https://labs.utsouthwestern.edu/bioinformatics-lab/positions

Candidates must have: a PhD degree in statistics, biostatistics or bioinformatics, extensive experience in analyzing high-dimensional data (microarray, SNP, CNVs) and of validation approaches. In addition, experience in penalized regression methods, data base manipulation; and strong programming skills in order to conduct Monte Carlo studies and applications (R). Candidate must have excellent communication skills (verbal, written and presentation), a strong proficiency in Linux system.

This position is available immediately and will be filled as soon as possible. Appointment could be extended beyond the first year based on additional funding.

For more information about the Department of Biostatistics and Bioinformatics, please visit our website: http://www.biostat.duke.edu.

For more info: http://biostat.duke.edu/sites/biostat.duke.edu/files/Halabi%20-%20Postdoc%20Job%20Posting%202013%20updated.pdf

Duke University is an Equal Opportunity/Affirmative Action Employer.

https://evomics.org/2024-workshop-on-genomics/

Address of the bookmark: https://evomics.org/2024-workshop-on-genomics/

About the person:

Essential criteria:

Hold or be about to obtain* a PhD in Computational biology, Bioinformatics, computing science or related subjects. (*must be obtained within 3 months of the closing date for the post) or MSc equivalent with at least 3 years' work experience in a relevant role.
Significant relevant research experience in genomics or work experience in a relevant technical/scientific role.
Significant experience in managing and analysing NGS data and other big data.
Experience in developing and maintaining analysis pipelines.
Experience working with Linux/UNIX environments.
Proficiency with python, bash, R and/or equivalent languages.
To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information document linked on our website.

More at https://hrwebapp.qub.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open

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

Foreign nationals intending to have certification are eligible to appear for BINC examination.
Minimum qualification includes a degree from a recognized university/institute in the areas listed in FAQ.
Formal training in the area of Bioinformatics is not a prerequisite.
Note that the foreign students will only be certified by DBT and are not eligible for the cash award as well as junior research fellowship.

Address of the bookmark: http://binc.scisjnu.ernet.in/

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.

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.