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Overview of attention for book
Attention for Chapter 4: NGSPanPipe: A Pipeline for Pan-genome Identification in Microbial Strains from Experimental Reads
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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6 X users

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Chapter title
NGSPanPipe: A Pipeline for Pan-genome Identification in Microbial Strains from Experimental Reads
Chapter number 4
Book title
Infectious Diseases and Nanomedicine III
Published in
Advances in experimental medicine and biology, January 2018
DOI 10.1007/978-981-10-7572-8_4
Pubmed ID
Book ISBNs
978-9-81-107571-1, 978-9-81-107572-8
Authors

Umay Kulsum, Arti Kapil, Harpreet Singh, Punit Kaur, Kulsum, Umay, Kapil, Arti, Singh, Harpreet, Kaur, Punit

Abstract

Recent advancements in sequencing technologies have decreased both time span and cost for sequencing the whole bacterial genome. High-throughput Next-Generation Sequencing (NGS) technology has led to the generation of enormous data concerning microbial populations publically available across various repositories. As a consequence, it has become possible to study and compare the genomes of different bacterial strains within a species or genus in terms of evolution, ecology and diversity. Studying the pan-genome provides insights into deciphering microevolution, global composition and diversity in virulence and pathogenesis of a species. It can also assist in identifying drug targets and proposing vaccine candidates. The effective analysis of these large genome datasets necessitates the development of robust tools. Current methods to develop pan-genome do not support direct input of raw reads from the sequencer machine but require preprocessing of reads as an assembled protein/gene sequence file or the binary matrix of orthologous genes/proteins. We have designed an easy-to-use integrated pipeline, NGSPanPipe, which can directly identify the pan-genome from short reads. The output from the pipeline is compatible with other pan-genome analysis tools. We evaluated our pipeline with other methods for developing pan-genome, i.e. reference-based assembly and de novo assembly using simulated reads of Mycobacterium tuberculosis. The single script pipeline (pipeline.pl) is applicable for all bacterial strains. It integrates multiple in-house Perl scripts and is freely accessible from https://github.com/Biomedinformatics/NGSPanPipe .

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 24%
Researcher 6 18%
Librarian 2 6%
Professor > Associate Professor 2 6%
Student > Ph. D. Student 2 6%
Other 3 9%
Unknown 11 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 21%
Agricultural and Biological Sciences 6 18%
Medicine and Dentistry 3 9%
Computer Science 2 6%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 14 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 June 2018.
All research outputs
#6,674,205
of 23,577,761 outputs
Outputs from Advances in experimental medicine and biology
#1,044
of 5,038 outputs
Outputs of similar age
#133,437
of 445,192 outputs
Outputs of similar age from Advances in experimental medicine and biology
#32
of 237 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 5,038 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 445,192 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 237 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.