http://www.gigasciencejournal.com The latest research articles published by GigaScience 2013-05-17T00:00:00Z With ever-increasing amounts of metabolomics data produced each year, there is an even greater need to disseminate data and knowledge produced in a standard and reproducible way. To assist with this a general purpose, open source metabolomics repository, MetaboLights, was launched in 2012. To promote a community standard, initially culminated as metabolomics standards initiative (MSI), COordination of Standards in MetabOlomicS (COSMOS) was introduced. COSMOS aims to link life science e-infrastructures within the worldwide metabolomics community as well as develop and maintain open source exchange formats for raw and processed data, ensuring better flow of metabolomics information. http://www.gigasciencejournal.com/content/2/1/8 Reza Salek Kenneth Haug Christoph Steinbeck GigaScience 2013, null:8 2013-05-17T00:00:00Z doi:10.1186/2047-217X-2-8 Enabling metabolomic data reuse There is a need to disseminate the increasing amount of metabolomic data in standard reproducible ways. Hosted and coordinated by the EBI, the open source MetaboLights repository, and COSMOS community standards consortium tackle this /content/figures/2047-217X-2-8-toc.gif GigaScience 2047-217X ${item.volume} 8 2013-05-17T00:00:00Z XML An increasing portion of biomedical research relies on the use of biobanks and databases. Sharing of such resources is essential for optimizing knowledge production. A major obstacle for sharing bioresources is the lack of recognition for the efforts involved in establishing, maintaining and sharing them, due to, in particular, the absence of adequate tools. Increasing demands on biobanks and databases to improve access should be complemented with efforts of end-users to recognize and acknowledge these resources. An appropriate set of tools must be developed and implemented to measure this impact.To address this issue we propose to measure the use in research of such bioresources as a value of their impact, leading to create an indicator: Bioresource Research Impact Factor (BRIF). Key elements to be assessed are: defining obstacles to sharing samples and data, choosing adequate identifier for bioresources, identifying and weighing parameters to be considered in the metrics, analyzing the role of journal guidelines and policies for resource citing and referencing, assessing policies for resource access and sharing and their influence on bioresource use. This work allows us to propose a framework and foundations for the operational development of BRIF that still requires input from stakeholders within the biomedical community. http://www.gigasciencejournal.com/content/2/1/7 Laurence Mabile Raymond Dalgleish Gudmundur Thorisson Mylène Deschênes Robert Hewitt Jane Carpenter Elena Bravo Mirella Filocamo Pierre Gourraud Jennifer Harris Paul Hofman Francine Kauffmann Maria Muñoz-Fernàndez Markus Pasterk Anne Cambon-Thomsen GigaScience 2013, null:7 2013-05-01T00:00:00Z doi:10.1186/2047-217X-2-7 Sharing bioresources rewarded <p>The Bioresource Research Impact Factor (BRIF) aims to address the lack of recognition for the sharing of bioresources such as databases and biobanks, enabling crediting and measurement of their impact and use</p> /content/figures/2047-217X-2-7-toc.gif GigaScience 2047-217X ${item.volume} 7 2013-05-01T00:00:00Z XML Background: Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications. A test-retest dataset was acquired to validate fMRI tasks used in pre-surgical planning. In particular, five task-related fMRI time series (finger, foot and lip movement, overt verb generation, covert verb generation, overt word repetition, and landmark tasks) were used to investigate which protocols gave reliable single-subject results. Ten healthy participants in their fifties were scanned twice using an identical protocol 2–3 days apart. In addition to the fMRI sessions, high-angular resolution diffusion tensor MRI (DTI), and high-resolution 3D T1-weighted volume scans were acquired.FindingsReliability analyses of fMRI data showed that the motor and language tasks were reliable at the subject level while the landmark task was not, despite all paradigms showing expected activations at the group level. In addition, differences in reliability were found to be mostly related to the tasks themselves while task-by-motion interaction was the major confounding factor. Conclusions: Together, this dataset provides a unique opportunity to investigate the reliability of different fMRI tasks, as well as methods and algorithms used to analyze, de-noise and combine fMRI, DTI and structural T1-weighted volume data. http://www.gigasciencejournal.com/content/2/1/6 Krzysztof Gorgolewski Amos Storkey Mark Bastin Ian Whittle Joanna Wardlaw Cyril Pernet GigaScience 2013, null:6 2013-04-29T00:00:00Z doi:10.1186/2047-217X-2-6 Neuroimaging data for pre-surgical planning Presented here is test-retest functional MRI dataset for motor, language and spatial attention functions to validate fMRI tasks used in pre-surgical planning for tumor resection, and providing a resource for new methods and algorithms /content/figures/2047-217X-2-6-toc.gif GigaScience 2047-217X ${item.volume} 6 2013-04-29T00:00:00Z XML Next-generation sequencing machines produce large quantities of data which are becoming increasingly difficult to move between collaborating organisations or even store within a single organisation. Compressing the data to assist with this is vital, but existing techniques do not perform as well as might be expected. The need for a new compression technique was identified by the Pistoia Alliance who commissioned an open innovation contest to find one. The dynamic and interactive nature of the contest led to some novel algorithms and a high level of competition between participants. http://www.gigasciencejournal.com/content/2/1/5 Richard Holland Nick Lynch GigaScience 2013, null:5 2013-04-18T00:00:00Z doi:10.1186/2047-217X-2-5 Sequence squeeze: compression competitions Aiming to tackle the genomics data-deluge, the sequence squeeze challenge was an open innovation contest to find sequence compression algorithms that used a novel dynamic leaderboard and cloud computing to assess entries in real-time /content/figures/2047-217X-2-5-toc.gif GigaScience 2047-217X ${item.volume} 5 2013-04-18T00:00:00Z XML Background: Next-generation-sequencing (NGS) technologies combined with a classic DNA barcoding approach have enabled fast and credible measurement for biodiversity of mixed environmental samples. However, the PCR amplification involved in nearly all existing NGS protocols inevitably introduces taxonomic biases. In the present study, we developed new Illumina pipelines without PCR amplifications to analyze terrestrial arthropod communities. Results: Mitochondrial enrichment directly followed by Illumina shotgun sequencing, at an ultra-high sequence volume, enabled the recovery of Cytochrome c Oxidase subunit 1 (COI) barcode sequences, which allowed for the estimation of species composition at high fidelity for a terrestrial insect community. With 15.5 Gbp Illumina data, approximately 97% and 92% were detected out of the 37 input Operational Taxonomic Units (OTUs), whether the reference barcode library was used or not, respectively, while only 1 novel OTU was found for the latter. Additionally, relatively strong correlation between the sequencing volume and the total biomass was observed for species from the bulk sample, suggesting a potential solution to reveal relative abundance. Conclusions: The ability of the new Illumina PCR-free pipeline for DNA metabarcoding to detect small arthropod specimens and its tendency to avoid most, if not all, false positives suggests its great potential in biodiversity-related surveillance, such as in biomonitoring programs. However, further improvement for mitochondrial enrichment is likely needed for the application of the new pipeline in analyzing arthropod communities at higher diversity. http://www.gigasciencejournal.com/content/2/1/4 Xin Zhou Yiyuan Li Shanlin Liu Qing Yang Xu Su Lili Zhou Min Tang Ribei Fu Jiguang Li Quanfei Huang GigaScience 2013, null:4 2013-03-27T00:00:00Z doi:10.1186/2047-217X-2-4 The insect squishome: new tools for biodiversity research A new PCR-free sequencing-based method for metabarcoding provides new tools for studying eukaryotic biodiversity and gain quantitative taxonomic information from a DNA-soup extracted from bulk arthropod samples /content/figures/2047-217X-2-4-toc.gif GigaScience 2047-217X ${item.volume} 4 2013-03-27T00:00:00Z XML Background: Although high-throughput sequencing of small subunit rRNA genes has revolutionized our understanding of microbial ecosystems, these technologies generate data at depths that benefit from automated analysis. Here we present AXIOME (Automation, eXtension, and Integration Of Microbial Ecology), a highly flexible and extensible management tool for popular microbial ecology analysis packages that promotes reproducibility and customization in microbial research.FindingsAXIOME streamlines and manages analysis of small subunit (SSU) rRNA marker data in QIIME and mothur. AXIOME also implements features including the PAired-eND Assembler for Illumina sequences (PANDAseq), non-negative matrix factorization (NMF), multi-response permutation procedures (MRPP), exploring and recovering phylogenetic novelty (SSUnique) and indicator species analysis. AXIOME has a companion graphical user interface (GUI) and is designed to be easily extended to facilitate customized research workflows. Conclusions: AXIOME is an actively developed, open source project written in Vala and available from GitHub (http://neufeld.github.com/axiome) and as a Debian package. Axiometic, a GUI companion tool is also freely available (http://neufeld.github.com/axiometic). Given that data analysis has become an important bottleneck for microbial ecology studies, the development of user-friendly computational tools remains a high priority. AXIOME represents an important step in this direction by automating multi-step bioinformatic analyses and enabling the customization of procedures to suit the diverse research needs of the microbial ecology community. http://www.gigasciencejournal.com/content/2/1/3 Michael Lynch Andre Masella Michael Hall Andrea Bartram Josh Neufeld GigaScience 2013, null:3 2013-03-13T00:00:00Z doi:10.1186/2047-217X-2-3 New tools for reproducibility and customization in microbial research AXIOME (Automated and eXtensible Integration Of Microbial Ecology) is a highly flexible and extensible management tool for a number of popular microbial ecology analysis packages /content/figures/2047-217X-2-3-toc.gif GigaScience 2047-217X ${item.volume} 3 2013-03-13T00:00:00Z XML Ash dieback is a devastating fungal disease of ash trees that has swept across Europe and recently reached the UK. This emergent pathogen has received little study in the past and its effect threatens to overwhelm the ash population. In response to this we have produced some initial genomics datasets and taken the unusual step of releasing them to the scientific community for analysis without first performing our own. In this manner we hope to ‘crowdsource’ analyses and bring the expertise of the community to bear on this problem as quickly as possible. Our data has been released through our website at oadb.tsl.ac.uk and a public GitHub repository. http://www.gigasciencejournal.com/content/2/1/2 Dan MacLean Kentaro Yoshida Anne Edwards Lisa Crossman Bernardo Clavijo Matt Clark David Swarbreck Matthew Bashton Patrick Chapman Mark Gijzen Mario Caccamo Allan Downie Sophien Kamoun Diane Saunders GigaScience 2013, null:2 2013-02-12T00:00:00Z doi:10.1186/2047-217X-2-2 Crowdsourcing the Ash Dieback Fightback We present a call to arms for the genomics community to join in an open-source collaborative project using GitHub to fight Ash Dieback, a devastating disease of ash trees that has swept across Europe /content/figures/2047-217X-2-2-toc.gif GigaScience 2047-217X ${item.volume} 2 2013-02-12T00:00:00Z XML Fostering and promoting more open and transparent science is one of the goals of GigaScience. One of the ways we have been doing this is by throwing light on the peer-review process and carrying out open peer-review as standard. In this editorial, we provide our rationale for undertaking this policy, give examples of our positive experiences to date, and encourage others to open up the normally opaque publication process. http://www.gigasciencejournal.com/content/2/1/1 Scott Edmunds GigaScience 2013, null:1 2013-01-24T00:00:00Z doi:10.1186/2047-217X-2-1 /content/figures/2047-217X-2-1-toc.gif GigaScience 2047-217X ${item.volume} 1 2013-01-24T00:00:00Z XML Background: There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions.FindingsTo overcome these challenges, we have developed its successor, SOAPdenovo2, which has the advantage of a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome. Conclusions: Benchmark using the Assemblathon1 and GAGE datasets showed that SOAPdenovo2 greatly surpasses its predecessor SOAPdenovo and is competitive to other assemblers on both assembly length and accuracy. We also provide an updated assembly version of the 2008 Asian (YH) genome using SOAPdenovo2. Here, the contig and scaffold N50 of the YH genome were ~20.9 kbp and ~22 Mbp, respectively, which is 3-fold and 50-fold longer than the first published version. The genome coverage increased from 81.16% to 93.91%, and memory consumption was ~2/3 lower during the point of largest memory consumption. http://www.gigasciencejournal.com/content/1/1/18 Ruibang Luo Binghang Liu Yinlong Xie Zhenyu Li Weihua Huang Jianying Yuan Guangzhu He Yanxiang Chen Qi Pan Yunjie Liu Jingbo Tang Gengxiong Wu Hao Zhang Yujian Shi Yong Liu Chang Yu Bo Wang Yao Lu Changlei Han David Cheung Siu-Ming Yiu Shaoliang Peng Zhu Xiaoqian Guangming Liu Xiangke Liao Yingrui Li Huanming Yang Jian Wang Tak-Wah Lam GigaScience 2012, null:18 2012-12-27T00:00:00Z doi:10.1186/2047-217X-1-18 SOAPdenovo2: state-of-the-art de novo genome assembly The latest version of BGI's popular SOAPdenovo assembler is designed with a new algorithm optimized for larger genomes, and is competitive with other short-read de novo assemblers on both assembly length and accuracy /content/figures/2047-217X-1-18-toc.gif GigaScience 2047-217X ${item.volume} 18 2012-12-27T00:00:00Z XML In the article 'Genome empowerment for the Puerto Rican parrot - Amazona vittata. GigaScience 2012 1:13' [1] the leadership of avian phylogenomic project [2] mentioned in commentary should have been Guojie Zhang, Erich Jarvis, and Tom Gilbert [2]. We regret this omission.1. O'Brien SJ: Genome empowerment for the Puerto Rican parrot - Amazona vittata. GigaScience 2012, 1:13.2. BGI Bird Phylogenomic Project homepage http://phybirds.genomics.org.cn/ http://www.gigasciencejournal.com/content/1/1/17 Stephen O¿Brien GigaScience 2012, null:17 2012-11-27T00:00:00Z doi:10.1186/2047-217X-1-17 /content/figures/2047-217X-1-17-toc.gif GigaScience 2047-217X ${item.volume} 17 2012-11-27T00:00:00Z XML