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    ear the town of Rifle, Colorado, lies the primary field site for Phase I of the Subsurface Systems Scientific Focus Area 2.0 (SFA 2.0, sponsored by the DOE Office of Biological and Environmental Research—BER).
    Waiting to Respire
    UC Berkeley and JGI researchers joined forces and data sets to describe bacterial genomes for related (“sibling”) lineages that diverged from the bacterial tree before Cyanobacteria and its contemporaries. The information was then used to predict the metabolic strategies applied by a common ancestor to all five lineages.

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    Field researchers studying drought responses in Panicum hallii at the UT Austin Brackenridge Field Lab. (David Gilbert)
    A Model System for Perennial Grasses
    The DOE supports research programs for developing methods for converting plant biomass into sustainable fuels for cars and jets. By studying a close relative model species like Panicum hallii, researchers can develop crop improvement techniques that could be applied to the candidate bioenergy feedstock switchgrass.

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    At high temperature, S. paradoxus cells die in the act of cell division, as seen by the dyads with cell bodies shriveled away from the outer cell wall. (Images by Carly Weiss, courtesy of the Brem Lab)
    Mapping Heat Resistance in Yeasts
    In a proof-of-concept study, researchers demonstrated that a new genetic mapping strategy called RH-Seq can identify genes that promote heat resistance in the yeast Saccharomyces cerevisiae, allowing this species to grow better than its closest relative S. paradoxus at high temperatures.

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    Jorge Rodrigues is interested in the biological causes of methane flux variation in the Amazon rainforest. (Courtesy of Jorge Rodrigues)
    Methane Flux in the Amazon
    Wetlands are the single largest global source of atmospheric methane. This project aims to integrate microbial and tree genetic characteristics to measure and understand methane emissions at the heart of the Amazon rainforest.

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    Vampirovibrio chlorellavorus in yellow on green host. (Courtesy of Judith Brown)
    Infections and Host-Pathogen Interactions of Chlorella
    The non-photosynthetic, predatory cyanobacterium Vampirovibrio chlorellavorus is a globally important obligate pathogen of Chlorella species/strains, which are of interest as biofuel feedstocks.

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    Morphological diversity of Sordariales growing in the lab. Pierre Gladieux's proposal explores functional diversity in Neurospora and its relatives. (Pierre Gladieux, INRA Montpellier)
    Insights into Functional Diversity in Neurospora
    This proposal investigates the genetic bases of fungal thermophily, biomass-degradation, and fungal-bacterial interactions in Sordariales, an order of biomass-degrading fungi frequently encountered in compost and encompassing one of the few groups of thermophilic fungi.

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    Click on the image above or click here (https://youtu.be/iSEEw4Vs_B4) to watch a CRISPR Whiteboard Lesson from the Innovative Genomics Institute, this one focuses on the PAM sequence.
    Mining IMG/M for CRISPR-Associated Proteins
    Researchers report the discovery of miniature CRISPR-associated proteins that can target single-stranded DNA. The discovery was made possible by mining the datasets in the Integrated Microbial Genomes and Microbiomes (IMG/M) suite of tools managed by the JGI. The sequences were then biochemically characterized by a team led by Jennifer Doudna’s group at UC Berkeley.

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    The Angelo Coast Range Reserve, from which soil samples were taken, protects thousands of acres of the upper watershed of South Fork of the Eel River (shown here) in Mendocino County. (Akos Kokai via Flickr, CC BY 2.0 https://www.flickr.com/photos/on_earth/17307333828/)
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    Through the JGI’s Emerging Technologies Opportunity Program (ETOP), researchers have developed and improved upon a tool that combines existing DNA sequence binning algorithms, allowing them to reconstruct more near-complete genomes from soil metagenomes compared to other methods. The work was published in Nature Microbiology.

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    Through the EMSL-JGI FICUS calls, users can combine EMSL’s unique imaging, omics and computational resources with cutting-edge genomics, DNA synthesis and complementary capabilities at JGI. This was the sixth FICUS call between EMSL and JGI since the collaborative science initiative was formed.

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    The molecule cyclic di-GMP plays a key role in controlling cellulose production and biofilm formation. To better understand cyclic di-GMP signaling pathways, the team developed the first chemiluminescent biosensor system for cyclic di-GMP and showed that it could be used to assay cyclic di-GMP in bacterial lysates. (Image courtesy of Hammond Lab, UC Berkeley)
    Innovative Technology Improves Our Understanding of Bacterial Cell Signaling
    Cyclic di-GMP (Guanine Monophosphate) is found in nearly all types of bacteria and interacts with cell signaling networks that control many basic cellular functions. To better understand the dynamics of this molecule, researchers developed the first chemiluminescent biosensors for measuring cyclic di-GMP in bacteria through work enabled by the JGI’s Community Science Program (CSP).

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    Truffles are the fruiting bodies of the ectomycorrhizal (ECM) fungal symbionts residing on host plant roots. In Nature Ecology & Evolution, an international team sought insights into the ECM lifestyle of truffle-forming species. They conducted a comparative analysis of eight Pezizomycete fungi, including four species prized as delicacies.

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    Blyttiomyces helicus on spruce pollen grain. (Joyce Longcore)
    Expanding Fungal Diversity, One Cell at a Time
    In Nature Microbiology, a team led by JGI researchers has developed a pipeline to generate genomes from single cells of uncultivated fungi. The approach was tested on several uncultivated fungal species representing early diverging fungi.

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Data & Tools
Home › Data & Tools › BBTools › BBTools User Guide › CalcUniqueness Guide

CalcUniqueness Guide

CalcUniqueness is designed to plot the fraction of unique reads produced by a sequencing run, as a function of the number of reads sequence. In other words, the output is similar to a rarefaction curve. It can help determine library complexity and whether additional sequencing might be useful. The way it determines whether a read has already been seen is probabilistic, by storing kmers from fixed locations (e.g., the first kmer in the read); if a kmer has already been seen, it is assumed that the read has already been seen. It also tracks pair uniqueness, using a hashcode generated from one kmer in read 1 and another in read 2.

*Notes*

Memory:

CalcUniqueness grabs all available memory, even though normally it doesn’t really need it. It needs approximately 50 bytes per unique read.

Legacy Aspects:

CalcUniqueness was designed to replace an existing, inefficient pipeline. And it was designed to provide output matching that old pipeline, which I did not design. As a result, some of the features do not make a lot of sense, such as using K=20 (which is too short) and the “random kmer” columns (which are of questionable utility; I ignore them).

Data Quality:

Kmer matches must be exact. As a result, low quality data will give artificially high uniqueness estimates. For the same reason, this program cannot be used on raw PacBio data. Interestingly, you can see where on the flowcell the sequencing machine has quality issues by looking at the graphs from this program; they show up as spikes.

Histogram Output:

There are 3 columns for single reads, 6 columns for paired:
count number of reads or pairs processed
r1_first percent unique 1st kmer of read 1
r1_rand percent unique random kmer of read 1
r2_first percent unique 1st kmer of read 2
r2_rand percent unique random kmer of read 2
pair percent unique concatenated kmer from read 1 and 2

One line is printed every X reads (default is 25000) showing the percent of reads that were unique in the last interval. “cumulative=t” will still print once per interval, but will print the number of reads that were unique overall (which is generally a higher number, and not useful in most cases).

*Usage Examples*

To generate a uniqueness plot:

bbcountunique.sh in=reads out=histogram.txt

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