(A) Regulatory System Analysis with respect to microRNAs

(B) Computational Epigenomics & Regulomics:

(C) Computational issues with Next Generation Sequencing:

Department of Biotechnology,
Institute of Himalyan Bioresources Technology
CSIR, Palampur(Himachal Pradesh), India.
Email: ravishihbt.res.in; ravish9gmail.com

More @ http://scbb.ihbt.res.in/SCBB_dept/Lab_Member.php

What Are lncRNAs?

lncRNAs are RNA transcripts longer than 200 nucleotides that do not code for proteins. Despite their non-coding nature, they play diverse roles in gene regulation, including chromatin remodeling, transcriptional control, and post-transcriptional processing. Unlike messenger RNAs (mRNAs), lncRNAs often function as scaffolds, decoys, or guides in cellular machinery, influencing biological processes such as cell differentiation, immune response, and even cancer metastasis.

Challenges in lncRNA Research

Identifying and understanding lncRNAs pose unique challenges:

1. High Sequence Variability: Unlike protein-coding genes, lncRNAs exhibit low sequence conservation across species, making functional predictions difficult.
2. Low Expression Levels: lncRNAs are often expressed at low levels, complicating their detection in transcriptomic data.
3. Diverse Functions: The multifunctional nature of lncRNAs requires advanced computational tools to decipher their roles in complex networks.

Bioinformatics: A Crucial Ally in lncRNA Research

Bioinformatics bridges the gap between raw biological data and meaningful insights, making it indispensable in lncRNA research. Here’s how:

1. Identification and Annotation

High-throughput sequencing technologies like RNA-seq generate vast amounts of data. Bioinformatics tools such as StringTie, Cufflinks, and HISAT2 help assemble and annotate lncRNAs from this data. Additionally, databases like NONCODE, LNCipedia, and Ensembl provide curated repositories of lncRNA sequences and annotations.

2. Functional Prediction

Bioinformatics algorithms predict the potential functions of lncRNAs by analyzing their interactions with DNA, RNA, and proteins. Tools like LncRNA2Function and RIblast utilize sequence motifs and secondary structure predictions to hypothesize about the roles of specific lncRNAs.

3. Network Construction

lncRNAs often act as regulatory hubs. Bioinformatics platforms such as Cytoscape enable the visualization of lncRNA-mediated networks, elucidating their roles in pathways like cell cycle regulation and apoptosis.

4. Epigenetic Studies

lncRNAs are known to interact with chromatin-modifying complexes, influencing gene expression epigenetically. Tools like ChIP-seq and ATAC-seq, combined with computational pipelines, identify these interactions and map them to the genome.

5. Clinical Applications

Bioinformatics aids in the discovery of lncRNA biomarkers for diseases like cancer and neurodegenerative disorders. Machine learning models analyze differential expression profiles, helping prioritize lncRNAs with therapeutic potential.

Case Study: lncRNAs in Cancer Research

lncRNAs such as HOTAIR and MALAT1 have been implicated in cancer progression. Bioinformatics analyses have revealed their roles in promoting metastasis and altering the tumor microenvironment. For example, transcriptome analysis in cancer patients identifies lncRNA expression signatures, enabling precision medicine approaches.

Future Directions

The fusion of bioinformatics with experimental biology is unlocking the secrets of lncRNAs. Advances in artificial intelligence, single-cell sequencing, and structural modeling promise to overcome current limitations. Here are some promising directions:

• Integrative Analysis: Combining multi-omics data to understand the interplay of lncRNAs with other biomolecules.
• CRISPR Screens: Leveraging bioinformatics to design CRISPR-based functional screens for lncRNAs.
• Therapeutic Development: Using bioinformatics to design lncRNA-based therapeutics, including antisense oligonucleotides and RNA interference tools.

Conclusion

lncRNAs are the hidden gems of the genome, and bioinformatics is the key to unearthing their full potential. As research progresses, lncRNAs could pave the way for novel diagnostics, targeted therapies, and personalized medicine, revolutionizing our approach to complex diseases.

The journey into the world of lncRNAs is only beginning, and bioinformatics will continue to play a pivotal role in decoding these molecular mysteries. Whether you’re a researcher, clinician, or bioinformatics enthusiast, the study of lncRNAs offers a fascinating frontier of discovery.

More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php

No. ACTREC/Advt./ 72 /2013

WALK IN INTERVIEW

1. JRF*
Genome-wide RNAi screen with human pooled tyrosine kinase shRNA libraries in head and neck squamous call carcinoma (HNSCC) cell lines
DBT A/C No. 3071, Dr. Amit Dutt

2. JRF
IRB Project ACTREC Funds
Dr. Amit Dutt

3. RA
Defining the cancer genome of Head and Neck Squamous Cell Carcinoma (HNSCC) with SNP arrays and next generation sequencing technology
A/C No. 2895, Dr. Amit Dutt

Duration of the Project: One year from the date of appointment, or as and when project terminates.

Consolidated Salary: RA : Rs. 40,000/- p.m.
JRF* (DBT): Rs. 20,800/- p.m.
JRF: Rs. 16,000/- p.m.
Date & Time: 6th November, 2013, at 10.00 a.m.

Venue: Conference Room

Minimum Qualifications and Experience:

RA: The ideal applicant should have a PhD in a relevant field. He/she should have a strong computational biology background, with demonstrated experience in coding using Perl, Python, Java or C++. He/she should be familiar with working in unix enviromnent, devising computational algorithms for data analysis, statistical data analysis in R and matlab and database programming using MySQL. Hands on experience in analyzing high throughput data would be an added advantage.

JRF* (DBT project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Valid UGC-CSIR/DBT/ICMR JRF qualification and laboratory experience in molecular biology. Previous experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team would be desirable.

JRF (ACTREC project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Minimum 2 yrs experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team is essential.

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

Candidates fulfilling above requirements should send their application by e-mail to
‘careers.duttlab@gmail.com. in the format given below so as to reach on or before
4th November, 2013.

Advertisement:

http://www.actrec.gov.in/data%20files/2013/AD-RA-JR-TECHN-6-NOV.pdf

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

• Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
• Computational Biologist: Combines computational techniques with biological research.
• Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
• Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

• Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
• Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
• Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
• Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

• Tailor your resume to each job, emphasizing skills mentioned in the job description.
• Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
• Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

• Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
• Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
• Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

• Internships: Seek out internships in research labs or biotech companies.
• Collaborations: Volunteer to work on projects with professors or peers.
• Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

• Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
• Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
• Understanding the Organization: Research their recent projects, publications, or products.
• Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

• Keep improving your skills by taking online courses or certifications.
• Stay updated with advancements in bioinformatics by following journals and blogs.
• Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

1. Project Junior Research Fellow / Project Assistant

Last Date: 11th Nov 2013

Qualification B.Tech (Comp. Sci.), B.Tech/M.Tech (Bioinformatics), MCA, M.Sc. (Mathematics/Statistics)

Desirable Qualifications: Programming in FORTRAN/ C /PERL, Web application technologies

Upper Age limit 28

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

General terms and conditions:

Positions are purely temporary and co-terminus with the project.

HRDG (CSIR) prevailing guidelines are applicable these positions.

All categories of applicants are required to submit online application.

Enhancement of stipend to Project JRF to Project SRF will be with the due recommendation of Principal Investigator and approval of the Director on the evaluation of the 3 member Standing Committee consisting of Chairperson at the level of Chief Scientist, Coordinator of the JRFs/RAs/PDFs and the Principal Investigator of the Project.

The age relaxation as per HRDG (CSIR) norms: SC/ST/OBC/Women/Physically Handicapped persons – five years.

The Stipend normally be fixed at Rs.22000/- for Research Associates/Post Doc. Fellows. However, a selected RA/PDF may be placed in the higher start of stipend if there is ample justification and such recommendation is made by the Selection Committee. Based on the recommendation with justification by the PI and approval of the Director, person getting stipend at lower rate may be elevated to higher rate subject to availability of the funds in the project.

Recruitment will be based on initial screening based on qualifications and experience criteria and also based on suitability of the candidates to the nature of research project. This screening will be followed by written test followed / interview. After completing this process, candidates will be shortlisted and appointed in specific project subjects as and when appropriate positions become available. The pool of selected candidates will be valid for six months.

Remunerations indicate are maximum admissible and will depend upon the availability of funds and subject to conditions applicable to projects from different funding agencies at the time of recruitment.

Apply : http://www.ccmb.res.in/positions/projects/temp_positions.php

Form download : http://www.ccmb.res.in/positions/projects/oct-2013/pdf_download.php

Lab page: http://www.oeb.harvard.edu/faculty/edwards/index.html

Yet, for many students, biologists, and even computer scientists, the question is: “Where do I begin?” With so many platforms, books, and tutorials available, it’s easy to feel overwhelmed.

To make it easier, I’ve compiled 10 excellent resources — ranging from beginner-friendly introductions to advanced computational genomics courses. Many of these are freely available, created by pioneers in the field, and widely used in classrooms and research labs worldwide.

Whether you are a complete beginner or looking to strengthen your foundations, these courses will help you build the skills needed to analyze biological data, design workflows, and think computationally about complex biological systems.

1. HarvardX Data Analysis for Genomics by Rafael Irizarry

From the almighty Rafa, this set of online courses (via edX/HarvardX) is a classic starting point for genomic data science and bioinformatics.

2. Applied Computational Genomics – Aaron Quinlan

Aaron Quinlan (creator of bedtools and many other tools) has made his course materials open. A practical, tool-driven genomics introduction.

3. Bioinformatics Algorithms (Coursera + Companion Book)

Find the highly visual video classes on Coursera, backed by the popular Bioinformatics Algorithms book.

4. The Biostar Handbook

Not a course per se, but a hands-on manual by Istvan (founder of Biostars.org) that’s even used in classes at Penn State.

5. Introduction to Bioinformatics and Computational Biology (by Shirley Liu)

A comprehensive introduction from Shirley Liu’s lab (Harvard DFCI). Covers both theory and computational practice.

6. Data Carpentry: Genomics Workshops

Community-driven training workshops that focus on practical, reproducible research. I was honored to serve as curriculum committee chair here.

7. Computational Genomics: Applied Comparative Genomics

From the Schatz Lab — applied comparative genomics with real-world data.

8. Introduction to Computational Biology (Mike Love, creator of DESeq2)

This course bridges statistics, biology, and computation — a solid primer for anyone entering computational biology.

9. MIT Computational Biology (6.047 / 6.878 / HST.507) by Manolis Kellis

Covers genomes, networks, evolution, and health. A deep-dive from MIT’s OpenCourseWare archive.

10. An Introduction to Applied Bioinformatics

An interactive textbook with Python code, designed for practical applied bioinformatics learning.

Last Date for Submitting Application: 25th November 2013

1. Senior Scientist: (01 position)
Pay Scale: Rs.40, 000/-
Qualifications: PhD/ Post Doctoral with Experience in CADD

2. Junior Scientist (10 positions)
Pay Scale: Rs. 22,000/-
Qualifications: MPhil / Masters Degree in Bioinformatics / Computational Biology / CADD / Ayurveda

3. Technical Assistant (01+01 positions)
Pay Scale: Rs.12,000/-
Qualifications: 1. BSc Computer Science/ MCA
Qualifications: 2. MSc Biotechnology / MSc Microbiology

4. Programmer (01 position)
Pay Scale: Rs.20,000/-
Qualifications: MSc Computer Science/ MCA / B Tech (Experience in MATLAB, C, C++) Industrial experience is desirable

5. Teaching Assistant (03 positions)
Pay Scale: Rs.10,000/-
Qualifications: MSc in Bioinformatics

6. Administration Assistant (02 positions)
Pay Scale: Rs.8,000/-
Qualifications: Degree + PGDCA

The Selection process comprises of written test and interview. Positions are purely temporary (initially for the period of one year) and co-terminus with the project. For more details mail to: cbi.uok [at] gmail.com

More detail @ https://sites.google.com/site/centreforbioinformatics/announcements/applicationsinvitedforapplicationforai-caddproject