DOE Joint Genome Institute

  • About Us
  • Phone Book
  • Contact Us
  • Our Science
    • DOE Mission Areas
    • Bioenergy Research Centers
    • Science Programs
    • Products
    • Science Highlights
    • Scientists
    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.

    Read more

    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.

    Read more

    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.

    Read more

  • Our Projects
    • Search JGI Project List
    • DOE Metrics/Statistics
    • Approved User Proposals
    • Legacy Projects
    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.

    Read more

    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.

    Read more

    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.

    Read more

  • Data & Tools
    • IMG
    • Genome Portal
    • MycoCosm
    • Phytozome
    • GOLD
    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.

    Read more

    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.

    Read more

    DOE JGI BOOST logo
    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.

    Read more

  • User Programs
    • Calls for User Proposals
    • Special Programs
    • User Support
    • Submit a Proposal
    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.

    Read more

    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.

    Read more

    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).

    Read more

  • News & Publications
    • News Releases
    • Blog
    • Publications
    • Scientific Posters
    • Newsletter
    • Logos
    • Photos
    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.

    Read more

    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.

    Read more

    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.

    Read more

Data & Tools
Home › Data & Tools › BBTools › BBTools User Guide › Add Adapters Guide

Add Adapters Guide

AddAdapters is designed for grading the performance of adapter-trimming tools. It can add adapters to reads, and annotate the reads with their correct post-trimming length; and it can be run on trimmed reads, to calculate the rates of correct and incorrect trimming. However, it does not understand insert size, so for adding adapters to paired reads, it’s better to use RandomReads.

*Usage Examples*

To add adapters to reads:
addadapters.sh in=a.fq out=b.fq adapters=adapters.fa

To grade trimmed reads:
addadapters.sh in=trimmed.fq grade

To use RandomReads instead, to add adapters in the correct location according to insert size:
randomreads.sh ref=ref.fa out=reads.fq len=150 paired reads=100k mininsert=50 maxinsert=350 fragadapter1=ACTG fragadapter2=ACTG
rename.sh in=reads.fq out=renamed.fq renamebytrim interleaved

The result of this will still be named correctly for grading by addadapters. “ACTG” would normally be a much longer adapter sequence.

Flag Descriptions

Description:  Randomly adds adapters to a file, or grades a trimmed file.
The input is a set of reads, paired or unpaired.
The output is those same reads with adapter sequence replacing some of the bases in some reads.
For paired reads, adapters are located in the same position in read1 and read2.
This is designed for benchmarking adapter-trimming software, and evaluating methodology.

Usage:  addadapters.sh in=file in2=file2 out=outfile out2=outfile2 adapters=file

in2 and out2 are for paired reads and are optional.
If input is paired and there is only one output file, it will be written interleaved.


Parameters:
ow=f                (overwrite) Overwrites files that already exist.
int=f               (interleaved) Determines whether INPUT file is considered interleaved.
qin=auto            ASCII offset for input quality.  May be 33 (Sanger), 64 (Illumina), or
		    auto.
qout=auto           ASCII offset for output quality.  May be 33 (Sanger), 64 (Illumina), or
		    auto (same as input).
add                 Add adapters to input files.  Default mode.
grade               Evaluate trimmed input files.
adapters=file       Fasta file of adapter sequences.
literal=sequence    Comma-delimited list of adapter sequences.
left                Adapters are on the left (3') end of the read.
right               Adapters are on the right (5') end of the read.  Default mode.
adderrors=t         Add errors to adapters based on the quality scores.
addpaired=t         Add adapters to the same location for read 1 and read 2.
arc=f               Add reverse-complemented adapters as well as forward.
rate=0.5            Add adapters to this fraction of reads.
  • 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

More topics:

  • News Releases
  • Science Highlights
  • Blog
  • CSP Plans
  • Featured Profiles
  • Careers
  • Contact Us
  • Events
  • User Meeting
  • MGM Workshops
  • Internal
  • Disclaimer
  • Credits
  • Emergency Info
  • Accessibility / Section 508 Statement
  • Facebook
  • Flickr
  • Google+
  • Instagram
  • LinkedIn
  • RSS
  • Twitter
  • YouTube
Lawrence Berkeley National Lab Biosciences Area
A project of the US Department of Energy, Office of Science

JGI is a DOE Office of Science User Facility managed by Lawrence Berkeley National Laboratory

© 1997-2019 The Regents of the University of California