eRNA: a graphic user interface-based tool optimized for large data analysis from high-throughput RNA sequencing

BMC Genomics

Liang Wang¹  Stephen N Thibodeau³  Lisa Boardman²  Manish Kohli²  Meijun Du¹  Rachel L Dittmar¹  Xiaoyi Huang¹  Tiezheng Yuan¹ 

[1] Department of Pathology and MCW Cancer Center, Medical College of Wisconsin, Milwaukee WI 53226, USA;Department of Oncology, Mayo Clinic, Rochester MN 55905, USA;Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester MN 55905, USA

关键词: Parallel processing;    Graphic user interface;    Bioinformatics tool;    RNA sequencing;   

Others  :  1217821
DOI  :  10.1186/1471-2164-15-176

 received in 2013-10-30, accepted in 2014-02-26,  发布年份 2014

【 摘 要 】

Background

RNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers.

Results

We developed a standalone tool with graphic user interface (GUI)-based analytic modules, known as eRNA. The capacity of performing parallel processing and sample management facilitates large data analyses by maximizing hardware usage and freeing users from tediously handling sequencing data. The module miRNA identification” includes GUIs for raw data reading, adapter removal, sequence alignment, and read counting. The module “mRNA identification” includes GUIs for reference sequences, genome mapping, transcript assembling, and differential expression. The module “Target screening” provides expression profiling analyses and graphic visualization. The module “Self-testing” offers the directory setups, sample management, and a check for third-party package dependency. Integration of other GUIs including Bowtie, miRDeep2, and miRspring extend the program’s functionality.

Conclusions

eRNA focuses on the common tools required for the mapping and quantification analysis of miRNA-seq and mRNA-seq data. The software package provides an additional choice for scientists who require a user-friendly computing environment and high-throughput capacity for large data analysis. eRNA is available for free download at https://sourceforge.net/projects/erna/?source=directory webcite.

【 授权许可】

   
2014 Yuan et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

【 参考文献 】

• [1]Huang P-J, Liu Y-C, Lee CC, Lin W-C, Gan RR, Lyu PC, Tang P: DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res 2010, 38:W385-W391.
• [2]Buermans HP, Ariyurek Y, van Ommen G, den Dunnen JT, ’t Hoen PA: New methods for next generation sequencing based microRNA expression profiling. BMC Genomics 2010, 11:716. BioMed Central Full Text
• [3]Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM: miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011, 39:W132-W138.
• [4]Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2011, 40(1):37-52.
• [5]Mathelier A, Carbone A: MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 2010, 26(18):2226-2234.
• [6]Wang W-C, Lin F-M, Chang W-C, Lin K-Y, Huang H-D, Lin N-S: miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinforma 2009, 10:328. BioMed Central Full Text
• [7]Ronen R, Gan I, Modai S, Sukacheov A, Dror G, Halperin E, Shomron N: miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 2010, 26(20):2615-2656.
• [8]Humphreys DT, Suter CM: miRspring: a compact standalone research tool for analyzing miRNA-seq data. Nucleic Acids Res 2013, 41(15):e147.
• [9]Zhu E, Zhao F, Xu G, Hou H, Zhou L, Li X, Sun Z, Wu J: mirTools: microRNA profiling and discovery based on high-throughput sequencing. Nucleic Acids Res 2010, 38:W392-W397.
• [10]Pantano L, Estivill X, Marti E: SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res 2009, 38(5):e34.
• [11]Li Y, Zhang Z, Liu F, Vongsangnak W, Jing Q, Shen B: Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic Acids Res 2012, 40(10):4298-4305.
• [12]Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 2012, 7(3):562-578.
• [13]Goncalves A, Tikhonov A, Brazma A, Kapushesky M: A pipeline for RNA-seq data processing and quality assessment. Bioinformatics 2011, 27(6):867-869.
• [14]Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, Koski M, Käki J, Korpelainen EI: Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics 2011, 12:507. BioMed Central Full Text
• [15]Friedman BA, Maniatis T: ExpressionPlot: a web-based framework for analysis of RNA-Seq and microarray gene expression data. Genome Biol 2011, 12(7):R69. BioMed Central Full Text
• [16]Cumbie JS, Kimbrel JA, Di Y, Schafer DW, Wilhelm LJ, Fox SE, Sullivan CM, Curzon AD, Carrington JC, Mockler TC, Chang JH: GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences. PLoS One 2011, 6(10):e25279.
• [17]Halbritter F, Vaidya HJ, Tomlinson SR: GeneProf: analysis of high-throughput sequencing experiments. Nat Methods 2012, 9(1):7-8.
• [18]Givan SA, Bottoms CA, Spollen WG: Computational analysis of RNA-seq. Methods Mol Biol 2012, 883:201-219.
• [19]Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, Usadel B: RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res 2012, 40:W622-W627.
• [20]Soderlund C, Nelson W, Willer M, Gang DR: TCW: transcriptome computational workbench. PLoS One 2013, 8(7):e69401.
• [21]Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM: The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 2009, 38(6):1767-1771.
• [22]Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009, 10(3):R25. BioMed Central Full Text
• [23]Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL: TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 2013, 14(4):R36. BioMed Central Full Text
• [24]Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L: Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 2013, 31(1):46-53.
• [25]Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol 2010, 11(10):R106. BioMed Central Full Text
• [26]Strobl C, Malley J, Tutz G: An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 2009, 14(4):323-348.
• [27]Huang X-Y, Yuan T-Z, Tschannen M, Sun Z, Jacob H, Du M-J, Liang M-H, Dittmar RL, Liu Y, Kohli M, Thibodeau SN, Boardman L: Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics 2013, 14:319. BioMed Central Full Text
• [28]Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2010, 39:D152-D157.
• [29]Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods 2012, 9(4):357-359.
• [30]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The sequence alignment/map format and SAMtools. Bioinformatics 2009, 25(16):2078-2079.
• [31]Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Rätsch G, Goldman N, Hubbard TJ, Harrow J, Guigó R, Bertone P, The RGASP Consortium: Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods 2013, 10(12):1185-1191.
• [32]Steijger T, Abril JF, Engström PG, Kokocinski F, Hubbard TJ, Guigó R, Harrow J, Bertone P, The RGASP Consortium: Assessment of transcript reconstruction methods for RNA-seq. Nat Methods 2013, 10(12):1177-1184.
• [33]Rapaport F, Khanin R, Liang Y-P, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 2013, 14(9):R95. BioMed Central Full Text
• [34]Khatri P, Sirota M, Butte AJ: Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 2012, 8(2):e1002375.