Address of the bookmark: http://www.genome.gov/27532724

Why Normalize RNA-Seq Data?

Normalization is a crucial step in RNA-Seq analysis for the following reasons:

• Sequencing depth: Different RNA-Seq experiments produce varying numbers of reads, making direct comparisons between samples misleading.

• Gene length: Longer genes inherently generate more reads, irrespective of their actual expression level.

• Bias reduction: Normalization mitigates technical biases, enabling meaningful biological interpretation.

TPM (Transcripts Per Million)

TPM measures the proportion of reads mapped to a transcript, normalized by transcript length and sequencing depth. It is calculated as:

Key Features:

1. Proportionality: TPM values sum to 1,000,000 across all transcripts in a sample, making it easier to compare between samples.

2. Intuitive interpretation: TPM values directly represent the abundance of transcripts in a sample.

3. Preferred for comparisons: TPM facilitates between-sample comparisons better than FPKM.

FPKM (Fragments Per Kilobase Million)

FPKM normalizes read counts by transcript length and sequencing depth, but without enforcing proportionality like TPM. It is defined as:

Key Features:

1. Historical significance: FPKM was one of the first normalization methods used for RNA-Seq.

2. Single-end vs. paired-end: In paired-end sequencing, FPKM becomes RPKM (Reads Per Kilobase Million).

3. Limited utility: FPKM values are not as robust as TPM for cross-sample comparisons due to lack of proportionality.

CPM (Counts Per Million)

CPM normalizes raw read counts by sequencing depth, without considering gene length. It is expressed as:

Key Features:

1. Simplicity: CPM is straightforward and computationally less intensive.

2. Application: Suitable for non-length-dependent analyses, such as comparing total expression levels or differential expression analysis.

3. Gene length agnostic: CPM does not correct for gene length, making it less ideal for measuring expression levels.

When to Use Each Method

• TPM: Best for comparing expression levels between samples, especially when transcript length and sequencing depth vary.

• FPKM: Useful for historical consistency but generally replaced by TPM.

• CPM: Ideal for differential expression analysis when gene length normalization is unnecessary.

Conclusion

Choosing the right normalization method depends on the specific objectives of your RNA-Seq analysis. TPM’s proportionality and robustness make it the preferred choice for most applications, while CPM serves well for differential expression studies. Although FPKM paved the way for RNA-Seq normalization, it has largely been supplanted by TPM in modern workflows. Understanding these methods and their nuances ensures accurate and meaningful interpretations of RNA-Seq data.

References:

1. Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics.

2. Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology.

3. Law, C. W., et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

miRNA Sequencing data analysis : http://tools.genxpro.net/omiras/

miRNApath( R based tool) :  http://www.bioconductor.org/packages/release/bioc/html/miRNApath.html

miRWalk DB : http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/

TargetScanHuman : http://www.targetscan.org/

RNAhybrid : http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/welcome.html

RNA22 predictor : http://cbcsrv.watson.ibm.com/rna22.html

miRNA predictor : http://www.microrna.org/microrna/home.do

Plant miRNA DB :http://bioinformatics.cau.edu.cn/PMRD/

miRBASE DB: http://www.mirbase.org/

Plant RNA predictor : http://plantgrn.noble.org/psRNATarget/

miRNA Interaction DB : http://starbase.sysu.edu.cn/

Sequencing based miRNA DB : http://mirgator.kobic.re.kr/

predicted A-to-I edited miRNA DB : http://microrna.osumc.edu/mireditar/

Animal, plant and virus miRNA DB : http://lemur.amu.edu.pl/share/php/mirnest/

Atlantic Salmon miRNAs DB : http://www.molgenv.com/ssa_mirnas_db_home.php

miRNA prediction on UTRs : http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html

Idea of analysing miRNA Sequencing data :

http://www.illumina.com/applications/epigenetics/small_rna_analysis.ilmn

More:

http://www.bioconductor.org/help/search/index.html?q=miRNA+target

genomic scale studies
endogenous mechanisms of mutations, germ line and somatic
computational aspects of immunology in cancer
signalling networks
three-dimensional organization of information in the nucleus
gene silencing
metastatic cross-talk
kinase signaling
personalized medicine
detection of biomarkers in cancer
historical DNA variation

From : http://www.ous-research.no/hovig/

Group address:
Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway
Email: ehovig@radium.uio.no

Breitbart lab is located in the College of Marine Science at the University of South Florida. She is chosen as top "10 Brilliant" scientist by Popular Science magazine.
http://www.popsci.com/science/article/2013-09/mya-breitbart

Lab Link:
https://sites.google.com/site/breitbartgenomicslab/
http://www.marine.usf.edu/faculty/mya-breitbart.shtml

https://www.crops.org/publications/tpg/articles/6/2/plantgenome2013.03.0001in

Address of the bookmark: https://www.crops.org/publications/tpg/pdfs/6/2/plantgenome2013.03.0001in

Apply to Genotypic Technology if you are a PhD in Life Sciences/ Molecular Biology/ Biotechnology/ Human Genetics/ Bioinformatics with minimum 4-5 years post doctoral experience as well as publications in peer reviewed journals.

Source: http://www.genotypic.co.in/Careers/2/Current-Openings.aspx

Position Paper Submission Extension: October 9, 2014 
Regular Paper Authors Notification: November 3, 2014 
Position Paper Authors Notification: November 6, 2014 
Regular and Position Paper Camera Ready and Registration: November 17, 2014 

Address of the bookmark: http://www.bioinformatics.biostec.org/

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693