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      I-ATAC: interactive pipeline for the management and pre-processing of ATAC-seq samples

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      1 , , 2 ,
      PeerJ
      PeerJ Inc.
      ATAC-seq, Genomics, Data, Management, Pipeline, Pre-processing

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          Abstract

          Assay for Transposase Accessible Chromatin (ATAC-seq) is an open chromatin profiling assay that is adapted to interrogate chromatin accessibility from small cell numbers. ATAC-seq surmounted a major technical barrier and enabled epigenome profiling of clinical samples. With this advancement in technology, we are now accumulating ATAC-seq samples from clinical samples at an unprecedented rate. These epigenomic profiles hold the key to uncovering how transcriptional programs are established in diverse human cells and are disrupted by genetic or environmental factors. Thus, the barrier to deriving important clinical insights from clinical epigenomic samples is no longer one of data generation but of data analysis. Specifically, we are still missing easy-to-use software tools that will enable non-computational scientists to analyze their own ATAC-seq samples. To facilitate systematic pre-processing and management of ATAC-seq samples, we developed an interactive, cross-platform, user-friendly and customized desktop application: interactive-ATAC (I-ATAC). I-ATAC integrates command-line data processing tools (FASTQC, Trimmomatic, BWA, Picard, ATAC_BAM_shiftrt_gappedAlign.pl, Bedtools and Macs2) into an easy-to-use platform with user interface to automatically pre-process ATAC-seq samples with parallelized and customizable pipelines. Its performance has been tested using public ATAC-seq datasets in GM12878 and CD4+T cells and a feature-based comparison is performed with some available interactive LIMS (Galaxy, SMITH, SeqBench, Wasp, NG6, openBIS). I-ATAC is designed to empower non-computational scientists to process their own datasets and to break to exclusivity of data analyses to computational scientists. Additionally, I-ATAC is capable of processing WGS and ChIP-seq samples, and can be customized by the user for one-independent or multiple-sequential operations.

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          Most cited references11

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          openBIS: a flexible framework for managing and analyzing complex data in biology research

          Background Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it. Results We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies. Conclusions openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.
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            Ergatis: a web interface and scalable software system for bioinformatics workflows

            Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users. Results: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects. Availability: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.net Contact: jorvis@users.sourceforge.net
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              ChIP-chip: a genomic approach for identifying transcription factor binding sites.

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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                22 November 2017
                2017
                : 5
                : e4040
                Affiliations
                [1 ]Department of Genetics and Genome Sciences, University of Connecticut Health Center , Farmington, CT, United States of America
                [2 ]The Jackson Laboratory For Genomic Medicine , Farmington, CT, United States of America
                Article
                4040
                10.7717/peerj.4040
                5702251
                29181276
                ac9b6e2a-c22f-46f4-ad2b-a0c0410ce78d
                ©2017 Ahmed and Ucar

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 21 April 2017
                : 25 October 2017
                Funding
                Funded by: Jackson Laboratory (JAX)
                The Jackson Laboratory (JAX) supports and owns this project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Computational Biology
                Genomics
                Human-Computer Interaction
                Computational Science

                atac-seq,genomics,data,management,pipeline,pre-processing
                atac-seq, genomics, data, management, pipeline, pre-processing

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