About
This website is free and open to all users and there is no login requirement.
NormSeq is is a freely accessible web-server tool for data normalization, batch-effect correction and differential expression analysis obtained from next generation sequencing platforms such as Illumina or SOLiD. The platform offers multiple visualization options and downloadable output plots and tables.
If you use the NormSeq webserver, please cite our publication:
Chantal Scheepbouwer et al., NORMSEQ: a tool for evaluation, selection and visualization of RNA-Seq normalization methods, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W372–W378, https://doi.org/10.1093/nar/gkad429
Developed by the Exosomes Research Group at Cancer Center Amsterdam
How to cite
If you use the NormSeq webserver, please cite our publication:
Chantal Scheepbouwer et al., NORMSEQ: a tool for evaluation, selection and visualization of RNA-Seq normalization methods, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W372–W378, https://doi.org/10.1093/nar/gkad429
Example Data
The test dataset for NormSeq corresponds to the mouse tissue samples from the GEO dataset GSE141436.
It consists of the tRNA-sequencing raw counts matrix, at tRNA isodecoder level, of 21 different samples from seven mouse tissues, derived from the central nervous system (CNS), liver, tibialis and heart mouse tissues.
Please, if you use the test dataset, cite the following publication:
Pinkard O, McFarland S, Sweet T, Coller J. Quantitative tRNA-sequencing uncovers metazoan tissue-specific tRNA regulation. Nat Commun. 2020 Aug 14;11(1):4104. doi: 10.1038/s41467-020-17879-x. PMID: 32796835; PMCID: PMC7428014.
Example Data
The test dataset for NormSeq corresponds to the mouse tissue samples from the GEO dataset SRP326090.
It consists of the miRNA-sequencing raw counts matrix, of 18 different samples, from 18 different persons belonging to two groups: a. Cancer patients with active Hodking Lymphoma; b. Healthy people.
Please, if you use the test dataset, cite the following publication:
Drees EEE, Roemer MGM, Groenewegen NJ, Perez-Boza J, van Eijndhoven MAJ, Prins LI, Verkuijlen SAWM, Tran XM, Driessen J, Zwezerijnen GJC, Stathi P, Mol K, Karregat JJJP, Kalantidou A, Vallés-Martí A, Molenaar TJ, Aparicio-Puerta E, van Dijk E, Ylstra B, Groothuis-Oudshoorn CGM, Hackenberg M, de Jong D, Zijlstra JM, Pegtel DM. Extracellular vesicle miRNA predict FDG-PET status in patients with classical Hodgkin Lymphoma. J Extracell Vesicles. 2021 Jul;10(9):e12121. doi: 10.1002/jev2.12121. Epub 2021 Jul 15. PMID: 34295456; PMCID: PMC8282992.
Input File
The input matrix can be directly uploaded or provided as an URL link in .txt/.csv/.tsv/.xls formats:
- Upload a file (typically .txt/.csv/.tsv/.xls)
- Provide a link/URL with your data (it will be downloaded and then analyzed)
- Provide an ID from a previous normSeq job to reuse an uploaded file (e.g. N2LM1BEIU8PW6B8)
Methods
The RNA class of interest will be analyzed and compared by the number of normalization methods of choice. Some normalization methods are specific to the properties of certain RNA classes. The user can also decide to analyze data without data normalization or by selecting all normalization methods if the user is uncertain by the method of choice:
| Experimental Bias | Normalization Method | Description | Reference |
|---|---|---|---|
| Library Size | Counts per Million (CPM) | Counts per million without consideration of transcript length Calculation: [number of mapped reads]/[total read count/106] | Dillies, Brief Bioinfor, 2013 |
| Total Count (TC) | Dillies, Brief Bioinfor, 2013 | ||
| Total Count (TC) | Dillies, Brief Bioinfor, 2013 | ||
| Upper Quartile (UQ) | Bullard, Bioinfor, 2010 | ||
| Median | Dillies, Brief Bioinfor, 2013 | ||
| Trimmed mean of M values (TMM) | Robinson, Genome Biol, 2010 | ||
| Quantile | Bolstad et al, Bioinfor, 2003 | ||
| Relative log expression (RLE) | Anders, Genome Biol, 2010 | ||
| Fragment per Kilobase million (FPKM) | Fragments per kilobase of transcript per million mapped reads
Calculation: [# fragments]/[transcript length/1000]/(total read count)/10 |
Mortazavi, Nat Meth, 2008 |
Choose annotation
Annotation
The annotation file can be directly uploaded or provided as an URL link in .txt/.csv/.tsv/.xls formats:
- Upload a file containing two columns and a number of rows that matches sample number (typically .txt/.csv/.tsv/.xls)
- Provide a link/URL with your data (it will be downloaded and then analyzed)
- Annotate column 1 as sample, annotate column to as group
- List sample names under “sample” and assign the different groups under “groups”
Parameters
Parameters for analysis can be set by providing the minimum read counts for inclusion.