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

Offer
We offer a full-time contract for two years. The contract starts between October 2021
and December 2021. The position involves no or extremely light teaching load, if the
candidate is interested. Salaries are competitive at the European level. The recruited
person will benefit from the Belgian social insurance scheme (health care, etc.) without
additional expenses.

Profile
Applicants are expected to show outstanding commitment to research and must have
obtained a PhD by the start of the position. A strong expertise in genomics is required.
More specifically, solid competences in bioinformatics (e.g. scripting pipelines) and in
genome evolution are needed. Knowledge or interest regarding recombination,
metazoan evolution, phylogenomics and population genomics is an added-value.

Application
Applications should be submitted via email to karine.van.doninck@ulb.be. The
application package should contain the following documents:
- A curriculum vitae with the complete list of publications
- A cover letter mentioning why the candidate is interested in the position
- Minimum 2 recommendation letters
Interviews: Interviews will be conducted with the selected candidates. Selected
candidates could also be invited to give a seminar to MBE ULB.
For any additional information, please contact karine.van.doninck@ulb.be

SciGenom Research Foundation (SGRF) and Institute of Genomics and Integrative Biology (IGIB) are pleased to host the Next-Generation Sequencing and Bioinformatics for Genomics & Healthcare conference.

In the ten years since the first human reference genome was completed for US$3 billion the sequencing technologies have radically changed leading to great reduction in sequencing cost. Today a human genome can be sequenced for under US$ 5000 in less than two weeks. It is expected that by the end of 2015 the cost of sequencing a human genome will drop to below thousand dollars. The next generation sequencing technologies over the past five years have enabled a large number of genomic studies that impact human health and disease. Also, this has made possible the growth of microbial, animal and plant genomics studies. While the data production has increased at a rapid pace challenges remain in analyzing and understanding the data. The conference will cover the next generation sequencing (NGS) technologies, bioinformatics for NGS and applications of NGS in many areas including personalized medicine.

For more info : http://www.scigenomconferences.com/2013/default.php

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.

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

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

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.

Address of the bookmark: http://www.rdatamining.com/

Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.

Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.

choose rows where column 3 is larger than column 5:

awk '$3>$5' input.txt > output.txt

extract column 2,4,5:

awk '{print $2,$4,$5}' input.txt > output.txt

awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt

show rows between 20th and 80th:

awk 'NR>=20&&NR<=80' input.txt > output.txt

calculate the average of column 2:

awk '{x+=$2}END{print x/NR}' input.txt

regex (egrep):

awk '/^test[0-9]+/' input.txt

calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:

awk '{print $0,$2+$3}' input.txt

awk '{$1=$2+$3;print}' input.txt

join two files on column 1:

awk 'BEGIN{while((getline<"file1.txt")>0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt > output.txt

count number of occurrence of column 2 (uniq -c):

awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt

apply "uniq" on column 2, only printing the first occurrence (uniq):

awk '!($2 in l){print;l[$2]=1}' input.txt

count different words (wc):

awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt

deal with simple CSV:

awk -F, '{print $1,$2}'

substitution (sed is simpler in this case):

awk '{sub(/test/, "no", $0);print}' input.txt

OK now here's where to read this stuff properly explained. roll

Two thorough tutorials:

http://www.gnu.org/software/gawk/manual/gawk.html

http://www.grymoire.com/Unix/Awk.html

A famous list of useful one-liners - though they're short, many are quite tricky:

http://www.pement.org/awk/awk1line.txt

And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!

http://www.catonmat.net/blog/awk-one-li … -part-one/

http://www.catonmat.net/blog/ten-awk-ti … -pitfalls/