# iscream vs Rsamtools::scanTabix

Both iscream and Rsamtools can be used to query records from tabixed BED files in R. Here we compare their usability and performance using the methscan dataset.

This vignette uses mouse WGBS data from methscan as in the Plotting TSS profiles tutorial and mouse promoter regions. Running it requires downloading 18MB of BED files and tabix indices from this Zenodo record: https://zenodo.org/records/14733834

    library("BiocFileCache") |> suppressPackageStartupMessages()
    cachedir <- BiocFileCache()
    methscan_zip_path <- bfcrpath(cachedir, "https://zenodo.org/records/14733834/files/methscan_data.zip")
    methscan_unzip <- file.path(tempdir(), "methscan")
    unzip(methscan_zip_path, exdir = methscan_unzip)
    methscan_dir <- file.path(methscan_unzip, "scbs_tutorial_data")

iscream normally uses htslib to query BED files and store the data in memory. However, iscream’s `tabix()` function can use the tabix executable to make tabix queries because the shell program’s stream-to-file approach is faster than allocating and storing strings in memory. By default, the `"tabix.method"` option is set to “shell” and iscream’s `tabix()` will look for the tabix executable, falling back to using htslib only if it’s not found. Here, the `"tabix.method"` option is set to “htslib” to make fair comparisons between Rsamtools and iscream since `scanTabix` does not use the executable, but stores the strings in memory.

    library(iscream)

    ## iscream using 1 thread by default but parallelly::availableCores() detects 16 possibly available threads. See `?set_threads` for information on multithreading before trying to use more.

    options("tabix.method" = 'htslib')
    options("iscream.threads" = 8)

Using `scanTabix` requires the input regions to be `GRanges`. `iscream::tabix` accepts strings, data frames and GRanges.

    library(data.table)
    library(GenomicRanges) |> suppressPackageStartupMessages()
    library(Rsamtools) |> suppressPackageStartupMessages()
    library(microbenchmark)
    library(pbapply)
    library(ggplot2)

    methscan_files <- list.files(
      methscan_dir,
      full.names = T,
      pattern = "*.cov.gz$"
    )
    mouse_promoters <- fread(file.path(methscan_dir, "mouse_promoters.bed"), col.names = c("chr", "start", "end"))
    mouse_promoters.gr <- GRanges(mouse_promoters)

# One file

`tabix_raw()` and `scanTabix` produce a list of unparsed or raw strings. The result of both are identical.

    bench_1_raw <- microbenchmark(
      `rsamtools 1 file raw` = rq <- scanTabix(methscan_files[1], param = mouse_promoters.gr),
      `iscream 1 file raw` = iq <- tabix_raw(methscan_files[1], mouse_promoters),
      times = 3
    )
    bench_1_raw

    ## Unit: milliseconds
    ##                  expr        min         lq      mean     median         uq
    ##  rsamtools 1 file raw 2760.55888 2765.35353 2769.3114 2770.14819 2773.68760
    ##    iscream 1 file raw   79.47893   80.34265   84.7329   81.20637   87.35989
    ##         max neval
    ##  2777.22701     3
    ##    93.51341     3

    autoplot(bench_1_raw)

    ## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
    ## ℹ Please use tidy evaluation idioms with `aes()`.
    ## ℹ See also `vignette("ggplot2-in-packages")` for more information.
    ## ℹ The deprecated feature was likely used in the microbenchmark package.
    ##   Please report the issue at <https://github.com/joshuaulrich/microbenchmark/issues/>.
    ## This warning is displayed once every 8 hours.
    ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

tabix vs scanTabix raw string output on 1 file

    iq[1:5]

    ## $`1:3669498-3673498`
    ## character(0)
    ##
    ## $`1:4407241-4411241`
    ## character(0)
    ##
    ## $`1:4494413-4498413`
    ## character(0)
    ##
    ## $`1:4783739-4787739`
    ## [1] "1\t4785488\t4785488\t0.000000\t0\t2"
    ## [2] "1\t4785513\t4785513\t0.000000\t0\t2"
    ## [3] "1\t4785522\t4785522\t0.000000\t0\t2"
    ## [4] "1\t4785533\t4785533\t0.000000\t0\t2"
    ## [5] "1\t4786780\t4786780\t100.000000\t1\t0"
    ## [6] "1\t4786886\t4786886\t100.000000\t1\t0"
    ## [7] "1\t4786958\t4786958\t100.000000\t1\t0"
    ##
    ## $`1:4805823-4809823`
    ## [1] "1\t4806176\t4806176\t100.000000\t1\t0"
    ## [2] "1\t4806221\t4806221\t100.000000\t1\t0"
    ## [3] "1\t4807572\t4807572\t0.000000\t0\t1"
    ## [4] "1\t4807669\t4807669\t0.000000\t0\t2"
    ## [5] "1\t4807682\t4807682\t0.000000\t0\t2"
    ## [6] "1\t4807872\t4807872\t0.000000\t0\t1"
    ## [7] "1\t4807890\t4807890\t0.000000\t0\t1"
    ## [8] "1\t4807940\t4807940\t0.000000\t0\t1"
    ## [9] "1\t4807950\t4807950\t0.000000\t0\t1"

    all.equal(iq, rq)

    ## [1] TRUE

# Multiple files

`tabix()` can also take multiple files in the same call. It returns the raw strings, but each input file is a list of it’s own. `scanTabix()` does not support this.

    bench_30_raw <- microbenchmark(
      `iscream 30 files raw` = iq <- tabix_raw(methscan_files, mouse_promoters),
      times = 3
    )
    bench_30_raw

    ## Unit: seconds
    ##                  expr      min       lq     mean   median       uq      max
    ##  iscream 30 files raw 1.346655 1.559583 1.859358 1.772512 2.115709 2.458907
    ##  neval
    ##      3

    names(iq)

    ##  [1] "cell_01" "cell_02" "cell_03" "cell_04" "cell_05" "cell_06" "cell_07"
    ##  [8] "cell_08" "cell_09" "cell_10" "cell_11" "cell_12" "cell_13" "cell_14"
    ## [15] "cell_15" "cell_16" "cell_17" "cell_18" "cell_19" "cell_20" "cell_21"
    ## [22] "cell_22" "cell_23" "cell_24" "cell_25" "cell_26" "cell_27" "cell_28"
    ## [29] "cell_29" "cell_30"

    iq[["cell_01"]][1:5]

    ## $`1:3669498-3673498`
    ## character(0)
    ##
    ## $`1:4407241-4411241`
    ## character(0)
    ##
    ## $`1:4494413-4498413`
    ## character(0)
    ##
    ## $`1:4783739-4787739`
    ## [1] "1\t4785488\t4785488\t0.000000\t0\t2"
    ## [2] "1\t4785513\t4785513\t0.000000\t0\t2"
    ## [3] "1\t4785522\t4785522\t0.000000\t0\t2"
    ## [4] "1\t4785533\t4785533\t0.000000\t0\t2"
    ## [5] "1\t4786780\t4786780\t100.000000\t1\t0"
    ## [6] "1\t4786886\t4786886\t100.000000\t1\t0"
    ## [7] "1\t4786958\t4786958\t100.000000\t1\t0"
    ##
    ## $`1:4805823-4809823`
    ## [1] "1\t4806176\t4806176\t100.000000\t1\t0"
    ## [2] "1\t4806221\t4806221\t100.000000\t1\t0"
    ## [3] "1\t4807572\t4807572\t0.000000\t0\t1"
    ## [4] "1\t4807669\t4807669\t0.000000\t0\t2"
    ## [5] "1\t4807682\t4807682\t0.000000\t0\t2"
    ## [6] "1\t4807872\t4807872\t0.000000\t0\t1"
    ## [7] "1\t4807890\t4807890\t0.000000\t0\t1"
    ## [8] "1\t4807940\t4807940\t0.000000\t0\t1"
    ## [9] "1\t4807950\t4807950\t0.000000\t0\t1"

    scanTabix(methscan_files, param = GRanges(mouse_promoters))

    ## Error in h(simpleError(msg, call)): error in evaluating the argument 'file' in selecting a method for function 'scanTabix': 'file' must be length 1

# Parsing records into a data frame

A parsed data frame is more useful than a list of raw strings - use `tabix()` instead of `tabix_raw()`. For `scanTabix`, this custom function parses the list of strings to make a similar data frame:

    rtbx <- function(bed) {
      q <- scanTabix(bed, param = GRanges(mouse_promoters)) |>
        lapply(strsplit, split = "\t") |>
        Filter(function(i) length(i) != 0, x = _) |>
        unlist(recursive = FALSE) |>
        do.call(rbind, args = _) |>
        as.data.table() |>
        setnames(c("chr", "start", "end", paste0("V", 1:3)))
      q
    }

    bench_1_df <- microbenchmark(
      `rsamtools 1 file data frame` = rq <- rtbx(methscan_files[1]),
      `iscream 1 file data frame` = iq <- tabix(methscan_files[1], mouse_promoters),
      times = 3
    )
    rq

    ##           chr     start       end         V1     V2     V3
    ##        <char>    <char>    <char>     <char> <char> <char>
    ##     1:      1   4785488   4785488   0.000000      0      2
    ##     2:      1   4785513   4785513   0.000000      0      2
    ##     3:      1   4785522   4785522   0.000000      0      2
    ##     4:      1   4785533   4785533   0.000000      0      2
    ##     5:      1   4786780   4786780 100.000000      1      0
    ##    ---
    ## 79900:      X 168673566 168673566   0.000000      0      1
    ## 79901:      X 168673577 168673577   0.000000      0      1
    ## 79902:      X 168674670 168674670   0.000000      0      1
    ## 79903:      X 168675047 168675047   0.000000      0      1
    ## 79904:      X 169318831 169318831 100.000000      1      0

    iq

    ##           chr     start       end    V1    V2    V3
    ##        <char>     <int>     <int> <num> <int> <int>
    ##     1:      1   4785488   4785488     0     0     2
    ##     2:      1   4785513   4785513     0     0     2
    ##     3:      1   4785522   4785522     0     0     2
    ##     4:      1   4785533   4785533     0     0     2
    ##     5:      1   4786780   4786780   100     1     0
    ##    ---
    ## 79900:      X 168673566 168673566     0     0     1
    ## 79901:      X 168673577 168673577     0     0     1
    ## 79902:      X 168674670 168674670     0     0     1
    ## 79903:      X 168675047 168675047     0     0     1
    ## 79904:      X 169318831 169318831   100     1     0

    all.equal(iq, rq, check.attributes = F)

    ## [1] "Datasets have different column modes. First 3: start(numeric!=character) end(numeric!=character) V1(numeric!=character)"

The column types are different but the data is identical.

    bench_1_df

    ## Unit: milliseconds
    ##                         expr       min       lq      mean    median        uq
    ##  rsamtools 1 file data frame 2169.4535 2229.954 2641.8171 2290.4544 2877.9989
    ##    iscream 1 file data frame  132.9694  134.259  141.7589  135.5487  146.1537
    ##        max neval
    ##  3465.5434     3
    ##   156.7588     3

    autoplot(bench_1_df)

tabix vs scanTabix parsed data frame from 1 file

# Multiple files as data frame

We can try to query multiple BED files using Rsamtools with this function that 8 cores like iscream does:

    partbx <- function(bedfiles) {
      pblapply(
        methscan_files,
        function(bed) {
          query <- rtbx(bed)[, file := tools::file_path_sans_ext(basename(bed), compression = TRUE)][]
          setnames(query, c("chr", "start", "end", "beta", "M", "U", "file"))
          query
        },
        cl = 8
      ) |>
        rbindlist()
    }

    bench_30_df <- microbenchmark(
      `rsamtools 30 file data frame` = rq <- partbx(methscan_files),
      `iscream 30 file data frame` = iq <- tabix(methscan_files, mouse_promoters, col.names = c("beta", "M", "U")),
      times = 3
    )
    bench_30_df

    ## Unit: seconds
    ##                          expr       min        lq      mean   median        uq
    ##  rsamtools 30 file data frame 24.823254 26.949417 28.924390 29.07558 30.974959
    ##    iscream 30 file data frame  1.074239  1.389884  1.660032  1.70553  1.952928
    ##        max neval
    ##  32.874338     3
    ##   2.200326     3

    autoplot(bench_30_df)

tabix vs scanTabix parsed data frame from 30 files

## All benchmarks

    bench_all <- rbind(bench_1_raw, bench_30_raw, bench_1_df, bench_30_raw, bench_30_df)

    bench.dt <- as.data.table(bench_all)[, .(
      time       = time / 1000000,
      package    = fifelse(grepl("iscream", expr), "iscream", "rsamtools"),
      files      = fifelse(grepl("1", expr), 1, 30),
      data.frame = fifelse(grepl("raw", expr), FALSE, TRUE)
    )][,
      `data structure` := fifelse(data.frame, "data.frame", "raw-strings")
    ]

### Runtime

    ggplot(bench.dt, aes(x = as.factor(files), y = time / 1000, color = as.factor(package))) +
      geom_boxplot(position = position_dodge(preserve = "single")) +
      scale_color_discrete(name = "Package") +
      scale_y_continuous(limits = c(0, 35), breaks = scales::extended_breaks(10)) +
      facet_wrap(~ `data structure`, labeller = "label_both") +
      labs(x = "File count", y = "Runtime (seconds)") +
      theme_bw()

    ## Warning: Removed 1 row containing non-finite outside the scale range
    ## (`stat_boxplot()`).

Comparing all benchmarked iscream and Rsamtools querying runtimes

## Session info

    sessionInfo()

    ## R version 4.5.1 (2025-06-13)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS/LAPACK: /nix/store/g5b7qnbhjmiac5q4jb5yvahy6pysd88g-blas-3/lib/libblas.so.3;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: America/Detroit
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] ggplot2_4.0.0        pbapply_1.7-4        microbenchmark_1.5.0
    ##  [4] Rsamtools_2.25.3     Biostrings_2.77.2    XVector_0.49.1
    ##  [7] GenomicRanges_1.61.5 Seqinfo_0.99.2       IRanges_2.43.5
    ## [10] S4Vectors_0.47.4     BiocGenerics_0.55.1  generics_0.1.4
    ## [13] data.table_1.17.8    iscream_0.99.9       BiocFileCache_2.99.6
    ## [16] dbplyr_2.5.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] rappdirs_0.3.3        bitops_1.0-9          RSQLite_2.4.3
    ##  [4] lattice_0.22-7        magrittr_2.0.4        RColorBrewer_1.1-3
    ##  [7] evaluate_1.0.5        grid_4.5.1            fastmap_1.2.0
    ## [10] blob_1.2.4            Matrix_1.7-4          DBI_1.2.3
    ## [13] purrr_1.1.0           scales_1.4.0          codetools_0.2-20
    ## [16] httr2_1.2.1           cli_3.6.5             rlang_1.1.6
    ## [19] crayon_1.5.3          parallelly_1.45.1     bit64_4.6.0-1
    ## [22] withr_3.0.2           cachem_1.1.0          tools_4.5.1
    ## [25] parallel_4.5.1        BiocParallel_1.43.4   memoise_2.0.1
    ## [28] dplyr_1.1.4           filelock_1.0.3        curl_7.0.0
    ## [31] vctrs_0.6.5           R6_2.6.1              lifecycle_1.0.4
    ## [34] stringfish_0.17.0     bit_4.6.0             pkgconfig_2.0.3
    ## [37] gtable_0.3.6          RcppParallel_5.1.11-1 pillar_1.11.1
    ## [40] glue_1.8.0            Rcpp_1.1.0            xfun_0.53
    ## [43] tibble_3.3.0          tidyselect_1.2.1      knitr_1.50
    ## [46] farver_2.1.2          labeling_0.4.3        compiler_4.5.1
    ## [49] S7_0.2.0