| Previous changeset 0:dbdb21b1d395 (2025-01-20) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/cnvkit commit fc1282ec68b346988203ead860e9b9d6a47e9efb |
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modified:
macros.xml seg.xml test-data/sample.cnv.vcf |
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| diff -r dbdb21b1d395 -r 40918fee953b macros.xml --- a/macros.xml Mon Jan 20 16:43:10 2025 +0000 +++ b/macros.xml Sat Mar 01 12:05:56 2025 +0000 |
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| b'@@ -1,10 +1,10 @@\n <macros>\n- <token name="@VERSION_SUFFIX@">1</token>\n- <token name="@TOOL_VERSION@">0.9.11</token>\n+ <token name="@VERSION_SUFFIX@">0</token>\n+ <token name="@TOOL_VERSION@">0.9.12</token>\n <xml name="requirements">\n <requirements>\n <requirement type="package" version="@TOOL_VERSION@">cnvkit</requirement>\n- <requirement type="package" version="1.4.2">scikit-learn</requirement>\n+ <requirement type="package" version="1.21">samtools</requirement>\n </requirements>\n </xml>\n <xml name="reference_interface">\n@@ -56,13 +56,13 @@\n <option value="wgs">whole genome sequencing </option>\n </param>\n <param argument="--segment-method" type="select" label="Method used in the \'segment\' step" help="">\n- <option value="cbs" selected="True">Circular Binary Segmentation CBS</option>\n- <option value="flasso">Fused lasso, hybrid flasso</option>\n- <option value="haar">a pure-Python implementation of HaarSeg, a wavelet-based method. Very fast and performs reasonably well on small panels, but tends to over-segment large datasets., hybrid haar</option>\n- <option value="none">simply calculate the weighted mean log2 value of each chromosome arm. Useful for testing or debugging, or as a baseline for benchmarking other methods., hybrid none</option>\n- <option value="hmm">experimental \xe2\x80\x93 a 3-state Hidden Markov Model suitable for most samples. Faster than CBS, and slower but more accurate than Haar. Requires the Python package pomegranate, as do the next two thods., hybrid hmm</option>\n- <option value="hmm-tumor">experimental \xe2\x80\x93 a 5-state HMM suitable for finer-grained segmentation of good-quality tumor samples. In particular, this method can detect focal amplifications within a larger-scale, smaller-amplitude copy number gain, or focal deep deletions within a larger-scale hemizygous loss. Training this model takes a bit more CPU time than the simpler hmm method., hybrid hmm-tumor</option>\n- <option value="hmm-germline">experimental \xe2\x80\x93 a 3-state HMM with fixed amplitude for the loss, neutral, and gain states corresponding to absolute copy numbers of 1, 2, and 3. Suitable for germline samples and single-cell sequencing of samples with mostly-diploid genomes that are not overly aneuploid., hybrid hmm-germline</option>\n+ <option value="cbs" selected="True">CBS: Circular Binary Segmentation (default, precise)</option>\n+ <option value="flasso">Flasso: Fused Lasso; smoother segments, fewer breakpoints</option>\n+ <option value="haar">Haar: Haar wavelet transform; detects abrupt changes</option>\n+ <option value="none">None: No segmentation; outputs bin-level data as segments</option>\n+ <option value="hmm">Hmm: Basic Hidden Markov Model (generic use)</option>\n+ <option value="hmm-tumor">Hmm-tumor: HMM tailored for tumor samples (somatic CNVs)</option>\n+ <option value="hmm-germline">Hmm-germline: HMM for germline (inherited) variants (diploid assumption)</option>\n </param>\n <param argument="--male-reference" type="boolean" checked="false" truevalue="--male-reference" falsevalue="" label="Use or assume a male reference" help="female samples will have +1 log-CNR of chrX; otherwise male samples would have -1 chrX" />\n <param argument="--countreads" type="boolean" checked="false" truevalue="--countreads" falsevalue="" label="Get read depths by counting read midpoints within each bin" help="" />\n@@ -70,7 +70,7 @@\n </xml>\n <xml name="create_CNV_reference_file">\n <param name="input_sample_file" type="data" format="bam" label="Sample BAM file" help="" />\n- <param argument="--normal" type="data" format="bam" label="Control BAM file" help="" />\n+ <param argument="--normal" o'..b'nt="--trend" type="boolean" checked="false" truevalue="--trend" falsevalue="" label="Draw a smoothed local trendline on the scatter plot" help=""/>\n <param argument="--y-max" optional="true" type="integer" label="y-axis upper limit" min="1" value="" help=""/>\n <param argument="--y-min" optional="true" type="integer" label="y-axis lower limit" min="1" value="" help=""/>\n <param argument="--fig-size" optional="true" type="float" label="Width and height of the plot in inches" value="" help="Example 6.4 4.8, the space between the two inputs is important"/>\n </xml>\n <xml name="segment_optional">\n- <param argument="--dataframe" type="text" optional="true" label="Data frame" value="" help="File name to save the raw R dataframe emitted by CBS or Fused Lasso, example dataframe.r"/>\n+ <param argument="--dataframe" type="text" label="Data frame" help="File name to save the raw R dataframe emitted by CBS or Fused Lasso, example dataframe.r"/>\n <param argument="--method" type="select" label="Segmentation method" help="">\n- <option value="cbs" selected="True">Circular Binary Segmentation CBS method,hybrid CBS</option>\n- <option value="flasso">Fused lasso, hybrid flasso</option>\n- <option value="haar">A pure-Python implementation of HaarSeg, a wavelet-based method. Very fast and performs reasonably well on small panels, but tends to over-segment large datasets., hybrid haar</option>\n- <option value="none">simply calculate the weighted mean log2 value of each chromosome arm. Useful for testing or debugging, or as a baseline for benchmarking other methods., hybrid none</option>\n- <option value="hmm">experimental \xe2\x80\x93 a 3-state Hidden Markov Model suitable for most samples. Faster than CBS, and slower but more accurate than Haar. Requires the Python package pomegranate, as do the next two methods., hybrid hmm</option>\n- <option value="hmm-tumor">experimental \xe2\x80\x93 a 5-state HMM suitable for finer-grained segmentation of good-quality tumor samples. In particular, this method can detect focal amplifications within a larger-scale, smaller-amplitude copy number gain, or focal deep deletions within a larger-scale hemizygous loss. Training this model takes a bit more CPU time than the simpler hmm method., hybrid hmm-tumor</option>\n- <option value="hmm-germline">experimental \xe2\x80\x93 a 3-state HMM with fixed amplitude for the loss, neutral, and gain states corresponding to absolute copy numbers of 1, 2, and 3. Suitable for germline samples and single-cell sequencing of samples with mostly-diploid genomes that are not overly aneuploid., hybrid hmm-germline</option>\n+ <option value="cbs" selected="True">CBS: Circular Binary Segmentation (default, precise)</option>\n+ <option value="flasso">Flasso: Fused Lasso; smoother segments, fewer breakpoints</option>\n+ <option value="haar">Haar: Haar wavelet transform; detects abrupt changes</option>\n+ <option value="none">None: No segmentation; outputs bin-level data as segments</option>\n+ <option value="hmm">Hmm: Basic Hidden Markov Model (generic use)</option>\n+ <option value="hmm-tumor">Hmm-tumor: HMM tailored for tumor samples (somatic CNVs)</option>\n+ <option value="hmm-germline">Hmm-germline: HMM for germline (inherited) variants (diploid assumption)</option>\n </param>\n <param argument="--threshold" optional="true" type="integer" label="Significance threshold" min="1" help="To accept breakpoints during segmentation. For HMM methods, this is the smoothing window size"/>\n <param argument="--drop-low-coverage" type="boolean" checked="false" truevalue="--drop-low-coverage" falsevalue="" label="Drop very-low-coverage bins before segmentation" help="To avoid false-positive deletions in poor-quality tumor samples"/>\n' |
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| diff -r dbdb21b1d395 -r 40918fee953b seg.xml --- a/seg.xml Mon Jan 20 16:43:10 2025 +0000 +++ b/seg.xml Sat Mar 01 12:05:56 2025 +0000 |
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| @@ -14,7 +14,7 @@ --output sample.cnv.seg ]]></command> <inputs> - <param name="input_segmented_file" type="data" format="tabular" label="Segmented copy ratio data file (cns file)" help="" /> + <param name="input_segmented_file" type="data" format="cns" label="Segmented copy ratio data file (cns file)" help="" /> <section name="advanced_settings" title="Advanced settings" expanded="false"> <param argument="--enumerate-chroms" type="boolean" checked="false" truevalue="--enumerate-chroms" falsevalue="" label="Enumerate Chroms" help="Replace chromosome names with sequential integer IDs" /> </section> @@ -24,12 +24,30 @@ </outputs> <tests> <test expect_num_outputs="1"> - <param name="input_segmented_file" ftype="tabular" value="sample.cns" /> + <param name="input_segmented_file" ftype="cns" value="sample.cns" /> <output name="CNVs_SEG" file="sample.cnv.seg" /> </test> </tests> <help><![CDATA[ Export the segmented copy number data (from a .cns file) to the standard SEG format. + +----- + +**Segmented log2 ratios (.cns)** + +Tabular file with smoothed, merged segments of stable copy number, derived from the .cnr file. Represents final CNV calls. + +.. csv-table:: + :header-rows: 0 + + "chromosome","start, end: Genomic coordinates of the segment" + "gene","Gene(s) overlapping the segment." + "log2","Mean log2 ratio of the segment." + "probes","Mean log2 ratio of the segment." + "depth","Average read depth." + "weight","Reliability weight." + "p_value","Statistical confidence (lower = more significant)." + ]]></help> <expand macro="citations" /> </tool> |
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| diff -r dbdb21b1d395 -r 40918fee953b test-data/sample.cnv.vcf --- a/test-data/sample.cnv.vcf Mon Jan 20 16:43:10 2025 +0000 +++ b/test-data/sample.cnv.vcf Sat Mar 01 12:05:56 2025 +0000 |
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| @@ -1,6 +1,6 @@ ##fileformat=VCFv4.2 -##fileDate=20250120 -##source=CNVkit v0.9.11 +##fileDate=20250203 +##source=CNVkit v0.9.12 ##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants"> ##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants"> ##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record"> |