Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics

Baichoo, Shakuntala; Souilmi, Yassine; Panji, Sumir; Botha, Gerrit; Meintjes, Ayton; Hazelhurst, Scott; Bendou, Hocine; Beste, Eugene de; Mpangase, Phelelani T.; Souiai, Oussema; Alghali, Mustafa; Yi, Long; O’Connor, Brian D.; Crusoe, Michael R.; Armstrong, Don; Aron, Shaun; Joubert, Fourie; Ahmed, Azza E.; Mbiyavanga, Mamana; Heusden, Peter van; Magosi, Lerato E; Zermeno, Jennie; Mainzer, Liudmila Sergeevna; Fadlelmola, Faisal M.; Jongeneel, C V; Mulder, Nicola

doi:10.1186/s12859-018-2446-1

Cited by 35 publications

(42 citation statements)

References 34 publications

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“…ics data analysis workflow with flexibility, extensibility and reproducibility [39,40,41,42,43,44]. Herein, we used Nextflow to encapsulate the seven data processing pipelines so that users can directly call different pipelines through a uniform set of APIs.…”

Section: Nextflow Pipeline Design and Featuresmentioning

confidence: 99%

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Gao

Ling

Tang

et al. 2020

Preprint

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With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNAseq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.Since the emergence of the first single-cell RNA sequencing (scRNA-seq) platform [1], many research achievements have been made at the cellular and subcellular levels with unprecedented resolutions with the aid of this technology. Recent advances in microfluidics and next generation sequencing (NGS) have further increased the efficiency and throughput of scRNA-seq, enabling more cells to be identified and the expression information of more genes for each cell to be quantified simultaneously [2,3,4]. From microcapillary pipettes in 2012 [5,6] and nanoliter droplet-based microfluidic chips in 2015 [7,8] to the latest liquid barcoding combined with split-pool methods [9], the number of cells analysed in parallel has increased from several to tens of thousands [10], bringing new challenges for the processing of a large amount of barcoded NGS data for efficient and accurate quantification of the transcript information of single cells.To meet the needs of high-throughput scRNA-seq data processing, several pipelines that integrate multiple functions have been developed. As one of the first high-throughput scRNA-seq platforms, Drop-seq [7] was introduced in 2015. Moreover, Drop-seq-tools in combination with Picard tools were introduced and provided a user-friendly Application Programming Interface (API) to process the data from Drop-seq. Later in 2017, three additional pipelines were published including Cell Ranger [11], umis [12] and UMItools [13]. Cell Ranger [11] was developed along with the widely adopted 10X Genomics platform which can process multiple biological samples separated by sample barcodes in paral...

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Section: Nextflow Pipeline Design and Featuresmentioning

confidence: 99%

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Gao

Ling

Tang

et al. 2020

Preprint

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“…GxE testing was performed using the PLINK "-gxe" option (Purcell et al, 2007;Chang et al, 2015) on the "awigen" branch of the automated workflow 4 (Baichoo et al, 2018). We screened the output for a genome-wide significance threshold (p-values < 5 E-08).…”

Section: Gene-environment Interaction (Gxe) Analysismentioning

confidence: 99%

Novel and Known Gene-Smoking Interactions With cIMT Identified as Potential Drivers for Atherosclerosis Risk in West-African Populations of the AWI-Gen Study

et al. 2020

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Introduction: Atherosclerosis is a key contributor to the burden of cardiovascular diseases (CVDs) and many epidemiological studies have reported on the effect of smoking on carotid intima-media thickness (cIMT) and its subsequent effect on CVD risk. Gene-environment interaction studies have contributed towards understanding some of the missing heritability of genome-wide association studies. Gene-smoking interactions on cIMT have been studied in non-African populations (European, Latino-American, and African American) but no comparable African research has been reported. Our aim was to investigate smoking-SNP interactions on cIMT in two West African populations by genome-wide analysis.Materials and methods: Only male participants from Burkina Faso (Nanoro = 993) and Ghana (Navrongo = 783) were included, as smoking was extremely rare among women. Phenotype and genotype data underwent stringent QC and genotype imputation was performed using the Sanger African Imputation Panel. Smoking prevalence among men was 13.3% in Nanoro and 42.5% in Navrongo. We analyzed gene-smoking interactions with PLINK after adjusting for covariates: age and 6 PCs (Model 1); age, BMI, blood pressure, fasting glucose, cholesterol levels, MVPA, and 6 PCs (Model 2). All analyses were performed at site level and for the combined data set. Results:In Nanoro, we identified new gene-smoking interaction variants for cIMT within the previously described RCBTB1 region (rs112017404, rs144170770, and rs4941649) (Model 1: p = 1.35E-07; Model 2: p = 3.08E-08). In the combined sample, two novel Frontiers in Genetics | www.frontiersin.org Discussion: This is the first gene-smoking interaction study for cIMT, as a risk factor for atherosclerosis, in sub-Saharan African populations. In addition to replicating previously known signals for RCBTB1, we identified two novel genomic regions (TBC1D8, near BCHE) involved in this gene-environment interaction.

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“…33 To achieve sustainability, capacity building must occur across the breadth of research activities, from grant writing and administration to bioinformaticians and scientists to ethics committees, while involving both junior and senior scientists. 29 The Pan African Bioinformatics Network (H3ABioNet) supports H3Africa projects with training activities, standard operating procedures and workflows, 34 infrastructure development, and a help desk to facilitate interactions with H3ABioNet members with relevant expertise. 35 H3ABioNet trains bioinformaticians, scientists, and health care professionals in data management, analysis, interpretation, and infrastructure develop-Training of the next generation of genomic researchers is seen as one of the main benefits of the H3Africa initiative by African scientists within the H3Africa initiative.…”

Section: Capacity Buildingmentioning

confidence: 99%

The Emergence of Genomic Research in Africa and New Frameworks for Equity in Biomedical Research

2019

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Individuals with African ancestry have the greatest genomic diversity in the world, yet they have been underrepresented in genomic research. To advance our understanding of human biology and our ability to trace human history, we must include more samples from Africans in genomic research. Additionally, inclusion of more samples from participants of recent African descent is imperative to provide equitable health care as genomics is increasingly used for diagnosis, treatment, and to understand disease risk. The Human Heredity and Health in Africa initiative (H3Africa) seeks to expand the number of Africans included in genomic research and to do so by expanding the research capacity on the continent. In this article, we discuss how H3Africa is endeavoring to achieve these goals while promoting equitable research collaborations.Ethn Dis. 2019;29(Suppl 1):179-186; doi:10.18865/ed.29.S1.179

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Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics

Cited by 35 publications

References 34 publications

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Novel and Known Gene-Smoking Interactions With cIMT Identified as Potential Drivers for Atherosclerosis Risk in West-African Populations of the AWI-Gen Study

The Emergence of Genomic Research in Africa and New Frameworks for Equity in Biomedical Research

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