GenPipes: an open-source framework for distributed and scalable genomic analyses

Bourgey, Mathieu; Dali, Rola; Eveleigh, Robert; Chen, Kuang Chung; Létourneau, Louis; Fillon, Joël; Michaud, Marc; Caron, Maxime; Sandoval, Johanna; Lefebvre, François; Leveque, Gary; Mercier, Eloi; Bujold, David; Marquis, Pascale; Van, Patrick Tran; Morais, David Anderson de Lima; Tremblay, Julien; Shao, Xiaojian; Henrion, Édouard; González, Emmanuel; Quirion, Pierre-Olivier; Caron, B.; Bourque, Guillaume

doi:10.1093/gigascience/giz037

Cited by 147 publications

(122 citation statements)

References 70 publications

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“…DRIP-seq analysis. FastQ files of DRIP-seq reads were trimmed with Trimmomatic (PE -phred33), using the GenPpipes ChIP-seq pipeline (steps 1-3) 64 . Reads with both mate pairs were aligned to the dm3 version of the Drosophila genome using Bowtie2/2.3.1(-fr -no-mixed -no-unal) 65 .…”

Section: Discussionmentioning

confidence: 99%

RNA-DNA strand exchange by the Drosophila Polycomb complex PRC2

et al. 2020

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Polycomb Group (PcG) proteins form memory of transient transcriptional repression that is necessary for development. In Drosophila, DNA elements termed Polycomb Response Elements (PREs) recruit PcG proteins. How PcG activities are targeted to PREs to maintain repressed states only in appropriate developmental contexts has been difficult to elucidate. PcG complexes modify chromatin, but also interact with both RNA and DNA, and RNA is implicated in PcG targeting and function. Here we show that R-loops form at many PREs in Drosophila embryos, and correlate with repressive states. In vitro, both PRC1 and PRC2 can recognize R-loops and open DNA bubbles. Unexpectedly, we find that PRC2 drives formation of RNA-DNA hybrids, the key component of R-loops, from RNA and dsDNA. Our results identify R-loop formation as a feature of Drosophila PREs that can be recognized by PcG complexes, and RNA-DNA strand exchange as a PRC2 activity that could contribute to R-loop formation.

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Section: Discussionmentioning

confidence: 99%

RNA-DNA strand exchange by the Drosophila Polycomb complex PRC2

et al. 2020

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“…Standard ChIP-seq analysis relies on aligning reads to a reference sequence followed by peak calling [1] [2]. While the reference genome is a good approximation of the sequence under study, it does not account for the millions of small genetic variants, the larger structural variants or the two haplotypes of the human genome [3].…”

Section: Introductionmentioning

confidence: 99%

Personalized and graph genomes reveal missing signal in epigenomic data

Groza

Kwan

Soranzo

et al. 2018

Preprint

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Background: Epigenomic studies that use next generation sequencing experiments typically rely on the alignment of reads to a reference sequence. However, because of genetic diversity and the diploid nature of the human genome, we hypothesized that using a generic reference could lead to incorrectly mapped reads and bias downstream results.Results: We show that accounting for genetic variation using a modified reference genome (MPG) or a denovo assembled genome (DPG) can alter histone H3K4me1 and H3K27ac ChIP-seq peak calls by either creating new personal peaks or by the loss of reference peaks. MPGs are found to alter approximately 1% of peak calls while DPGs alter up to 5% of peaks. We also show statistically significant differences in the amount of reads observed in regions associated with the new, altered and unchanged peaks. We report that short insertions and deletions (indels), followed by single nucleotide variants (SNVs), have the highest probability of modifying peak calls. A counter-balancing factor is peak width, with wider calls being less likely to be altered. Next, because high-quality DPGs remain hard to obtain, we show that using a graph personalized genome (GPG), represents a reasonable compromise between MPGs and DPGs and alters about 2.5% of peak calls. Finally, we demonstrate that altered peaks have a genomic distribution typical of other peaks. For instance, for H3K4me1, 518 personal-only peaks were replicated using at least two of three approaches, 394 of which were inside or within 10Kb of a gene.Conclusions: Analysing epigenomic datasets with personalized and graph genomes allows the recovery of new peaks enriched for indels and SNVs. These altered peaks are more likely to differ between individuals and, as such, could be relevant in the study of various human phenotypes.

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“…Workflow management systems together with Linux containers offer a solution to efficiently analyze large scale datasets in a highly reproducible, scalable and parallelizable manner. During the last decade, an increasing interest in the field has led to the development of different programs such as Snakemake (Köster andRahmann, 2012), NextFlow (Di Tommaso et al, 2017), Galaxy (Afgan et al, 2018), SciPipe (Lampa et al, 2019) or GenPipes (Bourgey et al, 2019), among others. These tools enable the prototyping and deployment of pipelines by abstracting computational processes and representing pipelines as directed graphs, in which nodes represent tasks to be executed and edges represent either data flow or execution dependencies between different tasks.…”

Section: Overview Of the Masterofpores Workflowmentioning

confidence: 99%

MasterOfPores: A Workflow for the Analysis of Oxford Nanopore Direct RNA Sequencing Datasets

et al. 2020

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The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline starts with a pre-processing module, which converts raw current intensities into multiple types of processed data including FASTQ and BAM, providing metrics of the quality of the run, quality-filtering, demultiplexing, base-calling and mapping. In a second step, the pipeline performs downstream analyses of the mapped reads, including prediction of RNA modifications and estimation of polyA tail lengths. Four direct RNA MinION sequencing runs can be fully processed and analyzed in 10 h on 100 CPUs. The pipeline can also be executed in GPU locally or in the cloud, decreasing the run time fourfold. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow simplifies direct RNA sequencing data analyses, facilitating the study of the (epi)transcriptome at single molecule resolution.

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GenPipes: an open-source framework for distributed and scalable genomic analyses

Cited by 147 publications

References 70 publications

RNA-DNA strand exchange by the Drosophila Polycomb complex PRC2

RNA-DNA strand exchange by the Drosophila Polycomb complex PRC2

Personalized and graph genomes reveal missing signal in epigenomic data

MasterOfPores: A Workflow for the Analysis of Oxford Nanopore Direct RNA Sequencing Datasets

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