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. 2020 Jul 16;21(1):314.
doi: 10.1186/s12859-020-03649-5.

PyIR: a scalable wrapper for processing billions of immunoglobulin and T cell receptor sequences using IgBLAST

Cinque Soto  1   2 , Jessica A Finn  3 , Jordan R Willis  1 , Samuel B Day  1 , Robert S Sinkovits  4 , Taylor Jones  1 , Samuel Schmitz  5 , Jens Meiler  5 , Andre Branchizio  1 , James E Crowe Jr  6   7   8
Affiliations

PyIR: a scalable wrapper for processing billions of immunoglobulin and T cell receptor sequences using IgBLAST

Cinque Soto et al. BMC Bioinformatics. .

Abstract

Background: Recent advances in DNA sequencing technologies have enabled significant leaps in capacity to generate large volumes of DNA sequence data, which has spurred a rapid growth in the use of bioinformatics as a means of interrogating antibody variable gene repertoires. Common tools used for annotation of antibody sequences are often limited in functionality, modularity and usability.

Results: We have developed PyIR, a Python wrapper and library for IgBLAST, which offers a minimal setup CLI and API, FASTQ support, file chunking for large sequence files, JSON and Python dictionary output, and built-in sequence filtering.

Conclusions: PyIR offers improved processing speed over multithreaded IgBLAST (version 1.14) when spawning more than 16 processes on a single computer system. Its customizable filtering and data encapsulation allow it to be adapted to a wide range of computing environments. The API allows for IgBLAST to be used in customized bioinformatics workflows.

Keywords: Antibody; CDR3; IgBLAST; Illumina; Immune repertoires.

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Conflict of interest statement

The authors declare they have no competing interests.

Figures

Fig. 1
Fig. 1
Multiprocessing performance for PyIR and multithreaded IgBLAST (version 1.14). a One million synthetic immunoglobulin sequences were used to time PyIR (dark grey, ♦) against multithreaded IgBLAST (version 1.14) (grey, ■) as a function of the number of processes. Idealized timings are shown as a black dashed line. Average timings were measured over the three trial runs for 1 million sequences and computed separately for both IgBLAST and PyIR. Standard deviations appear as error bars for both methods. X and Y axes are in log2 space. b The speedup of PyIR relative to multithreaded IgBLAST (version 1.14) as a function of the number of simultaneous processes. Timings were done on a workstation equipped with 4 Opteron 6278 hyper-threaded 8-core processors for a total of 64 CPU threads using the average timings from (a). The X and Y axes are in log2 space. c One billion synthetic immunoglobulin sequences were used to determine the speedup PyIR achieved over multithreaded IgBLAST (version 1.14) as a function of the number of sequences. Idealized speedups are shown as a black dashed line. Timings were done on a workstation equipped 4 Xeon Platinum 8280 hyperthreaded 28-core processors for a total of 224 CPU threads. X and Y axes are in log10 space
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