Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling

Stanton, Kelly P.; Jin, Jiaqi; Lederman, Roy R.; Weissman, Sherman M.; Kluger, Yuval

doi:10.1093/nar/gkx799

Cited by 11 publications

(12 citation statements)

References 62 publications

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“…DNase-seq data has been shown, if analysed correctly, to contain high resolution "footprints" of transcription factor binding within the open chromatin regions 20 . Similarly, it has been shown that the independent visualisation of the left and right hand reads in a paired end ChIP-seq experiment more precisely localises the bound protein 21 . To provide these highresolution data NGseqBasic plots the distribution of fragment ends which it derives from the aligned sequences.…”

Section: Introduction and Resultsmentioning

confidence: 88%

NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

Telenius

Hughes

2018

Preprint

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With decreasing cost of next-generation sequencing (NGS), we are observing a rapid rise in the volume of 'big data' in academic research, healthcare and drug discovery sectors. The present bottleneck for extracting value from these 'big data' sets is data processing and analysis. Considering this, there is still a lack of reliable, automated and easy to use tools that will allow experimentalists to assess the quality of the sequenced libraries and explore the data first hand, without the need of investing a lot of time of computational core analysts in the early stages of analysis.NGseqBasic is an easy-to-use single-command analysis tool for chromatin accessibility (ATAC, DNaseI) and ChIP sequencing data, providing support to also new techniques such as low cell number sequencing and Cut-and-Run. It takes in fastq, fastq.gz or bam files, conducts all quality control, trimming and mapping steps, along with quality control and data processing statistics, and combines all this to a single-click loadable UCSC data hub, with integral statistics html page providing detailed reports from the analysis tools and quality control metrics. The tool is easy to set up, and no installation is needed. A wide variety of parameters are provided to fine-tune the analysis, with optional setting to generate DNase footprint or high resolution ChIP-seq tracks. A tester script is provided to help in the setup, along with a test data set and downloadable example user cases.NGseqBasic has been used in the routine analysis of next generation sequencing (NGS) data in highimpact publications 1,2 . The code is actively developed, and accompanied with Git version control and Github code repository. Here we demonstrate NGseqBasic analysis and features using DNaseIseq data from GSM689849, and CTCF-ChIP-seq data from GSM2579421, as well as a Cut-and-Run CTCF data set GSM2433142, and provide the one-click loadable UCSC data hubs generated by the tool, allowing for the ready exploration of the run results and quality control files generated by the tool.Availability: Download, setup and help instructions are available on the NGseqBasic web site

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Section: Introduction and Resultsmentioning

confidence: 88%

NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

Telenius

Hughes

2018

Preprint

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“…To ensure identification of all ATAC-seq peaks a combination of the traditional MACS2 approach 106 (v2.0/10 callpeak -B -q 0.01) and digital signal processing with Ritornello 107 (v2.0 default settings) was used. Peak summits from both calls were extended to 500 bp and intersected with bedtools 105 (v2.25.0), and filtered for high ploidy regions in MIG viewer 108 to form peak calls for each cell type (Supplementary Tables 8a-p).…”

Section: Methodsmentioning

confidence: 99%

An integrated platform to systematically identify causal variants and genes for polygenic human traits

Downes

Hill

Nußbaum

et al. 2019

Preprint

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33Japan 34 35 ABSTRACT 37 Genome-wide association studies (GWAS) have identified over 150,000 links between 38 common genetic variants and human traits or complex diseases. Over 80% of these 39 associations map to polymorphisms in non-coding DNA. Therefore, the challenge is 40 to identify disease-causing variants, the genes they affect, and the cells in which 41 these effects occur. We have developed a platform using ATAC-seq, DNaseI 42 footprints, NG Capture-C and machine learning to address this challenge. Applying 43 this approach to red blood cell traits identifies a significant proportion of known 44 causative variants and their effector genes, which we show can be validated by direct 45 in vivo modelling.Identification of the variation of the genome that determines the risk of common chronic and 48 infectious diseases informs on their primary causes, which leads to preventative or 49 therapeutic approaches and insights. Whilst genome-wide association studies (GWASs) 50 have identified thousands of chromosome regions 1 , the identification of the causal genes, 51 variants and cell types remains a major bottleneck. This is due to three major features of the 52 genome and its complex association with disease susceptibility. Trait-associated variants 53 are often tightly associated, through linkage disequilibrium (LD), with tens or hundreds of 54 other variants, mostly single-nucleotide polymorphisms (SNPs), any one or more of which 55 could be causal; the majority (>85%) the variants identified in GWAS lie within the non-56 coding genome 2 . Although non-coding regions are increasingly well annotated, many 57 variants do not correspond to known regulatory elements, and even when they do, it is rarely 58 known which genes these elements control, and in which cell types. New technical 59 approaches to link variants to the genes they control are rapidly improving but are often 60 limited by their sensitivity and resolution [3][4][5][6] ; and because so few causal variants have been 61 unequivocally linked to the genes they affect, the mechanisms by which non-coding variants 62 alter gene expression remain unknown in all but a few cases; and, third, the complexity of 63 gene regulation and cell/cell interactions means that knowing when in development, in which 64 cell type, in which activation state, and within which pathway(s) a causal variant exerts its 65 effect is usually impossible to predict. Although significant progress is being made, currently, 66 none of these problems has been adequately solved. 68Here, we have developed an integrated platform of experimental and computational 69 methods to prioritise likely causal variants, link them to the genes they regulate, and 70 determine the mechanism by which they alter gene function. To illustrate the approach we 71 have initially focussed on a single haematopoietic lineage: the development of mature red 72 blood cells (RBC), for which all stages of lineage specification and differentiation from a 73 haematopoietic stem cell to a RBC are known, and can be r...

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“…ChIP-seq experiments are performed to identify sites of chromatin modification or protein binding, visualized as peaks. For genome wide analysis, peaks are identified bioinformatically, most commonly with MACS2, though newer methods use digital signal processing (Stanton et al , 2017) and Machine Learning e.g. (Hocking et al , 2017) .…”

Section: Identification Of Chip-seq False Positives With MLVmentioning

confidence: 99%

Multi Locus View : An Extensible Web Based Tool for the Analysis of Genomic Data

Sergeant

Hughes

Hentges

et al. 2020

Preprint

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MotivationTracking and understanding data quality, analysis and reproducibility are critical concerns in the biological sciences. This is especially true in genomics where Next Generation Sequencing (NGS) based technologies such as ChIP-seq, RNA-seq and ATAC-seq are generating a flood of genome-scale data. These data-types are extremely high level and complex with single experiments capable of mapping ten to hundreds of thousands of biologically meaningful events across the genome. However, such data are usually processed with automated tools and pipelines, generating tabular outputs and static visualizations. These are difficult to interact with and require substantial bioinformatic skills to manipulate and query. Similarly, interpretation is normally made at a high level without the ability to visualise the underlying data in detail and so the complexity and quality of the real underlying biological signal is lost. Also genomics datasets require integration with other genomics datasets to be properly interpreted and this integration with multiple tracks again requires substantial bioinformatics skills and is difficult to visualise across multiple pertinent datasets. Conventional genome browsers do allow for the detailed visualisation of multiple tracks but are limited to browsing single locations and do not allow for interactions with the dataset as a whole. MLV has been developed to allow users to fluidly interact with genomics datasets at multiple scales, from complete metadata labelled and clustered populations to detailed representations of individual elements. It has inbuilt tools to integrate signals across multiple datasets and to perform dimensionality reduction and clustering analysis based on the extracted signal, allowing for the high-level analysis of complex datasets while maintaining visualisation of the fine grain structure of the data. MLV's ability to visualise clustering within the data combined with efficient tools for large-scale tagging of individual elements makes it a unique tool for the generation of annotated datasets for modern machine learning approaches.

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Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling

Cited by 11 publications

References 62 publications

NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

An integrated platform to systematically identify causal variants and genes for polygenic human traits

Multi Locus View : An Extensible Web Based Tool for the Analysis of Genomic Data

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