To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

GA4GH core funders and sponsors enable our work and allow us to convene the international genomic data sharing community.

https://www.ga4gh.org/

Address of the bookmark: https://www.ga4gh.org/

According to a preliminary report posted on December 19 by the COG-UK Consortium scientists, as of December 15, 1,623 variant genomes have been sequenced. In a December 21 tweet, COG-UK Consortium said that it added 2,963 more genome sequences of SARS-CoV-2, of which 942 (32%) belong to the new variant. The Consortium intends to sequence 20,000 more SARS-CoV-2 genomes in the next two weeks to further ascertain the spread of the variant.

There is no clear proof, at least not yet, that it does cause severe pandemic. But there is a justification for seriously taking the possibility. Another coronavirus lineage in South Africa has acquired one specific mutation that is also present in B.1.1.7. This variant is increasingly spreading across South Africa's coastal regions. And doctors have observed in preliminary research that individuals infected with this variant bear a higher viral load-a higher concentration of the virus in their upper respiratory tract. In many viral diseases, this is associated with more severe symptoms.

About National Institute Of Plant Genome Research (NIPGR) http://www.nipgr.res.in/

The National Institute of Plant Genome Research is an autonomous institution supported by the Department of Biotechnology, Government of India. It is committed to make the institute a premier Institution for plant genomic research in the country. It was established to contribute in the achievement of such hopes as a part of national effort for meeting the challenges in the midst of fast pace of international genomic research and grasping of opportunities on long-term basis.

About the Internship:

The selected intern(s) will work in the area of in Bioinformatics under the BTISNET program of DBT in the Distributed Information Sub center (DISC) facility at NIPGR, New Delhi, under the supervision of Dr. Gitanjali Yadav, Scientist, NIPGR.

Who can apply:

Students currently pursuing the final year of Masters Degree (or equivalent) in Bioinformatics/Biotechnology with strong interest in Computational Biology and First class/division throughout academic career may apply.

https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://github.com/lindenb/jvarkit

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.

1. Research Associate
Emoluments*: 14880/- + HRA
Qualification needed: Ph.D/M.Sc in Bioinformatics or Ph.D/M.Sc in Agriculture or Biotechnology with advanced Diploma in Bioinformatics
Desirable: 2 year experience in Bioinformatics.

2 Senior Research Fellow
Emoluments*: 10230/-
Qualification needed: M.Sc/ M.tech in Bioinformatics or M.Sc in Agriculture/ Biotechnology with Diploma in Bioinformatics.
Desirable: One year experience in Bioinformatics

3 Junior Research Fellow
Emoluments*: 9300/-
Qualification needed: M.Sc/ M.tech in Bioinformatics or M.Sc in Agriculture/Biotechnology/Plant Sciences with Diploma in Bioinformatics.

4 .Trainee/Studentship Bioinformatics
Emoluments*: 5000/-
Qualification needed: M.Sc in Bioinformatics with good knowledge of Bioinformatics softwares and tools.

5 Trainee/ Studentship Biotechnology
Emoluments*: 5000/-
Qualification needed: M.Sc in Biotechnology, with working knowledge in tissue culture, molecular markers and cloning of genes.

Candidates with the required qualifications and experience may give an application in the prescribed format with attested copies of certificates to prove eligibility on or before 30th November 2014. The applications are to be addressed to The Associate Dean, College of Horticulture and send to "Professor & Coordinator, Bioinformatics Centre (DIC), IT-BT Complex, Kerala Agricultural University, Vellanikkara, Thrissur, Kerala 680 656”. The envelope may be superscribed “Application for the post at Bioinformatics Centre”.

*Emoluments are likely to be revised in 2014-2015

More at http://www.kaubic.in/downloads/Notification_bic.pdf
http://www.kaubic.in/downloads/Application%20form.pdf