The project is on bacterial gene gain and loss and emerging pathogenicity, and is described in full here: https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015 . The EMBL-EBI / Cambridge Computational Biomedical Postdoctoral (“EBPOD”)

The closing date for applications is 3 September 2015. Nick Goldman EMBL-European Bioinformatics Institute Nick Goldman

More at https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015

Gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases.

Lab homepage @ http://ritchielab.psu.edu/ritchielab/

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

Essential requirements: Experience of using bioinformatics tools.

Experience of working in Linux. Basic knowledge of computer network administration.

Desirable: Knowledge of Linux installation/administration and proficiency in either of the following:

Shell/PERL/Java/Python/VB/Oracle/MySQL/C/CUDA.

Qualification: PhD. or First class M.Sc degree in Bioinformatics or Biotechnology/life science with specialization in Bioinformatics.

Fellowships: Rs 22,000/- with HRA for PhD qualified, Rs 16000/- with HRA for NET/BET/BINC/GATE qualified and 12000/- with HRA for non NET qualified applicants.

Interested candidates may send their complete biodata along with a write-up of their experience and suitability for the position to Dr. Dinesh Gupta by email only to dinesh@icgeb.res.in within 15 days of publication of this advertisement. Kindly mark the email with subject “Application for BIF-RA-2013”

Closing date for applications: 18 October 2013

Only short listed candidates will be invited for an interview at ICGEB.

No TA/DA will be paid for attending the interview.

Advertisement: http://www.icgeb.org/tl_files/Vacancies/BIF-RA-Advt.pdf

We are looking for a highly motivated candidate with a PhD degree in
Bioinformatics or a related field. Candidates are expected to have a
strong background in evolutionary biology and/or comparative functional
genomics. Additional experiences in single cell functional genomics
analyses, statistics and computational data analyses are a plus, as is
an interest in comparative developmental (EvoDevo) questions.

We offer a dynamic and interactive research environment with state-of-the
art research facilities, good research funding and internationally
competitive salaries.

The Tschopp lab (www.evolution.unibas.ch/tschopp/research/)
studies the gene regulatory mechanisms of cell type
specification and evolution in vertebrates. See also our
preprints at https://doi.org/10.1101/2024.03.26.586769 and
https://doi.org/10.1101/2024.11.28.625862 Applications should include
a motivation letter, a CV, a list of publications, a statement about
research interests, as well as the names and contact details of at
least two referees. Applications (in the form of a single .pdf file)
should be sent to Patrick Tschopp (patrick.tschopp@unibas.ch); review
of applications will begin on January 1st 2025, and will continue until
the position is filled.

Patrick Tschopp

Applications on plain paper along with details of CV (relevant photocopies of their
qualifications/experience and reprints of published work to be attached) are invited from qualified candidates for Research Fellowship in CSIR- sponsored research project.

JRF/SRF/RA (one vacancy)

CSIR sponsored “In silico design, identification and in vitro validation of lead molecule inhibitors to Bcr-Abl kinase”

JRF: M.Sc in Chemistry/ Bioinformatics/ Biotechnology with I division and NET or GATE qualified

SRF: M.Sc in chemistry/ Bioinformatics/ Biotechnology with at least two years of post- M.Sc research experience as evidenced from published papers in standard refereed journals in relevant area

RA: PhD in chemistry/ Bioinformatics/ Biotechnology with research experience in
relevant area.

As per CSIR guidelines

Notes:
1) You may visit the University of Hyderabad website www.uohyd.ernet.in to learn more about the University of Hyderabad.
2) Applicants should note that the appointment to be made is purely temporary and there is no right for claiming for any regular appointment in the University.
3) No TA/DA will be paid for attending the interview or at the time of joining the post, if selected.
4) The application should be submitted by post/courier/in-person to the address given below on or before November 1st 2013.

Prof. Lalitha Guruprasad
W-103, Gurbakhsh Singh Building
School of Chemistry
University of Hyderabad
Hyderabad- 500 046
5) Short-listed candidates will be called for interview at a short notice.

Advertisement: http://www.uohyd.ac.in/images/recruitment/chemisry_advt_101013.pdf

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.

Available immediately until 30th November 2015, to work on the development of bioinformatics approaches to aid analysis of data derived from the metabolomic profiling of biological matrices. The successful applicant will lead research activities on an FP7 funded EU-wide collaborative project aimed at establishing biomarker-based strategies for high throughput diagnostic screening. Key tasks will involve multivariate analysis of large datasets, bioinformatic-based selection and validation of identified markers, construction of metabolomic spectral profile databases and development of machine learning/database searching approaches amenable to analytical screening techniques. This position will offer the opportunity to travel and undertake work with project collaborators based in the Republic of Ireland and Europe.

Informal enquiries may be directed to Dr Terry McGrath, email: terry.mcgrath@qub.ac.uk.

Anticipated interview date: Thursday 31st October 2013
Salary scale: £30,424 – £39,649 per annum (including contribution points)
Closing date: Monday 21st October 2013

Telephone (028) 90973044 FAX: (028) 90971040 or e-mail on personnel@qub.ac.uk

The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability.

Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

More @ https://hrwebapp.qub.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=5616943npO&WVID=6273090Lgx&LANG=USA

While the expectations are not entirely off base, the reality of life as a bioinformatician is a mix of exciting discoveries, troubleshooting, and, let’s admit it, a fair amount of frustration. Here’s what it’s really like:

1. Expectation: Seamlessly Working with Perfect Datasets

Reality: You often receive messy, incomplete, or poorly annotated datasets. Hours are spent cleaning, normalizing, and validating data before you even begin your analysis. "Garbage in, garbage out" is a constant reminder in your workflow. Tools designed to handle these problems exist, but they require significant customization, which adds another layer of complexity.

2. Expectation: Effortless Multidisciplinary Integration

Reality: Bridging biology and computational science is far from straightforward. You need to be proficient in both domains while keeping up with advancements in genomics, machine learning, and statistics. Additionally, collaborating with biologists who might not be fluent in computational jargon requires patience and effective communication skills.

3. Expectation: Rapid, Groundbreaking Results

Reality: Analysis often involves waiting—waiting for scripts to run, pipelines to complete, or software to install. Bioinformatics projects are iterative; you analyze, debug, and refine repeatedly. A single project might take months to complete due to unforeseen challenges, like computational bottlenecks or the need for additional experiments.

4. Expectation: Beautiful Visualizations with a Click

Reality: While tools like R, Python, and specialized software can create stunning plots, generating a publication-ready visualization requires significant effort. You’ll spend hours tweaking axes, labels, and color palettes, ensuring clarity and accuracy.

5. Expectation: All Work, No Bugs

Reality: Debugging is an integral part of the job. Whether it’s a misconfigured server, a script throwing unexpected errors, or a pipeline breaking due to an update, you’ll develop a knack for problem-solving under pressure.

6. Expectation: Ample Time for Skill Development

Reality: Bioinformatics moves fast. Juggling ongoing projects, tight deadlines, and the constant stream of new tools and algorithms leaves little time for leisurely learning. Staying updated requires proactive effort—evenings, weekends, or dedicated study breaks.

7. Expectation: Publishing Papers Regularly

Reality: Publishing in bioinformatics is a marathon, not a sprint. Your analysis needs to be thorough, reproducible, and supported by strong biological insights. Reviewers often demand additional experiments or clarifications, stretching the timeline even further.

8. Expectation: A Clear Career Path

Reality: Bioinformatics offers diverse career paths, from academia and industry to healthcare and government. However, the choice can be daunting, with each path requiring unique skill sets and presenting different challenges. Navigating these options takes time, research, and sometimes trial and error.

Finding Joy in the Chaos

Despite these challenges, being a bioinformatician is immensely rewarding. You are at the forefront of science, enabling discoveries that impact medicine, agriculture, and the environment. The thrill of uncovering insights hidden in complex datasets and the satisfaction of solving biological puzzles make the hard work worthwhile.

Advice for Aspiring Bioinformaticians

• Embrace Learning: The field is ever-evolving. Stay curious and adaptable.
• Develop Communication Skills: Bridging the gap between biology and computation is as much about explaining your methods as it is about applying them.
• Find a Community: Collaborate with peers, join forums, and attend conferences to stay inspired and updated.
• Celebrate Small Wins: Every cleaned dataset, successful script, or informative plot is a step forward.

Bioinformatics is a blend of science, technology, and artistry. While the reality might not match the polished expectations, the journey is nothing short of exhilarating. If you’re ready to embrace the chaos and keep learning, the field of bioinformatics will never cease to amaze you.