Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （...Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （Huarte, 2015）. Due to recent advances in next-generation sequencing technologies, tens of thousands of lncRNAs have been identified and annotated, a number of them have been proven to be functional in diverse biological processes through various mechanisms.展开更多
RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpre...RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpreting the RNA-seq data.With the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases explosively.The intricate biological process of transcriptome is more complicated and diversified beyond our imagination.Moreover,most of the remaining organisms still have no available reference genome or have only incomplete genome annotations.Therefore,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges.This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.展开更多
Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis p...Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis procedures that can be used in clinical practice.Methods:We studied RNA-seq data from triple-negative breast cancer patients.Specifically,we investigated the subsampling of RNA-seq data.Results:The main results of our investigations are as follows:(1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices;(2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads;and(3) for an abrogated feature selection,higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.Conclusions:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.展开更多
The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pr...The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.展开更多
RNA-Seq technology is becoming widely used in various transcriptomics studies;however,analyzing and interpreting the RNA-Seq data face serious challenges.With the development of high-throughput sequencing technologies...RNA-Seq technology is becoming widely used in various transcriptomics studies;however,analyzing and interpreting the RNA-Seq data face serious challenges.With the development of high-throughput sequencing technologies,the sequencing cost is dropping dramatically with the sequencing output increasing sharply.However,the sequencing reads are still short in length and contain various sequencing errors.Moreover,the intricate transcriptome is always more complicated than we expect.These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies.This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies,including short read mapping,exon-exon splice junction detection,gene or isoform expression quantification,differential expression analysis and transcriptome reconstruction.展开更多
Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is th...Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is the cell type identification.Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters.Among all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality reduction.In this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions.The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension.Motivated by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not.Finally,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.We perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.展开更多
High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized sRNAs such as microRNAs （miRNAs...High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized sRNAs such as microRNAs （miRNAs）, piwi-interacting RNAs （piRNAs）, and small nucleolar RNA （snoRNAs）, recent emerging studies have spotlighted on tRNA-derived sRNAs （tsRNAs） and rRNA-derived sRNAs （rsRNAs） as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipeline _optimized for rRNA- and tRNA-derived s_RNAs （SPORTS 1 .0）. SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users＇ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an opensource software and can be publically accessed at https：//github.com/junchaoshi/sports1.0.展开更多
基金supported by grants from the National Key R&D Program of China (2017YFA0106700)National Natural Science Foundation of China (81772614, U1611261, 81772586 and 81602461)+3 种基金China Postdoctoral Science Foundation (2017M610573)Young Elite Scientists Sponsorship Program by CAST (2017QNRC001)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017, to J. Zheng)Fundamental Research Funds for the Central Universities (SYSU:17ykzd32)
文摘Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （Huarte, 2015）. Due to recent advances in next-generation sequencing technologies, tens of thousands of lncRNAs have been identified and annotated, a number of them have been proven to be functional in diverse biological processes through various mechanisms.
文摘RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpreting the RNA-seq data.With the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases explosively.The intricate biological process of transcriptome is more complicated and diversified beyond our imagination.Moreover,most of the remaining organisms still have no available reference genome or have only incomplete genome annotations.Therefore,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges.This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.
基金supported In part by the Arkansas Biosciences Institute under Grant(No.UL1TR000039)the IDeANetworks of Biomedical Research Excellence(INBRE) Grant(No.P20RR16460)
文摘Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis procedures that can be used in clinical practice.Methods:We studied RNA-seq data from triple-negative breast cancer patients.Specifically,we investigated the subsampling of RNA-seq data.Results:The main results of our investigations are as follows:(1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices;(2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads;and(3) for an abrogated feature selection,higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.Conclusions:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.
文摘The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.
基金supported by the National Basic Research Program of China (Grant Nos. 2010CB945401, 2007CB108800)National Natural Science Foundation of China (Grant Nos. 30870575,31071162,31000590)Science and Technology Commission of Shanghai Municipality (Grant No. 11DZ2260300)
文摘RNA-Seq technology is becoming widely used in various transcriptomics studies;however,analyzing and interpreting the RNA-Seq data face serious challenges.With the development of high-throughput sequencing technologies,the sequencing cost is dropping dramatically with the sequencing output increasing sharply.However,the sequencing reads are still short in length and contain various sequencing errors.Moreover,the intricate transcriptome is always more complicated than we expect.These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies.This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies,including short read mapping,exon-exon splice junction detection,gene or isoform expression quantification,differential expression analysis and transcriptome reconstruction.
基金supported in part by the National Natural Science Foundation of China(No.U19A2064)the Hunan Provincial Science and Technology Program(No.2019CB1007)+1 种基金the Fundamental Research Funds for the Central Universities,CSU(No.2282019SYLB004)the Fundamental Research Funds for the Central Universities of Central South University(No.2020zzts593)。
文摘Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is the cell type identification.Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters.Among all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality reduction.In this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions.The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension.Motivated by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not.Finally,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.We perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.
基金supported by Start-up funds for Zhou and Chen labs from Reno School of Medicine,University of Nevada and from the National Institutes of Health,United States(Grant Nos.R01DK091336 and P01DK041315 to KMSGrant Nos.R01HD092431 and P30GM110767-03 to QC)
文摘High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized sRNAs such as microRNAs （miRNAs）, piwi-interacting RNAs （piRNAs）, and small nucleolar RNA （snoRNAs）, recent emerging studies have spotlighted on tRNA-derived sRNAs （tsRNAs） and rRNA-derived sRNAs （rsRNAs） as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipeline _optimized for rRNA- and tRNA-derived s_RNAs （SPORTS 1 .0）. SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users＇ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an opensource software and can be publically accessed at https：//github.com/junchaoshi/sports1.0.
