Qualification required: PhD in Life Sciences/ Molecular Biology/ Bioinformatics or related discipline.

Desirable Qualification:

1. Candidates having experience in molecular biology techniques and must be well versed with bioinformatics tool or expert in relevant fields will be preferred.
2. The candidate should have good communication skill, knowledge about designing experiments and analyzing data.
3. Sense of initiative, autonomy, organization and thoroughness, good relation qualities.
Remuneration: 2000USD per month.

Last date of submitting the complete applications furnishing Bio-data is 10th February, 2015.

Interested candidates should send their resume including all personal as well as academic details to the principal investigator Prof Seung-Won Park (email:microsw@cu.ac.kr)

The shortlisted candidates will be intimated within February 2015.

Link @ http://www.bioinf.uni-freiburg.de/

Name of Position : Research Associate
No of Post : One
Desired candidate Profile : Candidate should be PhD in Bioinformatics or equivalent or Post-Graduation Bioinformatics with 1st division or 60% marks. Preference will be given to candidates having experience in server management
Remuneration : Rs 24000/- pm + HRA for PhD holders
Age requirement- 40 years (5 years relaxation for SC/ST/women candidates and 3 years for OBC candidates) as on date of interview.

How to apply- Interested candidates can walk -in-interview on 30.05.2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow.

Ref- http://www.nbfgr.res.in/PDF/News%20&%20Events/Walk%20in%20interview%20on%2030.05.2015.pdf

In this project, you will use single-molecule techniques to label mRNA and DNA in (live and fixed) Drosophila embryos and fixed embryonic bodies. You will also use super-resolution microscopy to visualize protein condensates. Co-localization dynamics reflecting DNA-protein bindings and DNA looping events will be detected, analyzed, and used to test computational models of gene transcription.

Qualification:
MSc degree (or equivalent) in Biology, Biophysics, or Bioengineering

Experience in one or more of these areas: (1) molecular cloning, (2) imaging, (3) image analysis (using Matlab/Python/Java), (4) microfluidics, and (5) computational modeling.

How to Apply?
Send (1) your CV, (2) summary of research experience, and (3) email addresses of at least 2 references to ezzat.el-sherif@fau.de. Title your email ‘Transcription PhD Position’.

salary Grade.: E13
Total Time: 3 Jahre
Start: 01.01.2020.
End: 31.3.2020.

Address:
Dr. El-Sherif, Ezzat
Department Biologie
Professur für Zoologie (Entwicklungsbiologie) (Prof. Dr. Klingler)
Telefon 09131/85-28068, Fax 09131/85-28040, E-Mail: ezzat.el-sherif@fau.de

We are looking for highly motivated and bright individuals interested in high-throughput cutting-edge data analyses methods in genomics (computational positions). Available positions:

Applications are invited from suitable candidates in both, the Max Planck India Partner Program and the CRP Wheat Program for openings at the levels:

Minimum qualification Salary scale (per month)

Project assistant Bachelor’s / Master’s Rs. 10000 / Rs. 14000

Project fellow (junior data analyst) Masters + research experience Rs. 16000

Research fellow (senior data analyst) Masters + adequate research experience/desirable skill sets Rs. 22000

Research Associated PhD (<1yr) / > 1 yr experience Rs. 28000 / Rs. 32000

Condition to satisfactory performance, availability of funds and requirements of the project, the positions could be available upto a period of ~2 years (or funding of the project).

Essential qualification: MSc/BTech/MTech/PhD (or other suitable qualification) in discipline related to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance PostGraduate Diploma in Bioinformatics’. Proficiency in one of the programming languages or statistics (proficient in R for example) is compulsory.

Desirable qualification:

1. Programming experiences in at least one low level language such as C/C++ and one scripting language such as Perl/Python/PHP and knowledge of SQL/MySQL.

2. Substantial experience in the linux or other unix environments.

Application process: Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by email to Shree P. Pandey, Department of Biological Sciences, IISER-Kolkata, Mohanpur Campus, West Bengal within 2 weeks (Feb 19th 2015). E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in Brief description of the group: We are an interdisciplinary group focusing on small-RNA (miRNA, siRNA) mediated regulation of signaling and defense. Project equally involve bioinformatics and systems biology (specially microarrays and next-generation sequencing (NGS) data analysis and its use), along with plant molecular biology, genetic engineering, field biology, and analytical plant chemistry for understanding response of plants to biotic stresses. For more details visit: http://www.iiserkol.ac.in/~sppandey/

Advertisement:

www.iiserkol.ac.in/images/iiserk/advertisements/advertisement_7_spp_feb_2015.pdf

Protein-protein and protein-DNA/RNA docking based on a hybrid algorithm of template-based modeling and ab initio free docking.

The HDOCK server distinguishes itself from similar docking servers in its ability to support amino acid sequences as input and a hybrid docking strategy in which experimental information about the protein–protein binding site and small-angle X-ray scattering can be incorporated during the docking and post-docking processes.

Address of the bookmark: http://hdock.phys.hust.edu.cn/

Title of the Project
Research Associate
(One position)
DBTs Biotechnology/Bioinformatics training centre
PI Dr. Ashok Varma

Duration of the Project Six Months from the date of appointment, can be extended further for six month.

Date & Time: 17th February, 2015 at 10.00 a.m.

Venue: Meeting Room, 3rd floor, Khanolkar Shodhika, ACTREC

Essential Qualifications and Experience:

Ph.D. Degree in Basic Sciences from recognized University. Research experience in Bioinformatics or on gene cloning, protein purification, and crystallization.

*M.Sc. degree obtained after a one year course will not be considered.

Selected candidate will have to join at the earliest.

Consolidated Salary: Rs.28,600/- p.m. {Rs.22,000/- + 30% HRA}

The work progress of the candidate will be monitored and extension after 6 months will depend on satisfactory progress of the work.

Candidates fulfilling these requirements should pre-register by sending their application in the prescribed format with recent CV and contact details of 2 referees by e-mail to ‘program.office@actrec.gov.in’ latest by 17.00 hrs on 12-02-2015.
The interviews would be held on 17th February, 2015 and will be only for the pre-registered candidates. Candidates should report between 09.30 to 10.00 a.m. in Steno Pool, 3rd floor, Khanolkar Shodhika, ACTREC, Kharghar, Navi Mumbai.
No T.A./D.A. will be admissible for attending the interview.

At the time of Interview the candidate should bring original certificates along with CV with contact details of 2 referees and submit the photocopies (attested) of the certificates, with a recent passport size photograph.

All correspondence should be strictly made only to ‘program.office@actrec.gov.in’ as indicated.

Advertisement: www.actrec.gov.in/data%20files/2015/AV-RA-DBT-28-1-15.docx

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.

Who you are
You’re someone who wants to influence your own development. You’re looking for a company where you have the opportunity to pursue your interests across functions and geographies. Where a job title is not considered the final definition of who you are, but the starting point.

Qualifications:
Major: Bioinformatcis or biology major who is interested and wants to learn Biocomputing, At least 2 years of college.
Basic knowledge of LINUX and programming, e.g., perl, python, XML.

More at http://www.roche.com/careers/jobs/jobsearch/job.htm?id=E-00437679&locale=en&title=Summer%20Intern%20-%20Research%20Bioinformatics

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/