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.

• A simple intro to statistical distributions
• hypothesis testing
• linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

• Understanding basic intuition behind machine learning approaches.
• Using unsupervised learning to cluster and visualise data points
• Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

• Understanding and using supervised learning methods for predictive purposes
• How to measure prediction performance
• Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

ADVERTISEMENT NO. - URDIP/ 5/2014

Learning opportunity for young Science and Engineering professionals to make a career in Information Science Industry CSIR has set up a Unit for Research and Development of Information Products (CSIR-URDIP) at Pune to work in the area of Scientific Informatics (ChemBioinformatics/Patent Informatics/Phytoinformatics/Toxinformatics) and related
software development projects.

Applications are invited from CSIR - UGC NET Qualified Candidates for consideration as Project Fellow (PF) and/or Senior Project Fellow (SPF) based on the experience to work on existing and new projects at CSIRURDIP.

Project Fellow

Remuneration - (Rs. 16,000.00 + 20% HRA)

M. Sc. In Biochemistry/Microbiology/Bioinformatics [Post-code A02] only with minimum of 55% marks

Senior Project Fellow

Remuneration - (Rs. 18,000.00 + 20% HRA)

M. Sc. in Biochemistry/Microbiology/Bioinformatics [Post-code A05] only with minimum of 55% marks plus two years research or relevant informatics experience

Please visit www.urdip.res.in/career.htm to apply online by 30th September, 2014.

Successful candidates who have appeared for NET exam in 2012 and 2013 are only eligible to apply.

Advertisement: http://115.112.95.114/urhr/download/Advt5_2014.pdf

More at https://www.igib.res.in/bdmg/ScientistRecruitmentAdvt2023.pdf

Eligible candidates are invited for a walk-in-interview for recruitment of Project Assistant in SVIMS Bioinformatics centre under the BTISnet Project entitled “Creation of Bioinformatics Infrastructure Facility for promotion of Biology teaching through Bioinformatics” on 25.09.2014 at 11 AM in SVIMS, Tirupati. The engagement will be made purely on temporary basis for a period of one year and it can be terminated at any time without notice or without assigning any reason thereof by the Coordinator of the Project. The person engaged shall not be entitled for any claim implicit or explicit for absorption in the University.

1. Name of the post : Project Assistant

2. Qualification :
i) Essential : MSc Bioinformatics/MTech (Biotechnology/Bioinformatics)

ii) Desirable : Experience in Bioinformatics research work (Preference will be given to candidates qualified in BINC/UGC/CSIR/NET/GATE)

3. Remuneration : 16000 + 10% HRA for NET/GATE candidates 14000 + 10% HRA for M. Tech / M.Sc. Candidates

4. Place of posting : Tirupati

5. Duration of the Project : One year

Terms and conditions:

1. Candidates are required to submit the Biodata relevant certificates in support of their age and educational qualification etc., before the interview committee, SVIMS University, Tirupati.

2. Candidates called for interview will attend the interview at their own cost.
3. Interim enquiries will not be entertained.
4. The maximum age limit for Project Assistant is 28 years for general category and 33 years for SC and ST category candidates as on 25th September, 2014.

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http://svr98.ehostpros.com/~svimsb98/Project%20Assistant_notification.pdf

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.

More http://www.nieduszynski.org/index.php
http://www.path.ox.ac.uk/research/cell-biology-and-pathology/conrad-nieduszynski-group

"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

https://github.com/iansealy/coursera-comparinggenomes

Address of the bookmark: https://github.com/iansealy/coursera-comparinggenomes