Address of the bookmark: https://astrobiomike.github.io/

Salary: £27,854 - £29,541, with progression to £36,298

You will perform cutting edge computational biology within the Faculty of Medical Sciences, with a particular focus on the Northern Institute for Cancer Research (NICR), and contribute to the delivery of Faculty wide programmes of training, analytical services and skill transfer between Faculty Institutes.

You will have a relevant first degree or equivalent qualifications and/or experience in a relevant scientific/technical role, together with previous specialist experience at a senior level in bioinformatics. A PhD is desirable.

This position is part of the Bioinformatics Support Unit but physically located for the majority of the time in the NICR buildings.

Tenable for three years.

Informal enquiries to unit head Dr Simon Cockell: 0191 222 7253; simon.cockell@ncl.ac.uk

For more information visit @ https://www15.i-grasp.com/fe/tpl_newcastle02.asp?s=4A515F4E5A565B1A&jobid=50667,2552984041&key=70203469&c=725434237887&pagestamp=sepghtjhowdqpsxuyn

You can also find several other jobs @http://bsu.ncl.ac.uk/support/recruitment/

https://www.mbl.edu/research/research-organisms/rotifer
https://gribblebiolab.org/

Functioning of efflux systems in Gram-negative bacteria
Determinants of the compound-efflux system interactions
Action of inhibitors on efflux systems
Structural and dynamical features of the efflux systems

TatA
Assembly of the TatA system
Study of the dynamical features of the charge zipper

Methods
Setup of a kinetic Monte Carlo (KMC) scheme to study the flux of antibiotics through porins and efflux systems
Setup of protocol to integrate MD results in a ligand-based approach

Viral inhibitors
Interactions of selected compounds with RNA-dependent RNA polymerases (RdRps) of HCV and BVDV
Assessment of the role of mutations in RdRps
Antimicrobial peptides

Interactions of antimicrobial peptides with membranes: structure and dynamics
Interactions between antimicrobial peptides in the presence of different membranes
Protein-protein interactions
Effects of mutations

Lab Page
http://www.dsf.unica.it/~paolo/Site/Home.html

https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

Address of the bookmark: https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

Genome Annotation and Functional Genomics

Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences. Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.

Genome and Molecular Evolution
Text and Data Mining

More @ http://bergmanlab.smith.man.ac.uk/

Nature Biotechnology spoke with Altschul and several other originators of computational biology software programs widely used today (Table 1). The conversations explored what makes certain software tools successful, the unique challenges of developing them for biological research and how the field of computational biology, as a whole, can move research agendas forward. What follows is an edited compilation of interviews.

Detail @ http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html

News Source @ Nature

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.