scholarly journals Uncovering the Complexity of Transcriptomes with RNA-Seq

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Valerio Costa ◽  
Claudia Angelini ◽  
Italia De Feis ◽  
Alfredo Ciccodicola
Keyword(s):  
Massively Parallel Sequencing ◽  
Point Of View ◽  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific ◽  
Data Files ◽  
Sequencing Platforms ◽  
New Applications ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.

scholarly journals Recent Applications of RNA Sequencing in Food and Agriculture

2021 ◽  
Author(s):  
Venkateswara R. Sripathi ◽  
Varsha C. Anche ◽  
Zachary B. Gossett ◽  
Lloyd T. Walker
Keyword(s):  
Rna Sequencing ◽  
Alternative Polyadenylation ◽  
Cost Effective ◽  
Circular Rnas ◽  
Rna Seq ◽  
Complete Collection ◽  
Specific Expression ◽  
Food And Agriculture ◽  
Allele Specific ◽  
Next Generation Sequencing Ngs

RNA sequencing (RNA-Seq) is the leading, routine, high-throughput, and cost-effective next-generation sequencing (NGS) approach for mapping and quantifying transcriptomes, and determining the transcriptional structure. The transcriptome is a complete collection of transcripts found in a cell or tissue or organism at a given time point or specific developmental or environmental or physiological condition. The emergence and evolution of RNA-Seq chemistries have changed the landscape and the pace of transcriptome research in life sciences over a decade. This chapter introduces RNA-Seq and surveys its recent food and agriculture applications, ranging from differential gene expression, variants calling and detection, allele-specific expression, alternative splicing, alternative polyadenylation site usage, microRNA profiling, circular RNAs, single-cell RNA-Seq, metatranscriptomics, and systems biology. A few popular RNA-Seq databases and analysis tools are also presented for each application. We began to witness the broader impacts of RNA-Seq in addressing complex biological questions in food and agriculture.

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

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.

scholarly journals Replicate sequencing libraries are important for quantification of allelic imbalance

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.

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

Genetics ◽  
2013 ◽  
Vol 195 (3) ◽  
pp. 1157-1166 ◽  
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

scholarly journals Human Mitochondrial Control Region and mtGenome: Design and Forensic Validation of NGS Multiplexes, Sequencing and Analytical Software

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 599
Author(s):  
Cydne L. Holt ◽  
Kathryn M. Stephens ◽  
Paulina Walichiewicz ◽  
Keenan D. Fleming ◽  
Elmira Forouzmand ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Control Region ◽  
Massively Parallel Sequencing ◽  
Performance Testing ◽  
Mitochondrial Control Region ◽  
Read Depth ◽  
Situational Variables ◽  
Data Files ◽  
Access To Data ◽  
Next Generation Sequencing Ngs

Forensic mitochondrial DNA (mtDNA) analysis conducted using next-generation sequencing (NGS), also known as massively parallel sequencing (MPS), as compared to Sanger-type sequencing brings modern advantages, such as deep coverage per base (herein referred to as read depth per base pair (bp)), simultaneous sequencing of multiple samples (libraries) and increased operational efficiencies. This report describes the design and developmental validation, according to forensic quality assurance standards, of end-to-end workflows for two multiplexes, comprised of ForenSeq mtDNA control region and mtDNA whole-genome kits the MiSeq FGxTM instrument and ForenSeq universal analysis software (UAS) 2.0/2.1. Polymerase chain reaction (PCR) enrichment and a tiled amplicon approach target small, overlapping amplicons (60–150 bp and 60–209 bp for the control region and mtGenome, respectively). The system provides convenient access to data files that can be used outside of the UAS if desired. Studies assessed a range of environmental and situational variables, including but not limited to buccal samples, rootless hairs, dental and skeletal remains, concordance of control region typing between the two multiplexes and as compared to orthogonal data, assorted sensitivity studies, two-person DNA mixtures and PCR-based performance testing. Limitations of the system and implementation considerations are discussed. Data indicated that the two mtDNA multiplexes, MiSeq FGx and ForenSeq software, meet or exceed forensic DNA quality assurance (QA) guidelines with robust, reproducible performance on samples of various quantities and qualities.

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

Gene ◽  
2018 ◽  
Vol 641 ◽  
pp. 367-375 ◽  
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

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

2018 ◽  
Vol 34 (13) ◽  
pp. 2177-2184 ◽  
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

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

2020 ◽  
Vol 11 (1) ◽  
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.

RNA-Seq Analysis of Allele-Specific Expression in the Mouse Cochlea

2014 ◽  
Vol 151 (1_suppl) ◽  
pp. P226-P226
Author(s):  
Maria K. L. Ho ◽  
Yehudit Hasin ◽  
Aldons J. Lusis ◽  
Rick A. Friedman
Keyword(s):  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific
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