The microBEnet blog

Worth a read: Post by @SharonJPeacock & Claire Chewapreecha on tracking pathogen/biothreat behind #melioidosis

Quick post here.  Nice short post in the On Health Blog at Biomed Central on studies of the population genomics of Burkholderia.

Melioidosis is a frequently fatal infectious disease caused by a bacterium (Burkholderia pseudomallei) found in soil in certain parts of the world. We have known about melioidosis for many years, but it’s only in the last 25 years that we have started to understand it better. So, what’s changed?

Source: Tracking the movement of a deadly pathogen and biothreat agent – On Health

Important story by @HelenBranswell in @statnews: Why the advice to take all your antibiotics may be wrong

Quick post. This is a very important read by Helen Branswell in STAT for those interested in antibiotic resistance.

Patients are told to finish their antibiotics, even if they feel better, but that guidance may be exacerbating antibiotic resistance, some experts say.

Source: Why the advice to take all your antibiotics may be wrong

 


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Training the trainers: training for teaching computational workshops

This is a great idea —  Instructor training for teaching computational workshopsFrom my UC Davis colleague Titus Brown. I have copied details below.

As part of our Summer Institute in Data Intensive Biology, we will be running a week-long instructor training from June 18 to June 25 at the University of California, Davis.

The instructor training will include the following —

  • Hands-on training in good pedagogical practice!
  • Become a certified Software/Data Carpentry instructor!
  • Learn to repurpose and remix online training materials for your own needs!

This workshop is intended for people interested in teaching, reusing and repurposing the Software Carpentry, Data Carpentry, or Analyzing Next-Generation Sequencing Data materials. We envision this course being most useful to current teaching-intensive faculty, future teachers and trainers, and core facilities that are developing training materials.

The workshop fee will be $350 for the week. Applications will close March 17th.

Please see http://ivory.idyll.org/dibsi/instructor-training.html for more information, and contact dibsi.training@gmail.com if you have questions or suggestions.

Call for Papers MoBE 2017 Microbiome Special Issue

Call for Papers: MoBE 2017 Special Issue of BioMed Central’s Microbiome Journal  (Submission Guidelines)

We invite submissions of MoBE papers highlighting recent research and emerging hot topics along the theme of “MoBE Research to Applications” for our peer-reviewed MoBE special issue.

Publishing charges are sponsored by the MoBE meeting and BioMed Central’s Microbiome Journal. This special issue will be available by October 1st, 2017.

Please share this announcement among your MoBE colleagues !! 

Timeline:

March 1: paper topic submission (2-3 sentence outline).

These can be submitted via the MoBE 2017 contact form.

June 1:        full paper submission deadline

July 30:      reviews complete, notice to submitters

August 30: revisions due to BioMed Central’s Microbiome Journal

From genomes to phenotypes: Traitar, the microbial trait analyzer

There is an increasing number of studies with a large number genomes recovered from isolate, metagenome, or single cell sequencing. To bridge the gap between the available genome sequences and available phenotype information, we have developed Traitar, a bioinformatics software to phenotype bacteria based on their genome sequence (see workflow below) . Traitar includes phenotype models for predicting 67 traits such as the use of different substrates as carbon and energy sources, oxygen requirement, morphology, and antibiotic susceptibility, and it provides the means to inspect the protein families (Pfams) that gave rise to these phenotype predictions.

 

In a paper recently published in mSystems (https://doi.org/10.1128/mSystems.00101-16), we describe the application of Traitar to two novel Clostridiales species with partical genomes recovered from metagenome shotgun sequencing of commercial biogas reactors. Traitar could verify an expert  metabolic reconstruction and furthermore pinpoint additional traits that were missing in the manual metabolic reconstruction.

The software is easy to install and run. It only requires a nucleotide or protein FASTA file per sample as input. Users can inspect the phenotyping results of  from Traitar  for their genome sequences of two prediction modes (phypat and phypat+PGL) through heatmaps (see example of Traitar applied to single-assembled genomes below; Fig 5 in Traitar paper) and flat text files. For phenotyping a single genome, Traitar only requires a couple of minutes. Computation is multithreaded (parallelized) and scales to data sets with hundreds of genomes. We also offer a web service for data sets of up to around ten genomes. If you have larger data sets and troubles running the Traitar stand-alone tool get in touch with us (contact details below).

To build and validate the phenotype models in Traitar, we have used phenotype data from the Global Infectious Disease and Epidemiology Online Network (GIDEON) and Bergey’s Systematic Bacteriology. Internally, the models were created using a machine learning method, namely L1 regularized L2 loss support vector machine trained on information about the presence and absence of protein families as well as ancestral protein family gains and losses.

Some word of advice when applying Traitar for phenotyping your genomes:
The training data from GIDEON and Bergey’s does not cover all known bacterial taxa and some with more data than others. Thus, some of the phenotypes might be realized with different protein families in taxa that are less well represented here and classification accuracy for these taxa be less than for others. Since Traitar provides the Pfam families responsible for your phenotype prediction, you could cross reference the phenotypes predicted by Traitar and the associated protein families with a targeted metabolic reconstruction approach.

We are currently working on incorporating new phenotypes and on further extending the existing phenotype models. For instance, we will apply Traitar to several hundred isolate genomes of the pathogen Pseudomonas aeruginosa to learn phenotype models of antibiotic resistance. We will keep updating the software and models, so please regularly check out our GitHub or Twitter. Traitar is designed to easily incorporate new prediction models. If you have data for phenotypes of interest please get in touch with us. We’re  also preparing a stand-alone software to allow users to train their own phenotype models.

Aaron Weimann: @aaron_weimann
Andreas Bremges: @abremges
Alice C. McHardy: @alicecarolyn

GitHub: https://github.com/hzi-bifo/traitar
Web service: https://research.bifo.helmholtz-hzi.de/webapps/wa-webservice/pipe.php?pr=traitar
Web service and general BIFO software support: bifo-software@helmholtz-hzi.de

Interesting long read on Healthy Building efforts by @Google

Got pointed to a very interesting long read story by Erica Hartmann on Twitter:

The story is by Diana Budds at the FastCoDesign. It is definitely worth a look.

Our buildings can make us sick. In Google, the movement for healthy architecture may have gotten its most powerful ally yet.

Source: Google’s Plan To Make Our Buildings Less Poisonous | Co.Design | business + design

Of possible interest: #PLOSOne paper on #microbiome sharing between children, livestock and household surfaces

Quick post here:

Of possible interest: Microbiome sharing between children, livestock and household surfaces in western Kenya in PLOS One.  Abstract below

The gut microbiome community structure and development are associated with several health outcomes in young children. To determine the household influences of gut microbiome structure, we assessed microbial sharing within households in western Kenya by sequencing 16S rRNA libraries of fecal samples from children and cattle, cloacal swabs from chickens, and swabs of household surfaces. Among the 156 households studied, children within the same household significantly shared their gut microbiome with each other, although we did not find significant sharing of gut microbiome across host species or household surfaces. Higher gut microbiome diversity among children was associated with lower wealth status and involvement in livestock feeding chores. Although more research is necessary to identify further drivers of microbiota development, these results suggest that the household should be considered as a unit. Livestock activities, health and microbiome perturbations among an individual child may have implications for other children in the household.

 

ISIAQ Healthy Buildings 2017 Asia

Of interest to many microBEnet readers.   From the website:

On behalf of the Organizing Committee, we cordially invite you to attend the Healthy Buildings 2017-Asia (HB2017-Asia), being held from September 2 to 5, 2017, at the College of Medicine, National Cheng Kung University in Tainan, Taiwan. The HB2017-Asia inscribes itself on the path of a series of Conferences that, since 1988, have been organized around the world with a focus on the quality of the indoor environment viewed from a holistic perspective.