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www.nbgn21conference.com

Our research centres on the use of genetics/genomics and phenotypic studies to address complex questions in the ecology, epidemiology and evolution of microbes. Our most recent interest focuses upon comparative genome analysis to describe the core and flexible genome of pathogenic bacteria (Campylobacter, Acinetobacter, Escherichia coli, Helicobacter, Staphylococcus and Streptococcus suis) and how this is related to population genetic structuring, the maintenance of species, and the evolution of host/niche adaptation and virulence.

More at https://sheppardlab.com/research/

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.

Address of the bookmark: http://www.ogt.com/assets/0000/3190/NGS_Survey_2013_Infographic_Web.pdf

• The market size of the Indian Bioinformatics Industry , FY’2007-FY’2013
• Market segmentation of India bioinformatics industry by application by sectors, FY’2007-FY’2013
• Market Segmentation of India bioinformatics industry by products and services,FY’2007-FY’2013
• Market Segmentation of India bioinformatics industry by applications of bioinformatics ,FY’2007-FY’2013
• India bioinformatics industry trends and developments
• Government regulations and initiatives of India bioinformatics industry
• Major bioinformatics research institutes in India
• Market Share of leading players in bioinformatics industry in India,FY’2013
• Company profiles of major players in India bioinformatics industry
• Future outlook and projections on the basis of revenue in India bioinformatics market, FY’2014-FY’2018

                                                                                                         (Source: Ken Research)

Address of the bookmark: http://www.kenresearch.com/healthcare/biotechnology/india-bioinformatics-industry-research-report/392-91.html

"Robert Sapolsky is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia

Address of the bookmark: http://www.youtube.com/watch?v=_dRXA1_e30o

http://www.biospectrumindia.com/biospecindia/features/203849/supercomputing-indian-agriculture-fast-track-mode/page/1

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/

Abstract: Strand NGS includes comprehensive workflows for DNA-Seq, RNA-Seq, Small RNA-Seq, ChIP-Seq, MeDIP-Seq, and Methyl-Seq analysis. Each workflow includes a quality assessment and filter section, followed by a workflow-specific analysis section. The pipeline functionality in Strand NGS allows users to execute a sequence of analysis steps with specific parameters - all without any manual intervention. This simplifies the analysis in large scale sequencing projects where every sample needs to be processed identically.

In this webinar we will discuss the pre-packaged pipelines present in Strand NGS. The packaged pipelines have well-chosen default parameters and are suitable for users analyzing data for the first time in the tool. We will also show how advanced users can customize pipelines and share them with other Strand NGS users. Finally, we will show a brief glimpse of an elaborate pipeline that aligns reads, filters poor-quality matches, computes coverage metrics, identifies variants, checks for sample cross-contamination, and emails quality reports - all from within Strand NGS.

Speaker: Dr. Vamsi Veeramachaneni, Vice President - Bioinformatics, Strand Life Sciences

Details: Session 1: 2:30 PM IST, Session 2 : 10:30 PM IST
Register here: http://www.strand-ngs.com/webinar_registration

Address of the bookmark: https://sourceforge.net/projects/genoviz/