Running fluidigm2purc¶

The fluidigm2purc script combines the tasks of read filtering/trimming based on quality scores, merging filtered paired-end reads, and conversion of the resulting output to the proper format for running through PURC. Run fluidigm2purc -h to see options.

Steps:

Read trimming with Sickle

Paired read mergind with FLASH2

Conversion from FASTQ to PURC-compatible FASTA format (“PURCifying”)

Each of the steps listed above can be run individually as well. That way, if you want to rerun one of the step with different setting, you don’t have to start from scratch. A particular step can be specified using the -p flag and the name of the step you want to run (sickle, flash2, PURCify). By default, the script will run all three steps (all). The only mandatory option is the prefix for the paired-end FASTQ files (e.g., ‘FluidigmData’ for the files FluidigmData_R1.fastq.gz and FluidigmData_R2.fastq.gz), which is given using the -f flag.

# By default, the script will run all three steps (i.e., --program all)
fluidigm2purc -f FluidigmData

# To only run Sickle
fluidigm2purc -f FluidigmData -p sickle

# To only run FLASH2
fluidigm2purc -f FluidigmData -p flash2

# To only run the PURCifying step
fluidigm2purc -f FluidigmData -p PURCify

The final output is a directory named output-FASTA that has a single FASTA file for each locus that was present in FASTQ files used as input. The output part of the directory cab be substituted with whatever is supplied by the -o argument (default=output).

PURCifying¶

The conversion from FASTQ to FASTA is straightforward because FASTA only uses the first two lines of every four line sequence entry in the FASTQ file. The important bit here is that we grab the relevant information from the sequence header in the FASTQ file and print it so that it is compatible with PURC. The things that we want are the taxon name and the locus name. These are added by dbcAmplicons when the Fluidigm data are demultiplexed. Taxon names and locus names can’t have spaces in them. The code splits on spaces first, then on colons (“:”) so that it can grab the taxon and locus names (this is specific to the way the Fluidigm data are processed by dbcAmplicons). Merged reads from FLASH2 are processed first. Unmerged reads are then read in together and are artificially combined with eight N’s in between (“NNNNNNNN”).

Additional outputs¶

`output.log`¶

The fluidigm2purc script will output a log file that lists the taxa and loci found during the processing of the FASTQ files. It also lists the command line arguments that were used to generate the analysis.

`output-taxon-table.txt`¶

A two-column table listing each taxon and its ploidy level is generated so that users can specify what the ploidy level of the sample is when processing the clusters output by PURC (see the section on cluster crunching). By default, all of the ploidy values are set to None.

Example:

Taxon    Ploidy
taxon1   None
taxon2   None
.
.
.
taxonN   None

`output-locus-err.txt`¶

fluidigm2purc will also calculate the per locus error rate using the PHRED quality scores in the FASTQ files. It does this by calculating the average error for each read mapping to a locus, followed by the overall average across reads. These error values are used in the cluster crunching step to determine if a cluster output by PURC is sequencing error or not.