sequence data and mapping statistics for the three MinION sequenced isolates. Table S6. Number of Homozygous and Heterozygous calls per target. Table S7. Predictive performance of the different libraries. Figure S1. Schematic highlighting the main steps in the TBProfiler pipeline. Figure S2. Map showing the geographic origin and the resistance types of isolates used in this study. Figure S3. Example of the report output for an isolate. (PDF 1169 kb)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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