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/

About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.

In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.

Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html

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

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

Source:http://www.nejm.org/doi/full/10.1056/NEJMp1114866

Another video on precision medicine:

http://www.youtube.com/watch?v=Pi8W0yOXnzE

Precision Medicine basically intergrates bioinformatics, genomics , genetics, molecular biology and nanotechnology to deliver precise cure/diagnotics to a specific patient.

Examples:

• The drug imatinib (Gleevec) designed to inhibit an altered enzyme produced by a fused version of two genes found in chronic myelogenous leukemia.
• The breast cancer drug trastuzumab (Herceptin) works only for women whose tumors have a particular genetic profile called HER-2 positive.

E.g. source :

http://www.bionews-tx.com/news/2013/08/15/how-the-impact-of-cancer-genomics-on-precision-medicine-is-revolutionizing-cancer-treatment/

Address of the bookmark: http://www.cbsnews.com/video/watch/?id=50149783n

Finally, I find the underlying R code in a file created by GrapheR. For more details read also the package vignette, which is available in English, French and German!

Address of the bookmark: http://www.phyloxml.org/