About metabinR

metabinR performs abundance- and composition-based binning on metagenomic samples directly from FASTA or FASTQ files. Abundance-based binning (AB) analyzes long k-mers (k > 8); composition-based binning (CB) analyzes short k-mers (k < 8); hierarchical binning (ABxCB) chains the two.

The heavy lifting is implemented in Java and called via rJava. From v2.0.0 the R API returns an S4 MetabinResult object and accepts Biostrings / ShortRead inputs in addition to file paths.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("metabinR")

A JDK (Java >= 11) must be available before installing metabinR.

Preparation

JVM heap size

JVM flags are passed through the java.parameters option. The -Xmx flag controls the maximum heap: -Xmx1500M or -Xmx3G, etc. Set this before loading the package.

options(java.parameters = "-Xmx1500M")
library(metabinR)
library(ggplot2)
library(Biostrings)

To customise additional JVM flags (on top of the -Xmx heap setting), set options(metabinR.jvm.flags = c("-XX:+UseG1GC", ...)) before the package is loaded. The defaults returned by metabinR_jvm_options() already enable G1GC and string deduplication.

The `MetabinResult` class

Each binning function returns a MetabinResult S4 object:

assignments(res) — DataFrame of per-read cluster assignments.
nClusters(res) — number of clusters produced.
parameters(res) — list of parameters actually used.
algorithm(res) — "AB", "CB", or "ABxCB".
as.data.frame(res) — the v1.x tabular layout for back-compat.

The read_id column identifies each read. The column named by algorithm(res) contains its assigned bin, and columns such as AB.1 contain distances to candidate bins. Bin numbers are arbitrary labels; they do not identify genomes or abundance classes on their own.

Abundance based binning example

The toy simulated metagenome contains 26,664 Illumina reads (13,332 pairs of 2x150bp) sampled from 10 bacterial genomes across two abundance classes (high vs. low).

Abundance ground truth:

abundances <- read.table(
    system.file("extdata", "distribution_0.txt", package = "metabinR"),
    col.names = c("genome_id", "abundance", "AB_id"))

Read-level ground truth contains the genome of origin for each read. Add the abundance class of each genome for the AB evaluation:

reads.mapping <- read.delim(system.file(
    "extdata", "reads_mapping.tsv.gz", package = "metabinR"))
reads.mapping <- merge(reads.mapping,
                       abundances[, c("genome_id", "AB_id")],
                       by = "genome_id")

Run abundance-based binning with 10-mers into 2 clusters:

res.AB <- abundance_based_binning(
    system.file("extdata", "reads.metagenome.fasta.gz", package = "metabinR"),
    numOfClustersAB = 2,
    kMerSizeAB = 10,
    dryRun = FALSE,
    outputAB = "vignette"
)
res.AB
#> MetabinResult (AB)
#>   reads:     26,664
#>   clusters:  2
#>   inputs:    1 file(s)
#>     - /tmp/RtmpKcpJG7/Rinst1bc6586471a8/metabinR/extdata/reads.metagenome.fasta.gz

Inspect the assignments and summarize the observed bins. The distance summaries use only each read’s distance to its assigned bin:

head(as.data.frame(assignments(res.AB)))
#>   read_id AB        AB.1        AB.2
#> 1  S0R0/1  1 0.032307751 0.004981781
#> 2  S0R0/2  1 0.030796454 0.005614620
#> 3  S0R1/1  1 0.021667648 0.009437209
#> 4  S0R1/2  1 0.019364505 0.010336333
#> 5  S0R2/1  1 0.013742806 0.012755650
#> 6  S0R2/2  2 0.009775655 0.014416852
knitr::kable(as.data.frame(bin_summary(res.AB)), digits = 3)

bin	n_reads	proportion	mean_distance	median_distance
1	19761	0.741	0.024	0.024
2	6903	0.259	0.015	0.015

abundance_based_binning() wrote one FASTA per cluster and a k-mer count histogram:

histogram.AB <- read.table("vignette__AB.histogram.tsv", header = TRUE)
ggplot(histogram.AB, aes(x = counts, y = frequency)) +
    geom_area() +
    labs(title = "kmer counts histogram") +
    theme_bw()

Evaluate against the abundance-class ground truth. evaluate_bins() matches read_id to anonymous_read_id, so the two tables need not have the same row order. The confusion table has inferred bins as rows and known abundance classes as columns:

eval.AB <- evaluate_bins(res.AB, reads.mapping,
                         id = "anonymous_read_id", label = "AB_id")
eval.AB$confusion
#>    origin
#> bin     1     2
#>   1 18185  1576
#>   2  1891  5012
knitr::kable(as.data.frame(eval.AB$per_bin), digits = 3)

bin	n_reads	dominant_origin	dominant_reads	purity
1	19761	1	18185	0.920
2	6903	2	5012	0.726

knitr::kable(as.data.frame(eval.AB$overall), digits = 3)

n_reads	n_bins	n_origins	weighted_purity	weighted_recovery	adjusted_rand_index
26664	2	2	0.87	0.87	0.519

Per-bin purity is the fraction of reads in a bin from its dominant abundance class. The adjusted Rand index (ARI) compares the two partitions without assuming that their labels correspond.

Composition based binning example

Here the known label is the bacterial genome of origin, rather than the abundance class used above.

Run composition-based binning with 4-mers into 10 clusters:

res.CB <- composition_based_binning(
    system.file("extdata", "reads.metagenome.fasta.gz", package = "metabinR"),
    numOfClustersCB = 10,
    kMerSizeCB = 4,
    dryRun = TRUE,
    outputCB = "vignette"
)

Check bin sizes and inspect reads with close competing distances. The margin is an absolute distance difference on this algorithm’s scale, not a probability. A small result can mean either that few reads are close to a boundary or that the chosen margin is too narrow:

knitr::kable(as.data.frame(bin_summary(res.CB)), digits = 3)

bin	n_reads	proportion	mean_distance	median_distance
3	7056	0.265	15354.64	15238
9	3022	0.113	15887.64	15816
10	1485	0.056	16183.90	16048
1	1055	0.040	15581.03	15334
8	7554	0.283	16462.67	16457
5	1412	0.053	16083.25	15941
4	2376	0.089	16167.66	16031
6	1343	0.050	14975.23	14754
2	867	0.033	16734.70	16682
7	494	0.019	16047.81	15718

close.reads <- ambiguous_reads(res.CB, margin = 0.05)
nrow(close.reads)
#> [1] 22
knitr::kable(head(as.data.frame(close.reads)), digits = 3)

read_id	bin	best_distance	second_distance
S0R181/1	3	16726	16726
S0R772/1	1	18002	18002
S0R1244/1	5	19762	19762
S0R2642/2	8	18712	18712
S0R3141/2	3	15104	15104
S0R3678/2	6	15440	15440

Evaluate by genome of origin. Best-bin recovery is the fraction of an origin’s reads found in its single largest bin; it is not genome completeness. Weighted purity and recovery summarize these counts over all evaluated reads:

eval.CB <- evaluate_bins(res.CB, reads.mapping,
                         id = "anonymous_read_id", label = "genome_id")
knitr::kable(as.data.frame(eval.CB$per_origin), digits = 3)

origin	n_reads	dominant_bin	recovered_reads	best_bin_recovery
Genome15.0	14190	3	5953	0.420
Genome3.0	5886	4	1616	0.275
Genome2.0	538	8	219	0.407
Genome12.0	3782	8	3679	0.973
Genome11.0	1186	8	1152	0.971
Genome6.0	356	7	67	0.188
Genome17.0	594	5	238	0.401
Genome22.0	8	8	6	0.750
Genome23.0	80	8	80	1.000
Genome4.0	44	7	20	0.455

knitr::kable(as.data.frame(eval.CB$overall), digits = 3)

n_reads	n_bins	n_origins	weighted_purity	weighted_recovery	adjusted_rand_index
26664	10	10	0.649	0.489	0.15

Hierarchical (2-step ABxCB) binning example

res.ABxCB <- hierarchical_binning(
    system.file("extdata", "reads.metagenome.fasta.gz", package = "metabinR"),
    numOfClustersAB = 2,
    kMerSizeAB = 10,
    kMerSizeCB = 4,
    dryRun = TRUE,
    outputC = "vignette"
)
knitr::kable(as.data.frame(bin_summary(res.ABxCB)), digits = 3)

bin	n_reads	proportion	mean_distance	median_distance
1	19761	0.741	16731.08	16490
2	6903	0.259	16773.47	16304

eval.ABxCB <- evaluate_bins(res.ABxCB, reads.mapping,
                            id = "anonymous_read_id", label = "genome_id")
knitr::kable(as.data.frame(eval.ABxCB$overall), digits = 3)

n_reads	n_bins	n_origins	weighted_purity	weighted_recovery	adjusted_rand_index
26664	2	10	0.62	0.904	0.298

In hierarchical results, distances for bins outside a read’s parent abundance bin are missing. ambiguous_reads() ignores those missing distances and omits reads with fewer than two finite candidates.

In-memory inputs (Biostrings / ShortRead)

Instead of a path, you can pass a DNAStringSet, QualityScaledDNAStringSet, or ShortReadQ. The Java backend reads from disk, so non-file inputs are staged to a tempfile transparently.

reads <- Biostrings::readDNAStringSet(
    system.file("extdata", "reads.metagenome.fasta.gz", package = "metabinR"))

res.AB.mem <- abundance_based_binning(
    reads,
    numOfClustersAB = 2,
    kMerSizeAB = 10,
    dryRun = TRUE
)
identical(nrow(assignments(res.AB.mem)), nrow(assignments(res.AB)))
#> [1] TRUE

Clean up files written by the AB run:

unlink("vignette__*")

Session Info

utils::sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  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=en_US.UTF-8          
#>  [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
#> [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] Biostrings_2.81.9    Seqinfo_1.3.2        XVector_0.53.0      
#>  [4] IRanges_2.47.5       S4Vectors_0.51.10    BiocGenerics_0.59.12
#>  [7] generics_0.1.4       ggplot2_4.0.3        metabinR_2.1.1      
#> [10] BiocStyle_2.41.0    
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6             xfun_0.61                bslib_0.12.0            
#>  [4] hwriter_1.3.2.1          latticeExtra_0.6-31      rJava_1.0-18            
#>  [7] Biobase_2.73.2           lattice_0.23-1           vctrs_0.7.3             
#> [10] tools_4.6.1              bitops_1.1-0             parallel_4.6.1          
#> [13] tibble_3.3.1             pkgconfig_2.0.3          Matrix_1.7-6            
#> [16] checkmate_2.3.4          RColorBrewer_1.1-3       S7_0.2.2                
#> [19] cigarillo_1.3.1          lifecycle_1.0.5          compiler_4.6.1          
#> [22] farver_2.1.2             deldir_2.0-4             Rsamtools_2.29.0        
#> [25] codetools_0.2-20         htmltools_0.5.9          sys_3.4.3               
#> [28] buildtools_1.0.0         sass_0.4.10              yaml_2.3.12             
#> [31] pillar_1.11.1            crayon_1.5.3             jquerylib_0.1.4         
#> [34] BiocParallel_1.47.0      cachem_1.1.0             ShortRead_1.71.1        
#> [37] tidyselect_1.2.1         digest_0.6.39            dplyr_1.2.1             
#> [40] labeling_0.4.3           maketools_1.3.2          fastmap_1.2.0           
#> [43] grid_4.6.1               cli_3.6.6                magrittr_2.0.5          
#> [46] withr_3.0.3              scales_1.4.0             backports_1.5.1         
#> [49] rmarkdown_2.32           pwalign_1.9.1            jpeg_0.1-11             
#> [52] interp_1.1-6             otel_0.2.0               png_0.1-9               
#> [55] evaluate_1.0.5           knitr_1.52               GenomicRanges_1.65.4    
#> [58] rlang_1.3.0              Rcpp_1.1.2               glue_1.8.1              
#> [61] BiocManager_1.30.27      jsonlite_2.0.0           R6_2.6.1                
#> [64] GenomicAlignments_1.49.2

metabinR