I am new to methylation analysis and I am currently using sesame to do look at differential methylation in a human population with and without a treatment, the experiment was done on Illumina EpicV2.

summary = DML(se, ~Treatment, BPPARAM = BiocParallel::MulticoreParam(4))
test_result = summaryExtractTest(summary)

I was following Illumina's youtube series but some things have changed. In particular, I am looking at the top differentially methylated sites and would like to know what chromosome/region ,etc they are in, if I look at the top probes from test_result:

Probe_ID Est_X.Intercept. Est_TreatmentNegative
cg06025456 0.8529257 -0.6830048
cg13934406 0.7866247 -0.6451398
cg26861374 0.9101138 -0.6367773
Pval_X.Intercept. Pval_TreatmentNegative FPval_Treatment
6.708460e-04 0.002604867 0.002604867
5.992517e-05 0.000404208 0.000404208
2.802338e-02 0.097658895 0.097658895
Eff_Treatment
0.6830048
0.6451398
0.6367773

are there tools in sesame to then assign these to the corresponding chromosomes and genes? There seem to be some tools for visualizing but I don't know how to look at chromosomes or genes?

sessionInfo() R version 4.4.1 (2024-06-14) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.4 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-r0.3.20.so; LAPACK version 3.10.0

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: Etc/UTC tzcode source: system (glibc)

attached base packages: 1 parallel stats4 stats graphics grDevices [6] utils datasets methods base

other attached packages: 1 pals_1.9
2 broom_1.0.6 [3] RColorBrewer_1.1-3 [4] limma_3.60.4
[5] shiny_1.9.1.9000 [6] IlluminaHumanMethylationEPICv2manifest_1.0.0 [7] IlluminaHumanMethylationEPICv2anno.20a1.hg38_1.0.0 [8] FlowSorted.Blood.EPIC_2.8.0 [9] knitr_1.47
[10] sesame_1.22.2 [11] sesameData_1.22.0 [12] ExperimentHub_2.12.0 [13] AnnotationHub_3.12.0 [14] BiocFileCache_2.12.0 [15] dbplyr_2.5.0
[16] grateful_0.2.4 [17] lubridate_1.9.3 [18] forcats_1.0.0 [19] stringr_1.5.1 [20] dplyr_1.1.4 [21] purrr_1.0.2
[22] readr_2.1.5 [23] tidyr_1.3.1 [24] tibble_3.2.1
[25] ggplot2_3.5.1 [26] tidyverse_2.0.0 [27] shinyMethyl_1.41.1 [28] minfi_1.50.0
[29] bumphunter_1.46.0 [30] locfit_1.5-9.10 [31] iterators_1.0.14 [32] foreach_1.5.2 [33] Biostrings_2.72.1 [34] XVector_0.44.0 [35] SummarizedExperiment_1.34.0 [36] Biobase_2.64.0 [37] MatrixGenerics_1.16.0 [38] matrixStats_1.3.0 [39] GenomicRanges_1.56.1 [40] GenomeInfoDb_1.40.1 [41] IRanges_2.38.1 [42] S4Vectors_0.42.1 [43] BiocGenerics_0.50.0

loaded via a namespace (and not attached): 1 splines_4.4.1
later_1.3.2 [3] BiocIO_1.14.0 bitops_1.0-8 [5] filelock_1.0.3 preprocessCore_1.66.0 [7] XML_3.99-0.17 lifecycle_1.0.4 [9] lattice_0.22-6 MASS_7.3-61 [11] base64_2.0.1 scrime_1.3.5 [13] backports_1.5.0
magrittr_2.0.3 [15] rmarkdown_2.28 yaml_2.3.10 [17] httpuv_1.6.15 doRNG_1.8.6 [19] askpass_1.2.0 mapproj_1.2.11 [21] DBI_1.2.3
maps_3.4.2 [23] abind_1.4-5
zlibbioc_1.50.0 [25] quadprog_1.5-8
RCurl_1.98-1.16 [27] rappdirs_0.3.3
GenomeInfoDbData_1.2.12 [29] genefilter_1.86.0
annotate_1.82.0 [31] DelayedMatrixStats_1.26.0 codetools_0.2-20 [33] DelayedArray_0.30.1 xml2_1.3.6
[35] tidyselect_1.2.1 UCSC.utils_1.0.0 [37] beanplot_1.3.1 illuminaio_0.46.0 [39] GenomicAlignments_1.40.0 jsonlite_1.8.8 [41] wheatmap_0.2.0 multtest_2.60.0 [43] survival_3.7-0 tools_4.4.1 [45] Rcpp_1.0.13 glue_1.7.0 [47] SparseArray_1.4.8 xfun_0.45
[49] HDF5Array_1.32.1 withr_3.0.1 [51] BiocManager_1.30.24 fastmap_1.2.0 [53] rhdf5filters_1.16.0 fansi_1.0.6 [55] openssl_2.2.1 digest_0.6.37 [57] timechange_0.3.0 R6_2.5.1 [59] mime_0.12
colorspace_2.1-1 [61] dichromat_2.0-0.1
RSQLite_2.3.7 [63] utf8_1.2.4
generics_0.1.3 [65] data.table_1.16.0
rtracklayer_1.64.0 [67] httr_1.4.7
S4Arrays_1.4.1 [69] pkgconfig_2.0.3 gtable_0.3.5 [71] blob_1.2.4 siggenes_1.78.0 [73] htmltools_0.5.8.1 scales_1.3.0 [75] png_0.1-8
rstudioapi_0.16.0 [77] tzdb_0.4.0
reshape2_1.4.4 [79] rjson_0.2.22 nlme_3.1-165 [81] curl_5.2.2 cachem_1.1.0 [83] rhdf5_2.48.0 BiocVersion_3.19.1 [85] AnnotationDbi_1.66.0 restfulr_0.0.15 [87] GEOquery_2.72.0 pillar_1.9.0 [89] grid_4.4.1
reshape_0.8.9 [91] vctrs_0.6.5
promises_1.3.0 [93] xtable_1.8-4
evaluate_0.24.0 [95] GenomicFeatures_1.56.0 cli_3.6.3
[97] compiler_4.4.1 Rsamtools_2.20.0 [99] rlang_1.1.4 crayon_1.5.3 [101] rngtools_1.5.2 nor1mix_1.3-3 [103] mclust_6.1.1 plyr_1.8.9 [105] stringi_1.8.4
BiocParallel_1.38.0 [107] munsell_0.5.1 Matrix_1.7-0 [109] hms_1.1.3 sparseMatrixStats_1.16.0 [111] bit64_4.0.5 Rhdf5lib_1.26.0 [113] KEGGREST_1.44.1 statmod_1.5.0 [115] memoise_2.0.1 bit_4.0.5

There is a sesameAnno_attachManifest function which might be useful to this need. e.g., https://github.com/zwdzwd/sesame/blob/7b779e180c4a5d789902adb309733509d8d2d76a/R/sesameAnno.R#L194