%global _empty_manifest_terminate_build 0 Name: python-MACS2 Version: 2.2.8 Release: 1 Summary: Model Based Analysis for ChIP-Seq data License: BSD License URL: http://github.com/taoliu/MACS/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ef/4d/8158c671a5597b423189851e1004bd7065777ffd7d3258e161db8fdc1a69/MACS2-2.2.8.tar.gz BuildArch: noarch %description `callpeak` | Main MACS2 Function to call peaks from alignment results. `bdgpeakcall` | Call peaks from bedGraph output. `bdgbroadcall` | Call broad peaks from bedGraph output. `bdgcmp` | Comparing two signal tracks in bedGraph format. `bdgopt` | Operate the score column of bedGraph file. `cmbreps` | Combine BEDGraphs of scores from replicates. `bdgdiff` | Differential peak detection based on paired four bedGraph files. `filterdup` | Remove duplicate reads, then save in BED/BEDPE format. `predictd` | Predict d or fragment size from alignment results. `pileup` | Pileup aligned reads (single-end) or fragments (paired-end) `randsample` | Randomly choose a number/percentage of total reads. `refinepeak` | Take raw reads alignment, refine peak summits. We only cover `callpeak` subcommand in this document. Please use `macs2 COMMAND -h` to see the detail description for each option of each subcommand. ### Call peaks This is the main function in MACS2. It can be invoked by `macs2 callpeak` . If you type this command with `-h`, you will see a full description of command-line options. Here we only list the essentials. #### Essential Options ##### `-t`/`--treatment FILENAME` This is the only REQUIRED parameter for MACS. The file can be in any supported format -- see detail in the `--format` option. If you have more than one alignment file, you can specify them as `-t A B C`. MACS will pool up all these files together. ##### `-c`/`--control` The control, genomic input or mock IP data file. Please follow the same direction as for `-t`/`--treatment`. ##### `-n`/`--name` The name string of the experiment. MACS will use this string NAME to create output files like `NAME_peaks.xls`, `NAME_negative_peaks.xls`, `NAME_peaks.bed` , `NAME_summits.bed`, `NAME_model.r` and so on. So please avoid any confliction between these filenames and your existing files. ##### `--outdir` MACS2 will save all output files into the specified folder for this option. A new folder will be created if necessary. ##### `-f`/`--format FORMAT` Format of tag file can be `ELAND`, `BED`, `ELANDMULTI`, `ELANDEXPORT`, `SAM`, `BAM`, `BOWTIE`, `BAMPE`, or `BEDPE`. Default is `AUTO` which will allow MACS to decide the format automatically. `AUTO` is also useful when you combine different formats of files. Note that MACS can't detect `BAMPE` or `BEDPE` format with `AUTO`, and you have to implicitly specify the format for `BAMPE` and `BEDPE`. Nowadays, the most common formats are `BED` or `BAM` (including `BEDPE` and `BAMPE`). Our recommendation is to convert your data to `BED` or `BAM` first. Also, MACS2 can detect and read gzipped file. For example, `.bed.gz` file can be directly used without being uncompressed with `--format BED`. Here are detailed explanation of the recommanded formats: ###### `BED` The BED format can be found at [UCSC genome browser website](http://genome.ucsc.edu/FAQ/FAQformat#format1). The essential columns in BED format input are the 1st column `chromosome name`, the 2nd `start position`, the 3rd `end position`, and the 6th, `strand`. Note that, for `BED` format, the 6th column of strand information is required by MACS. And please pay attention that the coordinates in BED format are zero-based and half-open. See more detail at [UCSC site](http://genome.ucsc.edu/FAQ/FAQtracks#tracks1). ###### `BAM`/`SAM` If the format is `BAM`/`SAM`, please check the definition in (http://samtools.sourceforge.net/samtools.shtml). If the `BAM` file is generated for paired-end data, MACS will only keep the left mate(5' end) tag. However, when format `BAMPE` is specified, MACS will use the real fragments inferred from alignment results for reads pileup. ###### `BEDPE` or `BAMPE` A special mode will be triggered while the format is specified as `BAMPE` or `BEDPE`. In this way, MACS2 will process the `BAM` or `BED` files as paired-end data. Instead of building a bimodal distribution of plus and minus strand reads to predict fragment size, MACS2 will use actual insert sizes of pairs of reads to build fragment pileup. The `BAMPE` format is just a `BAM` format containing paired-end alignment information, such as those from `BWA` or `BOWTIE`. The `BEDPE` format is a simplified and more flexible `BED` format, which only contains the first three columns defining the chromosome name, left and right position of the fragment from Paired-end sequencing. Please note, this is NOT the same format used by `BEDTOOLS`, and the `BEDTOOLS` version of `BEDPE` is actually not in a standard `BED` format. You can use MACS2 subcommand `randsample` to convert a `BAM` file containing paired-end information to a `BEDPE` format file: ``` macs2 randsample -i the_BAMPE_file.bam -f BAMPE -p 100 -o the_BEDPE_file.bed ``` ##### `-g`/`--gsize` PLEASE assign this parameter to fit your needs! It's the mappable genome size or effective genome size which is defined as the genome size which can be sequenced. Because of the repetitive features on the chromosomes, the actual mappable genome size will be smaller than the original size, about 90% or 70% of the genome size. The default *hs* -- 2.7e9 is recommended for human genome. Here are all precompiled parameters for effective genome size: * hs: 2.7e9 * mm: 1.87e9 * ce: 9e7 * dm: 1.2e8 Users may want to use k-mer tools to simulate mapping of Xbps long reads to target genome, and to find the ideal effective genome size. However, usually by taking away the simple repeats and Ns from the total genome, one can get an approximate number of effective genome size. A slight difference in the number won't cause a big difference of peak calls, because this number is used to estimate a genome-wide noise level which is usually the least significant one compared with the *local biases* modeled by MACS. ##### `-s`/`--tsize` The size of sequencing tags. If you don't specify it, MACS will try to use the first 10 sequences from your input treatment file to determine the tag size. Specifying it will override the automatically determined tag size. ##### `-q`/`--qvalue` The q-value (minimum FDR) cutoff to call significant regions. Default is 0.05. For broad marks, you can try 0.05 as the cutoff. Q-values are calculated from p-values using the Benjamini-Hochberg procedure. ##### `-p`/`--pvalue` The p-value cutoff. If `-p` is specified, MACS2 will use p-value instead of q-value. ##### `--min-length`, `--max-gap` These two options can be used to fine-tune the peak calling behavior by specifying the minimum length of a called peak and the maximum allowed a gap between two nearby regions to be merged. In other words, a called peak has to be longer than `min-length`, and if the distance between two nearby peaks is smaller than `max-gap` then they will be merged as one. If they are not set, MACS2 will set the DEFAULT value for `min-length` as the predicted fragment size `d`, and the DEFAULT value for `max-gap` as the detected read length. Note, if you set a `min-length` value smaller than the fragment size, it may have NO effect on the result. For broad peak calling with `--broad` option set, the DEFAULT `max-gap` for merging nearby stronger peaks will be the same as narrow peak calling, and 4 times of the `max-gap` will be used to merge nearby weaker (broad) peaks. You can also use `--cutoff-analysis` option with the default setting, and check the column `avelpeak` under different cutoff values to decide a reasonable `min-length` value. ##### `--nolambda` With this flag on, MACS will use the background lambda as local lambda. This means MACS will not consider the local bias at peak candidate regions. ##### `--slocal`, `--llocal` These two parameters control which two levels of regions will be checked around the peak regions to calculate the maximum lambda as local lambda. By default, MACS considers 1000bp for small local region(`--slocal`), and 10000bps for large local region(`--llocal`) which captures the bias from a long-range effect like an open chromatin domain. You can tweak these according to your project. Remember that if the region is set too small, a sharp spike in the input data may kill a significant peak. ##### `--nomodel` While on, MACS will bypass building the shifting model. ##### `--extsize` While `--nomodel` is set, MACS uses this parameter to extend reads in 5'->3' direction to fix-sized fragments. For example, if the size of the binding region for your transcription factor is 200 bp, and you want to bypass the model building by MACS, this parameter can be set as 200. This option is only valid when `--nomodel` is set or when MACS fails to build model and `--fix-bimodal` is on. ##### `--shift` Note, this is NOT the legacy `--shiftsize` option which is replaced by `--extsize`! You can set an arbitrary shift in bp here. Please Use discretion while setting it other than the default value (0). When `--nomodel` is set, MACS will use this value to move cutting ends (5') then apply `--extsize` from 5' to 3' direction to extend them to fragments. When this value is negative, ends will be moved toward 3'->5' direction, otherwise 5'->3' direction. Recommended to keep it as default 0 for ChIP-Seq datasets, or -1 * half of *EXTSIZE* together with `--extsize` option for detecting enriched cutting loci such as certain DNAseI-Seq datasets. Note, you can't set values other than 0 if the format is BAMPE or BEDPE for paired-end data. The default is 0. Here are some examples for combining `--shift` and `--extsize`: 1. To find enriched cutting sites such as some DNAse-Seq datasets. In this case, all 5' ends of sequenced reads should be extended in both directions to smooth the pileup signals. If the wanted smoothing window is 200bps, then use `--nomodel --shift -100 --extsize 200`. 2. For certain nucleosome-seq data, we need to pile up the centers of nucleosomes using a half-nucleosome size for wavelet analysis (e.g. NPS algorithm). Since the DNA wrapped on nucleosome is about 147bps, this option can be used: `--nomodel --shift 37 --extsize 73`. ##### `--keep-dup` It controls the MACS behavior towards duplicate tags at the exact same location -- the same coordination and the same strand. The default `auto` option makes MACS calculate the maximum tags at the exact same location based on binomial distribution using 1e-5 as p-value cutoff; and the `all` option keeps every tag. If an integer is given, at most this number of tags will be kept at the same location. The default is to keep one tag at the same location. Default: 1 ##### `--broad` When this flag is on, MACS will try to composite broad regions in BED12 ( a gene-model-like format ) by putting nearby highly enriched regions into a broad region with loose cutoff. The broad region is controlled by another cutoff through `--broad-cutoff`. Please note that, the `max-gap` value for merging nearby weaker/broad peaks is 4 times of `max-gap` for merging nearby stronger peaks. The later one can be controlled by `--max-gap` option, and by default it is the average fragment/insertion length in the PE data. DEFAULT: False ##### `--broad-cutoff` Cutoff for the broad region. This option is not available unless `--broad` is set. If `-p` is set, this is a p-value cutoff, otherwise, it's a q-value cutoff. DEFAULT: 0.1 ##### `--scale-to ` When set to `large`, linearly scale the smaller dataset to the same depth as the larger dataset. By default or being set as `small`, the larger dataset will be scaled towards the smaller dataset. Beware, to scale up small data would cause more false positives. ##### `-B`/`--bdg` If this flag is on, MACS will store the fragment pileup, control lambda in bedGraph files. The bedGraph files will be stored in the current directory named `NAME_treat_pileup.bdg` for treatment data, `NAME_control_lambda.bdg` for local lambda values from control. ##### `--call-summits` MACS will now reanalyze the shape of signal profile (p or q-score depending on the cutoff setting) to deconvolve subpeaks within each peak called from the general procedure. It's highly recommended to detect adjacent binding events. While used, the output subpeaks of a big peak region will have the same peak boundaries, and different scores and peak summit positions. ##### `--buffer-size` MACS uses a buffer size for incrementally increasing internal array size to store reads alignment information for each chromosome or contig. To increase the buffer size, MACS can run faster but will waste more memory if certain chromosome/contig only has very few reads. In most cases, the default value 100000 works fine. However, if there are a large number of chromosomes/contigs in your alignment and reads per chromosome/contigs are few, it's recommended to specify a smaller buffer size in order to decrease memory usage (but it will take longer time to read alignment files). Minimum memory requested for reading an alignment file is about # of CHROMOSOME * BUFFER_SIZE * 8 Bytes. DEFAULT: 100000 #### Output files 1. `NAME_peaks.xls` is a tabular file which contains information about called peaks. You can open it in excel and sort/filter using excel functions. Information include: - chromosome name - start position of peak - end position of peak - length of peak region - absolute peak summit position - pileup height at peak summit - -log10(pvalue) for the peak summit (e.g. pvalue =1e-10, then this value should be 10) - fold enrichment for this peak summit against random Poisson distribution with local lambda, - -log10(qvalue) at peak summit Coordinates in XLS is 1-based which is different from BED format. When `--broad` is enabled for broad peak calling, the pileup, p-value, q-value, and fold change in the XLS file will be the mean value across the entire peak region, since peak summit won't be called in broad peak calling mode. 2. `NAME_peaks.narrowPeak` is BED6+4 format file which contains the peak locations together with peak summit, p-value, and q-value. You can load it to the UCSC genome browser. Definition of some specific columns are: - 5th: integer score for display. It's calculated as `int(-10*log10pvalue)` or `int(-10*log10qvalue)` depending on whether `-p` (pvalue) or `-q` (qvalue) is used as score cutoff. Please note that currently this value might be out of the [0-1000] range defined in [UCSC ENCODE narrowPeak format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12). You can let the value saturated at 1000 (i.e. p/q-value = 10^-100) by using the following 1-liner awk: `awk -v OFS="\t" '{$5=$5>1000?1000:$5} {print}' NAME_peaks.narrowPeak` - 7th: fold-change at peak summit - 8th: -log10pvalue at peak summit - 9th: -log10qvalue at peak summit - 10th: relative summit position to peak start The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 3. `NAME_summits.bed` is in BED format, which contains the peak summits locations for every peak. The 5th column in this file is the same as what is in the `narrowPeak` file. If you want to find the motifs at the binding sites, this file is recommended. The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 4. `NAME_peaks.broadPeak` is in BED6+3 format which is similar to `narrowPeak` file, except for missing the 10th column for annotating peak summits. This file and the `gappedPeak` file will only be available when `--broad` is enabled. Since in the broad peak calling mode, the peak summit won't be called, the values in the 5th, and 7-9th columns are the mean value across all positions in the peak region. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 5. `NAME_peaks.gappedPeak` is in BED12+3 format which contains both the broad region and narrow peaks. The 5th column is the score for showing grey levels on the UCSC browser as in `narrowPeak`. The 7th is the start of the first narrow peak in the region, and the 8th column is the end. The 9th column should be RGB color key, however, we keep 0 here to use the default color, so change it if you want. The 10th column tells how many blocks including the starting 1bp and ending 1bp of broad regions. The 11th column shows the length of each block and 12th for the start of each block. 13th: fold-change, 14th: *-log10pvalue*, 15th: *-log10qvalue*. The file can be loaded directly to the UCSC genome browser. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 6. `NAME_model.r` is an R script which you can use to produce a PDF image of the model based on your data. Load it to R by: `$ Rscript NAME_model.r` Then a pdf file `NAME_model.pdf` will be generated in your current directory. Note, R is required to draw this figure. 7. The `NAME_treat_pileup.bdg` and `NAME_control_lambda.bdg` files are in bedGraph format which can be imported to the UCSC genome browser or be converted into even smaller bigWig files. The `NAME_treat_pielup.bdg` contains the pileup signals (normalized according to `--scale-to` option) from ChIP/treatment sample. The `NAME_control_lambda.bdg` contains local biases estimated for each genomic location from the control sample, or from treatment sample when the control sample is absent. The subcommand `bdgcmp` can be used to compare these two files and make a bedGraph file of scores such as p-value, q-value, log-likelihood, and log fold changes. ## Other useful links * [Cistrome](http://cistrome.org/ap/) * [bedTools](http://code.google.com/p/bedtools/) * [UCSC toolkits](http://hgdownload.cse.ucsc.edu/admin/exe/) ## Tips of fine-tuning peak calling There are several subcommands within MACSv2 package to fine-tune or customize your analysis: 1. `bdgcmp` can be used on `*_treat_pileup.bdg` and `*_control_lambda.bdg` or bedGraph files from other resources to calculate the score track. 2. `bdgpeakcall` can be used on `*_treat_pvalue.bdg` or the file generated from bdgcmp or bedGraph file from other resources to call peaks with given cutoff, maximum-gap between nearby mergeable peaks and a minimum length of peak. bdgbroadcall works similarly to bdgpeakcall, however, it will output `_broad_peaks.bed` in BED12 format. 3. Differential calling tool -- `bdgdiff`, can be used on 4 bedGraph files which are scores between treatment 1 and control 1, treatment 2 and control 2, treatment 1 and treatment 2, treatment 2 and treatment 1. It will output consistent and unique sites according to parameter settings for minimum length, the maximum gap and cutoff. 4. You can combine subcommands to do a step-by-step peak calling. Read detail at [MACS2 wikipage](https://github.com/taoliu/MACS/wiki/Advanced%3A-Call-peaks-using-MACS2-subcommands) %package -n python3-MACS2 Summary: Model Based Analysis for ChIP-Seq data Provides: python-MACS2 BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-MACS2 `callpeak` | Main MACS2 Function to call peaks from alignment results. `bdgpeakcall` | Call peaks from bedGraph output. `bdgbroadcall` | Call broad peaks from bedGraph output. `bdgcmp` | Comparing two signal tracks in bedGraph format. `bdgopt` | Operate the score column of bedGraph file. `cmbreps` | Combine BEDGraphs of scores from replicates. `bdgdiff` | Differential peak detection based on paired four bedGraph files. `filterdup` | Remove duplicate reads, then save in BED/BEDPE format. `predictd` | Predict d or fragment size from alignment results. `pileup` | Pileup aligned reads (single-end) or fragments (paired-end) `randsample` | Randomly choose a number/percentage of total reads. `refinepeak` | Take raw reads alignment, refine peak summits. We only cover `callpeak` subcommand in this document. Please use `macs2 COMMAND -h` to see the detail description for each option of each subcommand. ### Call peaks This is the main function in MACS2. It can be invoked by `macs2 callpeak` . If you type this command with `-h`, you will see a full description of command-line options. Here we only list the essentials. #### Essential Options ##### `-t`/`--treatment FILENAME` This is the only REQUIRED parameter for MACS. The file can be in any supported format -- see detail in the `--format` option. If you have more than one alignment file, you can specify them as `-t A B C`. MACS will pool up all these files together. ##### `-c`/`--control` The control, genomic input or mock IP data file. Please follow the same direction as for `-t`/`--treatment`. ##### `-n`/`--name` The name string of the experiment. MACS will use this string NAME to create output files like `NAME_peaks.xls`, `NAME_negative_peaks.xls`, `NAME_peaks.bed` , `NAME_summits.bed`, `NAME_model.r` and so on. So please avoid any confliction between these filenames and your existing files. ##### `--outdir` MACS2 will save all output files into the specified folder for this option. A new folder will be created if necessary. ##### `-f`/`--format FORMAT` Format of tag file can be `ELAND`, `BED`, `ELANDMULTI`, `ELANDEXPORT`, `SAM`, `BAM`, `BOWTIE`, `BAMPE`, or `BEDPE`. Default is `AUTO` which will allow MACS to decide the format automatically. `AUTO` is also useful when you combine different formats of files. Note that MACS can't detect `BAMPE` or `BEDPE` format with `AUTO`, and you have to implicitly specify the format for `BAMPE` and `BEDPE`. Nowadays, the most common formats are `BED` or `BAM` (including `BEDPE` and `BAMPE`). Our recommendation is to convert your data to `BED` or `BAM` first. Also, MACS2 can detect and read gzipped file. For example, `.bed.gz` file can be directly used without being uncompressed with `--format BED`. Here are detailed explanation of the recommanded formats: ###### `BED` The BED format can be found at [UCSC genome browser website](http://genome.ucsc.edu/FAQ/FAQformat#format1). The essential columns in BED format input are the 1st column `chromosome name`, the 2nd `start position`, the 3rd `end position`, and the 6th, `strand`. Note that, for `BED` format, the 6th column of strand information is required by MACS. And please pay attention that the coordinates in BED format are zero-based and half-open. See more detail at [UCSC site](http://genome.ucsc.edu/FAQ/FAQtracks#tracks1). ###### `BAM`/`SAM` If the format is `BAM`/`SAM`, please check the definition in (http://samtools.sourceforge.net/samtools.shtml). If the `BAM` file is generated for paired-end data, MACS will only keep the left mate(5' end) tag. However, when format `BAMPE` is specified, MACS will use the real fragments inferred from alignment results for reads pileup. ###### `BEDPE` or `BAMPE` A special mode will be triggered while the format is specified as `BAMPE` or `BEDPE`. In this way, MACS2 will process the `BAM` or `BED` files as paired-end data. Instead of building a bimodal distribution of plus and minus strand reads to predict fragment size, MACS2 will use actual insert sizes of pairs of reads to build fragment pileup. The `BAMPE` format is just a `BAM` format containing paired-end alignment information, such as those from `BWA` or `BOWTIE`. The `BEDPE` format is a simplified and more flexible `BED` format, which only contains the first three columns defining the chromosome name, left and right position of the fragment from Paired-end sequencing. Please note, this is NOT the same format used by `BEDTOOLS`, and the `BEDTOOLS` version of `BEDPE` is actually not in a standard `BED` format. You can use MACS2 subcommand `randsample` to convert a `BAM` file containing paired-end information to a `BEDPE` format file: ``` macs2 randsample -i the_BAMPE_file.bam -f BAMPE -p 100 -o the_BEDPE_file.bed ``` ##### `-g`/`--gsize` PLEASE assign this parameter to fit your needs! It's the mappable genome size or effective genome size which is defined as the genome size which can be sequenced. Because of the repetitive features on the chromosomes, the actual mappable genome size will be smaller than the original size, about 90% or 70% of the genome size. The default *hs* -- 2.7e9 is recommended for human genome. Here are all precompiled parameters for effective genome size: * hs: 2.7e9 * mm: 1.87e9 * ce: 9e7 * dm: 1.2e8 Users may want to use k-mer tools to simulate mapping of Xbps long reads to target genome, and to find the ideal effective genome size. However, usually by taking away the simple repeats and Ns from the total genome, one can get an approximate number of effective genome size. A slight difference in the number won't cause a big difference of peak calls, because this number is used to estimate a genome-wide noise level which is usually the least significant one compared with the *local biases* modeled by MACS. ##### `-s`/`--tsize` The size of sequencing tags. If you don't specify it, MACS will try to use the first 10 sequences from your input treatment file to determine the tag size. Specifying it will override the automatically determined tag size. ##### `-q`/`--qvalue` The q-value (minimum FDR) cutoff to call significant regions. Default is 0.05. For broad marks, you can try 0.05 as the cutoff. Q-values are calculated from p-values using the Benjamini-Hochberg procedure. ##### `-p`/`--pvalue` The p-value cutoff. If `-p` is specified, MACS2 will use p-value instead of q-value. ##### `--min-length`, `--max-gap` These two options can be used to fine-tune the peak calling behavior by specifying the minimum length of a called peak and the maximum allowed a gap between two nearby regions to be merged. In other words, a called peak has to be longer than `min-length`, and if the distance between two nearby peaks is smaller than `max-gap` then they will be merged as one. If they are not set, MACS2 will set the DEFAULT value for `min-length` as the predicted fragment size `d`, and the DEFAULT value for `max-gap` as the detected read length. Note, if you set a `min-length` value smaller than the fragment size, it may have NO effect on the result. For broad peak calling with `--broad` option set, the DEFAULT `max-gap` for merging nearby stronger peaks will be the same as narrow peak calling, and 4 times of the `max-gap` will be used to merge nearby weaker (broad) peaks. You can also use `--cutoff-analysis` option with the default setting, and check the column `avelpeak` under different cutoff values to decide a reasonable `min-length` value. ##### `--nolambda` With this flag on, MACS will use the background lambda as local lambda. This means MACS will not consider the local bias at peak candidate regions. ##### `--slocal`, `--llocal` These two parameters control which two levels of regions will be checked around the peak regions to calculate the maximum lambda as local lambda. By default, MACS considers 1000bp for small local region(`--slocal`), and 10000bps for large local region(`--llocal`) which captures the bias from a long-range effect like an open chromatin domain. You can tweak these according to your project. Remember that if the region is set too small, a sharp spike in the input data may kill a significant peak. ##### `--nomodel` While on, MACS will bypass building the shifting model. ##### `--extsize` While `--nomodel` is set, MACS uses this parameter to extend reads in 5'->3' direction to fix-sized fragments. For example, if the size of the binding region for your transcription factor is 200 bp, and you want to bypass the model building by MACS, this parameter can be set as 200. This option is only valid when `--nomodel` is set or when MACS fails to build model and `--fix-bimodal` is on. ##### `--shift` Note, this is NOT the legacy `--shiftsize` option which is replaced by `--extsize`! You can set an arbitrary shift in bp here. Please Use discretion while setting it other than the default value (0). When `--nomodel` is set, MACS will use this value to move cutting ends (5') then apply `--extsize` from 5' to 3' direction to extend them to fragments. When this value is negative, ends will be moved toward 3'->5' direction, otherwise 5'->3' direction. Recommended to keep it as default 0 for ChIP-Seq datasets, or -1 * half of *EXTSIZE* together with `--extsize` option for detecting enriched cutting loci such as certain DNAseI-Seq datasets. Note, you can't set values other than 0 if the format is BAMPE or BEDPE for paired-end data. The default is 0. Here are some examples for combining `--shift` and `--extsize`: 1. To find enriched cutting sites such as some DNAse-Seq datasets. In this case, all 5' ends of sequenced reads should be extended in both directions to smooth the pileup signals. If the wanted smoothing window is 200bps, then use `--nomodel --shift -100 --extsize 200`. 2. For certain nucleosome-seq data, we need to pile up the centers of nucleosomes using a half-nucleosome size for wavelet analysis (e.g. NPS algorithm). Since the DNA wrapped on nucleosome is about 147bps, this option can be used: `--nomodel --shift 37 --extsize 73`. ##### `--keep-dup` It controls the MACS behavior towards duplicate tags at the exact same location -- the same coordination and the same strand. The default `auto` option makes MACS calculate the maximum tags at the exact same location based on binomial distribution using 1e-5 as p-value cutoff; and the `all` option keeps every tag. If an integer is given, at most this number of tags will be kept at the same location. The default is to keep one tag at the same location. Default: 1 ##### `--broad` When this flag is on, MACS will try to composite broad regions in BED12 ( a gene-model-like format ) by putting nearby highly enriched regions into a broad region with loose cutoff. The broad region is controlled by another cutoff through `--broad-cutoff`. Please note that, the `max-gap` value for merging nearby weaker/broad peaks is 4 times of `max-gap` for merging nearby stronger peaks. The later one can be controlled by `--max-gap` option, and by default it is the average fragment/insertion length in the PE data. DEFAULT: False ##### `--broad-cutoff` Cutoff for the broad region. This option is not available unless `--broad` is set. If `-p` is set, this is a p-value cutoff, otherwise, it's a q-value cutoff. DEFAULT: 0.1 ##### `--scale-to ` When set to `large`, linearly scale the smaller dataset to the same depth as the larger dataset. By default or being set as `small`, the larger dataset will be scaled towards the smaller dataset. Beware, to scale up small data would cause more false positives. ##### `-B`/`--bdg` If this flag is on, MACS will store the fragment pileup, control lambda in bedGraph files. The bedGraph files will be stored in the current directory named `NAME_treat_pileup.bdg` for treatment data, `NAME_control_lambda.bdg` for local lambda values from control. ##### `--call-summits` MACS will now reanalyze the shape of signal profile (p or q-score depending on the cutoff setting) to deconvolve subpeaks within each peak called from the general procedure. It's highly recommended to detect adjacent binding events. While used, the output subpeaks of a big peak region will have the same peak boundaries, and different scores and peak summit positions. ##### `--buffer-size` MACS uses a buffer size for incrementally increasing internal array size to store reads alignment information for each chromosome or contig. To increase the buffer size, MACS can run faster but will waste more memory if certain chromosome/contig only has very few reads. In most cases, the default value 100000 works fine. However, if there are a large number of chromosomes/contigs in your alignment and reads per chromosome/contigs are few, it's recommended to specify a smaller buffer size in order to decrease memory usage (but it will take longer time to read alignment files). Minimum memory requested for reading an alignment file is about # of CHROMOSOME * BUFFER_SIZE * 8 Bytes. DEFAULT: 100000 #### Output files 1. `NAME_peaks.xls` is a tabular file which contains information about called peaks. You can open it in excel and sort/filter using excel functions. Information include: - chromosome name - start position of peak - end position of peak - length of peak region - absolute peak summit position - pileup height at peak summit - -log10(pvalue) for the peak summit (e.g. pvalue =1e-10, then this value should be 10) - fold enrichment for this peak summit against random Poisson distribution with local lambda, - -log10(qvalue) at peak summit Coordinates in XLS is 1-based which is different from BED format. When `--broad` is enabled for broad peak calling, the pileup, p-value, q-value, and fold change in the XLS file will be the mean value across the entire peak region, since peak summit won't be called in broad peak calling mode. 2. `NAME_peaks.narrowPeak` is BED6+4 format file which contains the peak locations together with peak summit, p-value, and q-value. You can load it to the UCSC genome browser. Definition of some specific columns are: - 5th: integer score for display. It's calculated as `int(-10*log10pvalue)` or `int(-10*log10qvalue)` depending on whether `-p` (pvalue) or `-q` (qvalue) is used as score cutoff. Please note that currently this value might be out of the [0-1000] range defined in [UCSC ENCODE narrowPeak format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12). You can let the value saturated at 1000 (i.e. p/q-value = 10^-100) by using the following 1-liner awk: `awk -v OFS="\t" '{$5=$5>1000?1000:$5} {print}' NAME_peaks.narrowPeak` - 7th: fold-change at peak summit - 8th: -log10pvalue at peak summit - 9th: -log10qvalue at peak summit - 10th: relative summit position to peak start The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 3. `NAME_summits.bed` is in BED format, which contains the peak summits locations for every peak. The 5th column in this file is the same as what is in the `narrowPeak` file. If you want to find the motifs at the binding sites, this file is recommended. The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 4. `NAME_peaks.broadPeak` is in BED6+3 format which is similar to `narrowPeak` file, except for missing the 10th column for annotating peak summits. This file and the `gappedPeak` file will only be available when `--broad` is enabled. Since in the broad peak calling mode, the peak summit won't be called, the values in the 5th, and 7-9th columns are the mean value across all positions in the peak region. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 5. `NAME_peaks.gappedPeak` is in BED12+3 format which contains both the broad region and narrow peaks. The 5th column is the score for showing grey levels on the UCSC browser as in `narrowPeak`. The 7th is the start of the first narrow peak in the region, and the 8th column is the end. The 9th column should be RGB color key, however, we keep 0 here to use the default color, so change it if you want. The 10th column tells how many blocks including the starting 1bp and ending 1bp of broad regions. The 11th column shows the length of each block and 12th for the start of each block. 13th: fold-change, 14th: *-log10pvalue*, 15th: *-log10qvalue*. The file can be loaded directly to the UCSC genome browser. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 6. `NAME_model.r` is an R script which you can use to produce a PDF image of the model based on your data. Load it to R by: `$ Rscript NAME_model.r` Then a pdf file `NAME_model.pdf` will be generated in your current directory. Note, R is required to draw this figure. 7. The `NAME_treat_pileup.bdg` and `NAME_control_lambda.bdg` files are in bedGraph format which can be imported to the UCSC genome browser or be converted into even smaller bigWig files. The `NAME_treat_pielup.bdg` contains the pileup signals (normalized according to `--scale-to` option) from ChIP/treatment sample. The `NAME_control_lambda.bdg` contains local biases estimated for each genomic location from the control sample, or from treatment sample when the control sample is absent. The subcommand `bdgcmp` can be used to compare these two files and make a bedGraph file of scores such as p-value, q-value, log-likelihood, and log fold changes. ## Other useful links * [Cistrome](http://cistrome.org/ap/) * [bedTools](http://code.google.com/p/bedtools/) * [UCSC toolkits](http://hgdownload.cse.ucsc.edu/admin/exe/) ## Tips of fine-tuning peak calling There are several subcommands within MACSv2 package to fine-tune or customize your analysis: 1. `bdgcmp` can be used on `*_treat_pileup.bdg` and `*_control_lambda.bdg` or bedGraph files from other resources to calculate the score track. 2. `bdgpeakcall` can be used on `*_treat_pvalue.bdg` or the file generated from bdgcmp or bedGraph file from other resources to call peaks with given cutoff, maximum-gap between nearby mergeable peaks and a minimum length of peak. bdgbroadcall works similarly to bdgpeakcall, however, it will output `_broad_peaks.bed` in BED12 format. 3. Differential calling tool -- `bdgdiff`, can be used on 4 bedGraph files which are scores between treatment 1 and control 1, treatment 2 and control 2, treatment 1 and treatment 2, treatment 2 and treatment 1. It will output consistent and unique sites according to parameter settings for minimum length, the maximum gap and cutoff. 4. You can combine subcommands to do a step-by-step peak calling. Read detail at [MACS2 wikipage](https://github.com/taoliu/MACS/wiki/Advanced%3A-Call-peaks-using-MACS2-subcommands) %package help Summary: Development documents and examples for MACS2 Provides: python3-MACS2-doc %description help `callpeak` | Main MACS2 Function to call peaks from alignment results. `bdgpeakcall` | Call peaks from bedGraph output. `bdgbroadcall` | Call broad peaks from bedGraph output. `bdgcmp` | Comparing two signal tracks in bedGraph format. `bdgopt` | Operate the score column of bedGraph file. `cmbreps` | Combine BEDGraphs of scores from replicates. `bdgdiff` | Differential peak detection based on paired four bedGraph files. `filterdup` | Remove duplicate reads, then save in BED/BEDPE format. `predictd` | Predict d or fragment size from alignment results. `pileup` | Pileup aligned reads (single-end) or fragments (paired-end) `randsample` | Randomly choose a number/percentage of total reads. `refinepeak` | Take raw reads alignment, refine peak summits. We only cover `callpeak` subcommand in this document. Please use `macs2 COMMAND -h` to see the detail description for each option of each subcommand. ### Call peaks This is the main function in MACS2. It can be invoked by `macs2 callpeak` . If you type this command with `-h`, you will see a full description of command-line options. Here we only list the essentials. #### Essential Options ##### `-t`/`--treatment FILENAME` This is the only REQUIRED parameter for MACS. The file can be in any supported format -- see detail in the `--format` option. If you have more than one alignment file, you can specify them as `-t A B C`. MACS will pool up all these files together. ##### `-c`/`--control` The control, genomic input or mock IP data file. Please follow the same direction as for `-t`/`--treatment`. ##### `-n`/`--name` The name string of the experiment. MACS will use this string NAME to create output files like `NAME_peaks.xls`, `NAME_negative_peaks.xls`, `NAME_peaks.bed` , `NAME_summits.bed`, `NAME_model.r` and so on. So please avoid any confliction between these filenames and your existing files. ##### `--outdir` MACS2 will save all output files into the specified folder for this option. A new folder will be created if necessary. ##### `-f`/`--format FORMAT` Format of tag file can be `ELAND`, `BED`, `ELANDMULTI`, `ELANDEXPORT`, `SAM`, `BAM`, `BOWTIE`, `BAMPE`, or `BEDPE`. Default is `AUTO` which will allow MACS to decide the format automatically. `AUTO` is also useful when you combine different formats of files. Note that MACS can't detect `BAMPE` or `BEDPE` format with `AUTO`, and you have to implicitly specify the format for `BAMPE` and `BEDPE`. Nowadays, the most common formats are `BED` or `BAM` (including `BEDPE` and `BAMPE`). Our recommendation is to convert your data to `BED` or `BAM` first. Also, MACS2 can detect and read gzipped file. For example, `.bed.gz` file can be directly used without being uncompressed with `--format BED`. Here are detailed explanation of the recommanded formats: ###### `BED` The BED format can be found at [UCSC genome browser website](http://genome.ucsc.edu/FAQ/FAQformat#format1). The essential columns in BED format input are the 1st column `chromosome name`, the 2nd `start position`, the 3rd `end position`, and the 6th, `strand`. Note that, for `BED` format, the 6th column of strand information is required by MACS. And please pay attention that the coordinates in BED format are zero-based and half-open. See more detail at [UCSC site](http://genome.ucsc.edu/FAQ/FAQtracks#tracks1). ###### `BAM`/`SAM` If the format is `BAM`/`SAM`, please check the definition in (http://samtools.sourceforge.net/samtools.shtml). If the `BAM` file is generated for paired-end data, MACS will only keep the left mate(5' end) tag. However, when format `BAMPE` is specified, MACS will use the real fragments inferred from alignment results for reads pileup. ###### `BEDPE` or `BAMPE` A special mode will be triggered while the format is specified as `BAMPE` or `BEDPE`. In this way, MACS2 will process the `BAM` or `BED` files as paired-end data. Instead of building a bimodal distribution of plus and minus strand reads to predict fragment size, MACS2 will use actual insert sizes of pairs of reads to build fragment pileup. The `BAMPE` format is just a `BAM` format containing paired-end alignment information, such as those from `BWA` or `BOWTIE`. The `BEDPE` format is a simplified and more flexible `BED` format, which only contains the first three columns defining the chromosome name, left and right position of the fragment from Paired-end sequencing. Please note, this is NOT the same format used by `BEDTOOLS`, and the `BEDTOOLS` version of `BEDPE` is actually not in a standard `BED` format. You can use MACS2 subcommand `randsample` to convert a `BAM` file containing paired-end information to a `BEDPE` format file: ``` macs2 randsample -i the_BAMPE_file.bam -f BAMPE -p 100 -o the_BEDPE_file.bed ``` ##### `-g`/`--gsize` PLEASE assign this parameter to fit your needs! It's the mappable genome size or effective genome size which is defined as the genome size which can be sequenced. Because of the repetitive features on the chromosomes, the actual mappable genome size will be smaller than the original size, about 90% or 70% of the genome size. The default *hs* -- 2.7e9 is recommended for human genome. Here are all precompiled parameters for effective genome size: * hs: 2.7e9 * mm: 1.87e9 * ce: 9e7 * dm: 1.2e8 Users may want to use k-mer tools to simulate mapping of Xbps long reads to target genome, and to find the ideal effective genome size. However, usually by taking away the simple repeats and Ns from the total genome, one can get an approximate number of effective genome size. A slight difference in the number won't cause a big difference of peak calls, because this number is used to estimate a genome-wide noise level which is usually the least significant one compared with the *local biases* modeled by MACS. ##### `-s`/`--tsize` The size of sequencing tags. If you don't specify it, MACS will try to use the first 10 sequences from your input treatment file to determine the tag size. Specifying it will override the automatically determined tag size. ##### `-q`/`--qvalue` The q-value (minimum FDR) cutoff to call significant regions. Default is 0.05. For broad marks, you can try 0.05 as the cutoff. Q-values are calculated from p-values using the Benjamini-Hochberg procedure. ##### `-p`/`--pvalue` The p-value cutoff. If `-p` is specified, MACS2 will use p-value instead of q-value. ##### `--min-length`, `--max-gap` These two options can be used to fine-tune the peak calling behavior by specifying the minimum length of a called peak and the maximum allowed a gap between two nearby regions to be merged. In other words, a called peak has to be longer than `min-length`, and if the distance between two nearby peaks is smaller than `max-gap` then they will be merged as one. If they are not set, MACS2 will set the DEFAULT value for `min-length` as the predicted fragment size `d`, and the DEFAULT value for `max-gap` as the detected read length. Note, if you set a `min-length` value smaller than the fragment size, it may have NO effect on the result. For broad peak calling with `--broad` option set, the DEFAULT `max-gap` for merging nearby stronger peaks will be the same as narrow peak calling, and 4 times of the `max-gap` will be used to merge nearby weaker (broad) peaks. You can also use `--cutoff-analysis` option with the default setting, and check the column `avelpeak` under different cutoff values to decide a reasonable `min-length` value. ##### `--nolambda` With this flag on, MACS will use the background lambda as local lambda. This means MACS will not consider the local bias at peak candidate regions. ##### `--slocal`, `--llocal` These two parameters control which two levels of regions will be checked around the peak regions to calculate the maximum lambda as local lambda. By default, MACS considers 1000bp for small local region(`--slocal`), and 10000bps for large local region(`--llocal`) which captures the bias from a long-range effect like an open chromatin domain. You can tweak these according to your project. Remember that if the region is set too small, a sharp spike in the input data may kill a significant peak. ##### `--nomodel` While on, MACS will bypass building the shifting model. ##### `--extsize` While `--nomodel` is set, MACS uses this parameter to extend reads in 5'->3' direction to fix-sized fragments. For example, if the size of the binding region for your transcription factor is 200 bp, and you want to bypass the model building by MACS, this parameter can be set as 200. This option is only valid when `--nomodel` is set or when MACS fails to build model and `--fix-bimodal` is on. ##### `--shift` Note, this is NOT the legacy `--shiftsize` option which is replaced by `--extsize`! You can set an arbitrary shift in bp here. Please Use discretion while setting it other than the default value (0). When `--nomodel` is set, MACS will use this value to move cutting ends (5') then apply `--extsize` from 5' to 3' direction to extend them to fragments. When this value is negative, ends will be moved toward 3'->5' direction, otherwise 5'->3' direction. Recommended to keep it as default 0 for ChIP-Seq datasets, or -1 * half of *EXTSIZE* together with `--extsize` option for detecting enriched cutting loci such as certain DNAseI-Seq datasets. Note, you can't set values other than 0 if the format is BAMPE or BEDPE for paired-end data. The default is 0. Here are some examples for combining `--shift` and `--extsize`: 1. To find enriched cutting sites such as some DNAse-Seq datasets. In this case, all 5' ends of sequenced reads should be extended in both directions to smooth the pileup signals. If the wanted smoothing window is 200bps, then use `--nomodel --shift -100 --extsize 200`. 2. For certain nucleosome-seq data, we need to pile up the centers of nucleosomes using a half-nucleosome size for wavelet analysis (e.g. NPS algorithm). Since the DNA wrapped on nucleosome is about 147bps, this option can be used: `--nomodel --shift 37 --extsize 73`. ##### `--keep-dup` It controls the MACS behavior towards duplicate tags at the exact same location -- the same coordination and the same strand. The default `auto` option makes MACS calculate the maximum tags at the exact same location based on binomial distribution using 1e-5 as p-value cutoff; and the `all` option keeps every tag. If an integer is given, at most this number of tags will be kept at the same location. The default is to keep one tag at the same location. Default: 1 ##### `--broad` When this flag is on, MACS will try to composite broad regions in BED12 ( a gene-model-like format ) by putting nearby highly enriched regions into a broad region with loose cutoff. The broad region is controlled by another cutoff through `--broad-cutoff`. Please note that, the `max-gap` value for merging nearby weaker/broad peaks is 4 times of `max-gap` for merging nearby stronger peaks. The later one can be controlled by `--max-gap` option, and by default it is the average fragment/insertion length in the PE data. DEFAULT: False ##### `--broad-cutoff` Cutoff for the broad region. This option is not available unless `--broad` is set. If `-p` is set, this is a p-value cutoff, otherwise, it's a q-value cutoff. DEFAULT: 0.1 ##### `--scale-to ` When set to `large`, linearly scale the smaller dataset to the same depth as the larger dataset. By default or being set as `small`, the larger dataset will be scaled towards the smaller dataset. Beware, to scale up small data would cause more false positives. ##### `-B`/`--bdg` If this flag is on, MACS will store the fragment pileup, control lambda in bedGraph files. The bedGraph files will be stored in the current directory named `NAME_treat_pileup.bdg` for treatment data, `NAME_control_lambda.bdg` for local lambda values from control. ##### `--call-summits` MACS will now reanalyze the shape of signal profile (p or q-score depending on the cutoff setting) to deconvolve subpeaks within each peak called from the general procedure. It's highly recommended to detect adjacent binding events. While used, the output subpeaks of a big peak region will have the same peak boundaries, and different scores and peak summit positions. ##### `--buffer-size` MACS uses a buffer size for incrementally increasing internal array size to store reads alignment information for each chromosome or contig. To increase the buffer size, MACS can run faster but will waste more memory if certain chromosome/contig only has very few reads. In most cases, the default value 100000 works fine. However, if there are a large number of chromosomes/contigs in your alignment and reads per chromosome/contigs are few, it's recommended to specify a smaller buffer size in order to decrease memory usage (but it will take longer time to read alignment files). Minimum memory requested for reading an alignment file is about # of CHROMOSOME * BUFFER_SIZE * 8 Bytes. DEFAULT: 100000 #### Output files 1. `NAME_peaks.xls` is a tabular file which contains information about called peaks. You can open it in excel and sort/filter using excel functions. Information include: - chromosome name - start position of peak - end position of peak - length of peak region - absolute peak summit position - pileup height at peak summit - -log10(pvalue) for the peak summit (e.g. pvalue =1e-10, then this value should be 10) - fold enrichment for this peak summit against random Poisson distribution with local lambda, - -log10(qvalue) at peak summit Coordinates in XLS is 1-based which is different from BED format. When `--broad` is enabled for broad peak calling, the pileup, p-value, q-value, and fold change in the XLS file will be the mean value across the entire peak region, since peak summit won't be called in broad peak calling mode. 2. `NAME_peaks.narrowPeak` is BED6+4 format file which contains the peak locations together with peak summit, p-value, and q-value. You can load it to the UCSC genome browser. Definition of some specific columns are: - 5th: integer score for display. It's calculated as `int(-10*log10pvalue)` or `int(-10*log10qvalue)` depending on whether `-p` (pvalue) or `-q` (qvalue) is used as score cutoff. Please note that currently this value might be out of the [0-1000] range defined in [UCSC ENCODE narrowPeak format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12). You can let the value saturated at 1000 (i.e. p/q-value = 10^-100) by using the following 1-liner awk: `awk -v OFS="\t" '{$5=$5>1000?1000:$5} {print}' NAME_peaks.narrowPeak` - 7th: fold-change at peak summit - 8th: -log10pvalue at peak summit - 9th: -log10qvalue at peak summit - 10th: relative summit position to peak start The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 3. `NAME_summits.bed` is in BED format, which contains the peak summits locations for every peak. The 5th column in this file is the same as what is in the `narrowPeak` file. If you want to find the motifs at the binding sites, this file is recommended. The file can be loaded directly to the UCSC genome browser. Remove the beginning track line if you want to analyze it by other tools. 4. `NAME_peaks.broadPeak` is in BED6+3 format which is similar to `narrowPeak` file, except for missing the 10th column for annotating peak summits. This file and the `gappedPeak` file will only be available when `--broad` is enabled. Since in the broad peak calling mode, the peak summit won't be called, the values in the 5th, and 7-9th columns are the mean value across all positions in the peak region. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 5. `NAME_peaks.gappedPeak` is in BED12+3 format which contains both the broad region and narrow peaks. The 5th column is the score for showing grey levels on the UCSC browser as in `narrowPeak`. The 7th is the start of the first narrow peak in the region, and the 8th column is the end. The 9th column should be RGB color key, however, we keep 0 here to use the default color, so change it if you want. The 10th column tells how many blocks including the starting 1bp and ending 1bp of broad regions. The 11th column shows the length of each block and 12th for the start of each block. 13th: fold-change, 14th: *-log10pvalue*, 15th: *-log10qvalue*. The file can be loaded directly to the UCSC genome browser. Refer to `narrowPeak` if you want to fix the value issue in the 5th column. 6. `NAME_model.r` is an R script which you can use to produce a PDF image of the model based on your data. Load it to R by: `$ Rscript NAME_model.r` Then a pdf file `NAME_model.pdf` will be generated in your current directory. Note, R is required to draw this figure. 7. The `NAME_treat_pileup.bdg` and `NAME_control_lambda.bdg` files are in bedGraph format which can be imported to the UCSC genome browser or be converted into even smaller bigWig files. The `NAME_treat_pielup.bdg` contains the pileup signals (normalized according to `--scale-to` option) from ChIP/treatment sample. The `NAME_control_lambda.bdg` contains local biases estimated for each genomic location from the control sample, or from treatment sample when the control sample is absent. The subcommand `bdgcmp` can be used to compare these two files and make a bedGraph file of scores such as p-value, q-value, log-likelihood, and log fold changes. ## Other useful links * [Cistrome](http://cistrome.org/ap/) * [bedTools](http://code.google.com/p/bedtools/) * [UCSC toolkits](http://hgdownload.cse.ucsc.edu/admin/exe/) ## Tips of fine-tuning peak calling There are several subcommands within MACSv2 package to fine-tune or customize your analysis: 1. `bdgcmp` can be used on `*_treat_pileup.bdg` and `*_control_lambda.bdg` or bedGraph files from other resources to calculate the score track. 2. `bdgpeakcall` can be used on `*_treat_pvalue.bdg` or the file generated from bdgcmp or bedGraph file from other resources to call peaks with given cutoff, maximum-gap between nearby mergeable peaks and a minimum length of peak. bdgbroadcall works similarly to bdgpeakcall, however, it will output `_broad_peaks.bed` in BED12 format. 3. Differential calling tool -- `bdgdiff`, can be used on 4 bedGraph files which are scores between treatment 1 and control 1, treatment 2 and control 2, treatment 1 and treatment 2, treatment 2 and treatment 1. It will output consistent and unique sites according to parameter settings for minimum length, the maximum gap and cutoff. 4. You can combine subcommands to do a step-by-step peak calling. Read detail at [MACS2 wikipage](https://github.com/taoliu/MACS/wiki/Advanced%3A-Call-peaks-using-MACS2-subcommands) %prep %autosetup -n MACS2-2.2.8 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-MACS2 -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 2.2.8-1 - Package Spec generated