Tuesday, December 8, 2015


A quick look at Google Scholar the other day showed me that there have been over 1000 citations to my papers. The other citation metrics were interesting, too, like the h-index (currently 14, meaning that 14 of my papers have been cited 14 times or more) and the i10 index (currently 19, meaning that I have had 19 papers cited 10 times or more). These numbers only take into account the peer-reviewed publications, but I do have quite a number of additional non-peer-reviewed works. If I count up the peer-reviewed papers plus my other types of publications (abstracts, book reviews, annotated bibliographies, etc.), I now have over 100 publications!

As an aside, one metric having to do with publication that is commonly used is the 'impact factor.' Although this number is meant to evaluate journals as a whole, it is often used by institutions to evaluate researchers, using the journals in which they publish as a proxy for their overall impact as scientists. This is, of course, highly controversial. One aspect that makes it even more problematic is that metrics like impact factor can be highly dependent on the database used for compiling the raw citation data. My wife published a paper a few years back in which this issue was explored quantitatively (Gray & Hodkinson 2008). In a set of 50 journals, many rankings were seen to differ by numbers that ran up into the double digits depending on the database used, calling into question the accuracy of any given journal's impact factor.

- Brendan



Gray, E, and S. Z. Hodkinson. 2008. Comparison of Journal Citation Reports and Scopus Impact Factors for Ecology and Environmental Sciences Journals. Issues in Science and Technology Librarianship DOI:10.5062/F4FF3Q9G.
View publication (website)

Saturday, November 21, 2015

Skin Virome

The human skin is covered in many microbes, and until recently, the viral component has remained poorly understood. A recent paper presents the research into the human skin virome that I worked on while in the Dermatology Department at the University of Pennsylvania.

A cross-section of the skin (showing viruses and other microbes).

Thanks to Geof Hannigan for conceptualizing and taking the lead on this project. You can read his description of it and an overview of the results here:

There is also a good article for the general public here that describes the work:

- Brendan



Hannigan, G. D., J. S. Meisel, A. S. Tyldsley, Q. Zheng, B. P. Hodkinson, A. J. SanMiguel, S. Minot, F. D. Bushman, and E. A. Grice. 2015. The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome. mBio 6(5): e01578-15.
Download publication (PDF file)

Saturday, October 17, 2015

patPRO: Visualizing Longitudinal Microbiome Data

Recently some of my collaborators from the University of Pennsylvania and I released a new R package on CRAN (Comprehensive R Archive Network). It is called 'patPRO' (short for 'Patient Profiler'), and provides a number of functions to facilitate the visualization of longitudinal microbiome data. Although we developed it for examining changes in human microbiomes over time, it could just as easily be used for changes in microbial populations in water supplies, agricultural soils, or livestock, just to name a few possibilities.

This is an example of a plot that can be made, simultaneously showing changes in multiple dimensions of the microbiome:

Here is the official page for the package, with links to the reference manual and source code:

This link provides a version of the reference manual in a series of webpages:

- Brendan



Hannigan, G. D., M. A. Loesche, B. P. Hodkinson, S. Mehta, and E. A. Grice. 2015. patPRO: Visualizing Temporal Microbiome Data. R package version 1.0.0. http://cran.r-project.org/web/packages/patPRO/index.html
Download package (website)


Note: One implementation of R gave me an error indicating that 'stringi' was needed when I tried to install 'patPRO.' After installing 'stringi,' I ran across no further issues with patPRO.

Saturday, September 26, 2015

Insights into Parmeliaceae

The results of a large-scale collaborative Parmeliaceae systematics project, in which I participated, were recently published in New Phytologist. To read the article, check out the link at the bottom of this post; I have pasted the abstract below.
  • We studied the evolutionary history of the Parmeliaceae (Lecanoromycetes, Ascomycota), one of the largest families of lichen-forming fungi with complex and variable morphologies, also including several lichenicolous fungi.
  • We assembled a six-locus data set including nuclear, mitochondrial and low-copy protein-coding genes from 293 operational taxonomic units (OTUs).
  • The lichenicolous lifestyle originated independently three times in lichenized ancestors within Parmeliaceae, and a new generic name is introduced for one of these fungi. In all cases, the independent origins occurred c. 24 million yr ago. Further, we show that the Paleocene, Eocene and Oligocene were key periods when diversification of major lineages within Parmeliaceae occurred, with subsequent radiations occurring primarily during the Oligocene and Miocene.
  • Our phylogenetic hypothesis supports the independent origin of lichenicolous fungi associated with climatic shifts at the Oligocene–Miocene boundary. Moreover, diversification bursts at different times may be crucial factors driving the diversification of Parmeliaceae. Additionally, our study provides novel insight into evolutionary relationships in this large and diverse family of lichen-forming ascomycetes.
- Brendan



Divakar, P. K., A. Crespo, M. Wedin, S. D. Leavitt, L. Myllys, B. McCune, T. Randlane, J. W. Bjerke, Y. Ohmura, I. Schmitt, C. G. Boluda, D. Alors, B. Roca-Valiente, R. Del-Prado, Constantino Ruibal, K. Buaruang, J. Núñez-Zapata, G. Amo de Paz, V. J. Rico, M. C. Molina, J. A. Elix, T. L. Esslinger, I. K. K. Tronstad, H. Lindgren, D. Ertz, C. Gueidan, L. Saag, T. Tõrra, G. Singh, F. Dal Grande, S. Parnmen, A. Beck, M. N. Benatti, D. Blanchon, M. Candan, P. Clerc, T. Goward, M. Grube, B. P. Hodkinson, J.-S. Hur, G. Kantvilas, P. M. Kirika, J. Lendemer, J.-E. Mattsson, M. I. Messuti, J. Miadlikowska, M. Nelsen, J. I. Ohlson, S. Pérez-Ortega, A. Saag, H. J. M. Sipman, M. Sohrabi, A. Thell, G. Thor, C. Truong, R. Yahr, D. K. Upreti, D. L. Hawksworth, P. Cubas, and H. T. Lumbsch. 2015. Evolution of complex symbiotic relationships in a morphologically derived family of lichen-forming fungi. New Phytologist DOI: 10.1111/nph.13553.
Download publication (PDF file)

Monday, August 31, 2015

New Job

I have officially started a new job at Janssen Research & Development, a member of the Johnson & Johnson family of companies! I am working within Oncology Translational Research as a Post-Doctoral Fellow in Computational Biology. With Janssen, I look forward to many opportunities to do groundbreaking research and really make an impact in people's lives!

- Brendan

Friday, July 31, 2015

PICS-Ord on Mac

In a previous post, I wrote about how to get the program PICS-Ord to run easily on a PC. Other operating systems presented some special problems associated with getting the different dependencies to find one another. I recently found Homebrew, which solves a lot of the problems typically found with Mac computers, and I put together some instructions for running PICS-Ord on the current Mac OS.

Running PICS-Ord on Mac OS X (10.9 or 10.10):

(1) Download and install R from here:
Open the file and follow the instructions.

(2) Install CMake and Boost using Homebrew; use the following three commands in the terminal to first install Homebrew and then the two Ngila dependencies:
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew install cmake
brew install boost --with-python

(3) Install Ngila by using the following commands in the terminal (if you already have wget, you can ignore the first line):
brew install wget
wget http://scit.us/projects/files/ngila/Releases/ngila-release.tar.gz
tar zxvf ngila-release.tar.gz
cd ngila-1.3-release/
cmake . && make
make install
cd ..

(4) Download PICS-Ord from here:
Open the zip file to create the picsord directory.

(5) Run PICS-Ord by moving the FASTA files into the picsord directory, navigating to the picsord directory in the terminal, and running the following:
Rscript picsord.R region_1.fas > region_1.phy
Rscript picsord.R region_2.fas > region_2.phy
Rscript picsord.R region_3.fas > region_3.phy
Rscript picsord.R region_4.fas > region_4.phy

- Brendan



Lücking, R., B. P. Hodkinson, A. Stamatakis, and R. A. Cartwright. 2011. PICS-Ord: Unlimited Coding of Ambiguous Regions by Pairwise Identity and Cost Scores Ordination. BMC Bioinformatics 12: 10.
Download publication (PDF file)
Download R-based program (zipped program package)
View program wiki (website)

Friday, June 12, 2015

Shared Microbiota of Humans and Their Pets

I recently co-authored a paper from a study in which we examined the microbiota of humans and their pets.  The full text can be seen here:

Abstract -

Background: Staphylococcus aureus and other coagulase-positive staphylococci (CPS) colonize skin and mucous membrane sites and can cause skin and soft tissue infections (SSTIs) in humans and animals. Factors modulating methicillin-resistant S. aureus (MRSA) colonization and infection in humans remain unclear, including the role of the greater microbial community and environmental factors such as contact with companion animals. In the context of a parent study evaluating the households of outpatients with community MRSA SSTI, the objectives of this study were 1) to characterize the microbiota that colonizes typical coagulase-positive Staphylococcus spp. carriage sites in humans and their companion pets, 2) to analyze associations between Staphylococcus infection and carriage and the composition and diversity of microbial communities, and 3) to analyze factors that influence sharing of microbiota between pets and humans.

Results:We enrolled 25 households containing 56 pets and 30 humans. Sampling locations were matched to anatomical sites cultured by the parent study for MRSA and other CPS. Bacterial microbiota were characterized by sequencing of 16S ribosomal RNA genes. Household membership was strongly associated with microbial communities, in both humans and pets. Pets were colonized with a greater relative abundance of Proteobacteria, whereas people were colonized with greater relative abundances of Firmicutes and Actinobacteria. We did not detect differences in microbiota associated with MRSA SSTI, or carriage of MRSA, S. aureus or CPS. Humans in households without pets were more similar to each other than humans in pet-owning households, suggesting that companion animals may play a role in microbial transfer. We examined changes in microbiota over a 3-month time period and found that pet staphylococcal carriage sites were more stable than human carriage sites.

Conclusions: We characterized and identified patterns of microbiota sharing and stability between humans and companion animals. While we did not detect associations with MRSA SSTI, or carriage of MRSA, S. aureus or CPS in this small sample size, larger studies are warranted to fully explore how microbial communities may be associated with and contribute to MRSA and/or CPS colonization, infection, and recurrence.



Misic, A. M., M. F. Davis, A. S. Tyldsley, B. P. Hodkinson, P. Tolomeo, B. Hu, I. Nachamkin, E. Lautenbach, D. O. Morris, and E. A. Grice. 2015. The shared microbiota of humans and companion animals as evaluated from Staphylococcus carriage sites. Microbiome 3: 2.
Download publication (PDF file)