Analyze

ONTOLOGY’s Analyze step guides the user through analysis of the Run’s sequence data using ONTOLOGY’s DNA Barcoding and Metabarcoding bioinformatic workflows. Following bioinformatic analysis, Analyze provides technical and biological summaries of the Run and facilitates sequence and image upload to BOLD (DNA Barcoding only).

File import

The first step of ANALYZE is to import the .fastq files. You may analyze any number of .fastq and/or fq.gz files so long as they are in the same folder, or a single concatenated .fastq/fq.gz is also accepted. To analyze .fastq/fq/gz files, first make sure that all the intended and no unintended .fastq/fq.gz files are in a folder (other file types may be in the same folder). On the FILE IMPORT screen, select Choose Folder to import the sequence file.

After uploading a file, the filename will display on the screen. In addition, the number of .fastq files read and the total number of reads across all fastq files will be displayed.

After sequences are uploaded into the ONTOLOGY environment, you can proceed to select or confirm the reference library ONTOLOGY will use for ID.

Reference library

ONTOLOGY requires two reference library files: a SINTAX file and a VSEARCH file. We reccomend BOLDistilled libraries for use with ONTOLOGY because they are confirmed to work.

Users can download the most current BOLDistilled libraries directly from ONTOLOGY.

Please note: querying against reference libraries is generally very quick compared to other parts of the bioinformatics pipeline, so using smaller libraries will only save a fraction of the analysis time.

Bioinformatics

With the proper metadata and sequence data loaded into ONTOLOGY, bioinformatic analysis is as easy as clicking a button. Simply select, Start Analysis and ONTOLOGY will begin the process of analyzing your data to produce an ASV table.

The length of analysis depends on three things:

  1. Computer power—faster on more powerful machines

  2. Quantity of sequence data—faster with fewer reads

  3. Biological complexity—faster with fewer ASVs per sample

Detailed documentation can be found [link tbd]

Summary metrics

The four tabs at the top contain graphics that allow the user to explore the performance of their run.

  • Technical (Run-wide): Metrics looking at the entire run together

  • Technical (Per-plate): Metrics showing heatmaps of wells

  • Taxonomy: Metrics showing the distribution of taxa in the study, including which matched vs. did not match a BIN.

  • Table: Additional quantitative metrics about the run