Scott Edmunds

![Lisa Johnson ICG prize
winner](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa2-300x215.jpg){.alignright
.wp-image-2624 .size-medium loading="lazy" decoding="async" width="300"
height="215"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa2-300x215.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa2-768x551.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa2-1024x735.jpg 1024w"
sizes="(max-width: 300px) 100vw, 300px"}Yesterday we [published the
winning paper](https://doi.org/10.1093/gigascience/giy158) of the second
*GigaScience* prize, with additional detail and [coverage in
GigaBlog](http://gigasciencejournal.com/blog/microbial-genomics-goldmine/)
describing why we and the judging panel found it so novel. This was an
impressively case study in reproducibility, reassembling & reannotating
around 700 microbial eukaryotic transcriptomes to demonstrate this
approach can aid in revealing new biologically relevant findings and
missed genes from old data. Here in a follow-up blog is one of our
[author Q&A's](http://gigasciencejournal.com/blog/tag/qa/) with first
author [Lisa
Johnson](https://cbsapps.ucdavis.edu/GradStudents/Student/View/10642).
Lisa presented her work at our prize track session at ICG13 (the 13th
International Conference on Genomics) in Shenzhen, and we have the
[video](https://www.youtube.com/watch?v=WGmMOw0Jsqk&t=14s) and
[slides](https://www.slideshare.net/GigaScience/lisa-johnson-at-icg13-reassembly-quality-evaluation-and-annotation-of-678-microbial-eukaryotic-reference-transcriptomes)
from her talk available to view online. Lisa is in the Molecular,
Cellular, and Integrative Physiology lab of our [Editorial Board
Member](https://academic.oup.com/gigascience/pages/Editorial_Board) C.
Titus Brown at UC Davis.

With additional detail in an [accompanying
commentary](https://doi.org/10.1093/gigascience/giy15) on what the
broadly applicable take home messages for re-analyzing data are, in this
Q&A we ask Lisa for some more insight into why they carried out the
work, what the technical and cultural challenges were, and what the
ideal research infrastructure would be for this type of work.

**While raw sequencing data is well catered for why do you think
processed data such as assemblies annotations and results haven't been
supported and shared in the same manner?**

There are public repositories such as
[NCBI-TSA](https://www.ncbi.nlm.nih.gov/genbank/tsa/){target="_blank"
rel="noopener"} and
[NCBI-Genome](https://www.ncbi.nlm.nih.gov/genome){target="_blank"
rel="noopener"} for depositing data products such as assemblies and
annotations generated by assemblers and annotation pipelines. But, if
you aren't the owner of the original raw data, you can't submit
assemblies to these "official" repositories.

The original MMETSP transcriptomes assembled by the National Center for
Genome Research (NCGR) that we re-assembled are hosted by a
privately-hosted website,
[iMicrobe](https://www.imicrobe.us/#/projects/104){target="_blank"
rel="noopener"} and are available on an [ftp
site](ftp://ftp.imicrobe.us/projects/104/){target="_blank"
rel="noopener"}. These addresses and files can change without a change
log. (And they did throughout the three-year process of this paper.)

Re-assemblies are improvements to the original assemblies. The
processes, i.e. software and pipeline management for assemblies and
annotations are improving. We found that reference assemblies can change
and potentially provide additional useful information with newer
versions and different assembly tools.

**Why do people keep creating new sequencing data rather than going back
to re-use previously produced data?**

::: {#attachment_2625 .wp-caption .alignleft style="width: 310px"}
![ICG prize
winner](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa0-300x225.jpg){.size-medium
.wp-image-2625 loading="lazy" decoding="async"
aria-describedby="caption-attachment-2625" width="300" height="225"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa0-300x225.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa0-768x576.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa0-1024x768.jpg 1024w"
sizes="(max-width: 300px) 100vw, 300px"}

Lisa presenting this work at ICG13.
:::

The collection of RNA sequences expressed in a sample of cells is
dynamic -- unlike DNA sequences. New RNAseq data generated under
experimental conditions to help answer specific biological questions
allow us to see the overall physiological status of cells. But, a high
quality reference is needed to characterize RNAseq expression data. For
those of us whose study organisms that do not have a well-characterized
reference genome like the mouse or human, we have to generate *de novo*
assemblies and annotations to generate our own reference transcriptomes
or genomes. Having the best possible quality reference is necessary to
be able to accurately characterize new RNAseq data. This is especially
true if significant investments will be made downstream based on
differential expression results, as is sometimes the case with biomarker
and drug discovery in the agriculture, food and pharmaceutical fields.

**You provide some suggestions in [your
commentary](https://doi.org/10.1093/gigascience/giy159) to overcome the
technical challenges of data re-use, but what do you think the cultural
challenges are?**

I see funding, computing resources and training as the main challenges
for re-assembling old data to generate better references.

Domain-specific research funding seems to be primarily focused on
novelty: generating new data to answer new questions in the field. If
algorithms or software tools are developed in the pursuit of these
questions, great, but ongoing work to continue software development or
reference improvements are rarely supported, I believe. This work was
thankfully supported by the [Moore Foundation's Data-Driven-Discovery
initiative](https://www.moore.org/article-detail?newsUrlName=the-gordon-and-betty-moore-foundation-selects-awardees-for-$21-million-in-grants-to-stimulate-data-driven-discovery){target="_blank"
rel="noopener"} Investigator award to my advisor, C. Titus Brown. This
Moore Foundation initiative had the foresight to support scientific
discovery specifically in the area of data science towards advancing
solutions that can be used by others.

Right now, *de novo* assemblers and annotation pipelines are
computationally expensive, time-consuming and require training to use.
Running them again might seem like insanity. The pipeline I ran to
re-assemble the 678 MMETSP samples took \~8,000 wall-clock hours (678
assemblies \* avg 12 hours each) to run. The main de novo transcriptome
assembler, [Trinity requires at least 1 GB of RAM per 1 million
reads](https://github.com/trinityrnaseq/trinityrnaseq/wiki/Trinity-Computing-Requirements){target="_blank"
rel="noopener"}. Depending on the organism, more RAM than this is
usually better. I had to run our whole MMETSP pipeline twice and a
collection of samples a few times more to update the Trinity version and
re-assemble some of the problem samples. I ran the majority of samples
on the [Michigan State University's iCER high performance computer
(HPC)](https://icer.msu.edu/){target="_blank" rel="noopener"}, for
legacy reasons, and experienced some technical challenges associated
with running hundreds of de novo transcriptome assemblies in parallel
with the Trinity assembler. Some of the assemblies were also run on
[NSF-XSEDE's Jetstream cloud
platform](https://jetstream-cloud.org/){target="_blank" rel="noopener"}.
[NSF-XSEDE](https://www.xsede.org/){target="_blank" rel="noopener"} has
been a great avenue for providing free computing resources for US
researchers, and I highly recommend the [Bridges
HPC](https://www.psc.edu/bridges/user-guide/){target="_blank"
rel="noopener"} resource available through XSEDE.

We [run training workshops at UC
Davis](https://angus.readthedocs.io/en/2018/){target="_blank"
rel="noopener"} and meet people with data from many interesting
organisms and questions. We can help with generating assemblies and
annotations. But for many groups this is the only reference that they
will generate as it can be technically challenging and time-consuming to
even get to that point. Time is particularly valuable when pursuing
answers to the biological questions, funded by domain-specific research
grants and often doesn't allow for re-assemblies and evaluations.

**You used Zenodo to host the resulting outputs and data. What were the
advantages of that platform and what would the ideal repository or
hosting body be for this?**

The main advantage of [zenodo](http://about.zenodo.org/){target="_blank"
rel="noopener"} is that it offers persistent storage, funded by CERN and
will likely not go away in the next several decades. A DOI is generates
for the repository, which is versionable, and interfaces with GitHub
releases. We have direct control of the displayed text and uploaded file
content without having to go through a party to make changes. These
changes are automatically logged.

The main downfall of zenodo is the [50GB repository
limit](https://help.zenodo.org/whatsnew/){target="_blank"
rel="noopener"}. While this is an improvement from their previous 2
GB/file limit, for me the ideal platform is one with no file size or
repository limits so that raw data could be included. (I realize this is
expensive to maintain.) Another ideal feature is automated
upload/download, as zenodo has available through their [rest
api](http://developers.zenodo.org/#rest-api){target="_blank"
rel="noopener"}.

**You demonstrated this approach works with MMETSP data but where should
it go next?**

Automated and programmable pipelines that are extensible for an
arbitrary number of samples and supports software updates can be applied
to any data set. We've had some success in our lab [automating pipelines
with snakemake](https://github.com/dib-lab/eelpond){target="_blank"
rel="noopener"}, which is where I would like to see the MMETSP pipeline
go next.

![ICG prize
winner](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Prize_winners.jpg){.aligncenter
.size-full .wp-image-2623 loading="lazy" decoding="async" width="706"
height="592"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Prize_winners.jpg 706w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Prize_winners-300x252.jpg 300w"
sizes="(max-width: 706px) 100vw, 706px"}

**Finally it was great having you visit us in Shenzhen to present at the
ICG Prize track (pictured above). How did it feel being announced as the
2018 ICG prize winner?**

Thank you! I was surprised. All the prize track winners who presented at
ICG were very impressive and interesting. I really appreciated the
opportunity to attend ICG and meet the other speakers, attendees and
*GigaScience* staff.

**Further Reading  
**Johnson, LK et al. (2018): Re-assembly, quality evaluation, and
annotation of 678 microbial eukaryotic reference transcriptomes.
*GigaScience*. doi:
[10.1093/gigascience/giy158](https://doi.org/10.1093/gigascience/giy158)

Alexander, H et al. (2018): Keeping it light: (Re)analyzing
community-wide datasets without major infrastructure. *GigaScience*.
doi:
[10.1093/gigascience/giy159](https://doi.org/10.1093/gigascience/giy159)

The post [Meet the GigaScience ICG Prize Winner, Pt. 2: Lisa Johnson
Q&A](http://gigasciencejournal.com/blog/icg-prize-winner-lisa-johnson-qa/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


Yesterday we published the winning paper of the second

 GigaScience

prize, with additional detail and coverage in GigaBlog describing why we and the judging panel found it so novel. This was an impressively case study in reproducibility, reassembling &amp; reannotating around 700 microbial eukaryotic transcriptomes to demonstrate this approach can aid in revealing new biologically relevant findings and missed genes from old data.

Meet the GigaScience ICG Prize Winner, Pt. 2: Lisa Johnson Q&amp;A

*[Out today](https://doi.org/10.1093/gigascience/giy158)[![microbial
genomics
goldmine](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/old-goldmine-and-water-tanks-300x186.jpg){.alignright
.wp-image-2614 .size-medium loading="lazy" decoding="async" width="300"
height="186"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/old-goldmine-and-water-tanks-300x186.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/old-goldmine-and-water-tanks-768x477.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/old-goldmine-and-water-tanks-1024x636.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/old-goldmine-and-water-tanks.jpg 1885w"
sizes="(max-width: 300px) 100vw, 300px"}](https://doi.org/10.1093/gigascience/giy158)
is the winner of our ICG13 Prize, presenting work that can aid in
revealing new biologically relevant findings and missed genes from
previously generated transcriptome assemblies. Teaching old data new
tricks, and maximising every last nugget of information from previously
funded research. Here we present some insight into why the reviewers and
judges felt this work was so novel and these efforts to reprocess the
microbial genomics goldmine has such promise in the field of
reproducibility and data reuse.  
*

Analyses of genomic data often miss a large amount of information that
is present in the data due to the presence of so-called genomics "dark
data". This is information that is actually contained within the data,
but due to limitations in the analysis methods and variation in the
analyses tools used, it is usually missed. Titus Brown and his lab have
now created an automated pipeline to assemble and annotate
previously-analyzed raw data to dig out this hidden information. This
study mines a huge marine microbial dataset from the [Microbial
Transcriptome Sequencing Project
(MMETSP)](https://doi.org/10.1371/journal.pbio.1001889), demonstrating
that re-analysing old data with new tools can yield new results.

Previous [work on the
MMETSP](https://doi.org/10.1371/journal.pbio.1001889) sequenced 678
transcriptomes and assembled genes that spanned 396 different strains of
marine eukaryotes. This dataset has been an invaluable resource within
the oceanographic community, exponentially expanding the accessible
genetic information base of marine protistan life. In the 5 years since
the original analysis was completed, tools, techniques and databases
have been improved on. While analysis of this historical data could
potentially be carried out again to produce new and more accurate
findings, re-analysis of previously generated data with new tools is not
commonplace, and it is unclear what the best practice would be. Running
analyses again produces different results, and the effects of using
different pipelines are poorly understood, making it difficult to
determine the usefulness of the new results relative to the previous
findings.

The authors of this study tackled this challenge in a systematic manner.
They created an automated pipeline to assemble and annotate the original
raw data from the MMETSP data. The resulting new transcriptome
assemblies were then automatically evaluated in the pipeline and
compared against previously-generated assemblies from the original
assembly pipeline developed by the National Center for Genome Research.
As there is no one-size-fits-all protocol for transcriptome assembly,
and as software tools are constantly improving, this pipeline enabled
improvements to be tested and quantified. The new assemblies generated
containing the majority of the previous data as well as new content. On
average, 7.8% of the annotated sequence in the new assemblies had novel
gene names not found in the historical assemblies, demonstrating that
new findings can be gleaned from old data. Asked why having the most
accurate and up-to-date assembly is important, author Lisa Johnson
stated: "Having the best possible quality reference is necessary to be
able to accurately characterize new RNAseq data. This is especially true
if significant investments will be made downstream based on differential
expression results, as is sometimes the case with biomarker and drug
discovery in the agriculture, food and pharmaceutical fields."

::: {#attachment_2615 .wp-caption .aligncenter style="width: 1034px"}
![Microbial
goldmine](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa_ICG-1024x832.jpg){.wp-image-2615
.size-large loading="lazy" decoding="async"
aria-describedby="caption-attachment-2615" width="1024" height="832"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa_ICG-1024x832.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa_ICG-300x244.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/Lisa_ICG-768x624.jpg 768w"
sizes="(max-width: 1024px) 100vw, 1024px"}

Lisa Johnson presenting the work at ICG13 in Shenzhen.
:::

While raw sequencing data is commonly shared and well cared for by
government funded public archives, the resulting assembled genomes,
annotations and results generally are not. For the microbial genomics
work carried out in this article, the processed data and results of each
re-analysis would ideally be deposited in a discoverable location, with
versions and automated redirection to newer versions. The authors have
attempted to do just that, with the resulting outputs archived [in the
public Zenodo repository](https://doi.org/10.5281/zenodo.746048) hosted
by CERN, as well as snapshots from the study archived [in our GigaDB
database](http://dx.doi.org/10.5524/100522). If we are to take full
advantage of public data, this work demonstrates that researchers need
to make these products "forward discoverable", automatically notifying
users when a dataset is updated or changed. For researchers low on
resources, the benefit would make it possible to improve downstream work
without significant additional funding, experimentation or sequencing.

This microbial genomics work was selected by our international panel of
judges as the winner of our [second *GigaScience*
prize](http://gigasciencejournal.com/blog/icg13-gigascience-prize/), and
the first author Lisa Johnson came out to the International Conference
on Genomics in Shenzhen to present the work. In a follow up posting
we'll present a Q&A with Lisa going into more detail on this work, but
we also have [accompanying
commentary](https://doi.org/10.1093/gigascience/giy159) by the authors
expanding on the important lessons for reproducibility. The [video of
her talk](https://www.youtube.com/watch?v=WGmMOw0Jsqk&t=14s) is also
available to view below, and the slides are also [available to
view](https://www.slideshare.net/GigaScience/lisa-johnson-at-icg13-reassembly-quality-evaluation-and-annotation-of-678-microbial-eukaryotic-reference-transcriptomes).

**Further Reading  
**Johnson, LK et al. (2018): Re-assembly, quality evaluation, and
annotation of 678 microbial eukaryotic reference transcriptomes.
*GigaScience*. doi:
[10.1093/gigascience/giy158](https://doi.org/10.1093/gigascience/giy158)

Alexander, H et al. (2018): Keeping it light: (Re)analyzing
community-wide datasets without major infrastructure. *GigaScience*.
doi:
[10.1093/gigascience/giy159](https://doi.org/10.1093/gigascience/giy159)

The post [Reprocessing the Microbial Genomic Goldmine: Winner of the
ICG13
Prize](http://gigasciencejournal.com/blog/microbial-genomics-goldmine/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


Out today is the winner of our ICG13 Prize, presenting work that can aid in revealing new biologically relevant findings and missed genes from previously generated transcriptome assemblies. Teaching old data new tricks, and maximising every last nugget of information from previously funded research.

Reprocessing the Microbial Genomic Goldmine: Winner of the ICG13 Prize

**Pressing Challenges for the Global Research Community** 
Continuous growth of the world population is expected to double the
worldwide demand for food by 2050. Eighty-eight percent of countries
currently face a serious burden of malnutrition, especially in Africa
and South-East Asia. To diversify and stabilize global food supply,
enhance agricultural productivity and tackle malnutrition, greater use
of neglected or underutilized local plants through democratising data
and research on them is required. Researchers on the ground need to be
involved in this, but in a survey of academics and students in
sub-Saharan Africa, [80% polled
reported](https://www.nature.com/articles/d41586-018-07244-w) that they
have held unpaid research positions. Open Access is essential for them,
but how are Article Processing Charge models going to work in a world
where journals can charge over \$5,000 US dollars?

::: {#attachment_2607 .wp-caption .alignright style="width: 310px"}
![Democratising
Data](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/IDWteabreaks-300x217.jpg){.wp-image-2607
.size-medium loading="lazy" decoding="async"
aria-describedby="caption-attachment-2607" width="300" height="217"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/IDWteabreaks-300x217.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/IDWteabreaks-768x555.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/IDWteabreaks-1024x740.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/IDWteabreaks.jpg 1087w"
sizes="(max-width: 300px) 100vw, 300px"}

Monkey business at the IDW coffee breaks.
:::

Attempting to answer this question, *GigaScience* organised a session at
the [International Data Week
conference](https://www.scidatacon.org/IDW2018/) in Gaborone, Botswana
last month. Bringing together the Research Data Alliance and CODATA
meetings that we have previously attended, hosting the meeting in Africa
for the first time was a brilliant opportunity to bring together
different global perspectives and approaches to research data together.
The conference was opened by the Botswanan President Dr Mokgweetsi
Masisi, and delegates were extremely impressed to see a head of state
talking about the importances of research data management. With Over 850
attendees coming from 66 countries, and 60% African participation, for
it was refreshing to reach a critical mass of diverse voices that were
not the "usual subjects" from the well funded European and North
American institutions, and everyone who made it to Botswana felt it was
a historic and eye opening meeting because of it. Other than vervet
monkeys raiding the coffee breaks (pictured), the conference followed
its usual structure and themes and was of extremely high quality.

::: {#attachment_2608 .wp-caption .alignleft style="width: 323px"}
![Chris Democratising
Data](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/ChrisA_IDW-300x165.jpg){.wp-image-2608
loading="lazy" decoding="async"
aria-describedby="caption-attachment-2608" width="313" height="172"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/ChrisA_IDW-300x165.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/ChrisA_IDW-768x422.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/ChrisA_IDW-1024x563.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/ChrisA_IDW.jpg 1478w"
sizes="(max-width: 313px) 100vw, 313px"}

Chris Armit discussing the democratising of data.
:::

While these meetings usually have a generic data publishing track we
wanted to use the novel location and audience to do something a little
different, specifically organising a track [on "Democratising Data
Publishing: A Global
Perspective"](https://www.scidatacon.org/IDW2018/sessions/196/). With
*GigaScience* Data Scientist Chris Armit starting the track off and
providing some scene setting (see his [slides
here](https://www.slideshare.net/GigaScience/chris-armit-at-idw2018-democratising-data-publishing-a-global-perspective)),
we then heard Chinese perspectives to Data Publishing from Jianhui Li
(China Scientific Data and ScienceDB) and the Nature Publishing view
from Varsha Khodiyar (Scientific Data). We then heard from more
infrastructure related perspectives, our Editorial Board Member Susanna
Sansone talked about FAIRsharing and how making digital objects FAIR can
democratise data publishing (see
[slides](https://www.slideshare.net/SusannaSansone/fairsharing-at-scidatacon-idw-2018)),
Mark Parsons on Power and Persistent Identifiers, and Daniel Mietchen on
how distributed Wikidata and Wikibase approaches could tackle the
democratizing issue (slides
[here](https://docs.google.com/presentation/d/1lqrgbA-Z0BQmkXDqDZtdR6EbHqDl0MM9POeyqmHG6KE/edit)).
We ended with a more local perspective, Anwar Vahed giving a very
interesting overview on the South African national research data
infrastructure: [DIRISA](https://www.dirisa.ac.za/). To give some time
to discuss the topic in hand, reiterating the challenges, but more
importantly propose any more democratic ways ahead beyond the APC, we
ended with a panel. This featured several of the speakers and added
Elizabeth Marincola and Rebecca Lawrence to give us some insight into
how the African Academy of Sciences [Open Research journal and
platform](https://aasopenresearch.org/) is trying to tackle this.

**Rice fills the agricultural data gap** 
The specific example that Chris focussed on was agricultural data and
the [Rice3K project we published](https://doi.org/10.1186/2047-217X-3-7)
the first outputs of data in 2014. With the aim of improving the
productivity of the the crop that provides the majority of daily
calories for much of the worlds poor. The [13.4TB of data
quadrupled](http://gigasciencejournal.com/blog/publish-data-fight-world-hunger/)
the amount of rice genomic data in the public domain, but this and the
resulting [100TB of processed data made available in
AWS](https://registry.opendata.aws/3kricegenome/), while open was
impractical to download and use by most researchers due to computer
bandwidth and cost. What was data publishing did help democratise was
the early release of data, incentivised by the [Data Note article in
*GigaScience*](https://doi.org/10.1186/2047-217X-3-7) that helped the
data producers to get early publication credit 4 years before the
analysis paper was published (which eventually [appeared in
*Nature*](https://www.nature.com/articles/s41586-018-0063-9) this year).

While downloading and using compute on the raw and partially processed
data was prohibitively expensive for those working on rice breeding in
the regions dependent on the crop, researchers at IRRI have now managed
to genotype and analyse it down to such a level that it can be cost
effectively shared. The RiceGalaxy platform also has a GUI to allow
plant breeders to utilise this genetic data without coding skills, and
is currently running via AWS Singapore & local servers at a cost of
about \$100 USD/month. This [CGIAR Excellence in Plant
Breeding](http://excellenceinbreeding.org/) model is now rolling out to
other crops, demonstrating there is a way that the benefits of these
big-data approaches can indeed be democratised. This work was one of the
six winners of our [ICG-prize
track](http://gigasciencejournal.com/blog/icg13-gigascience-prize/) in
October, and you can [read the
preprint](https://www.biorxiv.org/content/early/2018/06/29/358754) and
watch the video of Venice Juanillas [presenting on our
GigaTV](https://youtu.be/HWKa3acoDUQ) youtube channel.

**The African Orphan Crops Consortium bears its first fruits 
**Following the example set by these rice genomes, other agricultural
crop species with great potential for genome-assisted improvement are
being sequenced by a number of consortia. The goal of the [African
Orphan Crops Consortium
(AOCC)](http://africanorphancrops.org/meet-the-crops/), an international
public--private partnership, primarily comprises African Union's New
Partnership for Africa's Development (AU-NEPAD Agency); World
Agroforestry Centre (ICRAF); BGI; and University of California, Davis.
With generous support from its partners, AOCC is [working on sequencing
the genomes of 101 plants](https://doi.org/10.1093/gigascience/giy152)
that contribute to traditional African food supplies by 2020. These
neglected or orphan plants have been seldom studied by scientists, but
are of major importance in many African countries. They are usually
grown by smallholder farmers, either for consumption or local sale, and
are a major food source for 600 million rural Africans.

Our co-publishers BGI has completed reference genome analysis for six
crops, five of which are published in a [new Data Note in *GigaScience*
today](https://doi.org/10.1093/gigascience/giy152). With six genomes
near to completion, and nearly 30 more in progress, expect to see more
fruits of this work being released in the near future. The first five
plants include the legume crops the Hyacinth Bean (*Lablab purpureus)*,
the Bambara Groundnut (*Vigna subterranea)*, and larger tree species
such as the Apple-Ring Acacia (*Faidherbia albida)*, Marula
(*Sclerocarya birrea)* and the medicinal Horseradish Tree (*Moringa
oleifera)*. All of which are important for food, fodder, shelter and
medicinal properties for smallholder farmers across Africa. Unlike many
agricultural staple species that have complex hybrid genomes, the
assembled genome sizes were relatively compact: 535 Mb, 395Mb, 653Mb,
330Mb, 216Mb respectively, accounting for 97.3%, 93.5%, 98.9%, 92.9%,
77.9% of the estimated genome size respectively. 
![Democratising
data](http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/AOCCplants.jpg){.aligncenter
.size-full .wp-image-2610 loading="lazy" decoding="async" width="416"
height="279"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/AOCCplants.jpg 416w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/12/AOCCplants-300x201.jpg 300w"
sizes="(max-width: 416px) 100vw, 416px"} 
This [comprehensive study in
*GigaScience*](https://doi.org/10.1093/gigascience/giy152) reports the
sequencing, assembly, and annotation of five genomes of underutilized
plants in Africa, along with details of their key evolutionary features.
Following the example set by rice, the draft genomes of these species
will serve as an important complementary resource for non-model food
crops, especially the leguminous plants, and will be valuable for both
agroforestry and evolutionary research. Improving these underutilized
plants using genomics-assisted tools and methods could help to bring
food security to millions of people. Author and AOCC consortium member
Prasad Hendre was supposed to present in our International Data Week
Democratising Data session, but as he unfortunately couldn't make his
talk some of this work was also previewed in [Chris Armit's
presentation](https://www.slideshare.net/GigaScience/chris-armit-at-idw2018-democratising-data-publishing-a-global-perspective).
Meeting the goals of democratisation outlined above, as the research is
published Open Access it can be read by researchers across the world
without restriction, and all the data is released without restriction
for plant researchers and breeders via
[NCBI](https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA474418) and
[GigaDB](http://dx.doi.org/10.5524/100504). The [protocols are
available](http://dx.doi.org/10.17504/protocols.io.pzqdp5w) in
protocols.io and data of these first five species is available for
download from the via the following DOIs:

Genomic data of the Apple-Ring Acacia (*Faidherbia albida*).
<http://dx.doi.org/10.5524/101054> 
Genomic data of the Bambara Groundnut (*Vigna subterranea*).
<http://dx.doi.org/10.5524/101055> 
Genomic data of the Hyacinth Bean (*Lablab purpureus*).
<http://dx.doi.org/10.5524/101056> 
Genomic data of Marula (*Sclerocarya birrea*).
<http://dx.doi.org/10.5524/101057> 
Genomic data of the Horseradish Tree (*Moringa oleifera*).
<http://dx.doi.org/10.5524/101058>

**Further Reading: 
**Chang Y et al. The draft genomes of five agriculturally important
African orphan crops. *GigaScience*. 2018. doi:
[10.1093/gigascience/giy152](http://dx.doi.org/10.1093/gigascience/giy152)

3,000 rice genomes project. The 3,000 rice genomes project.
*Gigascience*. 2014 May 28;3:7. doi:
[10.1186/2047-217X-3-7](https://doi.org/10.1186/2047-217X-3-7)

The post [Democratising Data: The African Orphan Crops Consortium &
International Data
Week](http://gigasciencejournal.com/blog/democratising-data-aocc/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.

Pressing Challenges for the Global Research Community

Continuous growth of the world population is expected to double the worldwide demand for food by 2050. Eighty-eight percent of countries currently face a serious burden of malnutrition, especially in Africa and South-East Asia.

Democratising Data: The African Orphan Crops Consortium &amp; International Data Week

![Deadline
Extended](http://gigasciencejournal.com/blog/wp-content/uploads/2018/06/TarasLogo_ICG13_extended-300x254.jpg){.alignleft
.wp-image-2418 loading="lazy" decoding="async" width="321" height="272"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/06/TarasLogo_ICG13_extended-300x254.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/06/TarasLogo_ICG13_extended-768x649.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/06/TarasLogo_ICG13_extended-1024x865.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/06/TarasLogo_ICG13_extended.jpg 1117w"
sizes="(max-width: 321px) 100vw, 321px"}Due to popular demand we are
keeping the paper submission deadline of our [ICG prize track
open](http://www.icg-13.org/navigation/show_navigation?nid=10117) until
the end of the month. We've already received more submissions than our
first competition last year, and a number of other submitters have been
putting their finish touches to submission. Its easy to submit as you
just have to have your unpublished work in bioRxiv and use [our
integrated b2J
functionality](http://gigasciencejournal.com/blog/shortcut-from-biorxiv-to-gigascience/)
to click it through to us. Winning entries will again get registration
and travel covered to come out and present their work in Shenzhen in
late October. Carrying out open and transparent peer review the
submissions so far can be viewed on our [Preprint.Space conference
page](http://preprint.space/conference/ICG13), and  you can see more [in
our blog](http://gigasciencejournal.com/blog/icg13-gigascience-prize/)
and the guidelines below.

#### **Submission Guidelines:**

- Paper submission deadline: June 30, 2018 \[extended\]
- First round of peer review: August 15, 2018
- Presentation decision: September 15, 2018

Papers submitted for review should follow our
[scope](https://academic.oup.com/gigascience/pages/About) and represent
original, previously unpublished work that use, produce, or provide
analysis tools for large data in 'omics fields.

1. Submit your paper through
 [bioRxiv](http://biorxiv.org/submit-a-manuscript), then use the
 [integrated functionality to transfer it
 to](http://gigasciencejournal.com/blog/shortcut-from-biorxiv-to-gigascience/)
 [*GigaScience*](http://gigasciencejournal.com/blog/shortcut-from-biorxiv-to-gigascience/).
2. Include a note in the covering letter or send us an email stating:
 "This is a submission to the *GigaScience* Prize Track".
3. Once your submission is live, we will add it to the Preprint.Space
 channel here: <http://preprint.space/conference/ICG13>
4. If the paper is one of the winners, once it is published it will
 also be highlighted in the *GigaScience* Prize Track page:
 <https://academic.oup.com/gigascience/pages/prize_track>

See the [icg-13.org](http://www.icg-13.org/) website and [competition
page](http://www.icg-13.org/navigation/show_navigation?nid=10117) for
more information, and for further questions concerning the submissions,
please contact the *GigaScience* editors at
[editorial](mailto:editorial@gigasciencejournal.com)[@gigasciencejournal.com.](mailto:editorial@gigasciencejournal.com)

**ConGen-ial Conferences** 
On the topic of deadline extensions, another meeting we are supporting
this summer is [ConGen (Recent Advances in Conservation
Genetics)](http://conservationgenetics.org/), where we are supporting
two *GigaScience* scholars (see
[GigaBlog](http://gigasciencejournal.com/blog/gigascience-supports-congen-2018/)).
Our EiC Laurie will be attending the meeting at Front Royal, Virginia,
U.S.A. from August 19 -- 30th. Late registration is still available for
a few places on the course, so make sure you sign up from their website
[here](http://conservationgenetics.org/). The course is co-directed by
winner of last years ICG prize Taras Oleksyky, so you can get a taste of
the type of research you will learn about from the video of his
[Solenodon genome](https://doi.org/10.1093/gigascience/giy025) talk
here.

We have a busy summer going to a number of other conferences, including
as silver sponsors and exhibitors at
[GCCBOSC](https://gccbosc2018.sched.com/) (hopefully getting more
submissions for our [popular Galaxy
series](https://academic.oup.com/gigascience/pages/galaxy_series_data_intensive_reproducible_research)),
and if you know anything about our [summer
traditions](http://gigasciencejournal.com/blog/?s=ISMB), [at
ISMB](https://www.iscb.org/ismb2018). With the journal originally
launching back at ISMB2012 in [Long
Beach](http://gigasciencejournal.com/blog/searching-for-the-open-source-at-long-beach/)
we've been celebrating our birthday every year, and in Chicago this year
we will have one of our famously [boisterous birthday
bashes](http://gigasciencejournal.com/blog/bohemian-bashfully-baroque-birthdays-and-bingo-in-prague/)
on Sunday 8^th^. For more details watch this space, and if you are at
any of these meetings we look forward to catching up.

The post [Meetings, Meetings And Extended Deadlines For The ICG-13
Prize](http://gigasciencejournal.com/blog/extended-deadlines/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.

Due to popular demand we are keeping the paper submission deadline of our ICG prize track open until the end of the month. We’ve already received more submissions than our first competition last year, and a number of other submitters have been putting their finish touches to submission.

Rogue Scholar Beiträge

Meet the GigaScience ICG Prize Winner, Pt. 2: Lisa Johnson Q&A

Reprocessing the Microbial Genomic Goldmine: Winner of the ICG13 Prize

Democratising Data: The African Orphan Crops Consortium & International Data Week

Meetings, Meetings And Extended Deadlines For The ICG-13 Prize