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What is aTAP?

aTAP is a comprehensive bioinformatics web-based platform for analyze RNA-Seq data. aTAP provides one-stop service for performing de novo assembly the RNA-seq data, annotating, and visualizing of gene expression from the analysis.

Easy, Quick and Effortless Platform



aTAP was design to be easy to use, quick to understand, and effortless to learn. You can use aTAP without requiring any computer programing or bioinformatics skills. aTAP can provide you the answer and result in few steps.

Bioinformatics Pipeline



The de novo assembly analysis is based on Trinity protocol by performing de novo assembly of transcripts, quantifying the transcripts and then visualizing the result.

The result visualizations was provided for user in several plots. These plots and visualization are created using D3 JavaScript and DataTable library. Therefore, user can directly interact and play with the data and extract them into the figures.



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aTAP services

aTAP provides these bioinformatics services:


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De novo Transcriptome
Assembly

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Evaluating Assembly
Results

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Quantification

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Differential Gene
Expression

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Automated System

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Interactive Visualization

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One-stop Service

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User-friendly / Easy to use

Analyzing Differential Gene Expression with aTAP

Interactive, Automatic and One-stop Service Platform.


Learn How to Start

References

Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M., et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 2013, 8, 1494-1512, doi:10.1038/nprot.2013.084.

Ewels, P.; Magnusson, M.; Lundin, S.; Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047-3048, doi:10.1093/bioinformatics/btw354.

Li, B.; Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 2011, 12, 323, doi:10.1186/1471-2105-12-323.

Bray, N.L.; Pimentel, H.; Melsted, P.; Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016, 34, 525-527, doi:10.1038/nbt.3519.

Patro, R.; Duggal, G.; Love, M.I.; Irizarry, R.A.; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017, 14, 417-419, doi:10.1038/nmeth.4197.

Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15, 550, doi:10.1186/s13059-014-0550-8.

Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139-140, doi:10.1093/bioinformatics/btp616.

Yoon, S.; Nam, D. Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data. Bmc Genomics 2017, 18, doi: 10.1186/s12864-017-3809-0.

Ewels, P.; Magnusson, M.; Lundin, S.; Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047-3048, doi:10.1093/bioinformatics/btw354.

Automated Transcriptome Analysis Platform on Web Application

aTAP is developed and undermaintained by Molecular Evolution and Computational Biology Research Unit,
Faculty of Science, Prince of Songkla University, Thailand.


A Hub of Next-generation Scientist