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.

Human genetic variation
Inference of human evolutionary history from genetic markers
Statistical analysis of population-genetic data
Mathematical models of gene genealogies
Theoretical population genetics
Combinatorics of evolutionary trees
The relationship between gene trees and species trees
The role of human evolutionary genetics in the search for genes that contribute to disease-susceptibility
More at https://web.stanford.edu/group/rosenberglab/index.html

Pakyong

F.No:NRCO/Admn/DBT /136 /

Walk-in-Interviews will be held at 737106, Sikkim for the post of 01 (One Project ‘DBT’s Twinning programme for the NE’ titled “Assessment of some fragrant orchids of north-east India for sustainable improvement of community livelihood”, indicated below. The appointment will be on contractual basis and the incumbents shall not have any regular appointment in ICAR.

‘DBT’s Twinning programme for the NE’ titled “Assessment of chemical and genetic divergence of some fragrant orchids of north-east India for sustainable improvement of community livelihood”

Junior Research Fellow (One post)

Essential Qualification : a. MSc (with NET qualification) / M.Tech degree (with or without NET) with minimum 55% marks in Biotechnology/ Bioinformatics/ Molecular Biology or any other related field.

Desirable Qualification: Computer Skills (Linux, Perl, Java, MySQL) with experience in advanced molecular Biology techniques

2nd June 2015

Advertisement: www.nrcorchids.nic.in/Employments/Vacancy%20-%20JRF.pdf

Solved with perl http://rosalind.info/problems/1a/

#Find the most frequent k-mers in a string.
#Given: A DNA string Text and an integer k.
#Return: All most frequent k-mers in Text (in any order).

use strict;
use warnings;

my $string="ACGTTGCATGTCGCATGATGCATGAGAGCT";
my $kmer=4;
my %myHash;
my $max=0;

for (my $aa=0; $aa<=(length($string)-4); $aa++) {
    my $myStr=substr  $string, $aa,$kmer;
    #print "$myStr\n";
    my $km=kmerMatch ($string, $myStr, $kmer);
    if ($km > $max) { $max = $km;}
    #print "$km\t$myStr\n";
    $myHash{$myStr}=$km;
    
}

#Print all key which have matching values
foreach my $name (keys %myHash){
    print "$name " if $myHash{$name} == $max;
}

sub kmerMatch { #Check the exact matching kmers with sliding window
my ($string, $myStr, $kmer)=@_;
my $count=0;
for (my $aa=0; $aa<=(length($string)-4); $aa++) {
    my $myWin=substr  $string, $aa,$kmer;
    if ($myWin eq $myStr) {
        #print "$myWin eq $myStr\n";
        $count++;
    }
}
return $count;
}

Script are moved to http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl

Qualifications : Ph. D.

Desirable : Experience on DNA Markers, plant genome mapping and bioinformatics

No. of Post : 03

Department : Genetics

Salary : Rs. 60,000/-
How to apply

The applicants are requested to register their names on the day of interview in the First Floor, Biotech Centre, Centre for Genetic Manipulation of Crop Plants, Department of Genetics before the stipulated time for the interview. Only the registered eligible candidates will be interviewed on the day in the Committee Room. Applicants are requested to bring all related documents, in original and a set of photocopy, for verification. Date and time of the interview : 25.06.2015 at 10:30 AM.

Click Here for Job Details http://www.du.ac.in/du/index.php?mact=News,cntnt01,detail,0&cntnt01articleid=5492&cntnt01returnid=83

Applications are invited from individuals who have high motivation to do research for the below mentioned position,

1. Research Associate - 1 No.
in the DBT sponsored project under the supervision of Dr. D. Prabaharan, Principal Investigator, National Facility for Marine Cyanobacteria, Dept. of Marine Biotechnology, Bharathidasan University, Tiruchirappalli-24.

Title of the Project: “Establishment of National Repository for Micro algae & Cyanobacteria” funded by Department of Biotechnology, Govt. of India

Qualification

1. Research Associate – 1 No. Rs. 36,000/38,000/40,000 per month for I, II and III year + 20% HRA

Essential : Doctoral degree in relevant subject from recognized University/ Institutes
Desirable: Research experience in molecular biology and bioinformatics

Interested candidates can send their complete CV in plain paper with a passport size photograph, with details of Marks secured in all subjects from plus two stage (with proof, full postal address, sex, date of birth, community etc., along with additional qualification or experiences and two address of references whom could be contacted (One of whom should be PG teacher/guide). Application should reach the Principal Investigator on or before 30.06.2015 by Email (nfmcbic@yahoo.com)/Registered post/ Speed post, with subject subscribed as “Application for Research Associate /Technical Assistant /Lab attendant”. Qualifying candidates will be short listed and communicated with date of interview. No TA and DA will be given for attending the interview. Addressfor Communication Dr. D. Prabaharan Principal Investigator National Facility for Marine Cyanobacteria Bharathidasan University Tiruchirappalli-620024, Tamil Nadu.

Advertisement: http://www.bdu.ac.in/adv/NFMC_Project_Positions.pdf

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

The candidate is expected to have experience in handling functional genomics tools to dissect defense responses against bacterial pathogens and drought stress tolerance. This project may involve use of bioinformatics tools, database development, large scale transcriptome profiling, virus-induced gene silencing and any other research work as assigned by the PI.

Qualification: Candidates having a Ph. D. degree in Bioinformatics/Plant Molecular Biology/Plant Physiology/Plant Pathology/Plant Breeding & Genetics and strong publication record can apply. Candidates having prior work experience in using advanced molecular biology tools in laboratory with strong bioinformatics knowledge are preferred.

The Fellowship amount for the position will be given at par with the similar fellowships by DBT/DST.

NIPGR reserves the right to select the candidate against the above post depending upon the qualifications and experience of the candidate. Reservation of post shall be as per Govt. of India norms.

Eligible candidates may apply by sending hard copy of complete application in the given format with a cover letter showing interest and specifying the position. The attested copies of the mark-sheets, certificates, proof of research experience/publications are to be attached. The application should reach at the address given below within 15 days from the date of advertisement. The envelope must be superscribed by "Application for the post of RA under NIPGR Short-term research fellowship programme". No TA/DA will be paid for attending the interview.

ONLY hard copy of the application in the given format will be accepted.
www.nipgr.res.in/files/careers/format_RA2.doc

Dr. Senthil-Kumar Muthappa
Staff Scientist - III,
National Institute of Plant Genome Research (NIPGR)
Aruna Asaf Ali Marg, P.O. Box NO. 10531,
New Delhi - 110067