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

ADVERTISEMENT NO: NITR/SR/CH-BIF/2014/30

Applications are invited on prescribed format for the following assignment in a purely time bound research project undertaken in the Department of Biotechnology & Medical Engineering of the Institute.

1. Name of the Temporary Post : Senior Research Fellow-01
2. Name of the Research Project: “ Bioinformatics Infrastructure Facility (BIF)”
3. Name of the Sponsoring Agency: DBT, Government of India, 4 Tenure of the Project : 12th Five year Plan
5 Tenure of the Assignment : 01 year [Likely to be extended for 04 more years]
6 Job Description : BIF Maintenance and Active Research in Bioinformatics
7. Consolidated monthly compensation / Fellowship: Rs.18,000/- P.M.

8. Essential Qualifications and experience: B.Tech with valid GATE Score or M.Tech degree in Biotechnology/Bioinformatics/Computer Science/Computational Biology
9. Desirable Qualifications/ Experiences: Experience of Programming in PERL,R, Python, Unix and Visual Studio + Knowledge in NGS data analysis work flows ,WGS and statistical packages such as CRAN-R,MATLAB etc.

10. Accommodation : Bachelor accommodation in the Institute may be provided subject to availability.
11. For technical information on the project, the candidate may contact the Principal Investigator at the following address:

Name : Prof. Mukesh K Gupta
Address : Dept. of Biotechnology & Medical Engineering,
N.I.T.Rourkela-769 008
Telephone No : 0661-2462294
E-mail : guptam@nitrkl.ac.in

Eligible persons may apply in the prescribed format (available in the Institute Website)affixed with coloured photographs to be submitted in duplicate along with photo copies of relevant certificates, grade/ mark sheets, publications etc., to Asst. Registrar, SRICCE,
National Institute of Technology, Rourkela–769 008 before 22.08.2014. The cover should be super- scribed clearly the post applied for & Name of the Project.

Mere possession of minimum qualification does not guarantee invitation to the interview.
Candidates will be short listed based on merit and need of the project.

Advertisement:

http://www.nitrkl.ac.in/IntraWeb/Jobs_Tenders/Jobs/ProjectFellowship/2014/141707192838_1.pdf

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

Studentship and Traineeship in Bioinformatics

Applications are invited on plain paper from suitable candidates for Studentship and Traineeship (One each) at Bioinformatics Sub-Center as detailed below:

1. Studentship: Studentship is for those who have completed M. Sc. Degrees in Life Science.

Number of seats : One

Duration : Six months

Eligibility : Passed M.Sc. degree in Life Sciences.

Fellowship : Rs. 5000/- (Five thousand only) per month

2. Traineeship: Traineeship is for those who have completed M. Sc. Degrees in Life Science/Registered Ph. D. student in Life Sciences.

Number of seats : One

Duration : Six months

Eligibility : Passed M.Sc. degree in Life Sciences/ Registered Ph. D. student in Life Sciences

Fellowship : Rs. 5000/- (Five thousand only) per month

Preferences will be given to person who has experience in Bioinformatics and Computer
sciences. The application along with detailed bio-data should reach the undersigned, on or before 25th August 2014. Both, the studentship and the traineeship are temporary, will be discontinued after the six months from the date of Joining. It may be discontinued in-between without any notice, if the work is not found satisfactory.

Advertisement www.bioinfobubpl.nic.in/Advertisement_st.pdf

Address of the bookmark: https://github.com/lh3/psmc

The vital metaphase stage of cell division, when the sister chromatids migrated to the centre and lined up in a row, and pulled apart using attached microtubules in such a way that half the DNA ends up in each daughter cell. However, before the mitotic spindle‐mediated movement gets start and pulled DNA apart, the chromosomes are free to undergo recombination which involves the exchange of genetic material either between multiple chromosomes or between different regions of the same chromosome.

During recombination, the precise breakage of each strand, exchange between the strands, and sealing of the resulting recombined molecules happens. The “chromosomal breakpoints” refers to these places where they break. Mostly, this process occurs with a high degree of accuracy at high frequency in both eukaryotic and prokaryotic cells. But occasionally this “break and sealing/ break and reattach” process goes wrong and the reattachment happens in the wrong place which usually create disaster (with few exceptions).These chromosome disaster or abnormalities involve the gain, loss or rearrangement of visible amounts of genetic material during cell division. These abnormalities are of two type, the first one is numerical abnormalities  where severe disorders are caused by the loss or gain of whole chromosomes, which affect the copy number of hundreds or even thousands of genes. The second are structural abnormalities which can be unbalanced or balanced. The former are similar to numerical abnormalities in that genetic material is either gained or lost. The natural defects in chromosome segregation are linked to cancer and several genetic diseases (http://en.wikipedia.org/wiki/List_of_genetic_disorders). Therefore, the enzymes involved in regulating cell division are still the attractive drug targets for many diseases.

Apart from certain chromosome abnormalities, these “crossing over” of segments of maternal and paternal chromosomes to form hybrid chromosomes have some evolutionary importance and considered as a driver of genetic variation. Moreover, the chromosome breakage in evolution is considered to be non-random in nature(http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020014). In addition the study of breakpoint regions and non-breakpoint (stable) regions of chromosomes indicates both the regions evolved in distinctly different ways ( http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). These breakage may lead to genetic diseases or participate to chromosomal rearranmgnets and contributed in development of new species.

I will try to explain the genome hotspots/Evolutionary Breakpoint Regions(EBRs)/fragile regions/weak fragments/  in my next blog.

Software for recombination detection:

RAT http://cbr.jic.ac.uk/dicks/software/RAT/

Breakpointer https://github.com/ruping/Breakpointer

DRP http://web.cbio.uct.ac.za/~darren/rdp.html

RB-finder http://www.ncbi.nlm.nih.gov/pubmed/18707535

LDhat2.0 http://ldhat.sourceforge.net/LDhat2.0/instructions.shtml

Reference:

http://www.nature.com/scitable/topicpage/genetic-recombination-514#

Image: Wikipedia , sciencelearn.org.nz

Recommended Articles:

http://www.friendshipcircle.org/blog/2012/05/22/13-chromosomal-disorders-youve-never-heard-of/

http://web.udl.es/usuaris/e4650869/docencia/segoncicle/genclin98/recursos_classe_%28pdf%29/revisionsPDF/chromosyndromes.pdf

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775595/table/T2/

http://learn.genetics.utah.edu/content/disorders/chromosomal/

http://www.ncert.nic.in/html/learning_basket/biology/cc&cd.pdf

MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to $5 per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.

In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for $1 million.

The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.

Powered by DNBseq™, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.

SOURCE MGI

https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/

http://en.mgitech.cn/

Walk in interview for guest faculty in Pondicherry University, India. For more information please visit http://www.bicpu.edu.in/bioinfor140814.pdf

Address of the bookmark: https://github.com/google/nucleus