Usage Vignette ============== Introduction ------------ Since the use of High-throughput sequencing (HTS) was first introduced to analyze immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires (Freeman et al, 2009; Robins et al, 2009; Weinstein et al, 2009), the increasing number of studies making use of this technique has produced enormous amounts of data and there exists a pressing need to develop and adopt common standards, protocols, and policies for generating and sharing data sets. The `Adaptive Immune Receptor Repertoire (AIRR) Community `__ formed in 2015 to address this challenge (Breden et al, 2017) and has stablished the set of minimal metadata elements (MiAIRR) required for describing published AIRR datasets (Rubelt et al, 2017) as well as file formats to represent this data in a machine-readable form. The ``airr`` R package provide read, write and validation of data following the AIRR Data Representation schemas. This vignette provides a set of simple use examples. AIRR Data Standards ~~~~~~~~~~~~~~~~~~~ The AIRR Community’s recommendations for a minimal set of metadata that should be used to describe an AIRR-seq data set when published or deposited in a AIRR-compliant public repository are described in Rubelt et al, 2017. The primary aim of this effort is to make published AIRR datasets FAIR (findable, accessible, interoperable, reusable); with sufficient detail such that a person skilled in the art of AIRR sequencing and data analysis will be able to reproduce the experiment and data analyses that were performed. Following this principles, V(D)J reference alignment annotations are saved in standard tab-delimited files (TSV) with associated metadata provided in accompanying YAML formatted files. The column names and field names in these files have been defined by the AIRR Data Representation Working Group using a controlled vocabulary of standardized terms and types to refer to each piece of information. Reading AIRR formatted files ---------------------------- The ``airr`` package contains the function ``read_rearrangement`` to read and validate files containing AIRR Rearrangement records, where a Rearrangement record describes the collection of optimal annotations on a single sequence that has undergone V(D)J reference alignment. The usage is straightforward, as the file format is a typical tabulated file. The argument that needs attention is ``base``, with possible values ``"0"`` and ``"1"``. ``base`` denotes the starting index for positional fields in the input file. Positional fields are those that contain alignment coordinates and names ending in "_start" and "_end". If the input file is using 1-based closed intervals (R style), as defined by the standard, then positional fields will not be modified under the default setting of ``base="1"``. If the input file is using 0-based coordinates with half-open intervals (python style), then positional fields may be converted to 1-based closed intervals using the argument ``base="0"``. Reading Rearrangements ~~~~~~~~~~~~~~~~~~~~~~ .. code:: r # Imports library(airr) library(tibble) # Read Rearrangement example file f1 <- system.file("extdata", "rearrangement-example.tsv.gz", package="airr") rearrangement <- read_rearrangement(f1) glimpse(rearrangement) :: ## Rows: 101 ## Columns: 33 ## $ sequence_id "SRR765688.7787", "SRR765688.35420", "SRR765688.36681", "SRR765688.33811", "SRR765688.44149", "SRR765688.15636", "SRR765688.20304", "SRR765688.13860", "SRR7656… ## $ sequence "NNNNNNNNNNNNNNNNNNNNGCTGACCTGCACCTTCTCTGGATTCTCACTCAGTACTAGTGCAGTGGGTGTACACTGGATCCGTCAGCCCCCAGGAAAGGCCCTGGAGTGGCTTGCACTCATTTATTGGGATGATGCCAAATATTACAGCACCAGCCC… ## $ rev_comp FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE… ## $ productive TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE… ## $ vj_in_frame TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T… ## $ stop_codon FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FAL… ## $ v_call "IGHV2-5*02", "IGHV5-51*01", "IGHV7-4-1*02", "IGHV7-4-1*02", "IGHV7-4-1*02", "IGHV2-5*02", "IGHV7-4-1*02", "IGHV6-1*01,IGHV6-1*02", "IGHV7-4-1*02", "IGHV4-39*0… ## $ d_call "IGHD5-24*01", "IGHD3-16*02,IGHD3-3*01,IGHD3-3*02", "IGHD3-22*01", "IGHD3-9*01", "IGHD1-26*01", "IGHD2-21*02", "IGHD1-26*01,IGHD2-21*02,IGHD3/OR15-3a*01", "IGH… ## $ j_call "IGHJ4*02", "IGHJ6*02,IGHJ6*04", "IGHJ4*02", "IGHJ6*02", "IGHJ6*01", "IGHJ4*02", "IGHJ5*02", "IGHJ4*02", "IGHJ4*02", "IGHJ4*02", "IGHJ5*02", "IGHJ6*02", "IGHJ6… ## $ c_call "IGHG", "IGHG", "IGHG", "IGHG", "IGHG", "IGHA", "IGHA", "IGHG", "IGHG", "IGHA", "IGHA", "IGHG", "IGHA", "IGHA", "IGHG", "IGHA", "IGHG", "IGHA", "IGHG", "IGHG",… ## $ sequence_alignment "...........................................................GCTGACCTGCACCTTCTCTGGATTCTCACTCAGT......ACTAGTGCAGTGGGTGTACACTGGATCCGTCAGCCCCCAGGAAAGGCCCTGGAGTGGCT… ## $ germline_alignment "CAGATCACCTTGAAGGAGTCTGGTCCT...ACGCTGGTGAAACCCACACAGACCCTCACGCTGACCTGCACCTTCTCTGGGTTCTCACTCAGC......ACTAGTGGAGTGGGTGTGGGCTGGATCCGTCAGCCCCCAGGAAAGGCCCTGGAGTGGCT… ## $ junction "TGTGCACACAGTGCGGGATGGCTGCCTGATTACTGG", "TGTGCGAGGCATGGATTATACGGTTGTGATCATACCGGCTGTTATACAAGCTTCTACTACTACGGGATGGACGTCTGG", "TGTGCGAGAGAAGAACGTCGAAGTAGTGGTTATTTT… ## $ junction_aa "CAHSAGWLPDYW", "CARHGLYGCDHTGCYTSFYYYGMDVW", "CAREERRSSGYFDHW", "CAREGYYFDTTGSPRSHGLDVW", "CARDSGGMDVW", "CVLSRRLGDSGVQKYYFDYW", "CAREGLWDGRVVTDLW", "CARTRYSS… ## $ v_cigar "20S56N21=1X11=1X7=1X9=3X62=6D2=1X1=2X2=2X50=1X7=1X4=1X22=1X30=", "20S40N15=1X15=1X11=1X2=1X1=1X1=2X3=1X7=1X41=2X2=1X10=1X3=1X1=1X5=2X5=1X4=1X9=1X19=1X24=2X9=1… ## $ d_cigar "274S5N7=", "305S29N7=", "293S13N12=", "290S9N8=", "283S4N7=", "273S12N8=", "289S6N6=", "267S9N9=", "281S7N5=", "278S7N5=1X7=", "277S8N7=", "297S9N7=", "265S9N… ## $ j_cigar "288S11N32=1X4=", "318S7N12=1X15=", "305S5N6=1X14=1X21=", "321S15N5=1X23=1X17=", "290S17N19=", "296S26=1X21=", "311S11N4=1X33=", "280S2N17=1X6=1X21=", "299S8N4… ## $ v_sequence_start 21, 21, 21, 21, 21, 21, 21, 20, 22, 21, 21, 20, 21, 21, 21, 21, 19, 21, 21, 21, 20, 21, 21, 21, 21, 21, 20, 23, 19, 21, 20, 21, 21, 20, 20, 21, 20, 22, 21, 21,… ## $ v_sequence_end 269, 276, 283, 283, 283, 264, 283, 259, 281, 266, 264, 294, 258, 283, 273, 279, 274, 259, 278, 280, 262, 271, 281, 262, 264, 283, 259, 279, 278, 280, 261, 260,… ## $ v_germline_start 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … ## $ v_germline_end 320, 320, 320, 320, 320, 320, 320, 318, 318, 320, 320, 320, 319, 319, 317, 316, 316, 320, 318, 320, 320, 315, 318, 320, 321, 320, 318, 316, 320, 317, 319, 316,… ## $ d_sequence_start 275, 306, 294, 291, 284, 274, 290, 268, 282, 279, 278, 298, 266, 292, 301, 285, 276, NA, 291, 284, 273, 279, 289, 277, 272, 299, 270, 292, 282, 295, 267, 265, … ## $ d_sequence_end 281, 312, 305, 298, 290, 281, 295, 276, 286, 291, 284, 304, 273, 296, 307, 289, 282, NA, 297, 295, 293, 290, 294, 283, 287, 307, 280, 302, 290, 301, 280, 270, … ## $ d_germline_start 6, 30, 14, 10, 5, 13, 7, 10, 8, 8, 9, 10, 10, 7, 22, 14, 4, NA, 24, 4, 2, 5, 8, 9, 6, 3, 1, 3, 7, 7, 4, 6, 11, 14, 12, 8, 2, 11, 8, 10, 5, 24, 4, 17, 5, 5, 4, … ## $ d_germline_end 12, 36, 25, 17, 11, 20, 12, 18, 12, 20, 15, 16, 17, 11, 28, 18, 10, NA, 30, 15, 22, 16, 13, 15, 21, 11, 11, 13, 15, 13, 17, 11, 17, 18, 19, 13, 8, 16, 20, 16, … ## $ j_sequence_start 289, 319, 306, 322, 291, 297, 312, 281, 300, 301, 289, 319, 276, 300, 317, 299, 296, 271, 336, 321, 303, 304, 300, 297, 293, 322, 289, 311, 315, 320, 283, 293,… ## $ j_sequence_end 325, 346, 348, 368, 309, 344, 349, 326, 339, 347, 335, 361, 326, 334, 350, 333, 346, 320, 370, 338, 332, 338, 339, 333, 340, 368, 332, 345, 342, 362, 327, 327,… ## $ j_germline_start 12, 8, 6, 16, 18, 1, 12, 3, 9, 2, 5, 20, 9, 14, 15, 14, 2, 13, 28, 18, 6, 14, 9, 15, 1, 16, 5, 14, 8, 5, 4, 12, 9, 1, 20, 15, 6, 6, 5, 6, 5, 9, 1, 6, 5, 5, 9, … ## $ j_germline_end 48, 35, 48, 62, 36, 48, 49, 48, 48, 48, 51, 62, 59, 48, 48, 48, 52, 62, 62, 35, 35, 48, 48, 51, 48, 62, 48, 48, 35, 47, 48, 46, 48, 44, 62, 48, 48, 51, 50, 53,… ## $ junction_length 36, 78, 45, 66, 33, 60, 48, 45, 36, 61, 51, 48, 51, 30, 54, 30, 48, 42, 71, 66, 78, 42, 36, 51, 57, 66, 51, 42, 72, 60, 45, 45, 45, 42, 36, 36, 57, 48, 51, 45,… ## $ np1_length 5, 29, 10, 7, 0, 9, 6, 8, 0, 12, 13, 3, 7, 8, 27, 5, 1, 11, 12, 3, 10, 7, 7, 14, 7, 15, 10, 12, 3, 14, 5, 4, 4, 6, 1, 7, 1, 5, 4, 5, 26, 5, 0, 15, 26, 26, 8, 7… ## $ np2_length 7, 6, 0, 23, 0, 15, 16, 4, 13, 9, 4, 14, 2, 3, 9, 9, 13, NA, 38, 25, 9, 13, 5, 13, 5, 14, 8, 8, 24, 18, 2, 22, 15, 3, 3, 11, 29, 11, 9, 5, 1, 5, 8, 0, 1, 1, 11… ## $ duplicate_count 3, 3, 13, 3, 2, 2, 4, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 5, 2, 2, 3, 2, 4, 2, 3, 4, 8, 2, 2, 2, 2, 3, 2, 4, 3, 4, 2, 5, 2, 2, 7, 3, 2,… Reading AIRR Data Models ~~~~~~~~~~~~~~~~~~~~~~~~ AIRR Data Model records, such as Repertoire and GermlineSet, can be read from either a YAML or JSON formatted file into a nested list. .. code:: r # Read Repertoire example file f2 <- system.file("extdata", "repertoire-example.yaml", package="airr") repertoire <- read_airr(f2) glimpse(repertoire) :: ## List of 1 ## $ Repertoire:List of 3 ## ..$ :List of 5 ## .. ..$ repertoire_id : chr "1841923116114776551-242ac11c-0001-012" ## .. ..$ study :List of 13 ## .. ..$ subject :List of 15 ## .. ..$ sample :List of 1 ## .. ..$ data_processing:List of 1 ## ..$ :List of 5 ## .. ..$ repertoire_id : chr "1602908186092376551-242ac11c-0001-012" ## .. ..$ study :List of 13 ## .. ..$ subject :List of 15 ## .. ..$ sample :List of 1 ## .. ..$ data_processing:List of 1 ## ..$ :List of 5 ## .. ..$ repertoire_id : chr "2366080924918616551-242ac11c-0001-012" ## .. ..$ study :List of 13 ## .. ..$ subject :List of 15 ## .. ..$ sample :List of 1 ## .. ..$ data_processing:List of 1 .. code:: r # Read GermlineSet example file f3 <- system.file("extdata", "germline-example.json", package="airr") germline <- read_airr(f3) glimpse(germline) :: ## List of 2 ## $ GermlineSet:List of 1 ## ..$ :List of 17 ## .. ..$ germline_set_id : chr "OGRDB:G00007" ## .. ..$ author : chr "William Lees" ## .. ..$ lab_name : chr "" ## .. ..$ lab_address : chr "Birkbeck College, University of London, Malet Street, London" ## .. ..$ acknowledgements : list() ## .. ..$ release_version : int 1 ## .. ..$ release_description : chr "" ## .. ..$ release_date : chr "2021-11-24" ## .. ..$ germline_set_name : chr "CAST IGH" ## .. ..$ germline_set_ref : chr "OGRDB:G00007.1" ## .. ..$ pub_ids : chr "" ## .. ..$ species :List of 2 ## .. ..$ species_subgroup : chr "CAST_EiJ" ## .. ..$ species_subgroup_type: chr "strain" ## .. ..$ locus : chr "IGH" ## .. ..$ allele_descriptions :List of 2 ## .. ..$ curation : NULL ## $ GenotypeSet:List of 1 ## ..$ :List of 2 ## .. ..$ receptor_genotype_set_id: chr "1" ## .. ..$ genotype_class_list :List of 1 Writing AIRR formatted files ---------------------------- The ``airr`` package contains the function ``write_rearrangement`` to write Rearrangement records to the AIRR TSV format. Writing Rearrangements ~~~~~~~~~~~~~~~~~~~~~~ .. code:: r x1 <- file.path(tempdir(), "airr_out.tsv") write_rearrangement(rearrangement, x1) Writing AIRR Data Models ~~~~~~~~~~~~~~~~~~~~~~~~ AIRR Data Model records can be written to either YAML or JSON using the ``write_airr`` function. .. code:: r x2 <- file.path(tempdir(), "airr_repertoire_out.yaml") write_airr(repertoire, x2, format="yaml") x3 <- file.path(tempdir(), "airr_germline_out.json") write_airr(germline, x3, format="json") Validating AIRR data structures ------------------------------- The ``airr`` package contains the function ``validate_rearrangement`` to validate tabular (``data.frame``) Rearrangement records and AIRR Data Model objects, respectively. .. code:: r # Validate Rearrangement data.frame validate_rearrangement(rearrangement) :: ## [1] TRUE .. code:: r # Validate an AIRR Data Model validate_airr(repertoire) :: ## [1] TRUE .. code:: r # Validate AIRR Data Model records individual validate_airr(germline, each=TRUE) :: ## GenotypeSet GermlineSet ## TRUE TRUE References ---------- 1. Breden, F., E. T. Luning Prak, B. Peters, F. Rubelt, C. A. Schramm, C. E. Busse, J. A. Vander Heiden, et al. 2017. Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. *Front Immunol* 8: 1418. 2. Freeman, J. D., R. L. Warren, J. R. Webb, B. H. Nelson, and R. A. Holt. 2009. Profiling the T-cell receptor beta-chain repertoire by massively parallel sequencing. *Genome Res* 19 (10): 1817-24. 3. Robins, H. S., P. V. Campregher, S. K. Srivastava, A. Wacher, C. J. Turtle, O. Kahsai, S. R. Riddell, E. H. Warren, and C. S. Carlson. 2009. Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. *Blood* 114 (19): 4099-4107. 4. Rubelt, F., C. E. Busse, S. A. C. Bukhari, J. P. Burckert, E. Mariotti-Ferrandiz, L. G. Cowell, C. T. Watson, et al. 2017. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. *Nat Immunol* 18 (12): 1274-8. 5. Weinstein, J. A., N. Jiang, R. A. White, D. S. Fisher, and S. R. Quake. 2009. High-throughput sequencing of the zebrafish antibody repertoire. *Science* 324 (5928): 807-10.