ICRISAT is headquartered in Patancheru near Hyderabad, India, with two regional hubs and five country offices in sub-Saharan Africa. ICRISAT, established in 1972, is a member of the CGIAR Consortium. For more details, see www.icrisat.org.

Job Responsibilities:
Planning and execution of different NGS/genomics data analysis
Apply, maintain, and support cutting-edge pipelines for the analysis and interpretation of NGS data
Analyze large-scale genomic datasets generated internally and through collaboration with others
Genome wide analysis- LD analysis, hapmap, genetic map construction and qtl mapping
Expression analysis based on RNA-seq, gene ontology and metabolic pathway data
Sequence level analysis like gene family analysis, orthology/paralogy etc.
Familiarity with genomic and biological information databases
Compilation and interpretation of results and writing reports
Experience working independently and in a team environment
Requirements:

PhD or M.Sc with 2-3 years experience from reputed institute in the area of life science or similar Knowledge in Next Generation Sequencing (NGS) data analysis. Should be familiar with various sequencing platforms. Sound knowledge of genomics and molecular biology is must. Proficient in any one of the programming/scripting in languages: Python, Perl, PHP, R, Shell Scripting. Must be experienced in working on Linux and CLI environment. Ability to work as team as well as independently with minimal support. Fluency in spoken and written English is essential.

NGS techniques like sequence alignment, variant calling based on whole genome re-sequencing and genotyping-by-sequencing (GBS), RNA-Seq/transcriptome analysis
Knowledge of various standard NGS related tool
Ability to solve complex problems in advanced genomics research areas
Ease and interest in working with people from diverse backgrounds
Excellent oral/written communication and interpersonal/networking skills
General:
This position is contract for a period of two years.

How to apply:
Applicants should apply on or before 27-July-2017, with latest Curriculum Vitae, and the names and contact information of three references that are knowledgeable about your professional qualifications and work experience. All applications will be acknowledged, however only short listed candidates will be contacted
Please CLICK HERE to submit your application.
ICRISAT is an equal opportunity employer

http://icrisat.careersitemanager.com/job-listings-Visiting-Scientist-Computational-Genomics-ICRISAT-Hyderabad-Secunderabad-2-to-4-years-060717000278

Developing New Molecular Methods

Genomic Approaches to Developmental Biology

Massively Parallel Functional Genomics

Translating Genomics to the Clinic

Genetic Basis of Human Disease

Genome Sequencing Technologies

http://krishna.gs.washington.edu/index.html
http://www.gs.washington.edu/faculty/shendure.htm

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.

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he CMG-biotools system presents a stand-alone interface for comparative microbial genomics. The package is a customized operating system, based on Xubuntu 10.10, available through the open source Ubuntu project. The system can be installed on a virtual computer, allowing the user to run the system alongside any other operating system. Source codes for all programs are provided under GNU license, which makes it possible to transfer the programs to other systems if so desired. We here demonstrate the package by comparing and analyzing the diversity within the class Negativicutes, represented by 31 genomes including 10 genera. The analyses include 16S rRNA phylogeny, basic DNA and codon statistics, proteome comparisons using BLAST and graphical analyses of DNA structures.

 Paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0060120

Address of the bookmark: http://www.cbs.dtu.dk/biotools/CMGtools/

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.

Advertisement:

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

Address of the bookmark: https://schneebergerlab.github.io/syri/

The University of Idaho is in Moscow, a small college town located in
Northern Idaho on the Washington border. Moscow is widely considered to
be a great place to live, and it's known for a pleasant downtown, active
farmer's market, and nearby recreational opportunities. All of Moscow
is within biking or walking distance of the University of Idaho. For
more information about Moscow, see https://visitmoscowid.com/.

The University of Idaho has very strong faculty in evolution and
genomics in multiple departments and interdisciplinary programs. Of
particular note are the Bioinformatics and Computational Biology
Program (BCB: https://www.uidaho.edu/sci/bcb/people/faculty) and
the Institute for Bioinformatics and Evolutionary Studies (IBEST:
https://www.ibest.uidaho.edu/index.php). In addition, the University of
Idaho is only eight miles from Washington State University in Pullman, and
faculty from the two institutions interact and collaborate extensively.

Minimum qualifications include: a Ph.D. in biological sciences,
bioinformatics, or a related discipline; experience conducting research
in genomics or evolutionary biology, as evidenced by publications
in peer-reviewed journals; and evidence of strong written and oral
communication skills. Experience analyzing next-generation sequence
data and familiarity with the genomics of marine fishes are desirable
but not required.

Apply at: https://uidaho.peopleadmin.com/postings/30003

Review of applications will begin January 15, 2021. The start date
is flexible.

The University of Idaho is an equal opportunity/Affirmative Action/equal
access employer.

Informal inquiries are encouraged and can be directed to Adam Jones
(adamjones@uidaho.edu).

"adamjones@uidaho.edu"

Compare structural and functional annotations of proteins between sequenced genomes.

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.