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

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.