# deNOPA **Repository Path**: bxxu/denopa ## Basic Information - **Project Name**: deNOPA - **Description**: deNOPA, a sensitive nucleosome positioning algorithm for sparse ATAC-seq data. - **Primary Language**: Python - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2021-01-16 - **Last Updated**: 2025-08-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # deNOPA ### Introduction *A*s the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. Thanks to its rather simple protocol, ATAC-seq has been rapidly adopted as a major tool for chromatin-accessible profiling at both bulk and single-cell level; however, to picture the arrangement of nucleosomes *per se* remains a challenge with ATAC-seq. We introduce a novel ATAC-seq analysis toolkit, named **deNOPA**, to predict nucleosome positions. Assessments showed that deNOPA not only outperformed state-of-the-art tools, but it is the only tool able to predict nucleosome position precisely with ultrasparse ATAC-seq data. The remarkable performance of deNOPA was fueled by the reads from short fragments, which compose nearly half of sequenced reads and are normally discarded from an ATAC-seq library. However, we found that the short fragment reads enrich information on nucleosome positions and that the linker regions were predicted by reads from both short and long fragments using Gaussian smoothing. See the basic workflow of deNOPA as follows: ![Fig.1](data:image/png;base64,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) ### Install ##### Pre-requirements The deNOPA package was initially developed using python 2.7. The support of python 3 has also been added at version 1.0.2 (tested python 3.7). The package was tested under the default environment of Anaconda-5.3.1, both python 2.7 and python 3.7 version. Besides a python environment, the following dependencies were also needed. * numpy * scipy * h5py * pysam * sklearn * statsmodels Please make sure they were properly installed ahead of the deNOPA package itself. Please also use the python 3 version as far as possible. Only this version is maintained now. We also offered a tested pre-requirements list in the requirements.txt. User can quickly build an environment using `pip install -r requirements.txt` ##### Install from source code Use the following commands to get deNOPA installed. ``` git clone https://gitee.com/bxxu/denopa.git cd denopa python setup.py install ``` ##### Using docker image. To unify the user experience, the denopa package has been containerized as a Docker image. Users can pull it from Docker Hub using the following command: ``` docker pull hepingshiming2007/denopa:latest ``` After pulling successfully, you can start the container with the following command: ``` docker run -it --name denopa docker.1ms.run/hepingshiming2007/denopa bash ``` After that, the `denopa` command inside the container will be immediately available. ### Usage The bam files should be indexed using samtools before running the package. The package was only tested in alignments from bowtie2. The compatibility to other aligners is not guaranteed. ``` usage: denopa [-h] -i INPUT [-o OUTPUT] [-b BUFFERSIZE] [-s CHROMSKIP] [-c CHROMINCLUDE] [-n NAME] [-m MAXLEN] [--proc PROC] [-p PARER] [-q QNFR] [-r] Decoding the nucleosome positions with ATAC-seq data at single cell level optional arguments: -h, --help show this help message and exit -i INPUT, --input INPUT The input bam files. The files should be sorted. This argument could be given multiple times for multiple input files. -o OUTPUT, --output OUTPUT The directory where the output files will go to. It will be created if not exists (default .). -b BUFFERSIZE, --bufferSize BUFFERSIZE Number of reads buffered in reading the bam file (default 1000000). -s CHROMSKIP, --chromSkip CHROMSKIP Names of chromosomes skiped from the processing. Multiple values should be sepaated by ',' (default chrY,chrM). -c CHROMINCLUDE, --chromInclude CHROMINCLUDE The regular expression of chromosome names included in the analysis, for human genome 'chr[1-9][0-9]{,1}|chrX' should be enough. It can be combined with -s. -n NAME, --name NAME The name of the project (default deNOPA). -m MAXLEN, --maxLen MAXLEN The maximun fragment length in the input files (default 2000). --proc PROC Number of processors used in the analysis (default 1). -p PARER, --pARER PARER The p-value threshold used in determining the ATAC-seq reads enriched regions (ARERs, default 0.1) -q QNFR, --qNFR QNFR The q-value threshold used in determining the nucleosome free regions (NFRs, default 0.1). -r, --removeIntermediateFiles The intermediate files will be removed if this flag is set. ``` ### Output files ##### Results * {NAME}_ARERs.txt: The bet like file of the detected ARERs. The chromosome name, start position, end position, local maximum point, maximum signal and p-value of each ARER were recorded. * {NAME}_NFR.txt: A standard broadPeak formatted bed file for detected NFRs. * {NAME}_nucleosomes.txt: A bed like file for all detected nucleosomes. For each nucleosome, the chromosome name, start position, end position, left inflection point, center position, right inflection point, number of sequencing fragments intersected, number of sequencing fragments covered it, number of Tn5 cutting sites in its inner, p-value indicating whether the number of sequencing fragments covering the nucleosome was large enough, p-value indicating whether the number of Tn5 cutting events in its inner was small enough, the combination of the about two p-values, whether the nucleosome was dynamic were listed respectively. ##### Intermediated files * {NAME}_candidates.pkl: All the detected nucleosome candidates before the DBSCAN outlier detection was applied. * {NAME}_frag_len.pkl: The fragment length distribution of the ATAC-seq library. * {NAME}_pileup_signal.hdf: The raw coverage and cutting sites signal profiles. * {NAME}_smooth.hdf: The smoothed signal profiles and their derivations. ### Test data Sparse data does not mean no data. When reads are too sparse, deNOPA cannot detect any ARER and will certainly fail. The package has been tested to work for data with 600K or more read pairs for *Saccharomyces cerevisiae* or 10M or more read pairs for human or mouse. Here we provided a test dataset in the "test" directory which contained about 25% (604K) aligned fragments in SRR1822145, together with the results. ### Citation Please add the following citation if you use deNOPA in your study: > Xu B, Li X, Gao X, et al. DeNOPA: decoding nucleosome positions sensitively with sparse ATAC-seq data[J]. Briefings in Bioinformatics, 2022, 23(1): bbab469. ### Contacts You can send questions, discussions, bug reports and other useful information to Zhihua Zhang (zangzhihua@big.ac.cn) or Bingxiang Xu (xubingxiang@sus.edu.cn).