Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments

Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma-style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.

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Genome-Wide Association Study and Transcriptome Differential Expression Analysis of the Feather Rate in Shouguang Chickens

Frontiers in Genetics ◽

10.3389/fgene.2020.613078 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xiayi Liu ◽

Zhou Wu ◽

Junying Li ◽

Haigang Bao ◽

Changxin Wu

Keyword(s):

Differential Expression ◽

Candidate Genes ◽

Rna Sequencing ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Serine Phosphorylation ◽

Differentially Expressed ◽

Genome Wide Association ◽

Sequencing Analysis ◽

Genome Wide

The feather rate phenotype in chicks, including early-feathering and late-feathering phenotypes, are widely used as a sexing system in the poultry industry. The objective of this study was to obtain candidate genes associated with the feather rate in Shouguang chickens. In the present study, we collected 56 blood samples and 12 hair follicle samples of flight feathers from female Shouguang chickens. Then we identified the chromosome region associated with the feather rate by genome-wide association analysis (GWAS). We also performed RNA sequencing and analyzed differentially expressed genes between the early-feathering and late-feathering phenotypes using HISAT2, StringTie, and DESeq2. We identified a genomic region of 10.0–13.0 Mb of chromosome Z, which is statistically associated with the feather rate of Shouguang chickens at one-day old. After RNA sequencing analysis, 342 differentially expressed known genes between the early-feathering (EF) and late-feathering (LF) phenotypes were screened out, which were involved in epithelial cell differentiation, intermediate filament organization, protein serine kinase activity, peptidyl-serine phosphorylation, retinoic acid binding, and so on. The sperm flagellar 2 gene (SPEF2) and prolactin receptor (PRLR) gene were the only two overlapping genes between the results of GWAS and differential expression analysis, which implies that SPEF2 and PRLR are possible candidate genes for the formation of the chicken feathering phenotype in the present study. Our findings help to elucidate the molecular mechanism of the feather rate in chicks.

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DEUS: an R package for accurate small RNA profiling based on differential expression of unique sequences

Bioinformatics ◽

10.1093/bioinformatics/btz495 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4834-4836

Author(s):

Tim Jeske ◽

Peter Huypens ◽

Laura Stirm ◽

Selina Höckele ◽

Christine M Wurmser ◽

...

Keyword(s):

Differential Expression ◽

Small Rna ◽

Sequence Similarity ◽

Differential Expression Analysis ◽

R Package ◽

Supplementary Information ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Rna Sequences ◽

Rna Profiling

Abstract Summary Despite their fundamental role in various biological processes, the analysis of small RNA sequencing data remains a challenging task. Major obstacles arise when short RNA sequences map to multiple locations in the genome, align to regions that are not annotated or underwent post-transcriptional changes which hamper accurate mapping. In order to tackle these issues, we present a novel profiling strategy that circumvents the need for read mapping to a reference genome by utilizing the actual read sequences to determine expression intensities. After differential expression analysis of individual sequence counts, significant sequences are annotated against user defined feature databases and clustered by sequence similarity. This strategy enables a more comprehensive and concise representation of small RNA populations without any data loss or data distortion. Availability and implementation Code and documentation of our R package at http://ibis.helmholtz-muenchen.de/deus/. Supplementary information Supplementary data are available at Bioinformatics online.

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MicroRNA Expression Analysis Using Small RNA Sequencing Discovery and RT-qPCR-Based Validation

Methods in Molecular Biology - Functional Genomics ◽

10.1007/978-1-4939-7231-9_13 ◽

2017 ◽

pp. 197-208

Author(s):

Alan Van Goethem ◽

Pieter Mestdagh ◽

Tom Van Maerken ◽

Jo Vandesompele

Keyword(s):

Rna Sequencing ◽

Expression Analysis ◽

Small Rna ◽

Microrna Expression ◽

Small Rna Sequencing

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Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1606043113 ◽

2016 ◽

Vol 113 (40) ◽

pp. E5802-E5811 ◽

Cited By ~ 36

Author(s):

Arul M. Varman ◽

Lian He ◽

Rhiannon Follenfant ◽

Weihua Wu ◽

Sarah Wemmer ◽

...

Keyword(s):

Differential Expression ◽

Rna Sequencing ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Value Added ◽

Tca Cycle ◽

Soil Bacterium ◽

Metabolic Energy ◽

Catabolic Pathway ◽

Lignin Valorization

Sphingobiumsp. SYK-6 is a soil bacterium boasting a well-studied ligninolytic pathway and the potential for development into a microbial chassis for lignin valorization. An improved understanding of its metabolism will help researchers in the engineering of SYK-6 for the production of value-added chemicals through lignin valorization. We used13C-fingerprinting,13C metabolic flux analysis (13C-MFA), and RNA-sequencing differential expression analysis to uncover the following metabolic traits: (i) SYK-6 prefers alkaline conditions, making it an efficient host for the consolidated bioprocessing of lignin, and it also lacks the ability to metabolize sugars or organic acids; (ii) the CO2release (i.e., carbon loss) from the ligninolysis-based metabolism of SYK-6 is significantly greater than the CO2release from the sugar-based metabolism ofEscherichia coli; (iii) the vanillin catabolic pathway (which is the converging point of majority of the lignin catabolic pathways) is coupled with the tetrahydrofolate-dependent C1 pathway that is essential for the biosynthesis of serine, histidine, and methionine; (iv) catabolic end products of lignin (pyruvate and oxaloacetate) must enter the tricarboxylic acid (TCA) cycle first and then use phosphoenolpyruvate carboxykinase to initiate gluconeogenesis; and (v)13C-MFA together with RNA-sequencing differential expression analysis establishes the vanillin catabolic pathway as the major contributor of NAD(P)H synthesis. Therefore, the vanillin catabolic pathway is essential for SYK-6 to obtain sufficient reducing equivalents for its healthy growth; cosubstrate experiments support this finding. This unique energy feature of SYK-6 is particularly interesting because most heterotrophs rely on the transhydrogenase, the TCA cycle, and the oxidative pentose phosphate pathway to obtain NADPH.

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sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression

Nucleic Acids Research ◽

10.1093/nar/gkz415 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W530-W535 ◽

Cited By ~ 22

Author(s):

Ernesto Aparicio-Puerta ◽

Ricardo Lebrón ◽

Antonio Rueda ◽

Cristina Gómez-Martín ◽

Stavros Giannoukakos ◽

...

Keyword(s):

Differential Expression ◽

Rna Sequencing ◽

Small Rna ◽

Pcr Amplification ◽

Small Rna Sequencing ◽

User Friendliness ◽

Rna Profiling ◽

Sequencing Studies ◽

Microrna Sequencing ◽

Downstream Analysis

Abstract Since the original publication of sRNAtoolbox in 2015, small RNA research experienced notable advances in different directions. New protocols for small RNA sequencing have become available to address important issues such as adapter ligation bias, PCR amplification artefacts or to include internal controls such as spike-in sequences. New microRNA reference databases were developed with different foci, either prioritizing accuracy (low number of false positives) or completeness (low number of false negatives). Additionally, other small RNA molecules as well as microRNA sequence and length variants (isomiRs) have continued to gain importance. Finally, the number of microRNA sequencing studies deposited in GEO nearly triplicated from 2014 (280) to 2018 (764). These developments imply that fast and easy-to-use tools for expression profiling and subsequent downstream analysis of miRNA-seq data are essential to many researchers. Key features in this sRNAtoolbox release include addition of all major RNA library preparation protocols to sRNAbench and improvements in sRNAde, a tool that summarizes several aspects of small RNA sequencing studies including the detection of consensus differential expression. A special emphasis was put on the user-friendliness of the tools, for instance sRNAbench now supports parallel launching of several jobs to improve reproducibility and user time efficiency.

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