--- title: "_igblastr_ overview" author: - name: Hervé Pagès affiliation: Fred Hutch Cancer Center, Seattle, WA - name: Kellie MacPhee affiliation: Fred Hutch Cancer Center, Seattle, WA - name: Ollivier Hyrien affiliation: Fred Hutch Cancer Center, Seattle, WA date: "Compiled `r BiocStyle::doc_date()`; Modified 7 October 2025" package: igblastr vignette: | %\VignetteIndexEntry{igblastr overview} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document --- ```{r setup, include=FALSE} library(BiocStyle) ``` # Introduction The Immunoglobulin Basic Local Alignment Search Tool (IgBLAST) is a specialized bioinformatics tool developed by the National Center for Biotechnology Information (NCBI) for the analysis of B-cell receptor (BCR) and T-cell receptor (TCR) sequences (Ye et al., 2013). IgBLAST performs sequence alignment and annotation, with key outputs including germline V, D, and J gene assignments; detection of somatic hypermutations introduced during affinity maturation; identification and annotation of complementarity-determining regions (CDR1–CDR3) and framework regions (FR1–FR4); and both nucleotide and protein-level alignments. `r Biocpkg("igblastr")` is an R/Bioconductor package that provides functions to conveniently install and use a local IgBLAST installation from within R. The package is designed to make it as easy as possible to use IgBLAST in R by streamlining the installation of both IgBLAST and its associated germline databases. In particular, these installations can be performed with a single function call, do not require root access, and persist across R sessions. The main function in the package is `igblastn()`, a wrapper to the `igblastn` _standalone executable_ included in IgBLAST. In addition to `igblastn()`, the package provides: - A function (`install_igblast()`) for conveniently downloading and installing a pre-compiled IgBLAST from NCBI. - Functions to download and install germline databases from the IMGT/V-QUEST download site, and to configure them for use with `igblastn()`. - A set of built-in germline databases from OGRDB, the AIRR Community’s Open Germline Reference Database. - A set of built-in constant region (C-region) databases (a.k.a. constant region databases) from IMGT/V-QUEST. - Utility functions to parse the results returned by `igblastn()`. - A simple tool to vizualize the annotated sequences in a browser. - Utility functions to download data from OAS, the Observed Antibody Space database, and prepare it for use with IgBLAST. - Etc. IgBLAST is described at Online IgBLAST: Please use to report bugs, provide feedback, request features (etc) about `r Biocpkg("igblastr")`. # Install and load the package Like any Bioconductor package, `r Biocpkg("igblastr")` should be installed with `BiocManager::install()`: ```{r, eval=FALSE} if (!require("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("igblastr") ``` `BiocManager::install()` will take care of installing the package dependencies that are missing. Load the package: ```{r, message=FALSE} library(igblastr) ``` # Install IgBLAST If IgBLAST is already installed on your system, you can tell `r Biocpkg("igblastr")` to use it by setting the environment variable `IGBLAST_ROOT` to the path of your IgBLAST installation. See `?IGBLAST_ROOT` for more information. Otherwise, simply call `install_igblast()` to install the latest version of IgBLAST. As of March 2025, NCBI provides pre-compiled versions of IgBLAST for Linux, Windows, Intel Mac and Mac Silicon. `install_igblast()` will automatically download the appropriate pre-compiled version of IgBLAST for your platform from the NCBI FTP site, and install it in a location that will be remembered across R sessions. ```{r} if (!has_igblast()) install_igblast() ``` See `?install_igblast` for more information. Note that we use `has_igblast()` to avoid reinstalling IgBLAST if `r Biocpkg("igblastr")` already has access to a working IgBLAST installation. Display basic information about the IgBLAST installation used by `r Biocpkg("igblastr")`: ```{r} igblast_info() ``` # Install and select a germline database The `r Biocpkg("igblastr")` package includes a FASTA file containing 8,437 paired heavy and light chain human antibody sequences (16,874 individual sequences) retrieved from OAS. These sequences will serve as our _query sequences_, that is, the immunoglobulin (Ig) sequences that we will analyze in this vignette. Before we can do so, the `igblastn` _standalone executable_ in IgBLAST (and, by extension, our `igblastn()` function) needs access to the germline V, D, and J gene sequences for humans. Before `igblastn` can use them, these germline sequences must first be stored in three separate BLAST databases. We will refer to these as the V-region, D-region, and J-region databases, respectively. When considered together, we will refer to them as the _germline database_. ## Built-in germline databases The `r Biocpkg("igblastr")` package includes a set of built-in germline databases for human and mouse that were obtained from the OGRDB database (AIRR community). These can be listed with: ```{r} list_germline_dbs(builtin.only=TRUE) ``` The last part of the database name indicates the date of the download in YYYYMM format. If our query sequences are from humans or from one of the mouse strains listed above, we can already select the appropriate database with `use_germline_db()` and skip the subsection below. ## Install additional germline databases AIRR/OGRDB and IMGT/V-QUEST are two providers of germline databases that can be used with IgBLAST. If, for any reason, none of the built-in AIRR/OGRDB germline databases is suitable (e.g., if your query sequences are not from human or mouse), you can use `install_IMGT_germline_db()` to install additional germline databases. Below, we show how to install the latest human germline database from IMGT/V-QUEST. First, we list the most recent IMGT/V-QUEST releases: ```{r} head(list_IMGT_releases()) ``` The organisms included in release 202518-3 are: ```{r} list_IMGT_organisms("202518-3") ``` Next, we install the human germline database from the latest IMGT/V-QUEST release: ```{r, message=FALSE} install_IMGT_germline_db("202518-3", organism="Homo sapiens", force=TRUE) ``` See `?install_IMGT_germline_db` for more information. Finally, we select the newly installed germline database for use with `igblastn()`: ```{r} use_germline_db("IMGT-202518-3.Homo_sapiens.IGH+IGK+IGL") ``` See `?use_germline_db` for more information. ## Display the full list of cached germline dbs To see the full list of cached germline dbs: ```{r} list_germline_dbs() ``` Note that the asterisk (`*`) displayed at the far right of the output from `list_germline_dbs()` indicates the currently selected germline database (you may need to scroll horizontally to see the asterisk). See `?list_germline_dbs` for more information. # Select a constant region database (optional) The `igblastn` _standalone executable_ in IgBLAST can also use constant region (C region) sequences to improve the analysis. As with the germline V, D, and J gene sequences, the C-region sequences should typically come from the same organism as the query sequences, and they must also be formatted as a BLAST database. We will refer to this database as the C-region database. The `r Biocpkg("igblastr")` package includes a set of built-in C-region databases for human, mouse, and rabbit, obtained from IMGT/V-QUEST. These can be listed using: ```{r} list_c_region_dbs() ``` The last part of the database name indicates the date of the download in YYYYMM format. If your query sequences are from human, mouse, or rabbit, you can select the appropriate database using `use_c_region_db()`: ```{r} use_c_region_db("_IMGT.human.IGH+IGK+IGL.202412") ``` Calling `list_c_region_dbs()` again should display an asterisk (`*`) at the far right of the output, indicating the currently selected C-region database. See `?list_c_region_dbs` for more information. # Use igblastn() Now that we have selected a germline and C-region database for use with `igblastn()`, we are almost ready to call `igblastn()` to perform the alignment. As mentioned earlier, the `r Biocpkg("igblastr")` package includes a FASTA file containing 8,437 paired heavy and light chain human antibody sequences retrieved from OAS. These serve as our _query sequences_, that is, the set of BCR sequences that we will analyse using `igblastn()`. To get the path to the query sequences, use: ```{r} query <- system.file(package="igblastr", "extdata", "BCR", "1279067_1_Paired_sequences.fasta.gz") ``` The `r Biocpkg("igblastr")` package also includes a JSON file containing metadata associated with the query BCR sequences: ```{r} json <- system.file(package="igblastr", "extdata", "BCR", "1279067_1_Paired_All.json") query_metadata <- jsonlite::fromJSON(json) query_metadata ``` The 8,347 paired sequences come from memory B cells isolated from peripheral blood mononuclear cell (PBMC) samples of a single human donor (age 38) with no known disease or vaccination history. The source for these sequences is the Jaffe et al. (2022) study; the DOI link to the publication is provided above. Before calling `igblastn()`, we first check the selected databases by calling `use_germline_db()` and `use_c_region_db()` with no arguments: ```{r} use_germline_db() use_c_region_db() ``` Now, let's call `igblastn()`. Since we are only interested in the best alignment for each query sequence, we set `num_alignments_V` to 1. This may take up to around 3 min on a standard laptop: ```{r} AIRR_df <- igblastn(query, num_alignments_V=1) ``` By default, the result is a tibble in AIRR format: ```{r} AIRR_df ``` You can call `igbrowser()` on `AIRR_df` to visualize the annotated sequences in a browser. For each sequence, the V, D, J, and C segments will be shown as well as the FWR1-4 and CDR1-3 regions. See `?igblastn` and `?igbrowser` for more information. # Downstream analysis examples ```{r, message=FALSE} library(ggplot2) library(dplyr) library(scales) library(ggseqlogo) ``` One common analysis of AIRR format data is to examine the distribution of percent mutation across BCR sequences. Here we analyze the percent mutation in the V segments of each chain type (heavy, kappa, and lambda). Note that V percent mutation is 100 - v\_identity. ```{r} AIRR_df |> ggplot(aes(locus, 100 - v_identity)) + theme_bw(base_size=14) + geom_point(position = position_jitter(width = 0.3), alpha = 0.1) + geom_boxplot(color = "blue", fill = NA, outliers = FALSE, alpha = 0.3) + ggtitle("Distribution of V percent mutation by locus") + xlab(NULL) ``` Another common analysis is to investigate the distribution of germline genes (e.g., V genes). In this case, we typically stratify the analysis by locus or chain type. ```{r} plot_gene_dist <- function(AIRR_df, loc) { df_v_gene <- AIRR_df |> filter(locus == loc) |> mutate(v_gene = sub("\\*[0-9]*", "", v_call)) |> # drop allele info group_by(v_gene) |> summarize(n = n(), .groups = "drop") |> mutate(frac = n / sum(n)) df_v_gene |> ggplot(aes(frac, v_gene)) + theme_bw(base_size=13) + geom_col() + scale_x_continuous('Percent of sequences', labels = scales::percent) + ylab("Germline gene") + ggtitle(paste0(loc, "V gene prevalence")) } ``` ```{r, fig.height=8} plot_gene_dist(AIRR_df, "IGH") ``` ```{r, fig.height=5.9} plot_gene_dist(AIRR_df, "IGK") ``` ```{r, fig.height=5.3} plot_gene_dist(AIRR_df, "IGL") ``` A third category of analysis focuses on CDR3 sequences, including their lengths and motifs, which are often visualized using sequence logo plots. ```{r, fig.height=4.5} AIRR_df$cdr3_aa_length <- nchar(AIRR_df$cdr3_aa) AIRR_df |> group_by(locus, cdr3_aa_length) |> summarize(n = n(), .groups = "drop") |> ggplot(aes(cdr3_aa_length, n)) + theme_bw(base_size=14) + facet_wrap(~locus) + geom_col() + ggtitle("Histograms of CDR3 length by locus") ``` ```{r} AIRR_df |> filter(locus == "IGK", cdr3_aa_length == 9) |> pull(cdr3_aa) |> ggseqlogo(method = "probability") + theme_bw(base_size=14) + ggtitle("Logo plot of kappa chain CDR3 sequences that are 9 AA long") ``` # Advanced usage ## Passing additional arguments to igblastn() The `igblastn` _standalone executable_ in IgBLAST supports many command line arguments. You can quickly list them with `igblastn_help()` (or with `igblastn_help(TRUE)` to get an expanded list with more details -- see `?igblastn_help`): ```{r} igblastn_help() ``` All these command line arguments can be passed to the `igblastn()` function using the usual `argument_name=argument_value` syntax. For example, to set the number of threads to 8 (`-num_threads` command line argument), do `igblastn(query, num_threads=8)`. ## Restrict the search to a subset of user-supplied gene alleles Some arguments of particular interest are the `germline_db_[VDJ]_seqidlist` arguments. They allow restricting the search of the germline database to a list of gene alleles supplied by the user. This list can be provided as a character vector of gene allele identifiers (e.g. `IGHV3-23*01`, `IGHV3-23*04`, etc..), or as the path to a file containing the identifiers (one identifier per line). For example: ```{r, eval=FALSE} V_alleles <- c("IGHV3-23*01", "IGHV3-23*04") igblastn(query, germline_db_V_seqidlist=V_alleles) ``` If the gene alleles are stored in a file, say in `path/to/my_V_gene_alleles.txt`, then use: ```{r, eval=FALSE} igblastn(query, germline_db_V_seqidlist=file("path/to/my_V_gene_alleles.txt")) ``` Note that in this case, the path to the file containing the gene alleles identifiers must be wrapped in a call to `file()`. See `?igblastn` for more information. ## Add novel gene alleles to the search The `r Biocpkg("igblastr")` package also provides facilities to add novel gene alleles to the search. This is done in two steps: 1. Use one of the `augment_germline_db_[VDG]()` functions to create a new augmented V, D, or J germline database. 2. Tell `igblastn()` to use the augmented V, D, or J germline database by pointing the `germline_db_V`, `germline_db_D`, or `germline_db_J` argument to it. Note that this functionality can also be used to combine germline databases from two different organisms. See `?augment_germline_db` for more information and some examples. Finally note that augmenting and restricting can be combined e.g. by pointing `germline_db_V` to an augmented V germline database _and_ by supplying the `germline_db_V_seqidlist` argument. # A TCR analysis example Even though NCBI IgBLAST primary use case is BCR analysis, it can also be used for TCR sequence analysis, and so does the `r Biocpkg("igblastr")` package. File `SRR11341217.fasta.gz` included in the package contains 10,875 human beta chain TCR transcripts running from 5' of reverse transcription reaction to beginning of constant region. See for more information about this dataset. ```{r} filename <- "SRR11341217.fasta.gz" query <- system.file(package="igblastr", "extdata", "TCR", filename) ``` To analyze this dataset with `igblastn()`, we need to install and select a human TCR germline database: ```{r, message=FALSE} db_name <- install_IMGT_germline_db("202518-3", organism="Homo sapiens", tcr.db=TRUE, force=TRUE) use_germline_db(db_name) list_germline_dbs() ``` See `?install_IMGT_germline_db` and `?use_germline_db` for more information. When calling `igblastn()`, we don't need to specify the `ig_seqtype` argument as it will be automatically set to `"TCR"` (see documentation of the `ig_seqtype` argument in `?igblastn` for more information): ```{r} AIRR_df <- igblastn(query) AIRR_df ``` # Future developments At the moment, the `r Biocpkg("igblastr")` package does not provide access to the full functionality of the IgBLAST software. Most notably, the `igblastp` _standalone executable_ included in IgBLAST has no counterpart in `r Biocpkg("igblastr")`. Some future developments include: - Implement `igblastp()`, a wrapper to the `igblastp` _standalone executable_ included in IgBLAST for protein-level alignments. - Add facilities to retrieve germline databases from OGRDB, the AIRR Community’s Open Germline Reference Database. # Session information Here is the output of `sessionInfo()` on the system on which this document was compiled: ```{r} sessionInfo() ```