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|SPAN Semi-supervised Peak Analyzer|
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SPAN Peak Analyzer is a multipurpose peak caller capable of processing a broad range of ChIP-seq, ATAC-seq, and
single-cell ATAC-seq datasets.
In semi-supervised mode it is capable to robustly handle multiple
replicates and noise by leveraging limited manual annotation information.
Open Access Paper: https://doi.org/10.1093/bioinformatics/btab376
Citation: Shpynov O, Dievskii A, Chernyatchik R, Tsurinov P, Artyomov MN. Semi-supervised peak calling with SPAN and JBR Genome Browser. Bioinformatics. 2021 May 21.
See releases section for actual information.
Download and install Java 8.
To analyze a single (possibly replicated) biological condition use analyze command. See details with command:
$ java -jar span.jar analyze --helpThe <output.bed> file will contain predicted and FDR-controlled peaks in the
ENCODE broadPeak (BED 6+3) format:
<chromosome> <peak start offset> <peak end offset> <peak_name> <score> . <coverage or fold/change> <-log p-value> <-log Q-value>
Examples:
- Regular peak calling
java -Xmx8G -jar span.jar analyze -t ChIP.bam -c Control.bam --cs Chrom.sizes -p Results.peak - Semi-supervised peak calling
java -Xmx8G -jar span.jar analyze -t ChIP.bam -c Control.bam --cs Chrom.sizes -l Labels.bed -p Results.peak - Model fitting only
java -Xmx8G -jar span.jar analyze -t ChIP.bam -c Control.bam --cs Chrom.sizes -m Model.span
The compare two (possibly replicated) biological conditions use the compare. See help for details:
$ java -jar span.jar compare --help| Parameter | Description |
|---|---|
-t, --treatment TREATMENTrequired |
Treatment file. Supported formats: BAM, BED, or BED.gz file. If multiple files are provided, they are treated as replicates. Multiple files should be separated by commas: -t A,B,C. Multiple files are processed as replicates on the model level. |
-c, --control CONTROL |
Control file. Multiple files should be separated by commas. A single control file, or a separate file per each treatment file is required. Follow the instructions for -t, --treatment. |
-cs, --chrom.sizes CHROMOSOMES_SIZESrequired |
Chromosome sizes file for the genome build used in TREATMENT and CONTROL files. Can be downloaded at UCSC. |
-b, --bin BIN_SIZE |
Peak analysis is performed on read coverage tiled into consequent bins of configurable size. |
-f, --fdr FDR |
False Discovery Rate cutoff to call significant regions. |
-g, --gap GAP |
Gap size to merge spatially close peaks. Useful for wide histone modifications. |
-p, --peaks PEAKS |
Resulting peaks file in ENCODE broadPeak* (BED 6+3) format. If omitted, only the model fitting step is performed. |
--labels LABELS |
Labels BED file. Used in semi-supervised peak calling. |
-m, --model MODEL |
This option is used to specify SPAN model path. Required for further semi-supervised peak calling. |
-w, --workdir PATH |
Path to the working directory. Used to save coverage and model cache. |
--bg-sensitivity SENSITIVITY |
Configures background sensitivity for peaks. Recommended value for generic ChIP-seq: 0.2.Recommended value for TFs and ATAC-seq: 0.8. |
--clip CLIP |
Clip peaks to improve peaks density using local signal coverage. Recommended value for generic ChIP-seq: 0.4.Recommended value for TFs and ATAC-seq: 0.8. |
--ext |
Save extended states information to model file. Required for model visualization in JBR Genome Browser. |
--fragment FRAGMENT |
Fragment size. If provided, reads are shifted appropriately. If not provided, the shift is estimated from the data. --fragment 0 recommended for ATAC-Seq data processing. |
-kd, --keep-duplicates |
Keep duplicates. By default, SPAN filters out redundant reads aligned at the same genomic position. Recommended for bulk single cell ATAC-Seq data processing. |
-i, --iterations |
Maximum number of iterations for Expectation Maximisation (EM) algorithm. |
--tr, --threshold |
Convergence threshold for EM algorithm, use --debug option to see detailed info. |
--threads THREADS |
Configure the parallelism level. |
-l, --log LOG |
Path to log file, if not provided, it will be created in working directory. |
-d, --debug |
Print debug information, useful for troubleshooting. |
-q, --quiet |
Turn off standard output. |
-kc, --keep-cache |
Keep cache files. By default SPAN creates cache files in working directory and removes them after computation is done. |
Step-by-step example with test dataset is available here.
SPAN can be used as a part of snakemake pipeline.
Example of ChIP-seq analysis pipeline from raw reads to visualization and peak calling can be
found here.
Clone bioinf-commons library under the project root.
git clone git@github.com:JetBrains-Research/bioinf-commons.git
Launch the following command line to build SPAN jar:
./gradlew shadowJar
The SPAN jar file will be generated in the folder build/libs.
- Q: What is the average running time?
A: SPAN is capable of processing a single ChIP-Seq track in less than 20 minutes on an average laptop. - Q: Which operating systems are supported?
A: SPAN is developed in modern Kotlin programming language and can be executed on any platform supported by java. - Q: Where did you get this lovely span picture?
A: From ascii.co.uk, the original author goes by the name jgs.
Use GitHub issues to suggest new features or report bugs.