The bioinformatics data processing pipeline executed on each node consists of three overall steps: i) data transfer and pre-processing; ii) mapping of reads to the reference; and iii) estimation of coverage (Fig. 2).
Its pipeline executes multiple, independent hardware threads to avoid costly, unpredictable bypassing, and its exposed memory hierarchy provides predictable latency.
Our pipeline executes the following steps: Run UCHIME (Edgar et al., 2011 ) against the reference alignment to remove chimeric query sequences.
The pipeline executes a series of programs to assess quality and nucleotides from chromatograms, then edits, and assembles the input DNA sequence information into a non-redundant data set.
A pipeline framework executes the pipeline tools on a cluster or cloud using a big data processing engine or a supercomputer job scheduler.
With demands on more realistic visual effects, graphics hardware began to support programmable shading, where some stages of the pipeline can execute a user-defined program to generate customized effects.
In this section, the main steps and alternatives offered by the pipeline to execute the flux analysis methods are briefly explained.
For bioinformatics analysis of the FASTQ data from exome sequencing, we used SeqMule (http://seqmule.usc.edu), which is an automated pipeline to execute multiple alignment algorithms and multiple variant calling software tools.
We describe the pipeline framework used by the pipeline, how the pipeline tools are executed, how the pipeline data are stored, and the execution environment.
Because every component of the pipeline is executed on a local machine with no need to access resources over the Internet, the pipeline is suitable for projects of a sensitive nature.
