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    Screencap of green algae video for PNAS paper
    Green Algae Reveal One mRNA Encodes Many Proteins
    A team of researchers has found numerous examples of polycistronic expression – in which two or more genes are encoded on a single molecule of mRNA – in two species of green algae.

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    Advances in Rapidly Engineering Non-model Bacteria
    CRAGE is a technique for chassis (or strain)-independent recombinase-assisted genome engineering, allowing scientists to conduct genome-wide screens and explore biosynthetic pathways. Now, CRAGE is being applied to other synthetic biology problems.

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    Maize can produce a cocktail of antibiotics with a handful of enzymes. (Sam Fentress, CC BY-SA 2.0)
    How Maize Makes An Antibiotic Cocktail
    Zealexins are produced in every corn variety and protect maize by fending off fungal and microbial infections using surprisingly few enzymes.

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    Poplar (Populus trichocarpa and P. deltoides) grow in the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory in Tennessee. Poplar is an important biofuel feedstock, and Populus trichocarpa is the first tree species to have its genome sequenced — a feat accomplished by JGI. (Image courtesy of Oak Ridge National Laboratory, U.S. Dept. of Energy)
    Podcast: Xiaohan Yang on A Plantiful Future
    Building off plant genomics collaborations between the JGI and Oak Ridge National Laboratory, Xiaohan Yang envisions customizing plants for the benefit of human society.

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    Synthesizing Microbial Expansins with Unusual Activities
    Expansin proteins from diverse microbes have potential uses in deconstructing lignocellulosic biomass for conversion to renewable biofuels, nanocellulosic fibers, and commodity biochemicals.

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    Pennycress – A Solution for Global Food Security, Renewable Energy and Ecosystem Benefits
    Pennycress (Thlaspi arvense) is under development as a winter annual oilseed bioenergy crop. It could produce up to 3 billion gallons of seed oil annually while reducing soil erosion and fertilizer runoff.

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    An Automated Tool for Assessing Virus Data Quality
    CheckV can be broadly utilized by the research community to gauge virus data quality and will help researchers to follow best practices and guidelines for providing the minimum amount of information for an uncultivated virus genome.

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    Unicellular algae in the Chlorella genus, magnified 1300x. (Andrei Savitsky)
    A One-Stop Shop for Analyzing Algal Genomes
    The PhycoCosm data portal is an interactive browser that allows algal scientists and enthusiasts to look deep into more than 100 algal genomes, compare them, and visualize supporting experimental data.

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    Artistic interpretation of how microbial genome sequences from the GEM catalog can help fill in gaps of knowledge about the microbes that play key roles in the Earth's microbiomes. (Rendered by Zosia Rostomian​, Berkeley Lab)
    Podcast: A Primer on Genome Mining
    In Natural Prodcast: the basics of genome mining, and how JGI researchers conducted it in IMG/ABC on thousands of metagenome-derived genomes for a Nature Biotechnology paper.

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    Utilizing long-read sequencing for metagenomics and DNA modification detection webinar
    Watch the webinar on how the JGI employs single-molecule, long-read DNA sequences to aid with genome assembly and transcriptome analysis of microbial, fungal, and plant research projects.

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    “SIP technologies at EMSL and JGI” Webinar
    The concerted stable isotope-related tools and resources of the JGI and the Environmental Molecular Sciences Laboratory (EMSL) may be requested by applying for the annual “Facilities Integrating Collaborations for User Science” (FICUS) call.

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    CSP Functional Genomics Call Ongoing
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    Aerial photo of the switchgrass diversity panel late in the 2020 season at the Kellogg Biological Station in Michigan. (Robert Goodwin)
    A Team Effort Toward Targeted Crop Improvements
    A multi-institutional team has produced a high-quality reference sequence of the complex switchgrass genome. Building off this work, researchers at three DOE Bioenergy Research Centers have expanded the network of common gardens and are exploring improvements to switchgrass.

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    Artistic interpretation of how microbial genome sequences from the GEM catalog can help fill in gaps of knowledge about the microbes that play key roles in the Earth's microbiomes. (Rendered by Zosia Rostomian​, Berkeley Lab)
    Uncovering Novel Genomes from Earth’s Microbiomes
    A public repository of 52,515 microbial draft genomes generated from environmental samples around the world, expanding the known diversity of bacteria and archaea by 44%, is now available .

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    Green millet (Setaria viridis) plant collected in the wild. (Courtesy of the Kellogg lab)
    Shattering Expectations: Novel Seed Dispersal Gene Found in Green Millet
    In Nature Biotechnology, a very high quality reference Setaria viridis genome was sequenced, and for the first time in wild populations, a gene related to seed dispersal was identified.

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

Data Preprocessing

Prior to doing anything with raw reads – mapping, clustering, assembly, etc – it is usually prudent to do certain preprocessing steps. And these steps are best done in a specific order, which I have detailed below, along with the suggest tool. Note that many of them (like quality-trimming) are optional, so if you do them, do them in this order; but you don’t have to do them. Others, like adapter-trimming, are not optional and should always be done.

These steps replicate the QA protocol implemented at JGI for Illumina reads. There is a program “RQCFilter” which implements them as a pipeline, but that is not publically available because it has numerous hard-coded paths to reference datasets of contaminants.

0) Format conversion, if necessary. The simplest format for the subsequent steps is gzipped fastq, with the reads interleaved in a single file if they are paired, but that’s not required. However, H5 and SRA formats are not supported, and unaligned bam should be converted to fastq first. Tool: Reformat, Samtools, SRA Toolkit, etc.

1) Adapter-trimming. Always recommended. Tool: BBDuk.
1b) If chastity-filtering and barcode-filtering were not already done, they can be done here.
1c) If reads have an extra base at the end (like 2x151bp reads versus 2x150bp), it should be trimmed here with the “ftm=5” flag. That will occur before adapter-trimming.

2) Contaminant filtering for synthetic molecules and spike-ins such as PhiX. Always recommended. Tool: BBDuk.
2b) Quality-trimming and/or quality-filtering. Optional; only recommended if you have very low-quality data or are doing something very sensitive to low-quality data, like calling very rare variants.

3) Nextera LMP library splitting. Mandatory when processing Nextera long-mate-pair libraries (NOT normal paired Nextera libraries). Tool: SplitNexteraLMP (splitnextera.sh).

4) Human contaminant removal. Optional; only for non-vertebrate studies. Should be done by mapping. JGI also removes cat, dog, and mouse sequences, and we use masked version of the references to avoid false positives. Tool: BBMap.

5) Quality recalibration. Optional; mainly for when quality scores are very inaccurate, or binned, as in the NextSeq or HiSeq3000+ platforms. Tool: BBMap plus BBDuk.
5b) This step requires mapping, which requires an assembly. If no assembly exists, one can be generated rapidly with Tadpole.

6) Deduplication. Optional; mainly for exome-capture. This is not actually part of RQCFilter because JGI does not typically do exon-capture. Tool: Either Dedupe or DedupeByMapping can be used if you have sufficient memory. If not, there are 3rd-party deduplication tools based on sorting that do not need much memory.

7) Normalization or subsampling. Optional; mainly for assembly of data with high or uneven coverage. Tool: BBNorm for normalization, Reformat for subsampling.

8) Error correction. Optional; requires adequate coverage. Tool: Tadpole, or BBNorm if Tadpole runs out of memory. If BBNorm will be used, and normalization is desired, error correction can be done at the same time as normalization.

9) Paired-read merging. Optional; mainly for assembly, clustering, or insert-size calculation. Tool: BBMerge.
9b) RQCFilter runs BBMerge on all paired libraries for insert-size calculation, and uses the “cardinality” flag to simultaneously calculate the approximate number of unique kmers in the dataset, which can help estimate memory needs for assembly.

10) Kmer depth distribution. Optional; mainly for assembly and contamination detection. Tool: BBNorm (khist.sh).

11) BLAST or similar search against wide-taxonomy database such as RefSeq Microbial or nt. This can be done on an assembly of the reads, or a handful of reads. Optional; just for checking for contamination before proceeding. Mainly useful on isolates of a known organism such as human or fruitfly. Tool: BLAST, LAST, etc.

At this point the data is ready to use!

  • BBTools User Guide
    • Installation Guide
    • Usage Guide
    • Data Preprocessing
    • Add Adapters Guide
    • BBDuk Guide
    • BBMap Guide
    • BBMask Guide
    • BBMerge Guide
    • BBNorm Guide
    • CalcUniqueness Guide
    • Clumpify Guide
    • Dedupe Guide
    • Reformat Guide
    • Repair Guide
    • Seal Guide
    • Split Nextera Guide
    • Statistics Guide
    • Tadpole Guide
    • Taxonomy Guide
  • BBTools FAQ and Support Forums

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