https://github.com/kakitone/MetaFinisherSC

https://github.com/kakitone/BIGMAC

Address of the bookmark: https://github.com/kakitone/BIGMAC

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

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/

Address of the bookmark: https://github.com/b-brankovics/grabb

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

Address of the bookmark: http://www.phrap.org/consed/consed.html

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

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

• FASTQC: For quality control of raw RNA-seq reads.
• Trimmomatic: For trimming and filtering RNA-seq reads.
• HISAT2/STAR: High-performance aligners for RNA-seq reads.
• FeatureCounts: For quantifying gene expression.
• DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

• Trinity: For de novo transcriptome assembly.
• StringTie: For transcript assembly and quantification from RNA-seq alignments.
• TransDecoder: To predict coding regions within assembled transcripts.
• TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

• Infernal: For identifying ncRNA sequences based on covariance models.
• Rfam: Database and tools for ncRNA families.
• miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

• RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
• RNAstructure: Tools for RNA secondary structure prediction and analysis.
• MEME Suite: For identifying motifs in RNA sequences.
• IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

• REDItools: For RNA editing analysis.
• m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

• FLAIR: For isoform-level analysis of long-read RNA-seq data.
• NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

• RBPmap: For identifying RNA-binding protein motifs.
• PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

• getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
• ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

• IGV: Genome browser for visualizing RNA-seq alignments.
• Circos: Circular visualization of RNA-seq data.
• DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.