Professor/Senior Lecturer of Comparative Genomics
Vacancy Ref: 153610
Salary: Professor, Grade 10 will be within the Professorial range and
subject to negotiation
Senior Lecturer, Grade 9, 57,696 - 64,914 per annum

The School of Biodiversity, One Health and Veterinary Medicine has an
exciting opportunity to appoint a Professor/Senior Lecturer in Comparative
Genomics. You will make a substantial and positive contribution to the
strategic direction of the School/College through leading and contributing
to research of international standard, high quality teaching at both
undergraduate and postgraduate level, securing research funding, and
providing academic leadership and management within the School/College.

Applications are invited from candidates of international standing with
an appropriate record of academic achievement in comparative genomics
and associated omics technologies. We are looking for a candidate who
will complement our existing strengths in clinical veterinary medicine,
evolutionary biology, and animal physiology, with a demonstrable interest
in using domestic mammals among their study systems. We are particularly
interested in applications from candidates with a track record of
studying health related traits and their underlying genomic basis in
companion animals. Traits of specific interest include those related
to metabolism, ageing, and disease (e.g. cancer, autoimmune diseases,
neuromuscular disorders).

The School of Biodiversity, One Health and Veterinary Medicine is home to
researchers studying organismal biology and animal health across a diverse
range of systems, approaches and disciplines with existing strengths
in infectious disease, physiology, ageing, veterinary epidemiology, and
evolution among others. You will be based on the University of Glasgow's
Garscube campus, where the majority of veterinary teaching and research
infrastructure is located. This includes the Small Animal Hospital (a
recent 15M investment) and our Veterinary Diagnostic Services, offering
excellent opportunities for collaborative research at the clinical and
translational interface, especially with respect to companion animals.

We welcome applications from candidates with a Scottish Credit and
Qualification Framework level 12 (PhD) in animal biology, genomics and
health or related discipline with an extensive and established reputation
in research and significant teaching experience within the subject area.

This post is full time and open ended.

Visit our website for further information on The University of
Glasgow's, School of Biodiversity, One Health & Veterinary Medicine,
https://www.gla.ac.uk/schools/bohvm/

Informal Enquiries should be directed to Professor Roman Biek,
Roman.Biek@glasgow.ac.uk

Apply online at:
https://my.corehr.com/pls/uogrecruit/erq_jobspec_version_4.jobspec?p_id=153610

2013 Oct Advertisement from Indian Statistical Institute

Post: Network Analysis

No. of Positions: 01

Educational Qualifications:

Candidate should have passed BE/B.Tech Or Equivalent in Computer Science / Electrical Engineering / Electronics / Information Technology / Bioinformatics / Biotechnology with throughout first Class
Experience:

(details of experience required)
Pay Scale: INR Rs.16000-20000/-P.M.

Walk-In-Interview : 22 Oct 2013 at 10:30 AM

Download Official Notification:
http://www.isical.ac.in/JobApplicationFiles/MIU_0310201311433700.pdf

What is RNA-Seq?

RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome—the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.

Workflow for RNA-Seq Analysis

RNA-Seq analysis involves several stages, each requiring computational tools and expertise.

1. Experimental Design and Data Acquisition

Before diving into analysis, bioinformaticians should consider:

• Biological Replicates: Ensure statistical power to detect meaningful differences.
• Sequencing Depth: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).
• Paired-End vs. Single-End: Paired-end sequencing provides more detailed information on transcript structure.

Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.

2. Quality Control and Preprocessing

Quality control (QC) ensures data integrity. Tools such as FastQC evaluate metrics like base quality, GC content, and adapter contamination.

Preprocessing Steps:

• Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences.
• Filtering: Discard reads below a certain quality threshold or length.

3. Read Alignment

Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:

• HISAT2: Handles large genomes efficiently and supports spliced alignments.
• STAR: High-speed aligner optimized for RNA-Seq.
• Bowtie2: Suitable for short-read alignment.

Output: A SAM/BAM file containing aligned reads.

4. Transcript Assembly and Quantification

This step involves identifying transcripts and quantifying their expression levels. Tools used include:

• StringTie: Assembles and quantifies transcripts from aligned reads.
• Salmon/Kallisto: Perform pseudo-alignment for rapid and accurate quantification.

Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).

5. Differential Expression Analysis

To identify genes with altered expression between conditions, bioinformaticians use tools such as:

• DESeq2: Accounts for data normalization and variability.
• edgeR: Handles overdispersed count data efficiently.
• Limma-voom: Combines linear modeling with RNA-Seq count data.

The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.

6. Functional Annotation and Pathway Analysis

Understanding the biological significance of DEGs involves:

• Gene Ontology (GO) Analysis: Tools like DAVID or clusterProfiler categorize genes based on their biological functions.
• Pathway Enrichment Analysis: Identifies pathways enriched in DEGs using tools like KEGG, Reactome, or GSEA.

7. Visualization

Visualizing results enhances interpretability. Common visualizations include:

• Heatmaps: Show expression patterns across samples (e.g., pheatmap).
• Volcano Plots: Highlight significant DEGs (e.g., ggplot2).
• PCA/UMAP: Assess sample clustering and variability (e.g., Seurat).

Challenges in RNA-Seq Analysis

1. Batch Effects: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like ComBat.
2. Low-Quality Samples: Poor-quality RNA impacts downstream analyses.
3. Computational Complexity: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.

Key Tools and Resources

• Bioconductor: A treasure trove of R packages for RNA-Seq analysis.
• Galaxy: A web-based platform for running RNA-Seq workflows.
• Nextflow/Snakemake: Workflow management tools to streamline analyses.

Applications of RNA-Seq

RNA-Seq is used in diverse research areas, including:

• Cancer Transcriptomics: Identifying tumor-specific expression profiles.
• Developmental Biology: Studying dynamic transcriptome changes.
• Drug Discovery: Screening genes modulated by therapeutic compounds.

Conclusion

RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.

Whether you’re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.

Research Interest
Genomic Data Integration

Microarray Analysis

Gene and Protein Function Prediction

Detection and Analysis of Chromosomal Abnormalities and Functional Evolution

Integration of Computation and Experiments

Identification of Biological Networks and Pathways

Evaluation and Validation of Computational Predictions

Scalable Visualization-Based Data Analysis

More @ http://reducio.princeton.edu/cm/
PI page @ http://reducio.princeton.edu/cm/ogt

National Bioinformatics Infrastructure Sweden (NBIS) (nbis.se) plays an important role in advancing life science research in Sweden by providing expert support and developing cutting-edge bioinformatics infrastructure. Operating as a truly national initiative, NBIS employs more than 120 bioinformaticians, system developers, and data stewards across multiple locations in Sweden. It serves as the bioinformatics platform at SciLifeLab, a national resource that facilitates research in molecular biosciences by offering access to state-of-the-art technologies and technical expertise. With strong ties to data-producing facilities and ongoing collaborations with leading research groups, NBIS is ideally positioned to support world-class bioinformatics analyses. Furthermore, NBIS is the Swedish node in ELIXIR, the European infrastructure for biological information.

NBIS is seeking an experienced bioinformatician to support both Swedish and international projects. As part of our dynamic team, you will work closely with researchers to process large-scale biological data and contribute to advancing our data analysis infrastructure. Strong problem-solving skills, attention to detail, and the ability to troubleshoot complex bioinformatics pipelines are essential for success in this role. Flexibility and a willingness to learn are also important, as NBIS continually adapts to meet the evolving needs of the Swedish research community.

More at https://www.uu.se/en/about-uu/join-us/jobs-and-vacancies/job-details?query=778701

Details of the Positions and Pay Structure:

03 Posts for Assistant Professor in Molecular Synthesis for Drug Discovery and Development

Essential Qualifications and Requirements:

1. PhD in Synthetic Organic Chemistry/Medicinal Chemistry with research publications in high quality international journals and first class grade at the preceding degree from recognised University/Institute in India or abroad with consistently good academic record.
2. Three Yrs of Post-doctoral experience in relevant area.
3. Below 35 Yrs of age at the time of application

Desirable Experience: Candidates having strong research background in organic synthesis, total synthesis of structurally complex and medicinally important natural products/drugs related to cancer, neurodegenerative diseases (neurotropically active molecules for Alzheimer's, Parkinson’s, dementia etc) and infectious diseases such as malaria, TB etc. will be preferred.

Interested candidates may apply with:

1. Filled up Application Form (download from CBMR Website: http://www.cbmr.res.in) along with the Cover Letter, Curriculum Vitae including academic record (Bachelor degree onwards), awards, honours, list of Publications and reprints of 5 best publications.
2. Proposed research plan (max 3-4 pages).
3. Names and address (with valid e-mail and Phone number) of at least 3 academic referees.
4. Online Payment Receipt with transaction reference no. of Rs. 1000/- (USD 100 or equivalent foreign currency) on following details.
Account Number: 30054847814 Name: Director, Centre of Biomedical Research
Bank: STATE BANK OF INDIA, SGPGI Campus Branch, LUCKNOW

IFSC Code: SBIN0007789
MICR No: 22602034

Applications can be sent by registered/speed post or by e-mail to the following address:

The Director,
Centre of Biomedical Research (CBMR),
Sanjay Gandhi PGI Campus,
Raebareli Road, Lucknow-226014
e-mail: cbmr.admin@cbmr.res.in,
gp.pandey@cbmr.res.in

More Info:

http://www.cbmr.res.in/career/Advertisement%20for%20the%20post%20of%20Professors%20and%20Assistant%20Professors.pdf

1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

We are seeking a highly motivated young PhD student with strong interest in high throughput data analysis.
Detailed descriptions of our recent research activities may be found here:
http://www.humanitas-research.org/condorelli-gianluigi-md-phd/

Position is available starting from October/November. A probationary period of one month will be required.

Please send a CV along with a cover letter stating the reasons for applying and contact details of one or more referees to Dr. Paolo Kunderfranco (paolo.kunderfranco@humanitasresearch.it).

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

Sector 8, Dwarka, Delhi -110077

WALK IN INTERVIEW

One position of project fellow is to be filled up in a DRL- funded research project on Molecular and morphological characterization of An. fluviatilis in North-eastern states and bordering areas. The position is purely temporary for one year and can be extended

Essential qualifications

Master’s degree in any branch of Life Sciences with hands on experience in molecular biology and/or bioinformatics.

Age limit: 28 years, (relaxation for SC/ST/OBC candidates as per government of India rules)

Stipend: Rs.12, 000.00 per month (fixed)

Eligible candidates may walk in for an interview on 15 November 2013 at 11 AM at the above mentioned address along with a copy of CV (with a passport size photograph affixed), photocopies of all mark sheets/certificates and originals (for verifications). No TA/DA will be paid for attending the interview .Registration of candidates will start at 10:00AM and end at 10:45 AM.

Advertisement: http://www.mrcindia.org/vacancy/add-4.doc