GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

Hadish, John A.; Biggs, Tyler D.; Shealy, Benjamin T.; Bender, M. Reed; McKnight, Coleman B.; Wytko, Connor; Smith, Melinda; Feltus, F. Alex; Honaas, Loren; Ficklin, Stephen P.

doi:10.1186/s12859-022-04629-7

Cited by 7 publications

(7 citation statements)

References 43 publications

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“…This trimming is recommended for QuantSeq 3’ FWD sequencing prior to genome alignment [ 35 ]. Transcripts were then aligned and counted using the GEMmaker workflow [ 36 ] using the Hisat2 [ 37 ]. The ‘Golden Delicious’ doubled-haploid genome (GDDH13) [ 38 ] was downloaded from the Genome Database for Rosaceae (GDR) [ 39 ] and used for alignment.…”

Section: Methodsmentioning

confidence: 99%

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica

Hadish,

Hargarten,

Zhang

et al. 2024

PLoS ONE

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Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the ‘Gala’ variety of apples across several postharvest storage regiments. Fruit texture in ‘Gala’ apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments.

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Section: Methodsmentioning

confidence: 99%

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica

Hadish,

Hargarten,

Zhang

et al. 2024

PLoS ONE

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“…To perform transcriptome guided annotation, same RNA-seq data from Khan et al (2022) (eight tissue types from six pome fruit cultivars including ‘WA 38’, BioProject: PRJNA791346) were first aligned to the ‘WA 38’ haplomes using the STAR aligner implemented in GEMmaker (v2.1.0) Nextflow workflow (Hadish et al . 2022).…”

Section: Methodsmentioning

confidence: 99%

“…To perform transcriptome guided annotation, same RNA-seq data from Khan et al (2022) (eight tissue types from six pome fruit cultivars including 'WA 38', BioProject: PRJNA791346) were first aligned to the 'WA 38' haplomes using the STAR aligner implemented in GEMmaker (v2.1.0) Nextflow workflow (Hadish et al 2022). The resulting read alignments were used as extrinsic evidence in BRAKER1 (Hoff et al 2016) to predict gene models in each softmasked haplome with the following parameters: --softmasking, --UTR=off, --species=malus_domestica.…”

Section: Braker1 -Annotation With Transcriptome Evidencementioning

confidence: 99%

A Haplotype-resolved, Chromosome-scale Genome for Malus domestica 'WA 38'

Zhang,

Ko,

Eaker

et al. 2024

Preprint

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Genome sequencing for agriculturally important Rosaceous crops has made rapid progress both in completeness and annotation quality. Whole genome sequence and annotation gives breeders, researchers, and growers information about cultivar specific traits such as fruit quality, disease resistance, and informs strategies to enhance postharvest storage. Here we present a haplotype-phased, chromosomal level, genome ofMalus domestica, ‘WA 38’, a new cultivar of apple released to market in 2017 as Cosmic Crisp ®. Using both short and long read sequencing our fully phased genome of ‘WA 38’ is about 650 Megabases, with a BUSCO score of 98.7% complete. The genome contains two haplomes (HapA and HapB) which are composed of 17 chromosomes each. A total of 48,227 and 54,479 genes were annotated from HapA and HapB, respectively. Additionally, we provide genome-scale comparisons to ‘Gala’, ‘Honeycrisp’, and other relevant cultivars highlighting major structural and quality differences. This assembly and annotation was done in collaboration with the American Campus Tree Genomes project that included ‘WA 38’ (WSU) and the ‘d’Anjou’ pear (Auburn University). To ensure transparency, reproducibility, and applicability for any genome project, our genome assembly and annotation workflow is recorded in detail and shared under a public GitHub repository. All software is containerized, offering a simple implementation of the workflow.

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“…The list of 74833 SRR accessions was used with the GEMmaker workflow [26] automatically retrieves from NCBI and processes the SRR files. For these data, GEMmaker was run using Kallisto [27] for expression quantification.…”

Section: Rna-seq Data Pre-processingmentioning

confidence: 99%

Predicting Phenotypic Traits Using a Massive RNA-seq Dataset

Hadish,

Honaas,

Ficklin

2023

Preprint

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Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly availableArabidopsis thalianadata with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing.Author SummaryLarge datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly availableArabidopsis thalianaRNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.

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GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

Cited by 7 publications

References 43 publications

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica

A Haplotype-resolved, Chromosome-scale Genome for Malus domestica 'WA 38'

Predicting Phenotypic Traits Using a Massive RNA-seq Dataset

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