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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/)
    DAS Tool for Genome Reconstruction from Metagenomes
    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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    New Software Tools Streamline DNA Sequence Design-and-Build Process
    Researchers from the U.S. Department of Energy Joint Genome Institute (DOE JGI) have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows.

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    Cropped image of switchgrass microcosm showing established root network. (James Moran)
    FY 2019 FICUS EMSL-JGI Projects Selected
    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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    Preparing for a Sequence Data Deluge
    The approved CSP 2019 proposals leverage new capabilities and higher throughput in DNA sequencing, synthesis and metabolomics. Additionally, just over half of the accepted proposals come from primary investigators who have never led any previously accepted JGI proposal.

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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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    One of the heated plots at the Harvard Forest (Jeff Blanchard)
    Hidden Giants in Forest Soils
    In Nature Communications, giant virus genomes have been discovered for the first time in a forest soil ecosystem by JGI and University of Massachusetts-Amherst researchers. Most of the genomes were uncovered using a "mini-metagenomics" approach that reduced the complexity of the soil microbial communities sequenced and analyzed.

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    Truffle orchard in Lorraine, France. (Francis Martin)
    Symbiosis a Driver of Truffle Diversity
    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 › BBMask Guide

BBMask Guide

BBMask is designed for masking sequence, primarily to prevent false-positive matches in highly-conserved or low-complexity regions of genomes. It was designed as a replacement for tools like Dust which are incredibly slow and do not work well for that purpose. It does three types of masking, all optional:
1) Low-entropy (complexity)
2) Tandem-repeated kmers
3) Sam-file coverage

*Notes*

Entropy:

Entropy is calculated using Shannon Entropy of kmers in a window, and varies from 0 (mask nothing) to 1 (mask everything). It’s a little hard to figure out exactly what threshold you should set, but for reference, the default of 0.7 masks only 107 bases in the E.coli genome and about 0.7% of the human genome. 0.9 masks about 8kbp of E.coli and 7% of the human genome.

Memory:

BBMask loads all sequences into memory to allow multiple masking operations. For that reason, it requires about 1 byte per base (for fasta).

Human, Cat, Dog, and Mouse removal:

Masked references of various vertebrate organisms were prepared using BBMask. These are used by JGI to remove contaminant reads from libraries with zero false-positives. To produce them, BBMask was used, with default settings. Additionally, all of Mycocosm and Phytozome were shredded into 80bp overlapping pieces and mapped to the genomes; the resulting sam files were used for masking. Some organisms such as Maize were not used due to a high level of human contamination; this was ascertained by manually looking for long regions in the mapped fungal and plant genomes that had mapped to human at very high identity (typically 100% over a few hundred bp). These were BLASTed; if they hit other primates but no other plants or fungus, they were determined to be human contamination in the other organism’s reference, and thus NOT masked. If they hit other related plants and fungi but no animals other than human, they were determined to be plant/fungal contamination in the human reference, and masked. Due to the relatively high level of apparent contamination in nonprimary human reference sequences (such as unplaced and alternate contigs), none of these are used. It is also possible to mask ribosomal sequences as well, since they are highly conserved. This was done in the case of human, using Silva.

A handful of animals were also used for masking, including zebra danio (the only vertebrate used). The goal here was to bottleneck homologous regions. This is a highly simplified explanation, but my justification is this: If a gene in human is shared, largely unchanged, with a fungus, that means it had to go through fish, since fish are evolutionarily between humans and fungi. Therefore, masking human with fish should remove any sequences that are shared largely unchanged between humans and fungi. It’s a safe operation since the goal here is to achieve zero false positive read removals.

*Usage Examples*

To mask using just entropy:

bbmask.sh in=ref.fa out=masked.fa entropy=0.7

To mask sequences in genome A similar to those in genome B, plus low-entropy sequences:
shred.sh in=B.fa out=shredded.fa length=80 minlength=70 overlap=40
bbmap.sh ref=A.fa in=shredded.fa outm=mapped.sam minid=0.85 maxindel=2
bbmask.sh in=A.fa out=masked.fa entropy=0.7 sam=mapped.sam

To filter low-entropy sequences rather than masking them:
See the BBDuk Guide.

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