Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures

Munro, Sarah A.; Lund, Steven P.; Pine, P. Scott; Binder, Hans; Clevert, Djork-Arné; Conesa, Ana; Dopazo, Joaquín; Fasold, Mario; Hochreiter, Sepp; Hong, Huasheng; Jafari, Nadereh; Kreil, David P.; Łabaj, Paweł P.; Li, Sheng; Liao, Yang; Lin, Simon; Meehan, Joe; Mason, Christopher E.; Santoyo-López, Javier; Setterquist, Robert A.; Shi, Leming; Shi, Wei; Smyth, Gordon K.; Stralis‐Pavese, Nancy; Su, Zhenqiang; Tong, Weida; Wang, Charles; Wang, Jian; Xu, Joshua; Zhan, Yanqiang; Yang, Yong; Yu, Ying; Salit, Marc

doi:10.1038/ncomms6125

Cited by 127 publications

(107 citation statements)

References 35 publications

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“…Unfortunately, this limitation is currently pervasive across environmental shotgun sequencing studies, largely because of technical challenges in estimating in situ DNA, mRNA, and protein concentrations (64). These challenges will likely be overcome in the future (65,66). Given this current caveat, the general agreement of the model with the shape of the multiomic profiles (Fig.…”

Section: Consequences For Geobiologymentioning

confidence: 99%

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Louca

Hawley

Katsev

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

120

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Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.M icrobial communities catalyze Earth's biogeochemical cycles through metabolic pathways that couple fluxes of matter and energy to biological growth (1). These pathways are encoded in evolving genes that, over time, spread across microbial lineages and today shape the conditions for life on Earth. High-throughput sequencing and mass-spectrometry platforms are generating multiomic sequence information (DNA, mRNA, and protein) that is transforming our perception of this microcosmos, but the vast majority of environmental sequencing studies lack a mechanistic link to geochemical processes. At the same time, mathematical models are increasingly used to describe local-and global-scale biogeochemical processes or predict future changes in global elemental cycling and climate (2, 3). Although these models typically incorporate the catalytic properties of cells, they fail to integrate the information flow from DNA to mRNA, proteins, and process rates as described by the central dogma of molecular biology (4). Hence, a mechanistic framework integrating multiomic data with geochemical information has...

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Section: Consequences For Geobiologymentioning

confidence: 99%

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Louca

Hawley

Katsev

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

120

View full text Add to dashboard Cite

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“…Also, the nucleotide sequences of ERCC are known and publicly available [11]. In previous studies, ERCC spike-ins have been used as the ground truth to assess dynamic range of expression, accuracy of fold-change recovery, and lower limit of detection for several sequencing platforms [12]. With known sequences, we propose that ERCC spike-ins can also be applied to examine sequencing errors that occur in RNA-seq experiments.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls

Yang

et al. 2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC RNA spike-in controls as the ground truth to facilitate error-correction performance evaluation. After aligning raw and corrected RNA-seq data, we characterized the quality of reads by three metrics: mismatch patterns (i.e., the substitution rate of A to C) of reads aligned with one mismatch, mismatch patterns of reads aligned with two mismatches and the percentage increase of reads aligned to reference. We observed that the mismatch patterns for reads aligned with one mismatch are significantly correlated between ERCC spike-ins and real RNA samples. Based on such observations, we conclude that ERCC spike-ins can serve as ground truths for error correction beyond their previous applications for validation of dynamic range and fold-change response. Also, the mismatch patterns for ERCC reads aligned with one mismatch can serve as a novel and reliable metric to evaluate the performance of error-correction tools.

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“…In addition, we investigate other methods for determining filtering thresholds, including percentiles of intergenic distribution [7] and LODR (limit of detection ratio) introduced in erecdashboard [8]. LODR is derived from the analysis of external spike-in RNA control ratio mixtures, and is defined as the minimum count above which a gene with an absolute log fold-change signal has a 100% chance of obtaining a statistically significant adjusted p-value [8].…”

Section: Methodsmentioning

confidence: 99%

“…Other methods for determining filtering thresholds include estimation based on the distribution of intergenic reads per kilobase per million mapped reads (RPKM) values [7] and estimation based on External RNA Controls Consortium (ERCC) spike-in controls [8]. However, there is no systematic comparison of different filtering methods, and no investigation of how different RNA-seq pipelines affect these filtering methods.…”

Section: Introductionmentioning

confidence: 99%

Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data

Sha

Phan

Wang

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We compare methods for filtering RNA-seq lowexpression genes and investigate the effect of filtering on detection of differentially expressed genes (DEGs). Although RNA-seq technology has improved the dynamic range of gene expression quantification, low-expression genes may be indistinguishable from sampling noise. The presence of noisy, low-expression genes can decrease the sensitivity of detecting DEGs. Thus, identification and filtering of these low-expression genes may improve DEG detection sensitivity. Using the SEQC benchmark dataset, we investigate the effect of different filtering methods on DEG detection sensitivity. Moreover, we investigate the effect of RNA-seq pipelines on optimal filtering thresholds. Results indicate that the filtering threshold that maximizes the total number of DEGs closely corresponds to the threshold that maximizes DEG detection sensitivity. Transcriptome reference annotation, expression quantification method, and DEG detection method are statistically significant RNA-seq pipeline factors that affect the optimal filtering threshold.

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Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures

Cited by 127 publications

References 35 publications

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls

Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data

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