Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

Employer: Jawaharlal Nehru University
Address: Dr. S. Gourinath, Principal Investigator, School Of Life Sciences, Jawaharlal Nehru University, New Delhi-110067
Email: not mentioned / provided for this job post
URL: http://www.jnu.ac.in/Career/currentjobs.htm
Phone: 011 2674 2575
Skills: not mentioned / required for this job post
Experience: Experience in molecular biology, structural biology and bioinformatics is desired
Education: M.Sc. in any field of life sciences.
Job Location: New Delhi, Delhi, India (View Jobs in New Delhi, Jobs in Delhi, Jobs in India)

Job Description: School of Life Sciences, Jawaharlal Nehru University vacancy of JRF / SRF / RA in CSIR funded Projec

Name of the Post: JRF / SRF / RA

Salary: As per rules

Required Job Profile:

Candidate must possess M.Sc. in any field of life sciences.

Desired Job Profile:

Candidate having NET - CSIR or UGC and experience in molecular biology, structural biology and bioinformatics is desired and experience with publication is preferred.

How to apply:

Eligible and interested candidates should need to apply with complete details to the above mentioned address on or before 08.05.2015.

Refer to http://www.jnu.ac.in/Career/currentjobs.htm

Address of the bookmark: https://rpubs.com/Devashish13/PCAGenomics

Research Fellows Systems Biology job vacancy in C.R.Rao Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS)

JRF : Qualification - M. Sc in Bioinformatics, Systems Biology, M. Sc statistics, or M. Tech in Bioinformatics,

Pay Scale : Rs. 25,000

SRF : Qualification- Qualification prescribed for JRF with 2 years of research experience.

Pay Scale : Rs. 28,000*

No.of Post: 2

Desirable: Candidates should have strong background in Computational biology, bioinformatics, statistics and algorithmic development. In addition to that previous experience of working on Linux, bio-informatics, NGS data analysis and Basic knowledge of biology is desirable. Programming on any one of the programming languages (C, C++, perl, python) and statistical framework (e.g. R, matlab, etc.) is highly desirable.

More at http://www.crraoaimscs.org/jrf_application_form_2015.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.

Online version and downloadable software package for offline use (source code in PERL) freely available at http://bat.ina.certh.gr/tools/circoletto/

Contact:ndarz@certh.gr

Address of the bookmark: http://tools.bat.infspire.org/circoletto/

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;
}