Three publications are featured in the Special 10th Anniversary issue.
The Science:
To mark its 10th anniversary, the journal Nature Methods released a Special Issue highlighting impactful articles. Among these noteworthy articles are three from DOE JGI researchers.
To accompany the Nature Methods paper on the software quality control tool GenePRIMP, lead author and DOE JGI OMICS Analysis Lead Amrita Pati recorded a short video presentation that can be viewed at http://bit.ly/JGIYTGenePRIMP. (Figure of GenePRIMP analysis of gene calls in the M. palustris genome by three gene callers from Pati A et al. GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nat Methods. 2010:7, 455 – 457. doi:10.1038/nmeth.1457)
The Impact:
Microbial Sequencing is one of the areas of methods development highlighted by the journal, and the DOE JGI papers referenced in this category have been useful in providing accurate and robust microbial and metagenomic sequence data.
Summary
In its October 2014 issue, the journal Nature Methods marked its 10th anniversary. For this special issue, the editors wrote, “we highlight our choice of the ten areas of methods development with the most impact on biological research over the past decade.” Among the publications they singled out in the Microbial Sequencing area were a few authored by researchers at the U.S. Department of Energy Joint Genome Institute (DOE JGI), a DOE Office of Science User Facility. Additionally, researchers working under DOE JGI Prokaryotic Super Program Head Nikos Kyrpides authored two of these three papers.
One of the DOE JGI publications fell under the Functional Analysis and Ecology category. OMICS Analysis head Amrita Pati was first author on the paper first published online May 2010 that describes a software tool known a GenePRIMP for annotating prokaryotic genomes. The paper described the tool’s ability to check the quality of the microbial genome sequences before they are submitted to the federally funded public archive GenBank. It allows researchers to find gene-calling errors, which can range from two percent to as much as 30 percent of the original genes identified in the genome.
“Generating accurate and robust microbial sequence data requires rigorous benchmarking and controls,” noted Nature Methods editors in the Anniversary Issue feature as they highlighted methods under the Quality Control and Bias category of the Microbial Sequencing area. Two other DOE JGI publications fell into this category. The older of the two publications was published online April 2007 and offered metagenomics researchers what was described as a “gold standard” for data analysis. The project relied on three simulated metagenomic datasets that allowed the team to survey existing data processing methods and spot any weaknesses. The second paper validated two methods used for metatranscriptomics analysis and was led by former DOE JGI Microbial Ecology Head Phil Hugenholtz, now director of the Australian Center for Ecogenomics at the University of Queensland, Australia. The methods were tested using artificial communities made by mixing RNA from multiple microbial species whose genomes had been sequenced.
Contact
Nikos Kyrpides
DOE Joint Genome Institute
nckyrpides@lbl.gov
Funding
- U.S. Department of Energy Office of Science
Publications
- Mavromatis K et al. Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nat Methods. 2007 Jun;4(6):495-500. doi:10.1038/nmeth1043. Epub 2007 Apr 29.
- Pati A et al. GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nat Methods 2010 Jun;7(6):455-7. doi: 10.1038/nmeth.1457. Epub 2010 May 2.
- He S et al. Validation of two ribosomal RNA removal methods for microbial metatranscriptomics. Nat Methods. 2010 Oct;7(10):807-12. doi: 10.1038/nmeth.1507. Epub 2010 Sep 19.