Abstract
BackgroundPre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files.ResultsProcessing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5′ and 3′ PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO.ConclusionsVDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.
Highlights
Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis
We compare the performance of VDJPipe v0.1.7 with that of another software tool specialized for immunosequencing data, pRESTO v0.5.3 [13]. pRESTO has an alternative design of providing a set of Python scripts, each of which performs one step in the pre-processing workflow
We use two example data sets provided by pRESTO [14, 15] and publically available from SRA under accession ID: ERP003950 and SRX190717
Summary
We compare the performance of VDJPipe v0.1.7 with that of another software tool specialized for immunosequencing data, pRESTO v0.5.3 [13]. pRESTO has an alternative design of providing a set of Python scripts, each of which performs one step in the pre-processing workflow. For the first data set, processing steps include merging the paired-end reads into a single read sequence, quality filtering, 5′ and 3′ primer matching, and collapsing duplicate reads. For the second data set, processing steps include length, homopolymer and quality filtering, generating compositional statistics, barcode demultiplexing, 5′ and 3′ primer matching, and collapsing duplicate reads. Together, these two data sets test all the main functions provided by VDJPipe (Table 1). Competing interests The authors declare that they have no competing interests
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