Scott Edmunds

*![Alun Luo presenting epigenome editing at KI
HK](http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun2-300x152.jpg){.alignright
.wp-image-2369 loading="lazy" decoding="async" width="289" height="146"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun2-300x152.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun2-768x388.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun2-1024x517.jpg 1024w"
sizes="(max-width: 289px) 100vw, 289px"}The CRISPR/Cas9 system has
opened new horizons in what can be done with genome editing. Here we
present an author Q&A with Alun Yonglun Luo discussing his new paper in
GigaScience moving this technology towards epigenome editing and testing
its efficacy and accuracy.  
*  
**Modulating Methylation: Epigenome Editing**  
With our scope focussing the "biological big data" beat means
*GigaScience* has covered in a lot of detail the implications from the
exponentiation leap in our ability to read genomes (as well as
publishing the raw material in our Data Note articles). The ability to
edit genomes has had equally revolutionary implications, taking the
field from hypothesis creation to application in model organism studies
(see our [review article](https://doi.org/10.1186/2047-217X-3-24)) to
implementation in the clinic as a new generation of molecular
therapeutics. Fusing DNA methyltransferase domains to the
nuclease-deficient CRISPR associated protein 9 (dCas9) means these
technologies can also be adapted for epigenome editing, programming DNA
methylation to silence or switch on already silenced genes. To take
these technologies forward their specificities need to be thoroughly
investigated to ensure there are not any problems with off-target
effects.

[Recently published
work](https://doi.org/10.1093/gigascience/giy011)[![Alun Luo and Lars
Bolund. Epigenome Editing
gurus.](http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun1-300x292.jpg){.alignleft
.wp-image-2370 .size-medium loading="lazy" decoding="async" width="300"
height="292"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun1-300x292.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun1-768x748.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/04/Alun1-1024x997.jpg 1024w"
sizes="(max-width: 300px) 100vw, 300px"}](https://doi.org/10.1093/gigascience/giy011)
in *GigaScience* has done just this, with authors presenting a new dCas9
fusion protein system and for testing its ability for targeted epigenome
editing on a genome-wide scale with single-base resolution. "Alun"
Yonglun Luo from Aarhus University has lead a team of research in
Denmark and China using quantitative bisulfite pyrosequencing, whole
genomebisulfite sequencing, and ChIP-seq to investigate the
characteristics of dCas9 methyltransferase-mediated DNA methylation in
human cells. Alun has been working with genomics and animal cloning
pioneer Lars Bolund (also an author on this paper) to scale up these
techniques and build new research platforms for regenerative medicine
with them in a new Lars Bolund Institute in Qingdao (see the
[announcement at
ICG12](http://www.genomics.cn/en/news/show_news?nid=105392)). We met up
with Alun and Lars (pictured) at a symposium at the Karolinska
Institute's new [Ming Wai Lau Centre for Reparative
Medicine](https://ki.se/en/research/ming-wai-lau-centre-for-reparative-medicine)
in Hong Kong and asked some questions about epigenomic editing and their
new paper. Following our traditional Q&A format here is Alun's insight
into the paper.

**The easy ability to edit the genome with the CRISPR/Cas9 system has
opened new horizons in what we can do in molecular biology. What
applications are people trying to do in editing the epigenome?**

Currently, the CRISPR epigenome editing system has been used to add or
remove methyl groups to CpG dinucleotide residuals and to create histone
modifications (acetylation/deacetylation). These technologies now allow
us to regulate gene expression through targeted epigenome editing.
Previously, the link between epigenetic modifications and their
biological functions could only be derived from association studies. Now
CRISPR-based epigenome editing tools to directly investigate e.g.
whether epigenetic modification of a certain genomic locus leads to gene
activation or inhibition. Aberrant epigenetic modifications are found in
various of cancers, the CRISPR epigenome editing system could potential
become a tool for targeted cancer therapy.

**Has the CRISPR/Cas9 system been used much for epigenomic editing, and
what has held back the utility of this technique so far? How does your
new work in *GigaScience* advance upon what has been done so far in
epigenome editing?**

Limits in specificity and efficiency have been the major issue holding
this back so far. Our study systematically highlights the precision and
limits of CRISPR-based DNA methylation editing.

**As applications for CRISPR/Cas9 are rapidly approaching clinical use,
it is crucially important to assess the specificity of these approaches.
How big a problem are off-target effects, and how are you and others
trying to do to overcome them? **

The off-target effects seem small enough not to severely jeopardize
experimental research on epigenetic programming and somatic cell
differentiation and evolution. However, improvements are needed for
clinical use. To ensure the results were supported by the data we
performed repeated (1+3) whole genome bisulfite sequencing on top of
localized methylation studies of selected loci.

**On top of sharing all the raw sequencing and RNA-seq, WGBS, and
ChIP-seq data in NCBI and [GigaDB](http://dx.doi.org/10.5524/100406),
you've also shared the [plasmids in
Addgene](https://www.addgene.org/Yonglun%20Luo/). How do you hope others
will build upon your work?**

The area of genome/epigenome editing has great promise in basic science
as well as clinical practice. We hope to contribute to global efforts in
this field -- and speed up international collaboration.

**What is next for this research? As you are working with your co-author
Lars Bolund in the set-up of the new Lars Bolund Institute in Qingdao,
will large-scale CRISPR/Cas9-based research be a focus of the new
centre?**

CRISPR/Cas9-based research will be an important part of the new
Institutes 2 x 2 strategy -- two technical platforms (CRISPR-editing and
cell/organoid cultures) and two experimental animal systems
(regenerative biology models and preclinical pig models).

**Further Reading:**

Chen L, Tang L, Xiang H, Jin L, Li Q, Dong Y, Wang W, Zhang G. Advances
in genome editing technology and its promising application in
evolutionary and ecological studies. *Gigascience*. 2014 Oct 30;3:24.
doi: [10.1186/2047-217X-3-24](https://doi.org/10.1186/2047-217X-3-24).

Lin L, Liu Y, Xu F, Huang J, Daugaard TF, Petersen TS, Hansen B, Ye L,
Zhou Q, Fang F, Yang L, Li S, Fløe L, Jensen KT, Shrock E, Chen F, Yang
H, Wang J, Liu X, Xu X, Bolund L, Nielsen AL, Luo Y. Genome-wide
determination of on-target and off-target characteristics for RNA-guided
DNA methylation by dCas9 methyltransferases. *Gigascience*. 2018 Mar
1;7(3):1-19. doi:
[10.1093/gigascience/giy011](https://doi.org/10.1093/gigascience/giy011).

The post [CRISPR-based Epigenome Editing: Author Q&A with Alun
Luo](http://gigasciencejournal.com/blog/epigenome-editing/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


The CRISPR/Cas9 system has opened new horizons in what can be done with genome editing. Here we present an author Q&amp;A with Alun Yonglun Luo discussing his new paper in GigaScience moving this technology towards epigenome editing and testing its efficacy and accuracy.

CRISPR-based Epigenome Editing: Author Q&amp;A with Alun Luo

*With the current annual data creation rate estimated to be in the tens
of [zettabytes](https://en.wikipedia.org/wiki/Zettabyte), the flood of
information currently being generated in every area of human life is
crashing up against limited data storage solutions. However, DNA, which
serves as a storage system for biological information,* [*has been
proposed*](http://dx.doi.org/10.1038/nature11875) [*as a potential
means*](http://dx.doi.org/%2010.1126/science.1226355) *to store an
unlimited amount of information. Here, instead of digital storage with
0's and 1's, the information is stored as A's, C's, T's, and G's. There
have been some studies that have assessed the potential by using DNA
inside living cells to store information. The problem was that the cells
replicate, and introduce errors, and they also die. Getting around this
problem,* [*George Church and colleagues
devised*](http://dx.doi.org/10.1038/nature23017) *a completely in silico
system, with the DNA being synthesized in short fragments on a glass
chip. The authors tested the system by synthesizing DNA to contain all
the information from an HTML version of a book of 53,426 words, 11
images, and one javascript file into DNA. They then read the information
back by sequencing the DNA, and could restore the entire book with an
error rate so low that it basically amounted to typos. While this is a
great step forward, the cost of this storage system, since it requires
both DNA synthesis and sequencing, still has extensive drawbacks.*

*![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/HYang-150x150.jpg){.alignleft
.size-thumbnail .wp-image-2109 loading="lazy" decoding="async"
width="150" height="150"}Below, in a guest blog, Professor Huanming Yang
presents his thoughts on the next method that could be used to get
around this issue, using human genome sequencing information as a
mechanism to eliminate the need for those two steps. Prof Yang is one of
the founders of BGI (our co-publisher), and has an interest in synthetic
biology, previously* [*publishing a guest
blog*](http://gigasciencejournal.com/blog/synthetic-genomics-redesign-and-synthesis-of-the-first-multicellular-eukaryotic-genome/)
*with us on synthetic genomes.  
*

**  
An** ***in silico*** **system for storage of information using the human
genome sequence**

So far, all the proposed methods or ideas for DNA information storage
require the following: 1) An *in silico* system with relevant programs
to encode and decode the information. This makes possible conversion
from the information stored in a binary code (0/1 of the digital
information) to then be converted to DNA sequence (A/T/C/G of the
nucleotide) and *vice versa*. 2) A DNA synthesizer to synthesize a
series of DNA nucleotides, stored either *in vitro* or *in vivo*. 3) A
DNA sequencer to read the DNA sequences, and the final steps to
re-convert the DNA sequence to the binary code from which it began and
can be machine read. While this is a completely feasible system using
current technologies, both steps 2 and 3 are very expensive and
laborious, limiting the feasibility of this approach for broad use.

Here, my colleagues at BGI and I propose a fully *in silico* system for
storing information and archiving data using DNA sequence that will be
universally applicable, reliable, convenient, and inexpensive. The major
advantage of our proposed system is that it eliminates the need for both
DNA synthesizers and sequencers. The basic premise of the protocol is to
store information in the human genome sequence.

Our proposed system contains the following elements:  1) The information
to store, which might be any text, images or video. These would be
converted to a machine-readable binary code, followed by further
conversion into DNA sequence.  (This part is the same as other proposed
methods or ideas)  2) A "template" sequence such as the entire human
genome DNA sequence or selected regions that are without known
variations, e.g. a cDNA sequence. Here, each nucleotide, A/T/C/G, in the
"template" sequence is labeled with a "position code". 3) A computer
program to (a) select each required nucleotide (N=1) or a string of
several consecutive nucleotides (N\>1) from the "template" sequence to
form the "information" sequence (Fig. 1).  This is a relatively simple
system that is composed of a "position code", which indicates the
position of a nucleotide in the "template" sequence, and a "grammar
code", which indicates, for example, spaces between words, beginning or
end of sentences, and other grammatical components, (b) convert the
"information" sequence into the binary code, and (c) reconvert the
binary code back to the initial information to store.  
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig1.jpg){.aligncenter
.size-full .wp-image-2107 loading="lazy" decoding="async" width="720"
height="405"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig1.jpg 720w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig1-300x169.jpg 300w"
sizes="(max-width: 720px) 100vw, 720px"}In addition, the "template"
sequence could also be any other reference genome or a human-designed
synthetic sequence. An "internationally standardized template sequence"
could be adopted and stored in DNA molecules either *in vivo* as a few
kb DNA insert in a vector replicating in a living organism, or *in
vitro* as synthesized oligonucleotides in lyophilized aliquots for
convenient manipulation and long-term storage.  
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig2.jpg){.aligncenter
.size-full .wp-image-2108 loading="lazy" decoding="async" width="720"
height="405"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig2.jpg 720w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/09/Fig2-300x169.jpg 300w"
sizes="(max-width: 720px) 100vw, 720px"}It is also worth noting that
this system could also be used to transfer protected or secret
information at a high level of security.

In summary, this proposed system has several advantages: 1) Greatly
eliminating the need to use expensive and laborious DNA synthesizers and
sequencers and 2) Its capacity allows storing an unlimited amount of
information. Finally, we specifically recommend use of the human genome
sequence information as it is freely available to follow the HGP Spirit:
"Owned by All, Done by All, and Shared by All" proposed by us and
[endorsed by the HGP
community](https://www.eurekalert.org/pub_releases/2006-04/bcom-bhg042506.php),
and that this DNA sequence will always be present in every human being
for ever and everywhere. It is fitting that storage of all of the
accumulated knowledge of the human race can be stored in the in the
material that encodes the information to build every human.

Feel free to add your feedback and comments via the blog commenting
system, and communications should be addressed to .

### **References**

1.  Shipman SL, Nivala J, Macklis JD, Church GM: CRISPR--Cas encoding of
    a digital movie into the genomes of a population of living bacteria.
    *Nature*. 2017; 547 (7663): 345-349.
2.  Erlich Y, and Zielinski D: DNA Fountain enables a robust and
    efficient storage architecture. *Science*. 2017; 355 (6328):
    950-954.
3.  Grass RN, Heckel R, Puddu M, Paunescu D, Stark WJ: Robust chemical
    preservation of digital information on DNA in silica with
    error-correcting codes. *Angew Chem Int Ed Engl*. 2015; 54
    (8):2552-2555.
4.  Goldman N, Bertone P, Chen S, Dessimoz C, LeProust EM, Sipos B,
    Birney E: Towards practical, high-capacity, low-maintenance
    information storage in synthesized DNA. *Nature*. 2013; 494 (7435):
    77-80.
5.  Church GM, Gao Y, and Kosuri S: Next-Generation Digital Information
    Storage in DNA. *Science*. 2012; 337 (6102): 1628.
6.   Cox, J. P. L: Long-term data storage in DNA. *Trends Biotechnol.*
    2001; 19: 247--250.

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The post [Guest Blog from Huanming Yang on using the human genome for
big-data
storage](http://gigasciencejournal.com/blog/guest-blog-from-huanming-yang-on-using-the-human-genome-for-big-data-storage/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


With the current annual data creation rate estimated to be in the tens of zettabytes, the flood of information currently being generated in every area of human life is crashing up against limited data storage solutions. However, DNA, which serves as a storage system for biological information,


 has been proposed


 as a potential means


 to store an unlimited amount of information.

Guest Blog from Huanming Yang on using the human genome for big-data storage

Nicole Nogoy

[![10530770_806106836068605_7801527962476975705_n](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/10530770_806106836068605_7801527962476975705_n-300x200.jpg){.alignleft
.size-medium .wp-image-1397 loading="lazy" decoding="async" width="300"
height="200"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/10530770_806106836068605_7801527962476975705_n-300x200.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/10530770_806106836068605_7801527962476975705_n-768x512.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/10530770_806106836068605_7801527962476975705_n.jpg 960w"
sizes="(max-width: 300px) 100vw, 300px"}](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/10530770_806106836068605_7801527962476975705_n.jpg)

When it comes to technology,
[*GigaScience*](http://www.gigasciencejournal.com/ "GigaScience homepage"){target="_blank"
rel="noopener"} has always been open and willing to embrace new ways of
integrating technology in its publishing processes, with the ultimate
goal of working towards more reproducible, interactive and executable
papers. So far, 2014 has been an extremely busy year for Team *Giga*
with regards to various technical developments as the journal continues
to push the boundaries of reproducible research and open data. Our team
have been busy working on our technical platforms, working on case
studies and exemplar articles, as well as presenting many of the early
results at conferences around the world (see for example [Peter Li's
talk at the Galaxy Community
conference](https://wiki.galaxyproject.org/Events/GCC2014/Abstracts#Events.2FGCC2014.2FAbstracts.2FTalks.A_journal.2BIBk-s_experiences_of_reproducing_published_data_analyses_using_Galaxy "Peter at GCC014"){target="_blank"
rel="noopener"} this year). With 2015 rapidly approaching, we thought we
would give you an end of year summary of recent and upcoming technical
developments, including some published examples that showcase them.

**Publishing Gets Technical** 
One of our biggest focuses since launch has been to integrate and
improve our [*Giga*DB
database](gigadb.org/ "GigaDB homepage"){target="_blank"
rel="noopener"}, as well as implement new programs and platforms. Much
of the work put into our version 2 of *Giga*DB was covered in [this
paper](http://database.oxfordjournals.org/content/2014/bau018.abstract?)
in the *DataBase Journal* that our lead curator Chris Hunter presented
at the [Biocuration
2014](http://biocuration2014.events.oicr.on.ca/ "Biocuration 2014 program"){target="_blank"
rel="noopener"} conference in April. We are currently working on v3, and
hope to release this early in the New Year. One project our Data
Scientist, Rob Davidson, has been working on is implementing
[OMERO](http://www.openmicroscopy.org/site/products/omero). Commonly
used in the imaging community, OMERO is an open source platform from the
[OME (Open Microscopy Environment)
community](http://www.openmicroscopy.org/site "OME homepage"){target="_blank"
rel="noopener"} that can handle imaging data in a secure, central
repository that can support over 130 different file formats, including
all major microscopy formats. Being open source has allowed plugins for
data types such as high content screens to be built, and overall allows
one to view, organize, analyse and share imaging data to minimise
incidents like the cherry picking of imaging data in the [retracted
*Nature* STAP
papers](http://www.nature.com/news/stap-retracted-1.15488 "STAP retraction editorial"){target="_blank"
rel="noopener"}. We are pleased to announce that our OMERO-based digital
viewer (*Giga*DV) will be launching in the coming months. This is
especially important as we are getting increasing amounts of large-scale
imaging datasets to showcase, including recently published ["prickly
pictures" of sea
urchins](http://www.gigasciencejournal.com/content/3/1/21/abstract); a
(39 Gb) [dataset](http://gigadb.org/dataset/100124) comprises of 141
high-resolution MRI scans from 98 species of sea urchins, and a similar
volume of [earthworm micro-CT data](http://gigadb.org/dataset/100092).

***Giga*Galaxy and "bring your own" data parties** 
Prior to joining the *GigaScience* team, Rob worked in Mark Viant's
group at the University of Birmingham -- [and through NERC funded work
with
us](http://blogs.biomedcentral.com/gigablog/2013/05/03/giga-galaxy-moves-into-the-metabolomics-universe/ "GigaGalaxy NERC/metabolomics blog"){target="_blank"
rel="noopener"} have developed the first end-to-end metabolomics
analysis pipeline in Galaxy, which we will be making available for
public use in the next in the next few months.  This will join other
workflows in our [*Giga*Galaxy
server](http://galaxy.cbiit.cuhk.edu.hk/ "GigaGalaxy server"){target="_blank"
rel="noopener"}, including implemented SOAPdenovo 2 workflows from [that
paper](http://www.gigasciencejournal.com/content/1/1/18) (look out for
the pre-print of the case study work we have been carrying out on this
with the
[ISA-](http://isacommons.org/ "ISA commons homepage"){target="_blank"
rel="noopener"}, [Research
Object](http://www.researchobject.org/ "ResearchObject Homepage"){target="_blank"
rel="noopener"} and
[Nanopublication](http://nanopub.org/ "nanopub.org homepage"){target="_blank"
rel="noopener"} communities), a population genomics toolkit, and another
in-press population genetics tool (Smilefinder). In addition to allowing
more interactive and reproducible methods and analyses, using
*Giga*Galaxy also allows much easier and clearer visualisation of
results in our papers (see more on its functionality and our Galaxy
series [in
*Giga*Blog](http://blogs.biomedcentral.com/gigablog/2014/02/06/rewarding-reproducibility-first-papers-in-our-galaxy-series-utilizing-our-gigagalaxy-platform/).
We've just published our [third
paper](http://www.gigasciencejournal.com/content/2/1/17) in the series
and have another one in press.

Given our drive to engage the community and further lower the barriers
for data release, in June we co-organised a metabolomics data hackathon
at our BGI Hong Kong office with attendees from local universities and
the UK metabolomics standards community. The fruitful interaction
between the participants resulted in the delivery of an ISA-Tab viewing
component for web browsers and the curation and release of a number
datasets that are currently being written up and reviewed as Data Note
articles. Owing to the success on this first data-a-thon (and other
similar 'bring your own data' events [encouraged by
ELIXIR](http://www.dtls.nl/dtl/events/join-elixir-nl-session-at-nbic-2014.html)),
the same teams will be organizing a follow-up meeting.  Check out [the
blog](http://blogs.biomedcentral.com/gigablog/2014/08/22/aint-no-party-like-a-bring-your-own-data-party/)
and please get in touch if you'd like to participate in future events.

**Papers Showcasing the Giga Approach** 
When working with human genome sequences, anonymization is almost
impossible when dealing with genomic epidemiological data and new
methods of sharing results whist keeping anonymity are required.
[Recently published in
*GigaScience*](http://www.gigasciencejournal.com/content/3/1/18 "GWATCH paper"){target="_blank"
rel="noopener"}, GWATCH is a dynamic web-based platform that enables
rapid search of large genome-wide datasets, visualisation of
disease-gene associations, 2D- and 3D-snapshots of gene regions along a
moving and colourful chromosome highway, including real-time validation
of candidate genes whilst preserving anonymity behind firewalls. Check
out the moving colourful chromosome highway in this video:

Something we had a lot of fun with, in addition to being worthwhile for
the community, was our project (also using Amazon Cloud) aiding the
speedy release of the [first *E. Coli* sequence
dataset](http://gigadb.org/dataset/100102) from Oxford Nanopore's
USB-powered,
MinION![™](https://s.w.org/images/core/emoji/14.0.0/72x72/2122.png){.wp-smiley
style="height: 1em; max-height: 1em;"} sequencer from Nick Loman's
group, which after rapid peer review we [recently
published](http://www.gigasciencejournal.com/content/3/1/22/abstract),
 This rapid release of the 135 GB of data before other databases were
geared up to handle it has already enabled the data to be [integrated in
teaching
materials](https://github.com/lexnederbragt/INF-BIOx121_fall2014_de_novo_assembly)
and become [reference
data](https://github.com/lexnederbragt/INF-BIOx121_fall2014_de_novo_assembly)
for new nanopore real-time analysis tools. Watch for forthcoming
projects and reports using this new technology.

Coronary artery disease is the most common cause of heart attacks and
diagnosis is key to prevent such events occurring. A useful tool in
diagnosis is magnetic resonance imaging (MRI) that is used to directly
examine blood flow to the myocardium of the heart. However, for MRI to
be most effective, it requires compensation for the breathing motion of
the patient, which is done using complex image processing methods. Thus,
there is a need to improve these tools and algorithms, and a key to
achieving this is the availability of large publicly available MRI
datasets to allow testing, optimization and development of new methods.
We have [published a virtual
box](http://www.gigasciencejournal.com/content/3/1/23) that aids the
fight against heart disease. Of course, in true *GigaScience*
"pushing-the-boundaries" fashion, we and the authors have published and
packaged the data alongside tools, scripts and the software required to
run the experiments to enable reproducible comparisons between new
tools. This is available for download from
[*Giga*DB](http://gigadb.org/dataset/100106) as a "virtual hard disk"
that specifically enable researchers to directly run the experiments
themselves, as well as add their own annotations to the data set. These
experiments in reproducibility follow on from the electrophysiology
paper we [published earlier in the
year](http://www.gigasciencejournal.com/content/3/1/3 "Reproducible neuroscience paper"){target="_blank"
rel="noopener"} that showcased dynamic ways of generating and publishing
papers that utilise R and Knitr (see
[GigaBlog](http://blogs.biomedcentral.com/gigablog/2014/04/16/qa-on-dynamic-documents/ "Knitr/dynamic doc blog"){target="_blank"
rel="noopener"}).

***Giga *GitHub** 
*GigaScience* has been using its GitHub repository, aka. [*Giga*
GitHub](https://github.com/gigascience), to host code from our own open
source projects and infrastructure, as well as helping authors of a
number of our papers to archive dynamical and updatable versions their
code (on top of the archived "version of record" snapshots we host in
*Giga*DB).  In a new experiment, last month we used GitHub to host a
supplementary table from a paper, to enable the contents to be dynamic
and updatable. The paper by Lei Chen and colleagues is a [review
article](http://www.gigasciencejournal.com/content/3/1/24/abstract)
 summarising the latest resources for genome editing using ZFN, TALEN
and CRISPR/Cas9 nuclease technologies, and as this is such a rapidly
developing field to keep it up to date we created a version (linked in
the paper) that can be edited, forked and more to enable others to
crowdsource content. Feel free to have a play here:
[https://github.com/gigascience/paper-chen2014/wiki ](https://github.com/gigascience/paper-chen2014/wiki%20)

**More Open Peer Review** 
As you are all aware of by now, improving transparency and
reproducibility of research is one of *GigaScience's* main goals, and
one way we achieve this is through [open peer
review](http://www.gigasciencejournal.com/content/2/1/10); however, we
have taken this one step further by partnering with
[Publons](https://publons.com/) -- the world's largest open peer-review
platform. Our reviewers can now get further recognized for their efforts
through Publons, announced via the [BMC
Blog](http://blogs.biomedcentral.com/bmcblog/2014/06/26/gigascience-helping-reviewers-get-credit-through-publons/).
We also got personal with Andrew Preston (co-founder of Publons) in a
[Q&A](http://blogs.biomedcentral.com/gigablog/2014/07/03/gigascience-making-open-peer-review-more-open-qa-with-publons-co-founder-andrew-preston/).
So, if you have reviewed for any of our papers you would have received
tokens via email; please set up your Publons profile and claim the
credit you deserve. We've also been carrying out a trial with
[AcademicKarma](http://academickarma.org/), which aims to speed up and
produce a more balanced peer review process. Sign up with your ORCID if
you are interested in trying it out too.

### **References**

[1. ](http://database.oxfordjournals.org/content/2014/bau018.abstract? "Database paper"){target="_blank"
rel="noopener"}Sneddon TP, Zhe XS, Edmunds SC, Li P, Goodman L, Hunter
CI: GigaDB: promoting data dissemination and reproducibility.
 *Database* 2014: bau018.
doi:[10.1093/database/bau018](http://database.oxfordjournals.org/content/2014/bau018.abstract? "Database paper"){target="_blank"
rel="noopener"}.

2\. Wollny, G; Kellman, P: Free breathing myocardial perfusion data sets
for performance analysis of motion compensation algorithms.
*GigaScience* 2014 **3**:23 <https://doi.org/10.1186%2F2047-217X-3-23>

3\. 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>

4\. Chen et al. Advances in genome editing technology and its promising
application in evolutionary and ecological studies. *GigaScience* 2014,
**3**:24 <https://doi.org/10.1186%2F2047-217X-3-24>

The post [Getting Techy With It: GigaScience Technology Update
2014](http://gigasciencejournal.com/blog/getting-techy-with-it-gigascience-technology-update-2014/){rel="nofollow"}
appeared first on
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When it comes to technology,

 GigaScience

has always been open and willing to embrace new ways of integrating technology in its publishing processes, with the ultimate goal of working towards more reproducible, interactive and executable papers.

Rogue Scholar Beiträge

CRISPR-based Epigenome Editing: Author Q&A with Alun Luo

Guest Blog from Huanming Yang on using the human genome for big-data storage

Getting Techy With It: GigaScience Technology Update 2014