{ "_id": "6a1b023e1d7bb097a0a00ab4", "Package": "spiky", "Type": "Package", "Title": "Spike-in calibration for cell-free MeDIP", "Description": "spiky implements methods and model generation for cfMeDIP\n(cell-free methylated DNA immunoprecipitation) with spike-in\ncontrols. CfMeDIP is an enrichment protocol which avoids\ndestructive conversion of scarce template, making it ideal as a\n\"liquid biopsy,\" but creating certain challenges in comparing\nresults across specimens, subjects, and experiments. The use of\nsynthetic spike-in standard oligos allows diagnostics performed\nwith cfMeDIP to quantitatively compare samples across subjects,\nexperiments, and time points in both relative and absolute\nterms.", "Version": "1.19.0", "Date": "2023-04-19", "Authors@R": "c(person(\"Samantha\", \"Wilson\", role=c(\"aut\")), \nperson(\"Lauren\", \"Harmon\", role=c(\"aut\")),\nperson(\"Tim\", \"Triche\", role=c(\"aut\",\"cre\"),\nemail=\"trichelab@gmail.com\"))", "biocViews": "DifferentialMethylation, DNAMethylation, Normalization,\nPreprocessing, QualityControl, Sequencing", "URL": "https://github.com/trichelab/spiky", "BugReports": "https://github.com/trichelab/spiky/issues", "License": "GPL-2", "RoxygenNote": "7.2.1", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "Config/pak/sysreqs": "make libbz2-dev liblzma-dev libxml2-dev libssl-dev\nxz-utils zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": 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"dedup", "title": "spike-in counts for two samples, as a wide data.frame", "object": "dedup", "class": [ "data.frame" ], "fields": [ "frag_grp", "read_count_6547", "read_count_6548" ], "rows": 26, "table": true, "tojson": true }, { "name": "genbank_mito", "title": "various mitochondrial genomes sometimes used as endogenous spike-ins", "object": "genbank_mito", "class": [ "DFrame" ], "fields": [], "table": false, "tojson": false }, { "name": "genomic_res", "title": "A Granges object with genomic coverage from chr21q22, binned every 300bp for the genomic contigs then averaged across the bin. (In other words, the default output of scan_genomic_contigs or scan_genomic_bedpe, restricted to a small enough set of genomic regions to be practical for examples.) 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(In other words, the default output of scan_spike_contigs or scan_spike_bedpe) This represents what most users will want to generate from their own spike-in BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package.", "object": "spike_res", "class": [ "GRanges" ], "fields": [], "table": false, "tojson": false }, { "name": "ssb_res", "title": "scan_spiked_bam results from a merged cfMeDIP CRAM file (chr22 and spikes)", "object": "ssb_res", "class": [ "CompressedGRangesList" ], "fields": [], "table": false, "tojson": false }, { "name": "testGR", "title": "a test GRanges with UMI'ed genomic sequences used as controls", "object": "testGR", "class": [ "GRanges" ], "fields": [], "table": false, "tojson": false } ], "_help": [ { "page": "add_frag_info", "title": "decode fragment identifiers for spike-in standards", "topics": [ "add_frag_info" ] }, { "page": "bam_to_bins", "title": "create a tiled representation of a genome from the BAM/CRAM file", "topics": [ "bam_to_bins" ] }, { "page": "bin_pmol", "title": "Binned estimation of picomoles of DNA present in cfMeDIP assays", "topics": [ "bin_pmol" ] }, { "page": "convertPairedGRtoGR", "title": "Convert Pairs to GRanges", "topics": [ "convertPairedGRtoGR" ] }, { "page": "covg_to_df", "title": "reshape 'scan_spiked_bam' results into data.frames for model_glm_pmol", "topics": [ "covg_to_df" ] }, { "page": "dedup", "title": "spike-in counts for two samples, as a wide data.frame", "topics": [ "dedup" ] }, { "page": "find_spike_contigs", "title": "find spike-in seqlevels in an object 'x', where !is.null(seqinfo(x))", "topics": [ "find_spike_contigs" ] }, { "page": "genbank_mito", "title": "various mitochondrial genomes sometimes used as endogenous spike-ins", "topics": [ "genbank_mito" ] }, { "page": "generate_spike_fasta", "title": "for CRAM files, a FASTA reference is required to decode; this builds that", "topics": [ "generate_spike_fasta" ] }, { "page": "genomic_res", "title": "A Granges object with genomic coverage from chr21q22, binned every 300bp for the genomic contigs then averaged across the bin. (In other words, the default output of scan_genomic_contigs or scan_genomic_bedpe, restricted to a small enough set of genomic regions to be practical for examples.) This represents what most users will want to generate from their own genomic BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package.", "topics": [ "genomic_res" ] }, { "page": "get_base_name", "title": "refactored out of rename_spikes and rename_spike_seqlevels", "topics": [ "get_base_name" ] }, { "page": "get_binned_coverage", "title": "tabulate read coverage in predefined bins", "topics": [ "get_binned_coverage" ] }, { "page": "get_merged_gr", "title": "get a GRanges of (by default, standard) chromosomes from seqinfo", "topics": [ "get_merged_gr" ] }, { "page": "get_spike_depth", "title": "get the (max, median, or mean) coverage for spike-in contigs from a BAM/CRAM", "topics": [ "get_spike_depth" ] }, { "page": "get_spiked_coverage", "title": "tabulate coverage across assembly and spike contig subset in natural order", "topics": [ "get_spiked_coverage" ] }, { "page": "kmax", "title": "simple contig kmer comparisons", "topics": [ "kmax" ] }, { "page": "kmers", "title": "oligonucleotideFrequency, but less letters and more convenient.", "topics": [ "kmers" ] }, { "page": "methylation_specificity", "title": "compute methylation specificity for spike-in standards", "topics": [ "methylation_specificity" ] }, { "page": "model_bam_standards", "title": "Build a Bayesian additive model from spike-ins to correct bias in *-seq", "topics": [ "model_bam_standards" ] }, { "page": "model_glm_pmol", "title": "Build a generalized linear model from spike-ins to correct bias in cfMeDIP", "topics": [ "model_glm_pmol" ] }, { "page": "parse_spike_UMI", "title": "parse out the forward and reverse UMIs and contig for a BED/BAM", "topics": [ "parse_spike_UMI" ] }, { "page": "phage", "title": "lambda and phiX phage sequences, sometimes used as spike-ins", "topics": [ "phage" ] }, { "page": "predict_pmol", "title": "predict picomoles of DNA from a fit and read counts (coverage)", "topics": [ "predict_pmol" ] }, { "page": "process_spikes", "title": "QC, QA, and processing for a new spike database", "topics": [ "process_spikes" ] }, { "page": "read_bedpe", "title": "read a BEDPE file into Pairs of GRanges (as if a GAlignmentPairs or similar)", "topics": [ "read_bedpe" ] }, { "page": "rename_spike_seqlevels", "title": "for spike-in contigs in GRanges, match to standardized spike seqlevels", "topics": [ "rename_spike_seqlevels" ] }, { "page": "rename_spikes", "title": "for BAM/CRAM files with renamed contigs, we need to rename 'spike' rows", "topics": [ "rename_spikes" ] }, { "page": "scan_genomic_bedpe", "title": "Scan genomic BEDPE", "topics": [ "scan_genomic_bedpe" ] }, { "page": "scan_genomic_contigs", "title": "scan genomic contigs in a BAM/CRAM file", "topics": [ "scan_genomic_contigs" ] }, { "page": "scan_methylation_specificity", "title": "tabulate methylation specificity for multiple spike-in BAM/CRAM files", "topics": [ "scan_methylation_specificity" ] }, { "page": "scan_spike_bedpe", "title": "Scan spikes BEDPE", "topics": [ "scan_spike_bedpe" ] }, { "page": "scan_spike_contigs", "title": "pretty much what it says: scan spike contigs from a BAM or CRAM file", "topics": [ "scan_spike_contigs" ] }, { "page": "scan_spike_counts", "title": "run spike_counts on BAM/CRAM files and shape the results for model_glm_pmol", "topics": [ "scan_spike_counts" ] }, { "page": "scan_spiked_bam", "title": "pretty much what it says: scan standard chroms + spike contigs from a BAM", "topics": [ "scan_spiked_bam" ] }, { "page": "seqinfo_from_header", "title": "create seqinfo (and thus a standard chromosome filter) from a BAM header", "topics": [ "seqinfo_from_header" ] }, { "page": "spike", "title": "spike-in contig properties for Sam's cfMeDIP spikes", "topics": [ "spike" ] }, { "page": "spike_bland_altman_plot", "title": "Bland-Altman plot for cfMeDIP spike standards", "topics": [ "spike_bland_altman_plot" ] }, { "page": "spike_counts", "title": "use the index of a spiked BAM/CRAM file for spike contig coverage", "topics": [ "spike_counts" ] }, { "page": "spike_cram_counts", "title": "spike-in counts, as a long data.frame", "topics": [ "spike_cram_counts" ] }, { "page": "spike_read_counts", "title": "spike-in counts, as a long data.frame", "topics": [ "spike_read_counts" ] }, { "page": "spike_res", "title": "A Granges object with spike-in sequence coverage, and summarized for each spike contig as (the default) 'max' coverage. (In other words, the default output of scan_spike_contigs or scan_spike_bedpe) This represents what most users will want to generate from their own spike-in BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package.", "topics": [ "spike_res" ] }, { "page": "spiky-methods", "title": "A handful of methods that I've always felt were missing", "topics": [ "plot,Rle,ANY-method", "plot,SimpleRleList,ANY-method", "spiky-methods" ] }, { "page": "ssb_res", "title": "scan_spiked_bam results from a merged cfMeDIP CRAM file (chr22 and spikes)", "topics": [ "ssb_res" ] }, { "page": "testGR", "title": "a test GRanges with UMI'ed genomic sequences used as controls", "topics": [ "testGR" ] }, { "page": "tile_bins", "title": "Tile the assembly-based contigs of a merged assembly/spike GRanges.", "topics": [ "tile_bins" ] } ], "_readme": "https://github.com/bioc/spiky/raw/HEAD/README.md", "_rundeps": [ "abind", "askpass", "bamlss", "BH", "Biobase", "BiocBaseUtils", 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spike-in controls", "author": "Samantha L Wilson and Lauren M Harmon", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Installation", "Load spike database, or create your own with process_spikes().", "Input: A Fasta file, GRanges, or dataframe of spike-in sequences, and a vector of booleans (0 or 1) describing whether each spike-in sequence is methylated.", "Output: The output contains a DataFrame with the following columns:", "Process the input files", "BAM Input", "BAM required columns", "Output: The output objects will be used downstream in the analysis, including", "BEDPE Input", "Methylation specificity", "Input: The output of the 'scan_spike_contigs' or 'scan_spike_bedpe' functions", "Output: methylation specificity mean and median", "Example", "Fit a Gaussian model to predict the molar amount of DNA sequences", "Output:", "Calculating molar amount on DNA sequences of interest", "Input: The output of the 'scan_genomic_contigs' or 'scan_genomic_bedpe' functions and the Gaussian generalized linear model", "Output: sample_pmol_data", "Adjusting molar amount to binned genomic windows", "Input: output dataframe produced from predict_pmol", "Output: sample_binned_data", "Session Info", "References" ], "created": "2020-09-17 16:58:06", "modified": "2022-09-16 16:39:29", "commits": 10 } ], "_score": 5.079181246047625, "_indexed": true, "_nocasepkg": "spiky", "_universes": [ "bioc", "trichelab" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.19.0", "date": "2026-05-30T10:47:50.000Z", "distro": "noble", "commit": "42d312e43dd2ee1ea1602049bb950643fbd17dd8", "fileid": "ca25fc96a07effeb91283a2c942dfdbb1bc759f71639e40ca2b89835320e5cd2", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26677106999" }, { "r": "4.6.0", "os": "linux", "version": "1.19.0", "date": "2026-05-30T10:47:01.000Z", "distro": "noble", "commit": "42d312e43dd2ee1ea1602049bb950643fbd17dd8", "fileid": 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