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    Data yielded from RIViT-seq increased the number of sigma factor-gene pairs confirmed in Streptomyces coelicolor from 209 to 399. Here, grey arrows denote previously known regulation and red arrows are regulation identified by RIViT-seq; orange nodes mark sigma factors while gray nodes mark other genes. (Otani, H., Mouncey, N.J. Nat Commun 13, 3502 (2022). https://doi.org/10.1038/s41467-022-31191-w)
    Streamlining Regulon Identification in Bacteria
    Regulons are a group of genes that can be turned on or off by the same regulatory protein. RIViT-seq technology could speed up associating transcription factors with their target genes.

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    Designer DNA: JGI Helps Users Blaze New Biosynthetic Pathways
    In a special issue of the journal Synthetic Biology, JGI scientific users share how they’ve worked with the JGI DNA Synthesis Science Program and what they’ve discovered through their collaborations.

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    A genetic element that generates targeted mutations, called diversity-generating retroelements (DGRs), are found in viruses, as well as bacteria and archaea. Most DGRs found in viruses appear to be in their tail fibers. These tail fibers – signified in the cartoon by the blue virus’ downward pointing ‘arms’— allow the virus to attach to one cell type (red), but not the other (purple). DGRs mutate these ‘arms,’ giving the virus opportunities to switch to different prey, like the purple cell. (Courtesy of Blair Paul)
    A Natural Mechanism Can Turbocharge Viral Evolution
    A team has discovered that diversity generating retroelements (DGRs) are not only widespread, but also surprisingly active. In viruses, DGRs appear to generate diversity quickly, allowing these viruses to target new microbial prey.

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    Photograph of a stream of diatoms beneath Arctic sea ice.
    Polar Phytoplankton Need Zinc to Cope with the Cold
    As part of a long-term collaboration with the JGI Algal Program, researchers studying function and activity of phytoplankton genes in polar waters have found that these algae rely on dissolved zinc to photosynthesize.

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    This data image shows the monthly average sea surface temperature for May 2015. Between 2013 and 2016, a large mass of unusually warm ocean water--nicknamed the blob--dominated the North Pacific, indicated here by red, pink, and yellow colors signifying temperatures as much as three degrees Celsius (five degrees Fahrenheit) higher than average. Data are from the NASA Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR SST) Analysis product. (Courtesy NASA Physical Oceanography Distributed Active Archive Center)
    When “The Blob” Made It Hotter Under the Water
    Researchers tracked the impact of a large-scale heatwave event in the ocean known as “The Blob” as part of an approved proposal through the Community Science Program.

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    A plantation of poplar trees. (David Gilbert)
    Genome Insider podcast: THE Bioenergy Tree
    The US Department of Energy’s favorite tree is poplar. In this episode, hear from ORNL scientists who have uncovered remarkable genetic secrets that bring us closer to making poplar an economical and sustainable source of energy and materials.

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    HPCwire Editor's Choice Award (logo crop) for Best Use of HPC in the Life Sciences
    JGI Part of Berkeley Lab Team Awarded Best Use of HPC in Life Sciences
    The HPCwire Editors Choice Award for Best Use of HPC in Life Sciences went to the Berkeley Lab team comprised of JGI and ExaBiome Project team, supported by the DOE Exascale Computing Project for MetaHipMer, an end-to-end genome assembler that supports “an unprecedented assembly of environmental microbiomes.”

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    With a common set of "baseline metadata," JGI users can more easily access public data sets. (Steve Wilson)
    A User-Centered Approach to Accessing JGI Data
    Reflecting a structural shift in data access, the JGI Data Portal offers a way for users to more easily access public data sets through a common set of metadata.

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    Phytozome portal collage
    A More Intuitive Phytozome Interface
    Phytozome v13 now hosts upwards of 250 plant genomes and provides users with the genome browsers, gene pages, search, BLAST and BioMart data warehouse interfaces they have come to rely on, with a more intuitive interface.

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    screencap from Amundson and Wilkins subsurface microbiome video
    Digging into Microbial Ecosystems Deep Underground
    JGI users and microbiome researchers at Colorado State University have many questions about the microbial communities deep underground, including the role viral infection may play in other natural ecosystems.

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    Yeast strains engineered for the biochemical conversion of glucose to value-added products are limited in chemical output due to growth and viability constraints. Cell extracts provide an alternative format for chemical synthesis in the absence of cell growth by isolating the soluble components of lysed cells. By separating the production of enzymes (during growth) and the biochemical production process (in cell-free reactions), this framework enables biosynthesis of diverse chemical products at volumetric productivities greater than the source strains. (Blake Rasor)
    Boosting Small Molecule Production in Super “Soup”
    Researchers supported through the Emerging Technologies Opportunity Program describe a two-pronged approach that starts with engineered yeast cells but then moves out of the cell structure into a cell-free system.

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    These bright green spots are fluorescently labelled bacteria from soil collected from the surface of plant roots. For reference, the scale bar at bottom right is 10 micrometers long. (Rhona Stuart)
    A Powerful Technique to Study Microbes, Now Easier
    In JGI's Genome Insider podcast: LLNL biologist Jennifer Pett-Ridge collaborated with JGI scientists through the Emerging Technologies Opportunity Program to semi-automate experiments that measure microbial activity in soil.

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    A view of the mangroves from which the giant bacteria were sampled in Guadeloupe. (Hugo Bret)
    Giant Bacteria Found in Guadeloupe Mangroves Challenge Traditional Concepts
    Harnessing JGI and Berkeley Lab resources, researchers characterized a giant - 5,000 times bigger than most bacteria - filamentous bacterium discovered in the Caribbean mangroves.

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    In their approved proposal, Frederick Colwell of Oregon State University and colleagues are interested in the microbial communities that live on Alaska’s glacially dominated Copper River Delta. They’re looking at how the microbes in these high latitude wetlands, such as the Copper River Delta wetland pond shown here, cycle carbon. (Courtesy of Rick Colwell)
    Monitoring Inter-Organism Interactions Within Ecosystems
    Many of the proposals approved through JGI's annual Community Science Program call focus on harnessing genomics to developing sustainable resources for biofuels and bioproducts.

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    Coloring the water, the algae Phaeocystis blooms off the side of the sampling vessel, Polarstern, in the temperate region of the North Atlantic. (Katrin Schmidt)
    Climate Change Threatens Base of Polar Oceans’ Bountiful Food Webs
    As warm-adapted microbes edge polewards, they’d oust resident tiny algae. It's a trend that threatens to destabilize the delicate marine food web and change the oceans as we know them.

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Home › Our Projects › DOE Metrics/Statistics

DOE Metrics/Statistics

FY 2023 DOE Metrics

Sequencing

Quarter Total Bases (Trillions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2023 150.0 170.477 114% 2,164 2,112 98%
Q2 2023 150.0 2,117
Q3 2023 150.0 2,140
Q4 2023 150.0 2,164
FY 2022 Total 600.0 8,585

*Includes Illumina and PacBio sequencing platforms.

**Operating Hours target is based on 98% of the total available hours.

FY 2022 DOE Metrics

Sequencing

Quarter Total Bases (Trillions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2022 87.500 151.270 173% 2,164 2,184 100.9%
Q2 2022 87.500 159.116 182% 2,117 2,160 102.0%
Q3 2022 87.500 167.731 191% 2,140 2,184 102.1%
Q4 2022 87.500 180.078 206% 2,164 2,208 102.0%
FY 2022 Total 350.0 658.195 188% 8,585 8,736 101.8%

*Includes Illumina and PacBio sequencing platforms.

**Operating Hours target is based on 98% of the total available hours.

 

FY 2021 DOE Metrics

There were 2,180 unique* JGI users in FY2021, which includes Principal Investigators (PIs), Co-PIs, and collaborators.

*Unique users are counted once, even though an individual may have several projects/roles.

Sequencing

Quarter Total Bases (Trillions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2021 62.500 112.109 179% 2,164 2,112 97.6%
Q2 2021 62.500 108.312 173% 2,117 2,064 97.5%
Q3 2021 62.500 127.333 204% 2,140 2,184 102.1%
Q4 2021 62.500 119.441 191% 2,164 2,208 102.1%
FY 2021 Total 250.0 467.195 187% 8,760 8,568 99.8%

*Includes Illumina and PacBio sequencing platforms.

**Operating Hours target is based on 98% of the total available hours.

FY 2020 DOE Metrics

There were 2,038 unique* JGI users in FY2020, which includes Principal Investigators (PIs), Co-PIs, and collaborators.

*Unique users are counted once, even though an individual may have several projects/roles.

Sequencing

Quarter Total Bases (Trillions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2020 37.000 67.184 182% 1,488 1,560 104.8%
Q2 2020 71.000 71.189 100.% 2,140 1,824 85.2%†
Q3 2020 71.000 28.127 40% 2,140 576 26.9%††
Q4 2020 71.000 123.993 175% 2,164 1,656 76.5% †††
FY 2020 Total 250.0 290.492 116% 7,932 5,616 70.8%

† Operational Goal = 89 days; Actual Operation = 76 days due to shelter in place order.

†† Operation Goal  = 91 days; Actual Operation = 24 days due to significant sequencer downtime related to the shelter-in-place.

††† Actual Operation = 69 days due to reduced lab activities related to the return-to-work plan in response to the pandemic.

FY 2019 Number of Unique Users

There were 1,933 unique* JGI users in FY2019, which includes Principal Investigators (PIs), Co-PIs, and collaborators.

*Unique users are counted once, even though an individual may have several projects/roles.

Sequencing

Quarter Total Bases (Trillions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2019 60.5 75,222 124.3% 2,164 2,160 99.8%
Q2 2019 60.5 73,329 121.2% 2,117 2,160 102.0%
Q3 2019 60.5 85,174 141.1% 2,140 2,184 102.1%
Q4 2019 60.5 92,765 153.3% 2,164 2,208 102.0%
FY 2019 Total 242.0 326.490 134.9% 8,584 8,712 101.5%

*Includes Illumina NovaSeq, Illumina NextSeq, Illumina MiSeq, and PacBio sequencing platforms.

**Operating Hour target is based on 98% of the total available hours.

FY 2018 Number of Unique Users

There were 1,882 unique* JGI users in FY2018, which includes Principal Investigators (PIs), Co-PIs, and collaborators.

*Unique users are counted once, even though an individual may have several projects/roles.

FY 2018 DOE Metrics

Sequencing

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2018 40,000  41,251  103.1% 2,164  2,160 99.8%
Q2 2018 48,000  52,966  110.3% 2,117  2,136  100.9%
Q3 2018 54,000 61,176 113.3% 2,140 2,184 102.1%
Q4 2018 58,000 69,906 120.5% 2,164 2,208 102.0%
FY 2018 Total 200,000  225,299 112.6% 8,584 8,688 101.2%

*Includes Illumina HiSeq, Illumina NextSeq, Illumina Miseq and PacBio Sequencing platforms.

**Operating Hour target is based on 98% of the total available hours.

FY 2017 DOE Metrics

1,598 unique* JGI users in FY2017

 Sequencing

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2017 37,500  40,942  109% 2,164  2,208  102%
Q2 2017 37,500  32, 178  86% 2,117  2,160  102%
Q3 2017 37,500  57,515  153% 2,140  2,184  102%
Q4 2017 37,500  47,501  127% 2,164  2,208  102%
FY 2017 Total 150,000  178,144  119% 8,584  8,760  102%

*Includes Illumina HiSeq, Illumina NextSeq, Illumina Miseq and PacBio Sequencing platforms.

**Operating Hour target is based on 98% of the total available hours.

FY 2016 DOE Metrics

1,391 unique* JGI users in FY2016

 Sequencing

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total** Actual % of Goal Goal (hours)*** Actual Total Actual % Goal
Q1 2016 34,000  28,574  84.0% 2,164  2,160  99.8%
Q2 2016 35,000  31,643  90.4% 2,140  2,136  99.8%
Q3 2016* 35,000  44,580  127.4% 2,140  2,184  102.1%
Q4 2016 20,000  37,073  185.4% 2,164  2,208  102.0%
FY 2016 Total 124,000 141,871 114.4% 8,608 8,688  100.9%

*Anticipated 50% reduced capacity for Q3 due to Clarity LIMS transition.

**Includes Illumina HiSeq, Illumina NextSeq, Illumina Miseq and PacBio Sequencing platforms.

***Operating Hour target is based on 98% of the total available hours.

FY 2015 Number of Unique Users

There were 958 unique* JGI users in FY2015, which includes Principal Investigators (PIs), Co-PIs, and collaborators.

*Unique users are counted once, even though an individual may have several projects/roles.

FY 2015 DOE Metrics

 Sequencing

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2015 24,500  29,857  122%  2,164  2,208  102%
Q2 2015 28,500  34,953  123%  2,117  2,160  102%
Q3 2015 28,500  41,036  144%  2,140  2,184  102%
Q4 2015 28,500  37,331  131%  2,164  2,208  102%
FY 2015 Total 110,000 143,177 130%  8,585 8,760  102%

* Includes Illumina HiSeq, MiSeq and PacBio sequencing platforms.
** Operating Hour target is based on 98% of the total available hours.

Other DOE Quarterly Metrics

Q1 – Report on new computational developments for improving microbial, metagenomic, or plant genome assemblies.  [Download PDF]

Q2 – Report on progress developing new experimental capabilities to analyze complex genomic or metagenomic datasets. [Download PDF]

Q3 – Report on new computational approaches for the annotation of genomic data. [Download PDF]

Q4 – Report on a new computational method for improving the interpretation of microbial, metagenomic, or plant genomes. [Download PDF]

End-of-year Goal – Develop one new computationally enabled approach to analyze complex genomic datasets. [Download PDF]

FY 2014 Overall Sequencing Progress, Updated Quarterly

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2014 15,000  18,827  126% 2,164  2,208  102%
Q2 2014 17,000  19,149  113% 2,117  2,160  102%
Q3 2014 18,000  24,309  135% 2,140  2,184  102%
Q4 2014 18,000  38,323  213% 2,164  2,208  102%
FY 2014 Total 68,000  100,608  148% 8,585 8,760 102%

* Includes Illumina HiSeq, MiSeq and PacBio sequencing platforms.
** Operating Hour target is based on 98% of the total available hours.

FY 2013 Overall Sequencing Progress, Updated Quarterly

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total* Actual % of Goal Goal (hours)** Actual Total Actual % Goal
Q1 2013 15,000 20,004 133% 2,164 2,208 102%
Q2 2013 15,000 18,882 126% 2,117 2,160 102%
Q3 2013 18,000 13,904 77% 2,140 2,184 102%
Q4 2013 18,000 18,073 100% 2,164 2,208 102%
FY 2013 Total 66,000 70,863 107% 8,585 8,760 102%

* Includes Illumina HiSeq, MiSeq and PacBio sequencing platforms.
** Operating Hour target is based on 98% of the total available hours.

FY 2012 Overall Sequencing Progress, Updated Quarterly

Quarter Total Bases (Billions) Operating Hours
Goal Actual Total Actual % of Goal Goal (hours)* Actual Total Actual % Goal
Q1 2012 10,000 12,923 129% 2,164 2,208 102%
Q2 2012 7,000 17,280 247% 1,848 2,184 118%
Q3 2012 13,755 15,099 110% 2,140 1,944 91%
Q4 2012 14,100 10,813 77% 2,164 2,208 102%
FY 2012 Total 44,855 56,115 125% 8,316 8,544 103%

* Operating Hour target is based on 98% of the total available hours. Reduced operating hour target in Q2 is due to planned upgrade of the Laboratory Information Management System (LIMS) and temporary shut down of the sequencing production line to accommodate data migration requirements. Q3 actual Operating Hours reflect a delay in implementation of the LIMS from Q2 to Q3. As a result of the delay in the LIMS implementation, the Q4 base total is below target.

FY 2011 Summary

Quarter Total Bases (Billions) Total Bases (Billions) by Platform Operating Hours*
Goal Actual Total Actual % of Goal 454 Illumina Goal (hours) Actual Total Actual % Goal
Q1 2011 1,500 3,233.30 216 73.27 3,160.03 2100 2208 105
Q2 2011 3,000 4,999.74 167 43.14 4,956.60 2100 2160 103
Q3 2011 5,000 7,893.75 158 6.38 7,887.37 2100 2184 104
Q4 2011 5,500 13,776.21 250 5.99 13,770.22 2100 2208 105
FY 2011 Total 15,000 29,903.00 199 128.78 29,774.22 8400 8760 104

* Number of hours a week that sequencing machines are available to produce data.

FY 2010 Summary

Quarter Total Bases (Billions) Total Bases (Billions) by Platform Operating Hours**
Goal Actual Total Actual % of Goal Sanger* 454 Illumina Goal Actual Total Actual % Goal
Q1 2010 275 640.365 233 1.223 45.865 593.278 2,100 2160 103
Q2 2010 275 1,784.271 649 0.735 81.792 1,701.744 2,100 2160 103
Q3 2010 275 1,609.752 585 0.974 108.558 1,500.22 2,100 2184 104
Q4 2010 275 2,006.41 730 1.196 124.393 1,880.818 2,100 2208 105
FY 2010 Total 1,100 6,040.796 549 4.128 360.608 5,676.060 8,400 8712 104

* Q20 bases — indicates good confidence in the assignment of a base.
** Number of hours a week that sequencing machines are producing data.

FY 2009 Summary

Quarter Total Bases (Billions) Total Bases (Billions) by Platform Operating Hours**
Goal Actual Total Actual % of Goal Sanger* 454 Illumina Goal Actual Total Actual % Goal
Q1 2009 39.9 124.21 311 6.02 23.01 95.18 2100 2088 99.4
Q2 2009 60.1 196.829 328 5.849 38.48 152.5 2100 2146 102
Q3 2009 71.2 236.566 332 5.251 63.127 168.188 2100 2184 104
Q4 2009 81.8 446.278 546 3.452 45.96 396.866 2100 2208 105
FY 2009 Total 253 1003.887 397 20.58 170.58 812.73 8400 8626 103

* Q20 bases — indicates good confidence in the assignment of a base.
** Number of hours a week that sequencing machines are producing data.

FY 2008 Summary

Quarter Total Bases (Billions) Total Bases (Billions) by Platform Operating Hours**
Goal Actual Total Actual % of Goal Sanger* 454 Illumina Goal Actual Total Actual % Goal
Q1 2008 9.8 7.2 73.40 4 3.2 – 2100 1512 72
Q2 2008 10 9.2 91.19 2.3 6.9 – 2100 1800 85.7
Q3 2008 11 41.17 374.27 5.99 8.28 26.9 2100 2184 104
Q4 2008 12 67.94 566.17 8.04 22.8 37.1 2100 2208 105
FY 2008 Total 42.8 125.51 293 20.3 41.17 64 8400 7704 92

* Q20 bases — indicates good confidence in the assignment of a base.
** Number of hours a week that sequencing machines are producing data.

FY05-07 Sequencing Goals and Results

Fiscal Year Sequence (GB) Operating Hours
Targets Actual* Targets Actual
2007 40 39.22 8400 8609
2006 30 32.74 8400 8734
2005 28 33.63 8400 8760

* Reflects actual data due to analysis beyond fiscal year-end close.

Sequencing Progress for Individual Organisms

See Sequencing Plans and Progress.

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