Advanced Applications of RNA Sequencing and Challenges

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

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Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Scientific Reports ◽

10.1038/s41598-021-83459-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Joseph Tomlinson ◽

Shawn W. Polson ◽

Jing Qiu ◽

Juniper A. Lake ◽

William Lee ◽

...

Keyword(s):

Broiler Chickens ◽

Nucleotide Polymorphisms ◽

Rna Seq ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Detection Tool ◽

Commercial Broiler ◽

Significant Phenomenon ◽

Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

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Replicate sequencing libraries are important for quantification of allelic imbalance

Nature Communications ◽

10.1038/s41467-021-23544-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil R. Sunyaev ◽

...

Keyword(s):

Allelic Imbalance ◽

False Positive Rate ◽

Error Rates ◽

Differential Analysis ◽

Rna Seq ◽

Specific Expression ◽

Technical Noise ◽

Specific Analysis ◽

Positive Rate ◽

Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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Analysis of Allele-Specific Expression in Mouse Liver by RNA-Seq: A Comparison With Cis-eQTL Identified Using Genetic Linkage

Genetics ◽

10.1534/genetics.113.153882 ◽

2013 ◽

Vol 195 (3) ◽

pp. 1157-1166 ◽

Cited By ~ 34

Author(s):

Sandrine Lagarrigue ◽

Lisa Martin ◽

Farhad Hormozdiari ◽

Pierre-François Roux ◽

Calvin Pan ◽

...

Keyword(s):

Mouse Liver ◽

Genetic Linkage ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption

Brain Sciences ◽

10.3390/brainsci9070155 ◽

2019 ◽

Vol 9 (7) ◽

pp. 155 ◽

Cited By ~ 6

Author(s):

Hitzemann ◽

Iancu ◽

Reed ◽

Baba ◽

Lockwood ◽

...

Keyword(s):

Large Scale ◽

Drug Exposure ◽

Regional Analysis ◽

Drug Effects ◽

Network Connectivity ◽

Transcriptome Profiling ◽

Rna Seq ◽

Reward Circuitry ◽

Brain Transcriptome ◽

Differential Gene

Transcriptome profiling can broadly characterize drug effects and risk for addiction in the absence of drug exposure. Modern large-scale molecular methods, including RNA-sequencing (RNA-Seq), have been extensively applied to alcohol-related disease traits, but rarely to risk for methamphetamine (MA) addiction. We used RNA-Seq data from selectively bred mice with high or low risk for voluntary MA intake to construct coexpression and cosplicing networks for differential risk. Three brain reward circuitry regions were explored, the nucleus accumbens (NAc), prefrontal cortex (PFC), and ventral midbrain (VMB). With respect to differential gene expression and wiring, the VMB was more strongly affected than either the PFC or NAc. Coexpression network connectivity was higher in the low MA drinking line than in the high MA drinking line in the VMB, oppositely affected in the NAc, and little impacted in the PFC. Gene modules protected from the effects of selection may help to eliminate certain mechanisms from significant involvement in risk for MA intake. One such module was enriched in genes with dopamine-associated annotations. Overall, the data suggest that mitochondrial function and glutamate-mediated synaptic plasticity have key roles in the outcomes of selective breeding for high versus low levels of MA intake.

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Design of the high-speed, high-precision and large-scale data acquisition system

Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004. ◽

10.1109/icosp.2004.1452724 ◽

2005 ◽

Author(s):

Hou Xiaomin ◽

Wang Xiaohong ◽

Long Teng

Keyword(s):

Data Acquisition ◽

High Precision ◽

High Speed ◽

Large Scale ◽

Data Acquisition System ◽

Acquisition System ◽

Large Scale Data ◽

Scale Data

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Variant calling from RNA-seq data of the brain transcriptome of pigs and its application for allele-specific expression and imprinting analysis

Gene ◽

10.1016/j.gene.2017.10.076 ◽

2018 ◽

Vol 641 ◽

pp. 367-375 ◽

Cited By ~ 6

Author(s):

Maria Oczkowicz ◽

Tomasz Szmatoła ◽

Katarzyna Piórkowska ◽

Katarzyna Ropka-Molik

Keyword(s):

Variant Calling ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Brain Transcriptome ◽

Allele Specific ◽

The Brain

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Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression

Bioinformatics ◽

10.1093/bioinformatics/bty078 ◽

2018 ◽

Vol 34 (13) ◽

pp. 2177-2184 ◽

Cited By ~ 33

Author(s):

Narayanan Raghupathy ◽

Kwangbom Choi ◽

Matthew J Vincent ◽

Glen L Beane ◽

Keith S Sheppard ◽

...

Keyword(s):

Hierarchical Analysis ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

Nature Communications ◽

10.1038/s41467-020-18320-z ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Emily Berger ◽

Deniz Yorukoglu ◽

Lillian Zhang ◽

Sarah K. Nyquist ◽

Alex K. Shalek ◽

...

Keyword(s):

Long Range ◽

Genetic Variants ◽

Read Length ◽

Rna Seq ◽

Sequencing Data ◽

Specific Expression ◽

Integrative Framework ◽

Whole Exome ◽

Allele Specific ◽

Diverse Data

Abstract Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10× faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.

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