Address of the bookmark: http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html

http://gmod.org/wiki/Diya

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

AI and science working together to manage the root causes of your aging 

Personalized plan built from your biomarkers and devices

Biologically-active treatments (cellular health). No drugs.

Source is Linkedln link :

https://www.linkedin.com/company/5143768/

Address of the bookmark: https://suisselifescience.com/

Our capacity building activities covers both general and specialized topics in translational genetics, and is designed to better acquaint scientists and clinicians with the tools and technologies of genetics and genomics.

OUR RESEARCH ACTIVITIES INCLUDE:

Address of the bookmark: http://www.nabda.gov.ng/departments/genetics-genomics-and-bioinformatics

Designation: Senior Scientist bioinformatics
Experience: 8-9 years of experience in bioinformatics analysis of various NGS applications such as WGS, RNASeq, Metagenome, small RNA.

Location: Bangalore

Roles & Responsibilities:
-Develop NGS pipeline for analysis and interpretation of NGS data
-Organizing and managing large scale genomic data
-Should have experience in NGS data analysis, such as WGS, RNASeq, Small RNA, Metagenome (16S, ITS, Whole metagenome)etc.
-Should also have good programming skills in perl or python, PHP,J Query, MySql.
-Manage project timelines and deliverables.
-Implement and execute data processing workflows and automate the pipelines.

If you are interested, please send your profile to me at arpitaghosh@eurofins.com with “Senior Scientist bioinformatics for Genomics” as the subject.

#NBGN21

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.

http://www.i-programmer.info/news/181-algorithms/4537-a-new-dna-sequence-search-compressive-genomics.html

http://en.wikipedia.org/wiki/Compression_of_Genomic_Re-Sequencing_Data

http://www.nature.com/nbt/journal/v30/n7/full/nbt.2241.html

http://bioinformatics.oxfordjournals.org/content/29/13/i283.full

http://groups.csail.mit.edu/cb/cast/

Address of the bookmark: http://gtpb.igc.gulbenkian.pt/bicourses/index.html