Applications are invited for the position of Lecturer in Evolutionary Biology (Bioinformatics).

We are seeking a person with a relevant doctorate, and demonstrated potential to develop as an outstanding researcher and teacher in evolutionary bioinformatics in the Department of Zoology. The position affords an exciting opportunity for an emerging scholar to research and teach in a vibrant and diverse Department. The successful candidate will develop a transformative and collaborative research program, supporting the university's commitment to excellence in research.

Your skills and experience

A PhD with a background in analysis of high-throughput sequencing data and evolutionary biology.
Knowledge of and familiarity with a range of bioinformatics skills, concepts, and practices as they relate to the biology of animals, including genomic, transcriptomic and metabarcoding data analyses.
A strong interest, and experience, in research and teaching of bioinformatics and evolutionary genomics.
An ability to contribute to teaching and learning environments that support engagement of students and staff with bioinformatics and genomics.
Be committed to and or have established connections or track record of working with national and local bioinformaticians.
Be committed to being a productive collaborator with a track record of working collegially.
Further details

This is a confirmation-path (tenure track) position at the level of Lecturer. The successful candidate is expected to take up duties by 1 July 2021.

To see a full job description and to apply online go to: https://otago.taleo.net/careersection/2/jobdetail.ftl?job=2100342

More at http://www.ircc.iitb.ac.in/IRCC-Webpage/rnd/RecruitmentGenerateCircular.jsp?srno=2013086

- Plan more efficient experiments
- Correctly interpret results
- Communicate results in publications more effectively

The course focus is on methodologies, not on particular software tools. After the course participants should be able to apply the methods in their respective environment. However, during the course, hands-on sessions will be performed using the Genedata Expressionist® software, which enables participants to quickly apply the discussed methods and visualize results. No previous knowledge on Expressionist® is required; access to the software is free of charge during the course.

More @ http://www.dixa-fp7.eu/dixa-training/dixa-training-agenda/genedata-academy#!

Plants represent an untapped resource of natural bioactive compounds that significantly contribute to plant resilience to pathogens, herbivores, and abiotic stresses, and may be applied for medicine or crop protection. In this project, you will design and/or apply innovative omics integration strategies for genomics, transcriptomics, and metabolomics data, to discover plant specialized metabolite biosynthetic pathways and study their evolution. You will work together with the other PhD candidate in this project, which will entail a combination of algorithm development, greenhouse experiments, integrative omics analysis, and evolutionary genomics. Extensive local and international collaboration is foreseen, including possibilities for a foreign research visit as part of your PhD project.

The research is embedded within the chairs of Bioinformatics and Biosystematics. The projects will be (co-)supervised by Dr. Marnix Medema, Dr. Justin van der Hooft, Dr. Klaas Bouwmeester and Prof. Dr. Eric Schranz.

We ask

We are looking for two enthusiastic and complementary team players with all or a subset of the following skills:

a solid academic record (MSc) in bioinformatics, biology, or biotechnology
experience in computational omics analysis and proficiency in programming (in, e.g., Python)
at least basic to intermediate statistical and mathematical skills
demonstrable experience in working with next-generation sequencing data or with greenhouse experiments with plants
affinity with plant science, metabolism and/or biosynthetic pathways
you meet all the entry requirements of the WUR PhD programme.
More information

For more information about this position, please contact Marnix Medema, Associate Professor Bioinformatics, by email (marnix.medema@wur.nl).
For more information about the procedure, please contact vacaturemeldingen.psg@wur.nl.

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/

The bioinformatics chat is produced by Roman Cheplyaka and hosted by Roman and Jacob Schreiber.

Address of the bookmark: https://bioinformatics.chat/

ECM3413 - Bioinformatics

Contents

General Information

Lecture Slides

Past Exam Paper

Continuous Assessments

Suggested Reading List

General Information

	This module runs in Semester 2.
	It is taught by Dr Ed Keedwell (Module Coordinator)
	Module Descriptor:  ECM3413
	Lecture Times: Tuesday 5pm,  171| Thursday, 171
	Workshop Times: Wednesday 11am Blue Room (Weeks 29,33 &40)
	Assessment: 2 CAs each worth 15% | 1 Examination worth 70%

Lecture Slides (if you have to print slides, to save your ink choose 'print in black and white' on the print menu)

	PPT|PDF| Lecture 1 - Introduction to Bioinformatics (& Biology)
	PPT|PDF| Lecture 2 - Genome Sequences: from fragments to sequences
	PPT|PDF| Lecture 3 - Sequence Alignment 1
	PPT|PDF| Lecture 4 - Global Pairwise Sequence Alignment
	PPT|PDF| Lecture 5 - Local Pairwise Sequence Alignment (Smith-Waterman & BLAST)
	DOC| Workshop 1 - Using BLAST and other Bioinformatics Databases
	PPT|PDF| Lecture 6 - Multiple Sequence Alignment
	PPT|PDF| Lecture 7 - BLAST (in more detail) & FASTA
	PPT|PDF| Lecture 8 - Sequence Alignment Conclusion & Other Sequence Analyses
	PPT|PDF| Lecture 9 - Markov Chains and Intro to Hidden Markov Models
	PPT|PDF| Lecture 10 - Hidden Markov Models
	PPT|PDF| Lecture 11 - Classification in Bioinformatics
	DOC|Workshop 2 - Using See5
	Workshop Data - Part 1 - adult.names | adult.data | adult.test, Part 3 - wdbc.names| wdbc.data
	PPT|PDF| Lecture 12 - Gene Expression Data
	PPT|PDF| Lecture 13 - Decision Trees and Gene Expression Classification
	PPT|PDF| Lecture 14 - Other Methods for Gene Expression Classification
	PPT|PDF| Lecture 15 - Gene Regulation
	PPT|PDF| Lecture 16 - Neural Networks in Gene Expression Analysis
	PPT|PDF| Lecture 17 - Genome Analysis
	PPT|PDF| Lecture 18 - Conclusion/Revision Lecture

For some reason best known to itself, my PDF creator doesn't like the slide with the substitution matrix on.  Therefore this has been removed from Lectures 3 and 7 for the PDF copy only - however, more information on these matrices can be found here.

Past Exam Paper

The paper from 2007/8 can be found here.

Continuous Assessments

	PDF|  CA1 - Manual Sequence Alignment
	PDF|Promoter.names|Promoter.data|ML.names|ML.data| CA2 - Data Mining in Bioinformatics

Suggested Reading List

General Bioinformatics

<="top">Xiong, J., (2006) Essential Bioinformatics, Cambridge University Press

	Lesk, A.M., (2002) Introduction to Bioinformatics, Oxford University Press
	Higgs, P.G., (2005) Bioinformatics and Molecular Evolution,  Blackwell Publishing

Machine Learning in Bioinformatics

	Baldi, P., Brunak, S., (2001) Bioinformatics: The Machine Learning Approach, MIT Press
	Keedwell, E., Narayanan, A., (2005) Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems, Wiley

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.