Bicycle: a bioinformatics pipeline to analyze bisulfite sequencing data

Graña, Osvaldo; López-Fernández, Hugo; Fdez-Riverola, Florentino; Pisano, David G.; Glez-Peña, Daniel

doi:10.1093/bioinformatics/btx778

Cited by 22 publications

(33 citation statements)

References 17 publications

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“…Since wg-blimp only integrates published software, and exhaustive evaluation of all conceivable pipeline setups would result in combinatorial explosion, we focus here on a feature-wise comparison of pipelines, similar to (19). We compared wg-blimp to BAT (20), bicycle (21), CpG_Me/DMRichR (10,(22)(23)(24), ENCODE-DCC's WGBS pipeline (25), Methy-Pipe (26), Nextflow methylseq (two available workflows) (27), PiGx (28) and snakePipes (19). Pipelines were compared with regards to technical setup (installation, workflow management), WGBS read processing (adapter trimming, alignment, methylation calling, quality control), and post-alignment analyses (DMR detection, segmentation, annotation).…”

Section: Resultsmentioning

confidence: 99%

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

Wöste

Leitão

Laurentino

et al. 2019

Preprint

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BackgroundAnalysing whole genome bisulfite sequencing datasets is a data-intensive task that requires comprehensive and reproducible workflows to generate valid results. While many algorithms have been developed for tasks such as alignment, comprehensive end-to-end pipelines are still sparse. Furthermore, previous pipelines lack features or show technical deficiencies, thus impeding analyses.ResultsWe developed wg-blimp (whole genome bisulfite sequencing methylation analysis pipeline) as an end-to-end pipeline to ease whole genome bisulfite sequencing data analysis. It integrates established algorithms for alignment, quality control, methylation calling, detection of differentially methylated regions, and methylome segmentation, requiring only a reference genome and raw sequencing data as input. Comparing wg-blimp to previous end-to-end pipelines reveals similar setups for common sequence processing tasks, but shows differences for post-alignment analyses. We improve on previous pipelines by providing a more comprehensive analysis workflow as well as an interactive user interface. To demonstrate wg-blimp’s ability to produce correct results we used it to call differentially methylated regions for two publicly available datasets. We were able to replicate 112 of 114 previously published regions, and found results to be consistent with previous findings. We further applied wg-blimp to a publicly available sample of embryonic stem cells to showcase methylome segmentation. As expected, unmethylated regions were in close proximity of transcription start sites. Segmentation results were consistent with previous analyses, despite different reference genomes and sequencing techniques.Conclusionswg-blimp provides a comprehensive analysis pipeline for whole genome bisulfite sequencing data as well as a user interface for simplified result inspection. We demonstrated its applicability by analysing multiple publicly available datasets. Thus, wg-blimp is a relevant alternative to previous analysis pipelines and may facilitate future epigenetic research.

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Section: Resultsmentioning

confidence: 99%

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

Wöste

Leitão

Laurentino

et al. 2019

Preprint

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“…With ARPEGGIO, the aim was to consolidate performant tools into a common approach that could be used as a start for further investigation; in our case downstream analyses leading to a list of genes. Reproducibility was another main feature present in ARPEGGIO that was lacking in many workflows, but appeared to be more prevalent in more recent publications (71,(75)(76)(77). Enhancing and promoting reproducibility is essential to ensure that discoveries stand the test of time (78).…”

Section: Comparison To Other Workflowsmentioning

confidence: 99%

“…All workflows support diploid data, which is considered the same as polyploid data with an available polyploid phased assembly. When comparing the presence of quality check, alignment and statistical analyses, most workflows included them all together, but some didn't include either quality check (74)(75)(76) or statistical analyses (79). For methylation contexts, only two workflows focused on CpG context only (77,80), while all the other allowed analyses for all contexts (CpG, CHG and CHH).…”

Section: Comparison To Other Workflowsmentioning

confidence: 99%

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

Milosavljevic

Kuo²,

Decarli

et al. 2020

Preprint

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Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid’s parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO’s design focuses on ease of use and reproducibility. ARPEGGIO was made simple to set up, run and interpret, and its implementation includes both package management and containerization. Here we discuss all the steps, challenges and implementation strategies; example datasets are provided to show how to use ARPEGGIO. In addition, we also test EAGLE-RC with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation.

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“…As a result of continuing drops 27 in sequencing costs, an increasing number of laboratories and international consortia (e.g. 28 IHEC, SYSCID, BLUEPRINT, EpiDiverse, NIH ROADMAP, Arabidopsis 1001 Epigenomes, 29 Genomes and physical Maps) are adopting WGBS as the method of choice to survey DNA 30 methylation in large population samples or in collections of cell lines and tissue types, either in 31 2/14 bulk or at the single-cell level [1,2]. Such surveys can easily generate hundreds or even thou- 32 sands of WGBS datasets in a single study.…”

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confidence: 99%

“…In an effort to help streamline the pre-processing 47 of WGBS data several pipelines have been published in recent years. These include nf-48 core/methylseq [29], gemBS [30], Bicycle [31] and Methylpy, some of which are currently 49 employed by several epigenetic consortia. gemBS, Bicycle and Methylpy integrate data pre-50 processing and analysis steps using their own custom trimming and/or alignment tools (see 51 Table 3).…”

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confidence: 99%

MethylStar: A fast and robust pre-processing pipeline for bulk or single-cell whole-genome bisulfite sequencing data

Dizaji

Hazarika

Johannes

2019

Preprint

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1 Background: Whole-Genome Bisulfite Sequencing (WGBS) is a Next Generation Sequenc-2 ing (NGS) technique for measuring DNA methylation at base resolution. Continuing drops 3 in sequencing costs are beginning to enable high-throughput surveys of DNA methylation in 4 large samples of individuals and/or single cells. These surveys can easily generate hundreds or 5 even thousands of WGBS datasets in a single study. The efficient pre-processing of these large 6 amounts of data poses major computational challenges and creates unnecessary bottlenecks 7 for downstream analysis and biological interpretation.8 9Results: To offer an efficient analysis solution, we present MethylStar, a fast, stable and flex-10 ible pre-processing pipeline for WGBS data. MethylStar integrates well-established tools for 11 read trimming, alignment and methylation state calling in a highly parallelized environment, 12 manages computational resources and performs automatic error detection. MethylStar offers 13 easy installation through a dockerized container with all preloaded dependencies and also fea-14 tures a user-friendly interface designed for experts/non-experts. Application of MethylStar to 15 WGBS from human, maize and Arabidopsis shows that it outperforms existing pre-processing 16 pipelines in terms of speed and memory requirements. 17 18 Conclusions: MethylStar is a fast, stable and flexible pipeline for high-throughput pre-19processing of bulk or single-cell WGBS data. Its easy installation and user-friendly inter-20 face should make it a useful resource for the wider epigenomics community. MethylStar is 21

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Bicycle: a bioinformatics pipeline to analyze bisulfite sequencing data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 22 publications

References 17 publications

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

MethylStar: A fast and robust pre-processing pipeline for bulk or single-cell whole-genome bisulfite sequencing data

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