View on GitHub

GOTTCHA

Accurate read-based metagenome characterization using a hierarchical suite of unique signatures

Download this project as a .zip file Download this project as a tar.gz file

What is GOTTCHA?

GOTTCHA (Genomic Origin Through Taxonomic CHAllenge) is an application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly smaller false discovery rates (FDR) that is also laptop deployable. Our algorithm was tested and validated on twenty synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

What's new?

GOTTCHA v1.0c released (2017/11/09):

  • Support long reads (read length >65535bp).
  • Use rolled up depth of coverage at strain level to calculate relative abundance by default. This change significantly improves abundance estimation of organisms with less than 1 depth of coverage.
  • Fix bugs.

    • GOTTCHA_database_v20150825 Released (2015/09/21):

  • We have updated GOTTCHA pre-computed bacterial and viral databases at 7 major levels. They are available at our ftp site. The new signature databases were generated using NCBI RefSeq complete genomes (as of 2015/08/25). For more details and improvements about the new databases, please visit the release note.

    • Updates (2015/07/20):

  • Databases can be downloaded through FTP server ftp://ftp.lanl.gov/public/genome/gottcha/. As a backup source, the human genome removed databases are also hosted at Google Drive.
  • Note that we took links off our web server due to LANL security policy.

    • GOTTCHA v1.0b released (2015/05/22):

  • Support multiple input files.
  • The mapping result in SAM format can be dumpped using "--dumpSam" option.
  • Fixed bugs.

    • Some major changes have been made in the v1.0 release:

  • The number of classified reads is reported in the READ_COUNT column of the output.
  • The number of split-trimmed reads that hit to plasmids is reported in the HIT_COUNT_PLASMID column of the output.
  • GOTTCHA can now remove the plasmid hits using "--noPlasmidHit" option.
    • Note: The plasmid-related results and the "--noPlasmidHit" option need the new parsed databases to work properly. For users who downloaded the databases before 30th March 2015, we encourage you to download the new parsed database (*.parsedGOTTCHA.dmp) to replace the old one. GOTTCHA v1.0 still supports old databases but plasmid relative results will show zero due to absence of the plasmid information. Please click here for more details.

    Can my system run GOTTCHA?

    Either Linux (2.6 kernel or later) or Mac OS (OSX 10.6 Snow Leopard or later) with a minimum of 8 GB of RAM is recommended. Perl v5.8 or above is required. The C/C++ compiling enviroment might be required for installing dependencies. Systems may vary. Please assure that your system has the essential software building packages (e.g. build-essential for Ubuntu, XCODE for Mac...etc) installed properly before running the installing script.

    GOTTCHA was tested successfully on our Linux servers (Ubuntu 12.10 w/ Perl v5.14.2; Ubuntu 10.04 w/ Perl v5.10.1) and Macbook Pro laptops (MAC OSX 10.8 w/ XCODE v5.1).

    How to install GOTTCHA?

    The installation guide and a quick tutorial can be found on the Github page. A more detailed description can be found in this section.

    Discussions / Bugs Reporting

    We have created a mailing list for GOTTCHA users. If you would like to recieve notifications about the updates and join the discussion, please join the mailing list by becoming the member of GOTTCHA-users groups.

    GOTTCHA user's google group

    Despite all these efforts, there are potential bugs and issues. Please help us to make it better by reporting them to GitHub issue tracker.

    GOTTCHA issue tracker

    Any other questions? You are welcome to contact Po-E (Paul) Li via po-e[at]lanl.gov.

    How to Run GOTTCHA? (The "I Can't Wait!" instructions)

    This is a quick example of profiling a "test.fastq" file using GOTTCHA with a species-level pre-computed bacterial database. The testing FASTQ file comes along with the GOTTCHA package in the "test" directory. More details are stated in the INSTRUCTION section.

    1. Obtaining the GOTTCHA package:

      $ git clone https://github.com/LANL-Bioinformatics/GOTTCHA.git gottcha

    2. Installing GOTTCHA:

      $ cd gottcha
$ ./INSTALL.sh

    3. Downloading lookup table and species-level database from our web server:

      $ wget ftp://ftp.lanl.gov/public/genome/gottcha/latest/GOTTCHA_lookup.tar.gz
$ wget ftp://ftp.lanl.gov/public/genome/gottcha/latest/GOTTCHA_BACTERIA_c4937_k24_u30_xHUMAN3x.species.tar.gz

      If you have any difficulty obtaining the databases, please contact us.

    4. Unpacking and decompressing the previous downloads:

      $ tar -zxvf GOTTCHA_lookup.tar.gz
$ tar -zxvf GOTTCHA_BACTERIA_c4937_k24_u30_xHUMAN3x.species.tar.gz

    5. Running gottcha.pl: 

      $ bin/gottcha.pl              \
      --threads 8             \
      --outdir ./             \
      --input test/test.fastq \
      --database database/GOTTCHA_BACTERIA_c4937_k24_u30_xHUMAN3x.species

    6. Enjoying the result at './test.gottcha.tsv'.

    What's the output?

    GOTTCHA reports profiling results in a neat summary table (*.gottcha.tsv) by default. The tsv file will list the organism(s) at all taxonomic levels from STRAIN to PHYLUM, their linear length, total bases mapped, linear depth of coverage, and the normalized linear depth of coverage. The linear depth of coverage (LINEAR_DOC) is used to calculate relative abundance of each organism or taxonomic name in the sample.

    Summary table:

    Column Description
    LEVEL taxonomic rank
    NAME taxonomic name
    REL_ABUNDANCE relative abundance (equivalent to NORM_COV by default)
    LINEAR_LENGTH number of non-overlapping bases covering the signatures
    TOTAL_BP_MAPPED sum total of all hit lengths recruited to signatures
    HIT_COUNT number of hits recruited to signatures
    HIT_COUNT_PLASMID number of hits recruited to signatures
    READ_COUNT number of reads recruited to signatures
    LINEAR_DOC linear depth-of-coverage (TOTAL_BP_MAPPED / LINEAR_LENGTH)
    NORM_COV normalized linear depth-of-coverage (LINEAR_DOC / SUM(LINEAR_DOC in certain level))

    How to visualize the result?

    Krona is an interactive browser that allows the exploration of hierarchical data with pie charts. Assuming you have Krona installed properly, you are going to create a Krona chart from a text file listing abundance and lineages. You must run GOTTCHA with the "--mode all" option; Use ".lineage.tsv" file found in the "_temp" directory to run krona.

    Use 'ktImportText' and save the chart to "test.krona.html":

        $ ktImportText test_temp/test.lineage.tsv -o test.krona.html

    Citation

    Please cite GOTTCHA in your publications:

    Tracey Allen K. Freitas, Po-E Li, Matthew B. Scholz and Patrick S. G. Chain (2015) Accurate read-based metagenome characterization using a hierarchical suite of unique signatures, Nucleic Acids Research (DOI: 10.1093/nar/gkv180)