Hans Zauner

**A new "multi-modal" data set on arm motion control, [published
recently in *GigaScience*](https://doi.org/10.1093/gigascience/giab043),
is an important contribution to develop robotic prosthetic devices and
other tools at the interface between human and machine.**

  
Robotic arms and other devices that support limb movements are getting
ever more sophisticated. New materials and new computational approaches
are paving the path for prosthetic devices that "blend in" with the
human organism. However, to navigate the interface between human and
machine, data is needed on all aspects of arm motion control, both in
healthy and disease conditions.

  
Developers of robotic arms and other human-machine interfaces that
support limb movements need quantitative data that describe how human
limbs operate during everyday tasks, such as lifting a cup of tea to
your lips or turning a page in a book.

## One of the biggest datasets on human motor control available {#one-of-the-biggest-datasets-on-human-motor-control-available .has-normal-font-size .wp-block-heading}

Although these tasks look trivial from the outside, the underlying
kinematics and involvement of diverse muscle groups is a complex affair;
even more if interaction with objects -- an apple, a cup, a page of a
book -- is involved. To really understand what is going on when we reach
for an apple and take a bite, for example, researchers need quantitative
information on arm movements and corresponding activity patterns of
muscles. They also need data from brain activity and other physiological
parameters connected to arm motion control.

<figure class="wp-block-image size-large">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/upperlimp.jpg"
class="wp-image-4052" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/upperlimp.jpg 720w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/upperlimp-300x244.jpg 300w"
sizes="(max-width: 720px) 100vw, 720px" width="720" height="585" />
</figure>

  
A collaboration coordinated by *[Istituto Italiano di
Tecnologia](https://www.iit.it/)*, with the participation of the
University of Pisa as well as of other scientists across Europe, has now
published one of the biggest datasets on human motor control available,
a "multi-modal" data collection of upper limb movements, both in healthy
and post-stroke conditions (read the *GigaScience* data note
[here](https://doi.org/10.1093/gigascience/giab043)). Corresponding
author Guiseppe Averta says,

"*It represents a previously unmatched source of information for the
research community, because it will enable a deeper analysis not only of
how the motor control works looking at a specific functional domains,
but also on the relationship between them.*"

  
The researchers met under the roof of
"[SoftPro](https://www.softpro.eu/)", an initiative with the goal to
"*study and design soft synergy-based robotics technologies to develop
new prostheses, exoskeletons, and assistive devices for upper limb
rehabilitation*". Averta says the aim is not only to develop new
technology and better performing devices, but also to contribute to
making the technology accessible to real people with real needs:

## Technology for real people with real needs {#technology-for-real-people-with-real-needs .has-normal-font-size .wp-block-heading}

*"It is the result of a strong integration and coordination effort.
U-Limb is now publicly available and published in the open access
journal GigaScience, coherently with the open approach to innovation
that characterized the Project*."

  
To go a bit into the details, the newly published article is based on 91
able-bodied and 65 post-stroke participants. The data combines tracking
of movements (kinematics) with data on muscle activity, heart rate and
brain function. This integration of different data sources is one of the
great strengths of the project, Averta explains:

  
*"It will be possible to study how certain cortical patterns reflect in
muscular activation, and how this influences the coordinate movement to
execute a certain task."*

<figure class="wp-block-image size-large">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/hand-1.jpg"
class="wp-image-4054" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/hand-1.jpg 478w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/07/hand-1-291x300.jpg 291w"
sizes="(max-width: 478px) 100vw, 478px" width="478" height="493" />
</figure>

  
To achieve such a multi-modal data collection, the researchers used
three protocols:

1\) **Participants performed a range of daily activities, such as
pointing at something**, grasping the handle of a suitcase, or removing
a tea bag from a cup. During these tasks, limb movements were recorded
on camera, based on defined marker positions, while muscle and brain
activity as well as heart rate were measured as well.

  
2) **Hand grasping was also studied in fMRI experiments.**

  
3) In addition, a device called **VPIT ("virtual peg insertion test")
was used to collect data on the coordination of arm and hand.**

To see how the VPIT works, watch this video from ETH RELAB:

<figure
class="wp-block-embed-youtube aligncenter wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio">
<div class="wp-block-embed__wrapper">
<div class="iframe">
<div id="player">

</div>
<div class="player-unavailable">
<h1 id="ein-fehler-ist-aufgetreten." class="message">Ein Fehler ist
aufgetreten.</h1>
<div class="submessage">
<a href="https://www.youtube.com/watch?v=TyJyd5uVN68"
target="_blank">Sieh dir dieses Video auf www.youtube.com an</a> oder
aktiviere JavaScript, falls es in deinem Browser deaktiviert sein
sollte.
</div>
</div>
</div>
</div>
</figure>

  
For some of the experiments, a subgroup of the participants wore a "full
body sensor suit", equipped with gyroscopes and accelerometers.  
The full data is openly available via the [Harvard dataverse
repository](https://doi.org/10.7910/DVN/FU3QZ9).

## Use cases for humans and robots {#use-cases-for-humans-and-robots .has-normal-font-size .wp-block-heading}

What are potential use cases for this new data? Averta explains more:

*"For example, a study of the coordination across joints could be used
to inform the design of under-actuated prostheses that can replace
missing limbs with a limited number of motors. On the other side, we can
exploit the knowledge of natural behavior to provide a comparison with
stroke patients, helping clinicians in assessing the pathology's
severity."*

There may even be applications in industrial robotics, for example in
robotic approaches where machines are trained to replicate human
behavior:

"*This would be an asset to develop manipulators, and in general
humanoid robots, that behave as a human would actually do in the same
situation."*

**Read the *GigaScience* Data Note:**

Averta G, Barontini F, Catrambone V, Haddadin S, Handjaras G, Held JPO,
Hu T, Jakubowitz E, Kanzler CM, Kühn J, Lambercy O, Leo A, Obermeier A,
Ricciardi E, Schwarz A, Valenza G, Bicchi A, Bianchi M. U-Limb: A
multi-modal, multi-center database on arm motion control in healthy and
post-stroke conditions. *Gigascience*. 2021 Jun 18;**10**(6):giab043.
doi:
[10.1093/gigascience/giab043](https://doi.org/10.1093/gigascience/giab043).

**Data available here:**

Averta G, Barontini F, Catrambone V, Haddadin S, Handjaras G, Held JPO,
Hu T, Jakubowitz E, Kanzler CM, Kühn J, Lambercy O, Leo A, Obermeier A,
Ricciardi E, Schwarz A, Valenza G, Bicchi A, Bianchi M. U-Limb: A
multi-modal, multi-center database on arm motion control in healthy and
post-stroke conditions. Harvard Dataverse, V3. 2020,
<https://doi.org/10.7910/DVN/FU3QZ9>

The post [Data on arm motion control for the human-machine
interface](http://gigasciencejournal.com/blog/arm-motion-control-data/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 A new “multi-modal” data set on arm motion control, published recently in
 <em>
  GigaScience
 </em>
 , is an important contribution to develop robotic prosthetic devices and other tools at the interface between human and machine.
</strong>
Robotic arms and other devices that support limb movements are getting ever more sophisticated.

Data on arm motion control for the human-machine interface

Scott Edmunds

*![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38-300x300.png){.wp-image-2126
.alignright loading="lazy" decoding="async" width="305" height="305"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38-300x300.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38-150x150.png 150w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38-768x768.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38-1024x1024.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-07-at-09.44.38.png 1532w"
sizes="(max-width: 305px) 100vw, 305px"}As a journal focussed on open
science we are big promoters of research parasites ([and research on
parasites](http://gigasciencejournal.com/blog/reproducible-research-resources-researching-parasites/)),
and try to feeds them with open data and tools. It is therefore
appropriate this is the second year GigaScience has supported and
sponsored the Research Parasite awards. In one of our
[Q&As](http://gigasciencejournal.com/blog/tag/qa/), organisers Casey
Greene and Brian Byrd tell us about the awards, how they came about, and
you can become a tropical research parasite by winning a travel grant to
the [Pacific Symposium on Biocomputing](http://psb.stanford.edu/) in
Hawaii.  
*  
*Casey Greene (CG) is an Assistant Professor of Systems Pharmacology and
Translational Therapeutics in the Perelman School of Medicine at the
University of Pennsylvania. His lab develops deep learning methods that
integrate distinct large-scale datasets to extract the rich and
intrinsic information embedded in such integrated data. J. Brian Byrd
(JBB) is an NIH-funded physician-scientist interested in high blood
pressure. His lab focuses on the clinical investigation of hypertension,
using proof of concept clinical trials, among other types of studies. In
addition, Dr. Byrd was an investigator for the SYMPLICITY-3 HTN pivotal
clinical trial of renal artery denervation for treatment-resistant high
blood pressure.*

**Tell us about the parasite award. What inspired you to set it up? What
sort of parasites are you looking for, and what can they win?**

**CG:** I, like many scientists, was annoyed at the tone of the [Longo
and Drazen
editorial](http://www.nejm.org/doi/full/10.1056/NEJMe1516564). The
behaviors that were described as parasitic, namely reanalyzing data and
potentially arriving at different conclusions, struck me as central to
the practice of good science and exactly what we try to teach students
at many phases of their training. Identifying a distinct interpretation
that is compatible with existing data is often an excellent start to a
qualifier exam. [Iddo Friedberg
tweeted](https://twitter.com/iddux/status/690563238979723267) a joke
about a research parasite award. It struck me as an excellent
opportunity to encourage, as opposed to discourage, these types of
behaviors, so I was happy to work to make it a reality.

> I propose a new science award: "The Research Parasite Award is given
> to those who used someone else's data to do some really cool sh\*t"
>
> --- Iddo Friedberg (@iddux) [January 22,
> 2016](https://twitter.com/iddux/status/690563238979723267)

  
For this award we're looking for scientists who have analyzed someone
else's data and published their findings independently of the data
generators. Our goal with the award was to reward precisely the approach
to science that was decried by the ["Research Parasites"
editorial](http://www.nejm.org/doi/full/10.1056/NEJMe1516564).
Candidates came from many fields, which really excited us, as we have a
very broad definition of Research Parasites. We view the term "Research
Parasite" as entirely complementary in this context, and we specifically
want to encourage individuals who identify with groups that are
underrepresented in their field to apply.

::: {#attachment_2130 .wp-caption .alignleft style="width: 381px"}
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/caseypic-300x200.jpg){.wp-image-2130
loading="lazy" decoding="async"
aria-describedby="caption-attachment-2130" width="371" height="248"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/caseypic-300x200.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/caseypic-768x512.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/caseypic-1024x683.jpg 1024w"
sizes="(max-width: 371px) 100vw, 371px"}

Casey lamprey modelling. Photo:Graham Perry
:::

There are two Research Parasite awards this year. The Senior Parasite
will receive a leather lamprey with a magnetic head that they can stick
to any ferromagnetic surface. The Junior Parasite will receive this
lamprey, as well as a \$500 cash prize and \$2950 in travel support to
attend [PSB](http://psb.stanford.edu/). We estimate that this is
sufficient to cover airfare from much of the world as well as hotel
costs for enough nights to receive the award.

**  
As this is the second year of the competition how was last years award
received and who won it? Were nominees proud to be parasites, and did
the people who coined the term "parasites" get or respond to the joke?**

**CG:** The winners were Kun-Hsing Yu and Erick Turner. As committee
members we enjoyed reading all of the applications, and the decision was
extremely difficult. We've got short bios of the winners plus their work
on our website, <http://researchparasite.com>. The nominees were clearly
proud parasites, which made us happy to see.

Last years award seemed to be well received. Dr. Jeff Drazen, one of the
authors of the editorial in question, was a good sport and attended PSB
and answered our questions immediately prior to the award presentation.
It's always hard to say what others find funny, but I think Jeff
understands where we were coming from with the award. I think that we
both would be happy to move beyond the negative research parasite term,
to more positively recognize the exemplars of science enabled by open
data.

**Were there any interesting lessons you learned from doing this, and
what are you doing differently this year? Tell us about the "symbiont"
award?**

**CG:** We had a wonderful time doing the awards, but it was a learning
process for us as well. We enjoyed reading the applications that were
submitted, and the serious purpose but tongue-in-cheek nature of the
awards led to some great application letters. I enjoyed hearing the
perspective of our winners during the awards ceremony. I would like to
see us record the ceremony this year (this reminds me that I need to
contact the PSB organizers!) so that it can be widely shared, including
to those who are unable to attend the meeting.

On the negative side, we were disappointed that we didn't simultaneously
recognize those who had shared data in ways that encouraged independent
reuse. In retrospect, this was an oversight on our part. We decided to
start a "host" award for those who were exemplars in data sharing.
However, we found that the terminology wasn't quite right, because often
the individuals whose data were reused were not negatively affected by
the reuse. We searched for a term for a bit, and ended up settling on
the term "symbiont" that refers to organisms that live together. The
relationship can be mutually beneficial because it increases the
scientific impact of those investigators, so we moved a away from the
term "host" even though it breaks the symmetry with the Parasite Awards.
The website for the Research Symbiont Awards is
<http://researchsymbionts.org>.

**![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/JBB-Photo-236x300.jpg){.alignleft
.size-medium .wp-image-2127 loading="lazy" decoding="async" width="236"
height="300"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/JBB-Photo-236x300.jpg 236w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/JBB-Photo-768x978.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/JBB-Photo-804x1024.jpg 804w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/JBB-Photo.jpg 855w"
sizes="(max-width: 236px) 100vw, 236px"}JBB:** I'm a clinical
investigator, and in that role, I run a laboratory & I have been
directly involved in clinical trials. As Casey and I got to know each
other, I shared my hope that something could be done to recognize people
who generate data and share the data. Casey & I share the vision that
data sharing is good for many reasons, and I am happy to be the
inaugural Chair of the Symbiont Awards Committee. The committee is a
diverse group with keen insight, and we look forward to selecting the
awardees. We have two awards for people who have shared data above and
beyond what is expected in their field. We have an award for an early
career researcher involved in human health research, and one for a more
senior researcher, who can be working in any scientific discipline. We
hope the Symbiont Awards help nudge the culture of researchers toward
more data sharing, and we are excited about the opportunity to recognize
those who already are setting the standard.

**As an open science/open data journal we try our best to help feed and
support the research parasite community, at the same time as giving
credit and sustenance to their hosts. Is there anything we and other
journals could and should be doing to help this process even more?**

**CG:** One of the things that we've seen is that researchers can resist
openly sharing their data for a number of reasons. One concern that we
hear frequently is that someone might download data and analyze it
without fully understanding its nuances, leading some of those
performing re-analyses to make findings that are trivial or incorrect.
[We'd support a
mechanism](http://www.nejm.org/doi/full/10.1056/NEJMc1705477) that
encourages authors of highly re-used datasets to write periodic reviews
to summarize the findings of reanalyses . This would also provide an
opportunity to independently weigh in on these efforts.

Of course, making it as easy as possible for researchers engaged in
reanalysis to directly recognize the datasets that they used in their
work is important. Right now, this is primarily done via citation.
However, some journals limit the number of references that can be used
in a given manuscript. I'd encourage journals to do away with these
reference limits and to concurrently work on supporting the option more
granular citation forms, which could enable researchers to separately
note the source of data, ideas, software, and other elements.

**Fortunately we [promote
citation](http://gigasciencejournal.com/blog/having-a-finger-on-the-pulse-of-data-citation/)
of all research objects (data, software, workflows, etc.)  and encourage
the inclusion of as much technical detail as possible in the body of
manuscripts.  
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-14-at-21.01.46-1024x490.png){.aligncenter
.wp-image-2131 loading="lazy" decoding="async" width="681" height="326"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-14-at-21.01.46-1024x490.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-14-at-21.01.46-300x144.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Screen-Shot-2017-09-14-at-21.01.46-768x368.png 768w"
sizes="(max-width: 681px) 100vw, 681px"}What is the plan for the awards
beyond 2018, and do you have any final thoughts on this type of
process?**

**CG:** We've received some generous financial gifts for these Awards
for Rigorous Secondary Data Analysis (colloquially called the Parasite
and Symbiont awards). These gifts have allowed us to establish an
endowment that will, starting with the 2019 award year, provide modest
prizes to recipients. If anyone would like to help us recognize data
sharing and reuse, we'd be happy to work with them and we have a URL
available for gifts to this fund:
<https://giving.apps.upenn.edu/giving/jsp/fast.do?fastStart=simpleForm&program=MED&fund=404893&bhcp=1>

In closing, I want to note that we've really enjoyed putting together
these awards. We think there's a very nice opportunity for copycat or
franchised awards at the society level. We'd love to see communities
build awards to specifically acknowledge their own exemplars of data
reuse and data sharing. The applications that we receive are quite
broad, and I'm sure that there are many societies who could find great
Parasites and Symbionts in their midst. I guess what I'm really saying
is that we'd like to see the "Research Parasite Award"-Parasites get
their acts together!*  
*

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The post [Research Parasites Wanted. Q&A with Casey Greene & Brian
Byrd](http://gigasciencejournal.com/blog/research-parasites-wanted-qa-with-casey-greene/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


*As a journal focussed on open science we are big promoters of research parasites (and research on parasites), and try to feeds them with open data and tools. It is therefore appropriate this is the second year GigaScience has supported and sponsored the Research Parasite awards.

Research Parasites Wanted. Q&amp;A with Casey Greene &amp; Brian Byrd

**Building an open, community-supported, e-infrastructure for medical
metabolomics data.**  
The European Union's Horizon 2020 research and innovation programme is
funding the [PhenoMeNal](http://phenomenal-h2020.eu/) (Phenome and
Metabolome aNalysis) project that aims to support data processing and
analysis pipelines for molecular phenotype data generated from
metabolomics applications. We aim to build an open, community-led and
community-supported e-infrastructure by leveraging existing cloud
infrastructures, tooling, and data repositories, under one umbrella of
services dedicated to the European biomedical community to begin with,
and eventually worldwide. However, the experience and tooling can be
applied to non clinical settings.

The project was formulated to address the complexity and high volumes of
data being generated, collected, and analysed that are quickly going
beyond current data management and computational capabilities. For
example, it is estimated that a single National Phenome Centre managing
only around 100,000 human samples per year might generate a velocity of
data amounting to more than 2PB annually. And this is a conservative
estimate.

When thinking of the near to middle-term future where phenotyping
efforts on a population-wide scale are on the horizon, such
e-infrastructures will need to deal with Exabyte-scales of biological
phenotyping data. It is essential to build such data infrastructures, in
particular for metabolomics phenotyping, as there is an urgent need to
improve the understanding of the causes and mechanisms underlying
health, healthy ageing and diseases beyond what can be done solely with
genomic approaches.

::: {#attachment_1811 .wp-caption .aligncenter style="width: 810px"}
![Strategy for knowledge generation enabled by PhenoMeNal
e-infrastructure. Source: PhenoMeNal
consortium.](http://gigasciencejournal.com/blog/wp-content/uploads/2016/07/url.png){.size-full
.wp-image-1811 fetchpriority="high" decoding="async"
aria-describedby="caption-attachment-1811" width="800" height="565"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2016/07/url.png 800w, http://gigasciencejournal.com/blog/wp-content/uploads/2016/07/url-300x212.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2016/07/url-768x542.png 768w"
sizes="(max-width: 800px) 100vw, 800px"}

Strategy for knowledge generation enabled by PhenoMeNal
e-infrastructure. Source: PhenoMeNal consortium.
:::

There are also additional challenges beyond simply addressing the
volumes, velocity and variety of data. Ethics and privacy are of central
concerns in the project since we aim to handle clinical metabolomics
data, and we are engaged with experts on ethics, legal, and social
implications of biological data. The project also aims to build
sustainable infrastructure that will live-on beyond the three years of
EU funding awarded to PhenoMeNal. This involves ensuring that all of the
infrastructure we develop is open source, well documented, and community
supported, and that we engage with existing research e-infrastructures
across Europe and worldwide.

**Building part public-part private clouds  
**So how are we building the PhenoMeNal e-infrastructure to address
these challenges?

In our first year, we have completed plenty of groundwork towards
setting up exemplar pipelines on public and private cloud computing
infrastructure. We are working on setting up the computing
infrastructure across European boundaries, with part hosting on the
European Bioinformatics Institute's EMBASSY Cloud in the UK, on an
installation at UPPMAX in Uppsala in Sweden (infrastructure previously
[covered by
*GigaScience*](https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-2-9)),
and within CRS4's datacenter in Sardinia, Italy. Alongside these, we are
also working with Google on creating a hybrid public-private cloud
infrastructure that we can then demonstrate the migration of computing
tools to data that needs to be ring-fenced within institutions (e.g.
within a clinic's own infrastructure where data cannot be moved) while
at the same time still leveraging the power of the public cloud.

We have also been working on packaging metabolomics software to become
'cloud-ready'. We are utilizing the [Docker
system](http://phenomenal-h2020.eu/home/2016/04/15/phenomenal-public-docker-registry-is-live/)
for containerizing software, where we have already packaged tools such
as
[BATMAN](http://bioinformatics.oxfordjournals.org/content/early/2012/05/25/bioinformatics.bts308)
(Bayesian AuTomated Metabolite Analyser for NMR spectra),
[OpenMS](http://www.openms.de/),
[XCMS](https://metlin.scripps.edu/xcms/), as well as data management
tooling such as the [ISA metadata framework](http://www.isa-tools.org)
API.

All of this work is in collaboration with clinical and research sites,
to support some example use cases. We work closely with clinical
metabolomics centers such as [CARAMBA](http://www.medsci.uu.se/caramba/)
(Clinical Analysis and Research Applying Mass spectrometry and
Bioinformatics at Akademiska) at Uppsala University (Sweden), the
[MRC-NIHR National Phenome
Centre](http://www.imperial.ac.uk/phenome-centre) (UK), the [Phenome
Centre Birmingham](http://www.birmingham.ac.uk/phenome-centre) (UK),
[Hospital Clínic of Barcelona](http://www.hospitalclinic.org/en)
(Spain), and the [Netherlands Metabolomics
Centre](http://www.metabolomicscentre.nl/) (Netherlands).

**Survey on Metabolomics Data: Call for participation  
**To gain a better understanding of the requirements for data
infrastructures for metabolomics research, PhenoMeNal has commissioned a
survey to gain insight into current practices, future needs, and
opinions, on how metabolomics research data is managed, published, and
disseminated. The survey welcomes contributions from the community of
researchers, including clinicians, and also from those involved with
data management, engineering and software development involved with
research and application of metabolomics.

If you work in any role relating to metabolomics, be it in clinical
application, basic research, or in technical support, please help us
build PhenoMeNal as an open, community-led, metabolomics infrastructure
by letting us know your thoughts.

You can contribute to the survey (closes end August 2016) here:
<https://www.surveymonkey.co.uk/r/metabolomics-data>

**Acknowledgements**  
The PhenoMeNal project is led by a consortium of 14 partners at: the
European Molecular Biology Laboratory-European Bioinformatics Institute
(UK), Imperial College of Science, Technology and Medicine (UK),
Leibniz-Institute of Plant Biochemistry (Germany), Universitat de
Barcelona (Spain), The University of Birmingham (UK), Consorzio
Interuniversitario Risonanze Magnetiche di Metallo Proteine (Italy),
Universiteit Leiden (Netherlands), The University of Oxford (UK), Swiss
Institute of Bioinformatics (Switzerland), Uppsala Universitet (Sweden),
Biobanking and BioMolecular Resources Research Infrastructure-ERIC
(Austria), Commissariat à l'énergie atomique et aux énergies
alternatives (France), Institut national de la recherche agronomique
(France), and Centro Di Ricerca, Sviluppo E Studi Superiori in Sardegna
(Italy).

The post [Guest posting: Building a PhenoMeNal metabolomics
e-infrastructure](http://gigasciencejournal.com/blog/guest-posting-building-phenomenal-metabolomics-e-infrastructure/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 Building an open, community-supported, e-infrastructure for medical metabolomics data.
</strong>
The European Union’s Horizon 2020 research and innovation programme is funding the PhenoMeNal (Phenome and Metabolome aNalysis) project that aims to support data processing and analysis pipelines for molecular phenotype data generated from metabolomics applications.

Guest posting: Building a PhenoMeNal metabolomics e-infrastructure

**Take a ride down chromosome highways with a novel web-based platform
that allows sharing of private genetic data while maintaining privacy
through a colourful dynamic visualization tool.**

[![B1uGkn6CIAIzP2h](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/B1uGkn6CIAIzP2h-300x194.png){.alignleft
.size-medium .wp-image-1361 loading="lazy" decoding="async" width="300"
height="194"}](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/B1uGkn6CIAIzP2h.png)The
Human Genome Project offered new hope that discovering the genetic
determinants which mediate chronic disease susceptibility would lead to
new avenues for drug development and targeted therapy, yet over decade
later the is disappointment that very few gene screens have made it into
the clinic to inform and improve treatment. Getting the most out of the
explosion of human genetic data has been difficult due to the massive
size of the data produced, as well as the challenges in balancing the
need to protect patient privacy with the need to be able to
independently inspect, replicate and build upon discoveries.
[GWATCH](http://gen-watch.org "GWATCH website"){target="_blank"
rel="noopener"} (Genome-Wide Association Tracks Chromosome Highway) is a
new tool [just published in
*GigaScience*](http://www.gigasciencejournal.com/content/3/1/18 "GWATCH paper"){target="_blank"
rel="noopener"}, that aims to address some of these issues. A new
web-based platform that provides visualization tools for identifying
disease-associated genetic markers from privacy-protected human data
without risk to patient privacy. This dynamic online tool, developed by
an international team of researchers from Russia, Australia, Canada, and
the US, allows and facilitates disease gene discovery via automation and
presentation of intuitive data visualization tools. Providing results in
three dimensions via a scrolling (Guitar Hero-like) chromosome highway,
this gives an extremely useful, visually appealing bird's-eye view of
positive disease-association results, while all sensitive information
and raw data remain secure behind firewalls.

Identification of genes that underlie deadly complex diseases, such as
heart disease, cancer and diabetes, and infections, including HIV-AIDS,
papilloma virus, and hepatitis B and C, is extremely difficult, as it
requires the availability of a huge amount of genetic information from
large numbers of patients and healthy controls. The advent of cheaper
and faster ways to sequence whole genomes --- with there likely to be
over [200,000 human genomes sequenced this
year](http://www.technologyreview.com/news/531091/emtech-illumina-says-228000-human-genomes-will-be-sequenced-this-year "MIT news estimate on # of genomes"){target="_blank"
rel="noopener"}--- has made producing this extensive amount of data
effectively a non-issue; however, issues over patient security and data
access extremely limit researchers' use of these amazing resources.
Identification of genes, replication of findings and independent
validation from 'potentially' available data is made nearly impossible
due to the necessarily complex and time consuming processes researchers
need to go through to obtain access to protected data. Thus, only a very
small percentage of data in protected databases is likely ever used. To
take full advantage of these data to uncover ways to treat or prevent
the [\~20 million deaths per
year](http://www.who.int/mediacentre/factsheets/fs310/en/ "WHO death stats"){target="_blank"
rel="noopener"} worldwide of people suffering from the most common
complex diseases, researchers need new, secure methods to access and
share these data.

As always the [peer reviews of the
article](http://www.gigasciencejournal.com/content/3/1/18/prepub "Prepub page"){target="_blank"
rel="noopener"} are available from the pre-publication history. One of
the peer reviewers of the article, [Dr Lachlan
Coin](http://www.imb.uq.edu.au/lachlan-coin "Lachlan homepage"){target="_blank"
rel="noopener"} from the University of Queensland, made noted the
importance of having such a tool, saying "The discovery of novel genetic
variants associated with complex disease has necessitated the formation
of large global research consortia to meta-analyse data from very large
sample sizes. However, sharing of this data has always been problematic.
GWATCH provides an innovative web-platform to facilitate sharing of
summary data from
[GWAS](http://en.wikipedia.org/wiki/Genome-wide_association_study "Genome Wide Association Study (via wikipedia)"){target="_blank"
rel="noopener"}, which will enable researchers to more quickly identify
and validate disease-associated genetic variation."

**Sharing Disease Data**  
[GWATCH](http://gen-watch.org "GWATCH website"){target="_blank"
rel="noopener"} allows investigators who were not involved in the
original study to access disease-associated genetic variation results
from GWAS (using whole genome sequence or SNP-arrays) rather than the
raw data that can be used to identify individuals. GWATCH has a
colourful and dynamic, user-friendly visualization tool that enables
researchers to effectively 'drive down chromosomes highways' and easily
see areas that associate with their disease of interest. Further they
can zoom in for greater detail on variation patterns and see and compare
different stages of disease (e.g., HIV infection, AIDS progression and
treatment outcome). The authors developed and tested GWATCH using an
often-requested huge NIH dataset of association data from more than
6,000 patients at risk for HIV-AIDS, which was used to discover
CCR5-∆32, the most powerful and useful AIDS resistance gene discovered
so far. GWATCH, however, can be used for any complex disease study by
importing in that study's association results.

The source code for GWATCH is freely available in
[Github](https://github.com/DobzhanskyCenter/GWATCH "GWATCH GitHub page"){target="_blank"
rel="noopener"}, an archived snapshot of the code used in this paper is
[available from a DOI in our GigaDB
repository](http://dx.doi.org/10.5524/100109 "GWATCH software DOI"){target="_blank"
rel="noopener"}, and access to on-going updated versions of GWATCH is
freely available at their website:
[http://gen-watch.org](http://gen-watch.org "GWATCH website"){target="_blank"
rel="noopener"}.

### **References**

1\. Svitin et al. GWATCH: a web platform for automated gene association
discovery analysis. *GigaScience* 2014, **3**:18
<https://doi.org/10.1186/2047-217X-3-18>  
[2.](http://dx.doi.org/10.5524/100109 "GWATCH software DOI"){target="_blank"
rel="noopener"} Svitin A, Malov S, Cherkasov N, Geerts P, Rotkevich M,
Dobrynin P, Shevchenko A, Guan L, Troyer J, Hendrickson-Lambert S,
Hutcheson Dilks H, Oleksyk TK, Donfield S, Gomperts E, Jabs DA, Sezgin
E, Van Natta M, Harrigan PR, Brumme ZL, O'Brien SJ. Software and
supporting material for: GWATCH: a web platform for automated gene
association discovery analysis (2014) GigaScience Database
[http://dx.doi.org/10.5524/100109](http://dx.doi.org/10.5524/10.5524/100109 "GWATCH software DOI"){target="_blank"
rel="noopener"}.

The post [Secure Genetic Data Moves into the Fast Lane of
Discovery](http://gigasciencejournal.com/blog/secure-genetic-data-moves-into-the-fast-lane-of-discovery/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 Take a ride down chromosome highways with a novel web-based platform that allows sharing of private genetic data while maintaining privacy through a colourful dynamic visualization tool.
</strong>
The Human Genome Project offered new hope that discovering the genetic determinants which mediate chronic disease susceptibility would lead to new avenues for drug development and targeted therapy, yet over decade later the is disappointment

Messaggi di Rogue Scholar

Data on arm motion control for the human-machine interface

Research Parasites Wanted. Q&A with Casey Greene & Brian Byrd

Guest posting: Building a PhenoMeNal metabolomics e-infrastructure

Secure Genetic Data Moves into the Fast Lane of Discovery