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Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data

Overview of attention for article published in Microbiome, January 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

blogs
2 blogs
twitter
33 tweeters
facebook
2 Facebook pages
wikipedia
1 Wikipedia page
googleplus
2 Google+ users

Citations

dimensions_citation
48 Dimensions

Readers on

mendeley
140 Mendeley
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Title
Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data
Published in
Microbiome, January 2017
DOI 10.1186/s40168-016-0224-8
Pubmed ID
Authors

Kelly M. Robinson, Jonathan Crabtree, John S. A. Mattick, Kathleen E. Anderson, Julie C. Dunning Hotopp

Abstract

A variety of bacteria are known to influence carcinogenesis. Therefore, we sought to investigate if publicly available whole genome and whole transcriptome sequencing data generated by large public cancer genome efforts, like The Cancer Genome Atlas (TCGA), could be used to identify bacteria associated with cancer. The Burrows-Wheeler aligner (BWA) was used to align a subset of Illumina paired-end sequencing data from TCGA to the human reference genome and all complete bacterial genomes in the RefSeq database in an effort to identify bacterial read pairs from the microbiome. Through careful consideration of all of the bacterial taxa present in the cancer types investigated, their relative abundance, and batch effects, we were able to identify some read pairs from certain taxa as likely resulting from contamination. In particular, the presence of Mycobacterium tuberculosis complex in the ovarian serous cystadenocarcinoma (OV) and glioblastoma multiforme (GBM) samples was correlated with the sequencing center of the samples. Additionally, there was a correlation between the presence of Ralstonia spp. and two specific plates of acute myeloid leukemia (AML) samples. At the end, associations remained between Pseudomonas-like and Acinetobacter-like read pairs in AML, and Pseudomonas-like read pairs in stomach adenocarcinoma (STAD) that could not be explained through batch effects or systematic contamination as seen in other samples. This approach suggests that it is possible to identify bacteria that may be present in human tumor samples from public genome sequencing data that can be examined further experimentally. More weight should be given to this approach in the future when bacterial associations with diseases are suspected.

Twitter Demographics

The data shown below were collected from the profiles of 33 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 139 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 20%
Student > Ph. D. Student 28 20%
Student > Master 16 11%
Unspecified 8 6%
Student > Bachelor 7 5%
Other 22 16%
Unknown 31 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 24%
Agricultural and Biological Sciences 27 19%
Medicine and Dentistry 14 10%
Unspecified 8 6%
Immunology and Microbiology 7 5%
Other 12 9%
Unknown 39 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 13 September 2022.
All research outputs
#896,454
of 22,675,759 outputs
Outputs from Microbiome
#268
of 1,417 outputs
Outputs of similar age
#21,569
of 417,565 outputs
Outputs of similar age from Microbiome
#12
of 37 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,417 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.0. This one has done well, scoring higher than 81% 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 417,565 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.