• Title, Summary, Keyword: RNA-Seq

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COEX-Seq: Convert a Variety of Measurements of Gene Expression in RNA-Seq

  • Kim, Sang Cheol;Yu, Donghyeon;Cho, Seong Beom
    • Genomics & Informatics
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    • v.16 no.4
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    • pp.36.1-36.3
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    • 2018
  • Next generation sequencing (NGS), a high-throughput DNA sequencing technology, is widely used for molecular biological studies. In NGS, RNA-sequencing (RNA-Seq), which is a short-read massively parallel sequencing, is a major quantitative transcriptome tool for different transcriptome studies. To utilize the RNA-Seq data, various quantification and analysis methods have been developed to solve specific research goals, including identification of differentially expressed genes and detection of novel transcripts. Because of the accumulation of RNA-Seq data in the public databases, there is a demand for integrative analysis. However, the available RNA-Seq data are stored in different formats such as read count, transcripts per million, and fragments per kilobase million. This hinders the integrative analysis of the RNA-Seq data. To solve this problem, we have developed a web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA-Seq. It provides a workflow that includes loading data set, selecting measurement formats of gene expression, and identifying gene names. COEX-seq is freely available for academic purposes and can be run on Windows, Mac OS, and Linux operating systems. Source code, sample data sets, and supplementary documentation are available as well.

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

  • Oh, Sunghee
    • Communications for Statistical Applications and Methods
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    • v.22 no.2
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    • pp.181-199
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    • 2015
  • In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of high-throughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.

Recent Trends in RNA-Seq Alignment Algorithms (RNA-Seq 정렬 알고리즘의 동향)

  • Yu, Seunghak;Choe, Min-Seok;Yoon, Sungroh
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.669-671
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    • 2014
  • High Throughput Sequencing (HTS) 기술의 발달로 인해 시퀀싱 비용이 감소함에 따라 다양한 분야에서 이를 활용한 융합 연구가 활발하게 진행되고 있다. HTS 기술에서 가장 중요한 부분은 수백만개의 short read 들을 표준유전체 (reference genome)에 정렬시키는 것인데 RNA 시퀀싱 (RNA-Seq) 의 경우 RNA splicing 으로 인해 일반적인 aligner 로 처리가 불가능하다. 복잡한 RNA-Seq 정렬 문제를 해결하기 위해 그동안 다양한 알고리즘들이 제안되어 왔다. 본 논문에서는 RNA-seq 정렬분야에서 잘 알려진 알고리즘들과 최신 알고리즘들을 살펴봄으로써 RNA-seq 정렬 알고리즘의 동향을 살펴보고자 한다.

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Analysis of Whole Transcriptome Sequencing Data: Workflow and Software

  • Yang, In Seok;Kim, Sangwoo
    • Genomics & Informatics
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    • v.13 no.4
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    • pp.119-125
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    • 2015
  • RNA is a polymeric molecule implicated in various biological processes, such as the coding, decoding, regulation, and expression of genes. Numerous studies have examined RNA features using whole transcriptome sequencing (RNA-seq) approaches. RNA-seq is a powerful technique for characterizing and quantifying the transcriptome and accelerates the development of bioinformatics software. In this review, we introduce routine RNA-seq workflow together with related software, focusing particularly on transcriptome reconstruction and expression quantification.

TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data

  • Lim, Jae Hyun;Lee, Soo Youn;Kim, Ju Han
    • Genomics & Informatics
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    • v.15 no.1
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    • pp.51-53
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    • 2017
  • High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.

RNA-seq Analysis Pipeline for Differential Gene Expression (유전자 발현량 비교를 위한 RNA-seq 분석 파이프라인 설계)

  • Jung, Minah;Kim, Dae-soo
    • Proceedings of the Korea Contents Association Conference
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    • pp.319-320
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    • 2018
  • 여러 단계를 걸쳐 이루어지는 RNA-seq 분석 과정을 한 번에 처리할 수 있는 shell script 파이프라인을 구축하였다. 연구자들로 하여금 trimming, quality control, mapping, assembly, quantification 등 개별 과정을 거치지 않고, 한 줄의 커맨드 라인(command line) 만으로 유전자 발현량과 상대적 발현량 차이를 확인할 수 있는 fold change(FC) 값까지 얻을 수 있도록 하였다.

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Identification of Alternative Splicing and Fusion Transcripts in Non-Small Cell Lung Cancer by RNA Sequencing

  • Hong, Yoonki;Kim, Woo Jin;Bang, Chi Young;Lee, Jae Cheol;Oh, Yeon-Mok
    • Tuberculosis and Respiratory Diseases
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    • v.79 no.2
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    • pp.85-90
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    • 2016
  • Background: Lung cancer is the most common cause of cancer related death. Alterations in gene sequence, structure, and expression have an important role in the pathogenesis of lung cancer. Fusion genes and alternative splicing of cancer-related genes have the potential to be oncogenic. In the current study, we performed RNA-sequencing (RNA-seq) to investigate potential fusion genes and alternative splicing in non-small cell lung cancer. Methods: RNA was isolated from lung tissues obtained from 86 subjects with lung cancer. The RNA samples from lung cancer and normal tissues were processed with RNA-seq using the HiSeq 2000 system. Fusion genes were evaluated using Defuse and ChimeraScan. Candidate fusion transcripts were validated by Sanger sequencing. Alternative splicing was analyzed using multivariate analysis of transcript sequencing and validated using quantitative real time polymerase chain reaction. Results: RNA-seq data identified oncogenic fusion genes EML4-ALK and SLC34A2-ROS1 in three of 86 normal-cancer paired samples. Nine distinct fusion transcripts were selected using DeFuse and ChimeraScan; of which, four fusion transcripts were validated by Sanger sequencing. In 33 squamous cell carcinoma, 29 tumor specific skipped exon events and six mutually exclusive exon events were identified. ITGB4 and PYCR1 were top genes that showed significant tumor specific splice variants. Conclusion: In conclusion, RNA-seq data identified novel potential fusion transcripts and splice variants. Further evaluation of their functional significance in the pathogenesis of lung cancer is required.

RNA-seq Profiles of Immune Related Genes in the Spleen of Necrotic Enteritis-afflicted Chicken Lines

  • Truong, Anh Duc;Hong, Yeong Ho;Lillehoj, Hyun S.
    • Asian-Australasian Journal of Animal Sciences
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    • v.28 no.10
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    • pp.1496-1511
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    • 2015
  • The study aimed to compare the necrotic enteritis (NE)-induced transcriptome differences between the spleens of Marek's disease resistant chicken line 6.3 and susceptible line 7.2 co-infected with Eimeria maxima/Clostridium perfringens using RNA-Seq. Total RNA from the spleens of two chicken lines were used to make libraries, generating 42,736,296 and 42,617,720 usable reads, which were assembled into groups of 29,897 and 29,833 mRNA genes, respectively. The transcriptome changes were investigated using the differentially expressed genes (DEGs) package, which indicated 3,255, 2,468 and 2,234 DEGs of line 6.3, line 7.2, and comparison between two lines, respectively (fold change ${\geq}2$, p<0.01). The transcription levels of 14 genes identified were further examined using qRT-PCR. The results of qRT-PCR were consistent with the RNA-seq data. All of the DEGs were analysed using gene ontology terms, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the DEGs in each term were found to be more highly expressed in line 6.3 than in line 7.2. RNA-seq analysis indicated 139 immune related genes, 44 CD molecular genes and 150 cytokines genes which were differentially expressed among chicken lines 6.3 and 7.2 (fold change ${\geq}2$, p<0.01). Novel mRNA analysis indicated 15,518 novel genes, for which the expression was shown to be higher in line 6.3 than in line 7.2 including some immune-related targets. These findings will help to understand host-pathogen interaction in the spleen and elucidate the mechanism of host genetic control of NE, and provide basis for future studies that can lead to the development of marker-based selection of highly disease-resistant chickens.

Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

  • Choi, Yoon Ha;Kim, Jong Kyoung
    • Molecules and Cells
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    • v.42 no.3
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    • pp.189-199
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    • 2019
  • Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

Identification of ERBB pathway-activated cells in triple-negative breast cancer

  • Cho, Soo Young
    • Genomics & Informatics
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    • v.17 no.1
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    • pp.3.1-3.4
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    • 2019
  • Intratumor heterogeneity within a single tumor mass is one of the hallmarks of malignancy and has been reported in various tumor types. The molecular characterization of intratumor heterogeneity in breast cancer is a significant challenge for effective treatment. Using single-cell RNA sequencing (RNA-seq) data from a public resource, an ERBB pathway activated triple-negative cell population was identified. The differential expression of three subtyping marker genes (ERBB2, ESR1, and PGR) was not changed in the bulk RNA-seq data, but the single-cell transcriptomes showed intratumor heterogeneity. This result shows that ERBB signaling is activated using an indirect route and that the molecular subtype is changed on a single-cell level. Our data propose a different view on breast cancer subtypes, clarifying much confusion in this field and contributing to precision medicine.