Read more]]> Last week we published our first neuroscience data note containing 10GB of fMRI data hosted and integrated into the paper by a DOI to our GigaDB database. While we have published a number of genomics datasets and data notes (see the Puerto Rican Parrot genome data note and its associated data DOI), this is a nice example of us providing a home for “orphan data”, the long tail of data types without community agreed curated repositories. Sharing of data enables re-use and new work to be created, all goals and reasons why we have built the infrastructure that makes up GigaScience and its integrated data hosting environment. This is even more timely, with a number of recent and high profile retractions and fraud cases in the areas such as psychology and animal cognition showing there is a growing need for increased transparency and access to supporting data to tackle this growing reproducibility gap.

The challenges sharing neuroscience data
Genomics has been a fertile area for data sharing through having a relatively limited number of platforms and resulting data types, and being unified by being focused on a particular technology. This has made it very different to an area such as neuroscience that falls into separate communities, each studying the brain at different levels and with different tools, which has made it much harder to provide motivation and a need to provide data to researchers outside their community. Compared to the (relatively) smooth rise of genomics data sharing led by the Human genome project and community guidelines formulated at the Bermuda and Fort Lauderdale meetings, moves to spur similar efforts in neuroscience have been more challenging. Organizations such the International Neuroinformatics Coordinating Facility (INCF) have been set up to coordinate and encourage neuroimaging data sharing, and have been producing tools and infrastructure to enable it.

One of the many tools promoted by the INCF has been the fMRI data center (fMRIDC) — a large-scale effort to gather, curate, and openly share fMRI data used in peer reviewed studies. Pioneering data sharing in neuroscience, despite receiving support from journals such as the Journal of Cognitive Neuroscience, the platform initially received skepticism from many of the community due to concerns over scooping and practicalities such as the time and effort it would require to deposit usefully curated data. The resource helped prove its utility over the following years by collecting over 100 complete studies, and enabling many publications that produced new results and/or conclusions from reusing this data, but unfortunately in the current difficult financial climate it did not receive long term funding and currently is not taking new submissions.

Promising moves on the horizon
Despite this setback, things are now more optimistic in this area, with the community looking more receptive to sharing their data, and work underway to overcome many of the roadblocks that have been holding things back (see our commentary on this subject). There are a number of promising moves from brain atlas, (e.g. the Allen Brain Atlas), and connectome projects (e.g. the human connectome project and CMRR) for providing huge resources of public data. In the functional imaging space, the openMRI project also provides a home for raw fMRI data, and has developed nice platform and data organisation standards.

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2003 to speed up drug development by validating imaging and biomarker data for Alzheimer’s disease clinical treatment trials, and set a new standard in neuroscience for data sharing without embargo. Despite data being de-identified, the consortium allows access only to approved members of the “scientific community” after their authorization, and there are stricter rules regarding data reuse and credit than the genomics community are used to. In contrast to these limitations we issue all of our data use a CC0 waiver to maximise its potential re-use, whilst giving all our hosted datasets a citable DataCite DOI to enable attribution and credit to the producers. The broader imaging community has shown that there are mechanisms and a large potential user base for sharing their data, with the Open Microscopy Environment (OME) community producing a suite of open visualization and conversion tools and data standards. OME’s open imaging platform OMERO (developed in part by our editorial board member Jason Swedlow), has a growing user base from labs organizing and presenting their data, and has worked with journals and societies to produce products such as the Journal of Cell Biology image viewer, and the American Society of Cell Biology “The Cell: an Image Library”. In the longer term the future is looking bright, the European Euro-Bioimaging initiative is also planning and aiming to build large-scale public biological and biomedical imaging infrastructure by 2017.

Data Publishing to the rescue?
Complementing these forthcoming subjects specific resources, the rise of a number of unstructured repositories such as Dryad and Figshare gives additional options and makes data sharing even easier. Structuring the data and storing necessary metadata to use it is essential to enable its reuse, and on top of providing the storage infrastructure the time and effort to carry this from data producers out needs to be credited. Data-publishing provides an incentive for data producers to make this effort, crediting early release of data with a citable publication, and allowing publication of the downstream analyses later.

Our recently published data note by Chris Gorgolewski and colleagues is a great example of this, providing test-retest functional MRI data for motor, language and spatial attention functions for 10 de-identified patients. This allows validation of fMRI tasks used in pre-surgical planning for tumor resection, and provides a valuable resource for the development of new methods and algorithms. On top of the GigaScience datanote the authors have already published an analysis using this, demonstrating that this model works. Overcoming some of the previous concerns about deposition of this type of data, all patient details have been deidentified, and the data is structured in the openfMRI manner. These authors are keen proponents of data-sharing, and have talked about the benefits of this type of data-publishing in the past, so it is great to see them use GigaScience as a vehicle for this.

Data publishing is currently very topical, and coming on top of the many other recent schemes we have written about in the past, Nature Publishing Group have recently announced their own foray into the field, with the upcoming launch of Scientific Data. Due to launch in spring 2014, their “data descriptors” are very similar to our data note articles, and also use the interoperable ISA-TAB metadata format that a number of our submitters have utilized. It is flattering that two years after we started publishing data in this way that Nature will be following what we have done, and it encourages and validates our approach, spurring us to take more data. We would data producers to take advantage of the fact that all article and data processing charges are currently covered by BGI until the end of the year, and contact us if you are interested in publishing data notes with us or submit here. For more on our data notes also see here.

Image credits: Charlie Llewellin, Flickr; Gorgolewski et al.

Further Reading
1. Gorgolewski KJ, Storkey A, Bastin ME, Whittle IR, Wardlaw JM, Pernet CR. A test-retest fMRI dataset for motor, language and spatial attention functions. Gigascience. 2013 2:6. http://dx.doi.org/10.1186/2047-217X-2-6.

2. Gorgolewski, KJ; Storkey, A; Bastin,ME; Whittle, IR; Wardlaw, JM; Pernet, CR (2013) A test-retest functional MRI dataset for motor, language and spatial attention functions. GigaScience Database http://dx.doi.org/10.5524/100051

3. Gorgolewski KJ, Storkey AJ, Bastin ME, Whittle I, Pernet C. Neuroimage. Single subject fMRI test-retest reliability metrics and confounding factors. 2013 69:231-43. http://dx.doi.org/10.1016/j.neuroimage.2012.10.085

4. Breeze JL, Poline JB, Kennedy DN. Data sharing and publishing in the field of neuroimaging. Gigascience. 2012 1:9. doi: http://dx.doi.org/10.1186/2047-217X-1-9

Sophisticated computational analyses must be performed on metabolomics data in order to measure the abundances of the metabolites. However, this typically requires expert knowledge in computer programming and biostatistics, restricting the usefulness of metabolomics to specialised laboratories. Thanks to funding from the UK’s Natural Environment Research Council, this project will develop a software platform based on Galaxy to make it much easier for non-specialist scientists to analyse their metabolomics datasets.

As the first metabolomics project in the recently announced Joint BGI-University of Birmingham Environment & Health Centre, the funding will enable Rob Davidson, a post-doctoral researcher from Mark Viant’s research group at the University’s School of Biosciences, to travel to Hong ...

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Sophisticated computational analyses must be performed on metabolomics data in order to measure the abundances of the metabolites. However, this typically requires expert knowledge in computer programming and biostatistics, restricting the usefulness of metabolomics to specialised laboratories. Thanks to funding from the UK’s Natural Environment Research Council, this project will develop a software platform based on Galaxy to make it much easier for non-specialist scientists to analyse their metabolomics datasets.

As the first metabolomics project in the recently announced Joint BGI-University of Birmingham Environment & Health Centre, the funding will enable Rob Davidson, a post-doctoral researcher from Mark Viant’s research group at the University’s School of Biosciences, to travel to Hong Kong and work with GigaScience in developing the popular Galaxy workflow system for use in metabolomics data analyses.

Rob arrived this week and will be working with us at BGI-Hong Kong for two months. Since Rob is a keen foodie, GigaScience is also looking forward to showing him the culinary delights of Hong Kong such as chickens feet dim sum stylee!

Read more]]> Singapore – a wonderful city of diverse cultures and culinary affair, is not just about the infamous chilli crab or signature cocktail, Singapore Sling, but from the 13-19 April the vibrant country plays host to one of the largest meetings in genetics and genomics. The 2013 Human Genome Meeting/International Congress of Genetics (HGM/ICG) meeting is the first of its kind in Asia – bringing together a genetics congress that has been held every five years since 1899 with the young upstart genomics experts from HUGO (the Human Genome Organisation), the 2013 meeting brought together over 1000 scientists from around the world at the Marina Bay Sands Convention Centre (a favourite location of ours since last years Bio-IT World Asia meeting).

Gigascience‘s Executive Editor, Scott Edmunds, and Commissioning Editor, Nicole Nogoy, attended the diverse meeting to learn the latest in the genetics and genomics world. Although having a heavy human focus, the meeting covered a wide range of topics from plant, animal and medical applications, as well as database and privacy. With a fantastic line-up spanning big science (workshops and talks from ENCODE and Ewan Birney, pictured) to monogenic gene-disorders, many of our editorial board were present, and our BGI colleagues also hosted a workshop.

A few highlights this week included Patrick Tan’s (from the Genome Institute of Singapore) presentation from a study of Aristolochia plants; these plants produce aristolochic acid (AA) – and is found in many Chinese medicine and herbal supplements commonly used to treat a variety of ailments from arthritis to menstrual cramps. AA is highly mutagenic and has been shown to cause mutations leading to kidney and urinary tract cancers. An increasing global burden in AA-related urinary tract cancers in China and Taiwan, as well as outbreaks in the USA and Europe, sparked Tan’s interest to study its potential relationship in other cancer types, and it was nice to see him re-analyse data produced by the Asian Cancer Research Group (ACRG) that is hosted in our GigaDB database.

Dennis Lo from the Chinese University of Hong Kong presented his pioneering work in the field of non-invasive pre-implantation genetic diagnosis of trisomy 21, 13 and 18. His latest work focuses on twin pregnancies which present a new challenge due to mono- or dizygocity. They applied a targeting sequencing approach and found differences in allele expression between mono and dizygotic twins, useful in determining zygosity and noninvasive testing for genetic disorders.

Michael Hayden of the Translational Medicine Lab at A*STAR gave an interesting presentation on how genetics is helping drug discovery. His group are particularly interested in ‘black swan‘ events – events that are completely unexpected and have extreme impact. Hayden emphasised we no longer have a sequencing issue, but a phenotype issue. He gave several nice examples of how target validation is tightly linked to a phenotype which led to drug discovery and clinical translation/treatment. An example ‘black swan’ event was his work on sclerosteosis – an opposite phenotype to osteoporosis consisting of increased bone density; their analyses lead to the creation of a humanized monoclonal antibody to inhibit sclerostin for osteoporosis.

Mike Snyder (Stanford) gave an update on his “Synderome” personal genome work, iPOP (integrated personal omics profiling), and highlighted their genome phasing approach. Being a big fan of crowdsourcing and community annotation approaches (see our OpenAshDB and “peoples parrot” papers) – there is currently 50Tb of Snyder’s own data consisting of genomic (blood, saliva and maternal), transcriptome and protein array, as well as proteomic and metabolomic data, all available from his website: snyderome.standford.edu. Peer Bork also presented his my.microbes project to catalog, share and compare microbiomes between interested parties. His concept of people with specific microbiota enterotypes using social networks to discuss which brand of yoghurts are best for stomach upsets shows how important genomic advances are increasingly becoming part of our every day lives.

Nicole Nogoy, Commissioning Editor, GigaScience

Promoting open access publishing in Singapore
Tomorrow morning, GigaScience will be participating in an Open Access workshop held at A*Star’s Fusionopolis kindly organised by Kostas Repanas, in a bid to promote open access publishing and data sharing in Singapore. In addition to GigaScience, there will be presentations from BioMed Central, Springer Open, and Wiley Open Access, and if you are at A*STAR or in Singapore see the program below and please drop by:

Date: Thursday 18th April
Time: 9:00-12:00
Venue: Infuse, Level 14, Connexis South Tower, Fusionopolis
1 Fusionopolis Way

A*STAR Open Access (OA) Workshop

9:00-9:20 Coffee/Tea

9:20-9:30 Opening remarks by Sir David Lane, A*STAR Chief Scientist

9:30-10:00 OA, open-data, open-source and open-review: GigaScience, and how licensing can change the way we do research

Dr Scott Edmunds, Executive Editor, GigaScience journal
Dr Nicole Nogoy, Commissioning Editor, GigaScience journal

10:00-10:30 OA and the future of publishing

Mr Robert Long, Vice President and Sales Director, Asia Pacific, Wiley
Mr Anthony Lau, Vice President, International Development, Asia, Wiley

10:30-10:50 Tea break

10:50- 11:10 OA publishing at BioMed Central: a pioneer’s perspective

Dr Nandita Quaderi, Publisher, BioMed Central
Dr Rebecca Furlong, Editor, Genome Medicine

11:10-11:30 OA repositories and Self-Archiving / SpringerOpen Journals

Mr Leo Cheung, Open Access Publishing Manager, BioMed Central
Mr Bin Walters, SpringerOpen Journal Manager, Springer Asia

11:30-12:00 Open discussion/debate

Update 20/04/13: here are the slides from our talk:

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The 2013 Galaxy Community Conference (GCC2013) and GigaScience are today announcing a call for papers for a special thematic focused series on studies utilizing large-scale datasets and workflows. Galaxy is an open, web-based platform for data intensive biomedical research allowing their growing community of users to reproduce and share analyses. GigaScience, with its aims to increase reproducibility and transparency of research has just launched its own Giga-Galaxy server, enabling the hosting and implementation of Galaxy-based workflows and methods. To see examples of how this works, see the slides from our flashtalk at the recent Beyond the PDF2 meeting, and examples of workflows implemented from our recent SOAPdenovo2 paper.

All accepted oral presentations from the meeting will be eligible for consideration in the GigaScience series, and working with the GCC2013 scientific committee, peer review will be coordinated, thorough and timely. BGI has been generously covering the open-access article-processing charges for the journal’s launch, and this offer will be extended to all submissions from the 2013 conference, which is being held on the 30th June-2nd July in Oslo.

Covering the themes of the conference, discussion and research is considered highlighting best practice for local Galaxy installation, management and use, as well as interesting tools, data sources, or novel uses of Galaxy. Addressing many of the goals of Galaxy to enable more accessible, reproducible, and transparent genomic science, submissions can utilize our novel format, where all of the workflows, tools and supporting data can be hosted and integrated into accepted papers using independently citable DataCite DOIs from the journals Giga-Galaxy server and GigaDB database. For more on GigaScience’s efforts to promote reproducibility see the recent blog post on how this worked for some of our recent publications.

Please contact the conference organizers or GigaScience editors for further information, or submit a manuscript or conference abstract, mentioning you would like to be considered in the series. The deadline for consideration for oral presentations to the meeting is 12th April, but later submissions for exceptional poster presentations (deadline 3rd May) may also be considered. This series will remain open in a similar manner to our Genomic Standards Consortium and beyond: best practice in genomics research series, so related work utilizing Galaxy can also be continued to be added to the virtual issue. Follow @gigascience and #usegalaxy on twitter, and the Galaxy news page for further updates and news.

Dr Xin Zhou is Director of the Environmental Genomics research group at Read more]]> Following from our previous blog posting, here we profile and interview Dr Xin Zhou, lead author of our recent “squishome” insect goo metabarcoding paper. This NGS (next generation sequencing)-based work has already generated a lot of interest (see this write-up in Wired and this blog posting for nice examples), and here Dr Zhou gives more insight into the potential for the technique in studying biodiversity, as well as some of the quirky findings his team made validating the technique behind their laboratory in China.

Dr Xin Zhou is Director of the Environmental Genomics research group at BGI, and Director of the Bio-resource Bank of the China National GeneBank. A biodiversity expert and Entomologist by training, Dr Zhou carried out his postgraduate studies and postdoctoral training at Rutgers University and University of Guelph, managing barcoding projects for the International Barcode of Life. From assembling and curating barcode reference libraries for a number of aquatic insect groups, his work has moved to the development of sequencing based analytical pipelines for bulk insect samples at Guelph, and since moving to China in October 2010, at BGI.

How is this method of PCR-free metabarcoding an improvement on previous techniques?

XZ: In PCR-based metabarcoding approaches, various primer sets are used to amplify target DNA fragments, which almost always introduce taxonomic biases, such that some organisms are easier to be detected while others are constantly missing or under-represented. This artificial bias poses a serious problem to all biodiversity studies where species composition is of a concern. Our work is the first of its kind that shows analyzing natural biodiversity samples doesn’t have to rely on PCR therefore bypassing the primer issue. In addition, our work demonstrates that the PCR-free pipeline may potentially reveal species abundance from the mixed arthropod sample, providing yet another piece of crucial information to ecologists alike.

What does this NGS-based technology bring to it over just manually barcoding the collected samples?

XZ: Significantly reduced time and labor in sample processing as well as overall cost to analyze bulk samples.

What can you actually do with this the data, and what potential new applications does this technique enable? If you can say, what are you planning to do with this technique next?

XZ: This paper is a proof of concept demonstrating that natural bulk samples can be analyzed using NGS without having to relying on PCR amplification. This is the first step towards empirical applications of the new methodology in ecological and biodiversity related researches. We demonstrate that this new pipeline CAN work while a few technical issues can be improved for its wide implementation, such as in mitochondrial enrichment and tissue preservation. While trying to improve these technical details, we plan on increasing diversity scales of arthropod samples by analyzing those collected in tropical regions and arrays of insect samples collected from real-world ecological sampling designs.

Why did you collect the specimens you validated it with behind your lab at BGI [based in the large urban city of Shenzhen], and were you surprised by the number of specimens you detected?

XZ: This was an advantage working in a subtropical region where biological samples are relatively easy to obtain. Although the sampling was not comprehensive in terms of intensity of traps and number of species, we were surprised to see what we managed to collect in the middle of a community township. The 2 sampling sites were very close to each other yet there were merely ~10% of the total species being shared between them. Also, the fact that only very few of our barcoded specimens received a sequence match from the Barcode of Life Data Systems, the world’s largest barcode reference database, suggests that much of China’s arthropod fauna still remains as a mystery, at least from a molecular aspect. On top of all that, we thought it would be an interesting idea to present BGI’s headquarters in a scientific publication for the first time, with its GPS coordinates recorded in a meta-database.

Does this example say anything useful about the biodiversity in Shenzhen and the area around the BGI HQ?

XZ: as stated above, although the 2 arthropod bulk samples represent typical fauna of a secondary forestry ecosystem in Southern China, the overlap between samples was minimum and much of the community was poorly understood both morphologically and molecularly. We believe there is an urgent need to improve our knowledge on China’s arthropod fauna. And we will start from where we live. We have a plan to barcode and metabarcode insects and plants of the Shenzhen municipal area.

In the paper it was interesting you found a novel COI [cytochrome oxidase subunit I – a common mitochondrial marker used in barcoding studies] from a Lepidoptera species not found in the reference library. Can you say a little more on this example, and is this a potentially new species?

XZ: Based on the quality of the nucleotide sequences and overall coverage of the novel barcode, we tend to believe that this is a real taxon that was somehow not detected in our morphological and barcode examinations. However, given the protocols used in this work, it is not possible to identify the exact source of this novel COI sequence. We listed a few potential possible reasons, including gut content, small residual tissues in the bulk sample, extracellular DNA etc. This novel sequence doesn’t get a sequence match in any existing barcode databases. But this is not a big surprise as we know that Chinese insect species are not well-sequenced. The ultra-deep sequencing capacity of the NGS platforms opens up a new prospective where we are now capable of revealing diversity of the even-smaller-things-that-run-the-world via detecting their molecules. This would not have been possible if we had to rely on the visual cues of these organisms. In some sense, the contribution of NGS technology to biodiversity research is equivalent to what microscopes did to microbiology.

On that subject, what potential does this technique have to help discover new species, and how much can you actually tell about them using it?

XZ: NGS technology creates an alternative way to analyze biodiversity pattern and its temporal and spatial variations by detecting molecular or genomic heterogeneity (MOTUs) in bulk environmental samples. However, to make sense of these molecular operational units, one would have to compare this sequence information to well-curated sequence databases that are tied to conventional biological species concepts. A good example of these databases is the Barcode of Life Data Systems, where millions of barcode sequences are linked to voucher specimens. My feeling is that the construction of sequence reference databases will remain critical in future molecular/genomic biodiversity research as it is a crucial step to provide linkages to the classic school of organismal science. However, NGS cataloging of world biodiversity can be performed in parallel. As long as meta-data are maintained for the bulk samples, biodiversity can be registered as MOTUs at first in a much accelerated fashion, and then be compared against existing reference databases available at the time. Known and (potentially) new species can be gradually revealed during this procedure. As biodiversity registration can be significantly accelerated using NGS, understanding biodiversity and especially interactions among species will be a long-term endeavor.

What are the implications for this technique in the growth of data taxa in the databases? As it is more high-throughput does it have the potential to massively increase the number of new entries?

XZ: This PCR-free approach can produce more accurate result in terms of species composition for bulk biological samples.

In terms of impact in increasing data entries in the databases, I believe this will be the future trend in biodiversity genomics. As the emergence of new technologies and rapid reduction in costs, the research community will be able to analyze much more biological samples in much shortened periods of time. The outcome will be an improved understanding of biodiversity changes based on consistent and standardized analysis procedures and intensified sampling (in terms of numbers of sampling sites across space and time and specimen numbers).

Is there anything else that you want to tell me about the technique?

XZ: the new PCR-free pipeline we created in this paper has further potentials in terms of construction reference genomes, such as mitochondrial and chloroplast genomes, in a much more economically efficient way. Based on our findings in the present work, much of the other mitochondrial genes of most of the insect species from the mixed sample can also be assembled with a decent N50 value. For instance, the largest scaffold we managed to assemble from the insect soup was a moth representing almost the entire length of its mitochondrial genome. This means that with some tweak of the current pipeline, we would be able to sequence and assemble small genomes for many different species in one shot. Having a comprehensive reference library for mitochondrial genomes can solve many of the difficult questions faced in the classic barcoding community, such as primer designs for the standard barcode region for difficult groups, e.g., Hymenoptera. Also this potential opens up the door to expanding classic barcoding methods from the current single-molecule approach to genomic screening.

Further reading:
1. Zhou X; et al., Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification GigaScience 2013 2:4 http://dx.doi.org/10.1186/2047-217X-2-4

Creepy crawlies are important indicators of diversity, as arthropods make up 80% of described species, and with an estimated only Read more]]> Insect goo aids biodiversity research
Apologies to Jonathan Eisen (see Badomics in the journal), but today in GigaScience we publish a new “squishomics” approach for assessing and understanding biodiversity, using the slightly wacky sounding method of combining DNA-soup made from crushed-up insects and the latest sequencing technology. This bulk-collected insect goo has the potential to rapidly and cheaply reveal the diversity and make-up of both known and unknown species collected in a particular time and place.

Creepy crawlies are important indicators of diversity, as arthropods make up 80% of described species, and with an estimated only 20% of insects characterized to date. The new method devised by Xin Zhou and colleagues at BGI, is a more accurate and quantitative version of a new biodiversity analysis technique called metabarcoding. Doing some initial validation on the analyses has already revealed how diverse and poorly characterized insect communities (or diversity) can be, even from two small sites within the researchers’ own backyard— literally.

Combining DNA barcoding, which utilizes a standard gene fragment for species identification with next generation sequencing technologies; previous metabarcoding methods, however, have required a step to amplify the amount of DNA collected that uses PCR. This step can introduce problematic errors into the analysis. The authors of this study have found a way to carry out this method without this step, giving it the potential to be more accurate. In addition to assessing species diversity, it also allows the researcher to determine the total quantity of mitochondrial DNA present for each species, making it possible to reveal relative abundance and biomass of each species. Allowing more consistent and rapid sampling, this may simplify the study of changes in biodiversity over space and time and transform the way we study ecosystems.

What really lies at the bottom of your garden
Testing the technique on species collected on a hillside behind their laboratory, the authors were very surprised by what they managed to find in their own neighborhood. BGI, the world’s largest genomics organization, is situated on the edge of Shenzhen, a city of 12 million people in the densely urbanized Pearl River delta. Setting up two traps close to each not only revealed how much diversity there was, but also detected species not currently present in online databases. The findings demonstrated how little is known about insect diversity in China, and by opening up the ability to carry out these types of systematic and high-throughput analyses — enabling it to be tested if this is the case everywhere else in the world. Unfortunately this hillside is currently being leveled and built upon for new building projects as the relentless urban and industrial development in China continues (see picture), so this surprisingly rich environment is not likely to remain as diverse for much longer.

Of the study, Dr. Zhou said: “The 2 sampling sites were very close to each other, yet there were only around 10% of the total species being shared between them. The fact that only very few of our barcoded specimens received a sequence match from the Barcode of Life Data Systems, the world’s largest barcode reference database, suggests that much of China’s arthropod fauna still remains as a mystery, at least from a molecular aspect.” With the ability to detect and discover tiny organisms, stomach contents and partial samples without the usual visual cues, he also adds, “In some sense, the contribution of NGS technology to biodiversity research is equivalent to what microscopes did to microbiology.”

Open Science: the best way of proving insect data isn’t “buggy”
Following from our recent SOAPdenovo2 paper, this study is the second time we have integrated into the paper separate DOIs for making all of the supporting data and pipelines available. Hosted in our GigaDB database, the ability to independently cite these rewards the authors for making them available, and also boosts the transparency, reproducibility and utility of this work. As the pipeline is adapted from the open-source SOAPdenovo2 application all of the code has to be released under a similar license, and we have hosted it in our GitHub repository. To further boost the utility, the authors have worked hard to follow best practices for metadata laid out by the Genomic Standards Consortium (GSC). Contextual information is essential for environmental studies such as this, and while there are currently not modules for this new data type, the authors have built upon the GSC MIMs checklist and provided all of the information they thought relevant as a starting point for building new standards. With a transparent and open-review process (see the pre-publication history here), and with work currently underway to implement the workflows in our Giga-Galaxy platform, we hope that this paper presents another good example of what we are attempting to do at GigaScience to our papers more transparent and reproducible. As a really exciting study potentially opening up huge new areas of research, we hope this additional work pays dividends allowing future users to recreate and adopt the technique much quicker and easier.

Further reading:
1. Zhou X; et al., Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification GigaScience 2013 2:4 http://dx.doi.org/10.1186/2047-217X-2-4

2. Zhou, X; Li, Y; Liu, S; Yang, Q; Su, X; Zhou, L; Tang, M; Fu, R; Li, J (2013): Raw data, assembly and annotation results for: “Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification”. GigaScience Database http://dx.doi.org/10.5524/100045

3. Zhou, X; Li, Y; Liu, S; Yang, Q; Su, X; Zhou, L; Tang, M; Fu, R; Li, J; Huang, Q (2013): Software and supporting material for: “Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification”. GigaScience Database http://dx.doi.org/10.5524/100046

UPDATE 2/4/13: check out the related Q&A with the lead author in a follow-up posting.

Read more]]> The BBSRC has just released an excellent video and article on crowdsourcing killer disease outbreaks very relevant to our recent commentary and blog postings on the OpenAshDB (the Ash Dieback disease crowdsourcing) project. Featuring interviews from Nick Loman and Lisa Crossman (also an author on our OpenAshDB paper), key contributors to the 2011 E. coli O104:H4 outbreak genome crowdsourcing effort, it gives a very good overview of the how our initial release of public domain genomic data via twitter helped kick-start a burst of crowd-sourced, curiosity-driven analyses around the world aimed at understanding and fighting the outbreak.

We have written a lot about this twitter driven “tweenome” analysis in the past, but this video and article tries to learn lessons from the E. coli project and use these experiences for the related effort to tackle the devastating spread of Ash Dieback disease in Europe. Since the publication of the OpenAshDB paper (currently our most viewed article this month), further analyses and data continues to be added to the GitHub based repository, including geospatial data and new secretome predictions. The BBSRC is taking a big interest in the potential of this method of potentially speeding up research practices, with the recent announcement of funding for up to £2M proposals to develop and deploy crowdsourcing approaches to complex, large-scale scientific problems. Further interesting viewing on this subject include Mark Pallen’s excellent talks on open-source genomics, and the brilliant TEDx talk by Jennifer Gardy on 21st century public health. This talk was extremely prescient in 2009, and highlighted early efforts using wiki based resources for tackling that years H1N1 influenza pandemic.

As the cost of and speed of sequencing continues to drop, these early examples hopefully will inspire and encourage what our editorial board member Mike Schatz terms in another recent GigaScience commentary: the rise of a “digital immune system”, by observing the microbial landscape, detecting potential threats, and neutralizing them before they spread beyond control. The ultimate aim of these projects is to shape the way we tackle future infectious disease outbreaks, speeding up the response to such an extent we potentially stop these outbreaks before they even happen. These are laudable goals and a fantastic example of what can be enabled by open-science, and GigaScience will continue to do what we can to help promote and encourage such schemes in the future.

Further Reading
1. MacLean, D; et al., Crowdsourcing genomic analyses of ash and ash dieback — power to the people. GigaScience 2013, 2:2
2. OpenAshDB Website: http://oadb.tsl.ac.uk/
3. Schatz, MC & Phillippy, AM The rise of a digital immune system. GigaScience 2012, 1:4

Read more]]> Bye Bye Bluebells
Bluebell woods, the dense carpets of violet–blue flowers found in ancient woodland are a spectacular and famous springtime sight in Britain, but this picture postcard scene is threatened as never before. Chalara fraxinea or ash dieback, a devastating fungal disease of ash trees has swept across northern Europe, and has now reached Britain, a country particular susceptible to its potential onslaught, as the estimated 80 million ash trees make up 30 per cent of woodland across the country. Ash trees particularly encourage biodiversity, as the tree branches are ideally spaced for light to pass through and let the bluebells and other species on the forest floor grow. Spreading over mainland Europe since its discovery in Poland in 1992, the disease has been particularly virulent in Northern Europe, with 90% of ash trees in Denmark affected. It had been hoped that Britain could act as a bulwark against the disease, but in October last year due to slow government response and a shortage of qualified plant pathologists everyone’s worst fears were realized, when the fungus was for the first time found growing growing in mature trees in Eastern England.

Crowdsourcing and Open-Science to the Rescue!
With hope of quarantine and eradication now over, the latest government strategy is to slow its spread and develop and restructure the woodland with resistant trees. To keep on top of an evolving highly infectious pathogen with a wind-borne spread that can spread in the wind is a particularly onerous task, particularly with the lack of experts and scientists on the ground. Crowdsourcing, opening up the fight against the pathogen to the global wisdom of the crowds, as well as harnessing the rapid transfer of information on social networks and “hive mind” of the web is one potentially way to address this. Already a team of developers and scientists have developed AshTag, a smartphone app that the public can use to report suspected cases of infection.

Following on from this, this week in GigaScience we publish a paper from a community of scientists taking an “open-source genomics” approach to engage and use the global genomics community in this fight. To kick start genomic analyses of the pathogen and host, Dan MacLean and colleagues from OpenAshDB present a call to arms to the research community entitled “Crowdsourcing genomic analyses of ash and ash dieback – power to the people”. Taking an usual step to immediately release the initial genomics datasets as soon as it is produced, they have producing a website (oadb.tsl.ac.uk) and GitHub based platform to share and analyse the data and results. While there have been attempts to crowdsource human disease outbreaks before (see this excellent TEDx talk from Jennifer Gardy on H1N1), this is the first time it has been attempted on a plant disease of such importance. This open-source genomics approach also follows on and learns lessons from the deadly 2011 European E. coli 0104:H4 outbreak, and the approach that our colleagues at the BGI and others followed to crowdsource the analysis of the pathogens genome via twitter, blogs and GitHub.

Using a very similarly structured collaborative GitHub-based platform that the Era7 team built upon the original E. coli data released by the BGI as the first data DOI in our GigaDB database, the OpenAshDB project aim to take this open-science approach even further, with plans for collaborative authorship for contributors and to work with pre-print servers before publication of the final products. On top of the altruistic reasons of scientific curiosity and wanting to protect biodiversity and the environment, one of the key incentives for taking part in a project such as this is obviously the traditional one of obtaining scientific credit. While everything has to be quickly released into the public domain to maximize its use, contributions can still be tracked through GitHub via commit number and traditional mechanisms such as citation. Working with DataCite we issued our first DOI for the E. coli genome, and the altmetrics community using tools such as Impact Story have shown it is also possible to track GitHub use through similar means (see this example for the E. coli GitHub).

The Tweenome Revisited
Crowdsourcing and open-science is an area we at GigaScience are keen to promote and support, and this paper comes on top of recent papers published on community sponsored/assembled Parrot genomes (AKA the Peoples Parrot), and personal genomics analysis via blogs. We have written previously on how our and collaborators at UMC Hamburg-Eppendorf release of CC0 (the most open public domain waiver) E. coli genome data via twitter enabled others (with special mention of early work from Nick Loman and the Era7 team who helped get the ball rolling) to kick-start a burst of crowd-sourced, curiosity-driven analyses from bioinformaticians around the world. Dubbed by some as the first “Tweenome”, this project led to a high profile paper in New England Journal of Medicine, and now over 18 months on it is a good time to look back and see the downstream consequences, and if any lessons can be learned for OpenAshDB and other projects with similar aims.

Among the over 110 citations to the paper so far (according to google scholar), the study provided insight into the pathogenicity, evolution, and treatment of the pathogen as well as assisting platform comparison studies. Obviously the main aim of doing science in this accelerated way was speed up diagnosis and treatments, and the E. coli data enabled the rapid development of diagnostic tests and anti-microbial agents. These are useful examples, as better diagnostic tests and potential therapies are obviously important downstream outcomes and goals that the OpenAshDB project could help enable.

Probably the projects biggest legacy is as an example of open-science, data-citation, and the use of CC0 data. After releasing the data under a CC0 license this allowed truly open-source analysis, and the UK HPA and github members followed suit in releasing their work in this way. Following this example, a team at Pacific-Biosystems also released their related data in a similar manner, using the example of their fellow E. coli data producers to allow them to release their data without wasting time on legal wrangling. This example has subsequently been used as an example for future UK and EU science policy, with the Royal Society in the UK using the E. coli crowsourcing as an example of “the power of intelligently open data”, and highlighting it on the cover of their influential “Science as an Open Enterprise” report.

We hope that the OpenAshDB project leaves a similar legacy, and being Ash Wednesday we hope that the many in the genomics community join the effort to study and fight this devastating ecological threat. It will not only enable future generations to continue to appreciate the beauty of bluebell woods, but provide an example of how science can be more collaborative, faster and more efficient in this new era of open-science and open data. As the authors of the paper state in the working title of the article – power to the people!

Further Reading
1. MacLean, D; et al., Crowdsourcing genomic analyses of ash and ash dieback — power to the people. GigaScience 2013, 2:2
2. OpenAshDB Website: http://oadb.tsl.ac.uk/
3. Notes from a Tweenome: http://blogs.biomedcentral.com/gigablog/2011/08/03/notes-from-an-e-coli-tweenome-lessons-learned-from-our-first-data-doi/
4. Rohde, H; et al., Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4. N Engl J Med 2011, 365:718-724.

Read more]]> With everyone in a reflective mood as the year comes to a close, one of the big scientific trends of 2012 has obviously been the high profile that open-access and more open methods of carrying out science has received. With the Elsevier boycott, UK Finch report, and launch of a number of innovative new schemes in publishing open-access research and data (including F1000 Research, eLife, PeerJ and of course GigaScience), 2012 has been talked of as the year of an “academic spring” that has started to shake up the centuries old, stuffy and closed system of scientific discourse.

On top of changes to the way scientists and readers are demanding they can access and mine the literature and data, new incentives and mechanisms to release and publish data (of which we have written extensively), the process of peer-review has also come under the spotlight, and there has been a lot of talk on the deficiencies of this system. Many newly launched journals have tried to make the system more transparent, using systems such as post-publication peer-review (e.g. F1000 Research), pre-print servers (e.g. the increasing acceptance of arXiv in biology), providing access to anonymized (e.g. EMBO journals) or partial (elife) parts of the peer-review history, or encouraging reviewers to opt-into open peer-review (PeerJ, and experimented with a little at PLOS). At GigaScience we have decided to take this process one step further and ask for open peer-review as default, and as our aims are to promote more open, reproducible and transparent-science we feel it promotes accountability, fairness, and importantly gives credit to reviewers for their hard efforts. A new publication in the journal today provides a particularly useful example of how this process has worked, so we have decided to highlight it here in GigaBlog, and would welcome feedback and comments on our approach.

What is SOAPdenovo2?
Today we publish an updated version of BGI’s popular SOAPdenovo software application (the original version having 460 citations according to googlescholar), a start of the art tool for de novo genome assembly. De novo assembly – piecing together genomes from sequencing data without the aid of a previously assembled reference, is a particularly computationally intensive and technically challenging task. BGI and their SOAPdenovo tool has been particularly adept in this area, using it to assemble hundreds of new plant and animal species genomes, as well as finding huge amounts of previously undetected structural changes when applied to individual human genomes. De novo genome assembly is an important and competitive area in bioinformatics, and there have been a number of assembly competitions and genome assembler “bake offs” to compare and benchmark the various applications and methods available for carrying this out, the Assemblathon and GAGE assembly competitions and evaluations being notable examples of this.

New developments in version 2 of SOAPdenovo have focused on using more efficient algorithms and data structures to reduce the memory requirements, better optimizing and handling of errors and low coverage or heterozygous regions, as well as improved closing of gaps. To demonstrate the improvements and that the application truly is the state-of-the-art for de novo assembly of large vertebrate genomes, the authors reassembled BGI’s YH Asian reference genome with the new and original versions of SOAPdenovo, version 2 producing contig sizes 3 times larger, and with nearly two thirds of the maximum memory consumption. Doing comparisons against other state of the-art assemblers such as ALLPATHS-LG, SOAPdenovo2 outperformed them for many metrics on the Assemblathon and GAGE benchmark datasets tested, really showcasing and demonstrating the potential power and utility of this new application for the bioinformatics community.

Open peer-review, GigaScience style
Stating that SOAPdenevo2 can perform better than other state-of-the-art assembly tools is one thing, but to justify and prove this review and testing by independent peers is needed, and the larger and more complicated an application is (particularly an issue for us being a journal that focuses on data heavy research studies), the more challenging this can be. In order to ease, throw light and credit the reviewers in this process GigaScience uses a much more transparent, accountable and open peer-review process. Tailoring the process for such data heavy studies our criteria for publication is based more on relative amount of data created or used, and transparency and availability more than subjective and unpredictable measures such as supposed “impact”. For software and methods papers what is being presented obviously has to be an improvement on what is currently available, but for scenarios such as genome assembly where there is obviously no “one-size-fits-all” solution, assessment has to be based on the new method being an improvement in at least one potential application.

During peer review we host all of the supporting information and data (totaling 78GB in this case) and our curators work and make all of it available to the peer-reviewers from our ftp servers. In this case we worked with three groups of expert reviewers (8 independent experts in total) who thoroughly tested the software against various tools and datasets provided to ensure the claims made by the authors were correct. On top of providing all of the test data and scripts and tools that support the paper, to aid the process the authors also provide detailed pipelines with the tools and configured packages including commands and necessary utilities to reproduce the different tests carried out in the paper.

Whilst used in a number of medical journals, almost unprecedentedly in biology we ask as default all of the reviewers to carry out open peer-review, and in this case all 8 of them consented and signed their names to the reports that are now available to view from the pre-publication history section associated with our published articles. To see how this looks you can follow the history of the SOAPdenovo2 paper here.

While we do have the option for reviewers to opt-out and anonymize their reports if they have concerns about this process, it is encouraging that for all of the papers we have reviewed so far none have asked to do this. A number of new journals are starting to encourage reviewers to sign their reports, but this is the default option for GigaScience, with the option of opting out if the referees have reasons to remain anonymous. We also give the reviewers the option of making confidential comments to the editors (particularly on ethical and policy issues), but so far the quality of the reports has generally been very constructive, and previous studies on open peer-review have also found that quality and courteousness of reviews were increased, with little if any negative effects. By making the process more open and transparent competing interests and biases are reduced, and reviewers are able to take credit for the hard efforts they have put into the review process, and even declare and include it in their CV if they wish as we would like to put the content of accepted papers reviews under a CC-BY license. The benefits of this increased transparency to readers are also useful, as they do not have to take it on trust that published manuscripts were reviewed by qualified reviewers, and for educational purposes they can see good examples of how peer review operates.

Promoting reproducibility, GigaScience style
On top of boosting transparency and reproducibility of peer-review of data-heavy studies, GigaScience also carries this over to the publication process, and this paper is also an excellent example of this goal. On top of SOAPdenovo2 meeting our requirements of being open source and having its code in a repository (sourceforge), the authors also provide detailed pipelines with the tools and configured packages including commands and necessary utilities to reproduce the different tests carried out in the paper. With 78GB of test data and 30MB of tools and scripts being much larger than any other journal is able to handle, we have made all of these available from our GigaDB database as separate citable DOIs. Taking this a step further, on top of being able to be downloaded by ftp and our Aspera license (allowing up to 10-100X faster access), the software and analyses are also currently being integrated into our Galaxy-workflow system based data platform.

While we have previously published software articles and pipeline studies combining reference datasets and tools before (see our methylome pipeline paper with 84GB of supporting information), this is the first paper that we have given separate DOIs to the tools and data. The logic for doing this is that both can now be credited to potentially different groups of authors, the data and methods/analyses may be used and cited independently of each other, and each can be tracked and credited to each author via DOIs listed in their ORCID account. We feel that it is important to credit method as well as data production, and while DataCite currently recognizes “Software” as a resource type, we are encouraging and working with them to add “Worklow” to their list of handled objects.

Work is ongoing on the workflow and data platform side, but we are currently in the process of reviewing a number of other software articles (called Technical Note in GigaScience), and if you have similar studies you are interested in having reviewed in a more transparent and constructive manner please contact us at editorial@gigasciencejournal or submit it through our online submission system. As process is still currently evolving and being fine-tuned we would welcome any feedback via this blog, twitter or email. We would like to thank the team of reviewers of this and our other manuscripts so far for their hard efforts, as well as the authors for being so helpful in making their work and data available in such a reproducible manner. Many journals are tentatively starting to experiment going down a partially more open route, but from our positive experiences so far we would encourage them and others to be more bold and go all of the way.

Further Reading
1. Luo R et al., SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler GigaScience 2012, 1:18
2. van Rooyen S et al., Effect of open peer review on quality of reviews and on reviewers’ recommendations: a randomised trial. BMJ 1999, 318:23-7
3. Walsh E et al., Open peer review: a randomised controlled trial. Br J Psychiatry 2000, 176:47-51.
4. Wang, J; et al., (2012): Updated genome assembly of YH: the first diploid genome sequence of a Han Chinese individual (version 2, 07/2012). GigaScience Database. http://dx.doi.org/10.5524/100038
5. Luo, R; et al., (2012): Software and supporting material for “SOAPdenovo2: An empirically improved memory-efficient short read de novo assembly”. GigaScience Database. http://dx.doi.org/10.5524/100044

UPDATE 24th Jan 2013: we have produced an editorial on our peer-review policies based on this blog and the feedback we received on it. Also check out the great work the Homolog_us blog has done testing and studying SOAPdenovo2 making the source-code even more transparent via this wiki: http://homolog.us/wiki/index.php?title=SOAPdenovo2

Read more]]> Regular reader of this blog will be aware of our efforts to promote data citation using digital object identifiers (DOIs), and this week, alongside Rebecca Lawrence from F1000 Research and Kevin Ashley from the Digital Curation Centre, our Editor in Chief Laurie Goodman has a correspondence in Nature strongly making this case. The motivation to cite datasets comes from a recognition that data generated in the course of research are just as valuable to the ongoing academic discourse as papers, and should therefore be treated in the same manner. The correspondence makes this case, and is timely with the recent launch of Thomson-Reuters data citation index. While datasets can be linked to database accessions and other identifiers, DOIs are more stable and permanent than URLs, and have the crucial advantage over alternatives in that they are already familiar to researchers, publishers, and libraries. With the new ORCID system allowing DOIs to be imported and linked to an authors other research works, funders such as NSF allowing datasets to be listed in biosketches, and data citation indexes now allowing datasets to be tracked and credited to data producers, there are finally tangible benefits and incentives for data producers in creating and citing data DOIs.

We have previously written about this subject and making the point in high profile journal such as Nature, the authors hope this can promote this point to a much wider audience, and also help directly lobby publishers such as NPG. This is tempered slightly by having to make these arguments in a closed access forum, especially as this means we are not able to reproduce the content here, but we are currently double checking we can put the pre-print version up. The published letter “Data-set visibility: Cite links to data in reference lists” is very short, but in the limited word limit allowed the authors ask publishers, funders, researchers and institutions that Datasets should be more prominently linked to their associated research articles as standard practice, and link to the DCC best practice guidelines to give more detailed instructions on how to do this. They also make a further argument that this increased visibility and accessibility of datasets would also benefit peer-reviewers and readers by “raising standards of data analysis, promoting more detailed review, encouraging data curation and boosting reproducibility and data reuse”.

A brief history of data citation
Working with DataCite and the British Library, our GigaDB databases first DOI (the genome of the deadly outbreak strain E. coli) was issued in June 2011 and we and the growing number of data publishers (including our co-authors on this correspondence F1000 Research) have been working closely with a number of publishers to allow the citation of datasets. The Nature commentary makes the point that at present very few journals are currently doing this, but after our initially unsuccessful attempts at getting DOIs included in the NEJM E. coli paper and DOIs into a Nature Biotechnology paper, our experiences of working with publishers has generally been more positive. Of the journals polled by F1000, only Cell Press and Ann Oncol have said they would have an issue with the pre-publication release of data in this manner. After working closely with the editors of Genome Biology to include the DOI of the Sorghum genome in the references of a paper in November 2011, BioMed Central have used this example in their instructions for authors as how to cite datasets, and we published a commentary in the BMC Research Notes Data Sharing and Standardization series highlighting this best practice. Since the Sorghum example, PLoS, Springer, Science, and a number of publishers have now started properly integrating DOIs from Dryad, Figshare and a number of other data publication platforms into their references.

Two Steps Forward, One Step Back
Following some initial difficulties getting the DOIs from Macaque genomes into a Nature Biotechnology publication, in February 2012 the editors agreed to allow the first DOIs to be cited in the journal. In October this year two papers in the same issue of the main Nature journal included GigaDB datasets in the references, and this letter is a further welcome sign of support from Nature and NPG. That there is still much work to be done is still clear though, and we have a demonstration of the challenges that are still to be overcome in the very week that this letter has been published. Having the correct editorial policies is one thing, but these need made to be made clear in the instructions the authors, editors and production departments follow, and in a paper just published in Nature Genetics on Panda population genomics, the DOIs for the Panda and Polar Bear genomes were moved by the journal from the references list to the URLs section of the journal. Relegating the datasets in this way not only prevents the data producers receiving due credit for making their data publicly available, not having the data in the references means they have also lost the ability to count and track the citations in the data citation index.

As the DOIs in the article were changed into the URLs that they redirect to (e.g. http://dx.doi.org/10.5524/100004 was changed to the current GigaDB URL http://gigadb.org/giant-panda/), this loses the stability and persistence that DOIs bring, and there is much greater risk in the future that the URLs may change and will no longer become resolvable. This was one of the reasons we have bought DOIs from the British Library and are using them for our datasets, and as we are currently migrating GigaDB to a new location and platform, DOIs give us the flexibility to move domains and locations without downstream users losing access, and preventing the need for citations having to change. Unlike the data Accessions section of the journal that is publicly accessible, and the reference section that is mined by citation indexes, the URLs section is the Nature Genetics is totally hidden behind a paywall and so is not mineable or accessible in any way, totally losing the advantages of linking literature and data. Coming out a few days after this particular example occurred, this letter in Nature will hopefully increase awareness and prevent such incidents happening in the future, and speed up the acceptance and treatment of data as first-class records of research.

Further Reading
1. Goodman, L., Lawrence, R. & Ashley, K. Data-set visibility: Cite links to data in reference lists. Nature 492, 356 (2012).
2. Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nature Genetics (2012).doi:10.1038/ng.2494
3. Edmunds, S. et al. Adventures in data citation: sorghum genome data exemplifies the new gold standard. BMC Research Notes 5, 223 (2012).