<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/21443?offset=720 https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach Sat, 14 Dec 2024 12:31:14 -0600 https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach <![CDATA[Exploring Bacterial Comparative Genomics: A Bioinformatics Approach]]> In the world of microbiology, bacteria have long fascinated scientists for their diversity, adaptability, and crucial roles in ecosystems and human health. Comparative genomics—a field that involves analyzing and comparing the genomes of different organisms—has revolutionized our understanding of bacterial evolution, adaptation, and pathogenicity. By leveraging bioinformatics tools and techniques, researchers can uncover genomic insights that were once hidden. This blog delves into the principles, methodologies, and applications of bacterial comparative genomics from a bioinformatics perspective.

What is Bacterial Comparative Genomics?

Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:

  • Identify conserved and unique genes.

  • Explore genetic determinants of pathogenicity.

  • Understand bacterial evolution and phylogenetics.

  • Investigate horizontal gene transfer and its role in antibiotic resistance.

Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.

Key Steps in Bacterial Comparative Genomics

  1. Genome Sequencing and Assembly: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.

  2. Genome Annotation: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.

  3. Genome Alignment: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.

  4. Comparative Analyses:

    • Core and Pan-genome Analysis: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.

    • Phylogenetic Analysis: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.

    • Functional Enrichment Analysis: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.

 Recommended Bioinformatics Tools for Comparative Genomics

Here are some additional bioinformatics tools that can aid bacterial comparative genomics:

  • OrthoFinder: For accurate ortholog identification across multiple genomes.

  • PanOCT: Specifically designed for pan-genome clustering and annotation.

  • FASTANI: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.

  • CIRCOS: For visually comparing genomic data through circular genome plots.

  • Galaxy Platform: A user-friendly web-based platform offering numerous genomic analysis tools.

  • BLAST: Essential for sequence alignment and similarity searches.

  • PhyloSift: Focused on phylogenetic analysis of microbial genomes using marker genes.

These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.

Applications of Bacterial Comparative Genomics

  1. Understanding Pathogenicity: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of Escherichia coli strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.

  2. Antibiotic Resistance Research: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.

  3. Microbial Ecology and Evolution: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.

  4. Vaccine Development: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like Neisseria meningitidis.

  5. Biotechnology Applications: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.

Challenges in Bacterial Comparative Genomics

While the field has made significant strides, several challenges remain:

  • Data Overload: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.

  • Genome Plasticity: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.

  • Annotation Accuracy: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.

  • Interpreting Non-Coding Regions: Understanding the functional significance of non-coding genomic regions remains a challenge.

Future Directions

The integration of bacterial comparative genomics with other ‘omics’ approaches—such as transcriptomics, proteomics, and metabolomics—promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.

Conclusion

Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.

]]> LEGE https://bioinformaticsonline.com/blog/view/44705/pirna-and-bioinformatics-decoding-the-guardians-of-the-genome Sat, 07 Dec 2024 02:15:11 -0600 https://bioinformaticsonline.com/blog/view/44705/pirna-and-bioinformatics-decoding-the-guardians-of-the-genome <![CDATA[piRNA and Bioinformatics: Decoding the Guardians of the Genome]]> In the symphony of small RNAs, PIWI-interacting RNAs (piRNAs) stand out as the protectors of genomic integrity. These small, non-coding RNAs play critical roles in silencing transposable elements, regulating gene expression, and maintaining germline stability. The rise of bioinformatics has revolutionized our understanding of piRNAs, enabling researchers to decipher their biogenesis, functions, and evolutionary significance.

What Are piRNAs?

piRNAs are the largest class of small non-coding RNAs, typically 24–32 nucleotides in length. Unlike microRNAs (miRNAs) and small interfering RNAs (siRNAs), piRNAs do not rely on Dicer enzymes for maturation. Instead, they are processed from long single-stranded precursors and associate with PIWI proteins, a subclass of the Argonaute protein family.

The primary functions of piRNAs include:

  1. Silencing Transposable Elements: By targeting transposons, piRNAs prevent genomic instability, particularly in germline cells.
  2. Regulating Gene Expression: piRNAs modulate gene expression at transcriptional and post-transcriptional levels.
  3. Epigenetic Modulation: They guide epigenetic modifications, such as DNA methylation, to specific genomic loci.

Challenges in piRNA Research

Studying piRNAs is fraught with challenges, including:

  • Short Length: Their small size complicates sequencing and alignment.
  • Lack of Sequence Conservation: Unlike miRNAs, piRNAs exhibit limited sequence conservation across species.
  • Complex Biogenesis: The intricate pathways of piRNA generation require sophisticated computational tools to unravel.

Bioinformatics: Illuminating the World of piRNAs

Bioinformatics has emerged as an indispensable tool for studying piRNAs, facilitating their discovery, annotation, and functional analysis. Here's how bioinformatics is transforming piRNA research:

1. Identification and Annotation

The discovery of piRNAs relies on next-generation sequencing (NGS) data. Bioinformatics tools such as piRNApredictor and Piano identify piRNA clusters and predict potential targets. Databases like piRBase and piRNAdb curate information about known piRNAs, their sequences, and associated proteins.

2. Mapping and Alignment

piRNAs often originate from repetitive regions, making their alignment challenging. Tools like Bowtie and STAR handle the unique mapping requirements of piRNAs, enabling accurate identification of piRNA clusters in genomes.

3. Functional Analysis

Bioinformatics approaches predict piRNA functions by analyzing their interactions with transposons, genes, and epigenetic marks. Algorithms such as TargetFinder and RIblast explore piRNA-mRNA interactions, shedding light on regulatory networks.

4. Evolutionary Studies

piRNAs are evolutionarily diverse, reflecting their roles in species-specific genomic defense. Comparative genomics tools help trace the evolution of piRNA clusters and their associated PIWI proteins across species.

5. Epigenomic Insights

piRNAs are key players in epigenetic regulation. Bioinformatics pipelines integrate piRNA data with chromatin immunoprecipitation sequencing (ChIP-seq) and DNA methylation data to uncover their role in shaping the epigenome.

Case Study: piRNAs in Germline Integrity

One of the hallmark functions of piRNAs is the suppression of transposable elements in the germline. For example, in Drosophila melanogaster, piRNAs target retrotransposons like gypsy and copia. Bioinformatics analyses revealed that these piRNAs guide PIWI proteins to transposon-derived RNA, ensuring genome stability during gametogenesis.

Clinical Relevance of piRNAs

Recent studies suggest that piRNAs may serve as biomarkers for diseases such as cancer, infertility, and neurodegenerative disorders. For instance:

  • Cancer: Dysregulated piRNA expression has been linked to tumorigenesis, making them potential targets for cancer therapies.
  • Infertility: Aberrant piRNA pathways are implicated in male infertility due to their role in spermatogenesis.
  • Neurodegeneration: piRNAs may regulate neuronal gene expression, highlighting their potential in neurological research.

Future Directions

The integration of bioinformatics with emerging technologies offers exciting opportunities for piRNA research:

  • Single-Cell Sequencing: Unveiling cell-specific piRNA expression and function.
  • Machine Learning: Predicting piRNA functions and targets with greater accuracy.
  • CRISPR-Based Tools: Editing piRNA clusters to explore their roles in vivo.

Conclusion

piRNAs are the unsung guardians of the genome, safeguarding genetic material from transposable elements and contributing to gene regulation and epigenetic programming. Bioinformatics has opened the floodgates of discovery, unraveling the complexities of piRNAs and their myriad roles in biology and disease.

As we continue to decode the piRNA landscape, these small RNAs promise to unveil big secrets about genome stability, evolution, and human health, cementing their place as a fascinating frontier in molecular biology.

]]> LEGE https://bioinformaticsonline.com/blog/view/44720/a-beginners-guide-to-using-kraken-for-taxonomic-classification Fri, 13 Dec 2024 11:29:03 -0600 https://bioinformaticsonline.com/blog/view/44720/a-beginners-guide-to-using-kraken-for-taxonomic-classification <![CDATA[A Beginner's Guide to Using Kraken for Taxonomic Classification]]> Kraken is a popular bioinformatics tool designed for fast and accurate taxonomic classification of metagenomic sequences. Its efficiency and precision make it a go-to resource for analyzing microbial communities, including bacteria, viruses, archaea, and fungi. Whether you're new to bioinformatics or experienced in the field, Kraken is an indispensable tool for taxonomic analysis.

In this blog, we’ll walk through the basics of Kraken, from installation to running an analysis, and highlight its key features and applications.

What is Kraken?

Kraken is a sequence classification tool that assigns taxonomic labels to DNA sequences using exact k-mer matching. It uses a reference database of genomes, dividing sequences into k-mers and identifying matches in a computationally efficient way.

Key Features of Kraken

  • Speed: Kraken processes data much faster than alignment-based methods.
  • Accuracy: It uses a precise k-mer matching algorithm for high-resolution taxonomic assignments.
  • Scalability: It can handle large metagenomic datasets.
  • Custom Databases: You can build and use custom databases tailored to your research needs.

Installing Kraken

  1. System Requirements

    • A Unix-based operating system (Linux/macOS).
    • Sufficient computational resources for database building (RAM and disk space).

  2. Installation Steps

    • Clone the Kraken repository from GitHub:
       
      git clone https://github.com/DerrickWood/kraken.git cd kraken
    • Compile the Kraken binaries:
       
      make
    • Add Kraken to your PATH for easy access:
       
      export PATH=$PATH:/path/to/kraken

Preparing a Database

Kraken requires a database of reference genomes. You can use a pre-built database or create a custom one.

  1. Downloading a Pre-built Database
    Kraken offers pre-built databases, such as the MiniKraken database, which is lightweight and suitable for smaller datasets. Download it using:

    kraken-build --download-library minikraken

  2. Building a Custom Database
    To include specific genomes, download FASTA files and build the database:

    kraken-build --download-library bacteria --threads 4 --db my_database kraken-build --build --db my_database

    This process may take considerable time and resources, depending on the size of the database.

Running Kraken

Once the database is ready, you can classify sequences.

  1. Basic Usage
    Use the following command to classify sequences:

    kraken --db my_database --threads 4 --fastq-input input_sequences.fastq --output kraken_output.txt

    Key options:

    • --db: Specifies the database.
    • --threads: Number of threads for parallel processing.
    • --fastq-input: Indicates input file format (FASTQ/FASTA).

  2. Interpreting Results
    Kraken generates an output file with columns for sequence IDs, taxonomic classifications, and the confidence score.

Visualizing Kraken Results

Kraken results can be visualized using tools like Krona or converted to human-readable reports using kraken-report.

  1. Generate a Report

    kraken-report --db my_database kraken_output.txt > kraken_report.txt

  2. Krona Visualization
    Install Krona and convert Kraken output for visualization:

    cut -f2,3 kraken_output.txt | ktImportTaxonomy -o krona_output.html

    Open the HTML file in your browser to interactively explore the taxonomic classifications.

Advanced Usage

  1. Confidence Thresholds
    Adjust the confidence threshold for classification using the --confidence option. Higher values reduce false positives but may miss some true positives:

    kraken --db my_database --confidence 0.1 --fastq-input input.fastq

  2. Paired-End Reads
    For paired-end sequencing data, use:

    kraken --db my_database --paired reads_1.fastq reads_2.fastq

  3. Customizing K-mers
    Kraken allows you to set custom k-mer lengths during database building for specific applications.

Applications of Kraken

  • Microbial Ecology: Characterizing microbial communities in soil, water, and the human microbiome.
  • Pathogen Detection: Identifying pathogens in clinical samples.
  • Fungal Research: Analyzing fungal diversity in metagenomic datasets.
  • Environmental Monitoring: Tracking microbial populations in diverse habitats.

Conclusion

Kraken is a versatile and efficient tool for taxonomic classification in metagenomics. Its speed, accuracy, and flexibility make it a favorite among bioinformaticians. By following this guide, you can set up and use Kraken to unlock insights into microbial and fungal communities, paving the way for discoveries in ecology, medicine, and biotechnology.

]]> Neel https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools Tue, 04 Nov 2025 07:55:53 -0600 https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools <![CDATA[Predicting Pathogen Virulence Using Bioinformatics Tools]]> In the genomic era, the ability to predict the virulence potential of pathogens has become an indispensable part of infectious disease research. With the exponential growth of microbial genome data, bioinformatics tools now enable scientists to identify virulence factors, model pathogen behavior, and even forecast outbreak risks — all from sequence data.

In an age where pathogens continue to evolve and cross boundaries, understanding what makes them virulent—that is, capable of causing disease—has become a critical focus in modern microbiology and genomics. Virulence prediction bridges computational biology, genomics, and machine learning to forecast the pathogenic potential of microbes before they strike.

What Is Virulence?

Virulence refers to the degree of damage a pathogen can inflict on its host. It is determined by a combination of genetic factors—called virulence factors (VFs)—that allow the organism to attach, invade, evade, and harm the host. These include genes coding for toxins, secretion systems, adhesins, and enzymes that disrupt host defenses.

Understanding virulence factors not only helps in deciphering the mechanisms of infection but also provides early warning signs for emerging threats.

Why Predict Virulence?

Traditional virulence studies relied heavily on experimental infection models, which, although accurate, are time-consuming, expensive, and ethically constrained.
Today, the availability of whole-genome sequences and large-scale pathogen databases has paved the way for in silico virulence prediction—a computational approach that can screen thousands of genomes within hours.

This approach enables researchers to:

  • Rapidly identify potential high-risk strains.

  • Prioritize pathogens for containment, surveillance, or further study.

  • Guide vaccine development and drug target discovery.

  • Support One Health frameworks, linking animal, human, and environmental health data.

How Is Virulence Predicted?

Virulence prediction combines bioinformatics pipelines with machine learning and comparative genomics. The process generally involves:

  1. Genome Annotation: Identifying genes and coding sequences in microbial genomes.

  2. Feature Extraction: Comparing sequences with curated databases like VFDB (Virulence Factor Database), PATRIC, or Victors.

  3. Pattern Recognition: Using algorithms (e.g., Random Forest, SVM, or deep learning models) to classify genes or strains as virulent or non-virulent based on sequence patterns, motifs, and protein domains.

  4. Scoring and Visualization: Assigning a virulence score or confidence level and visualizing it through heatmaps or genome maps.

Tools and Resources for Virulence Prediction

A number of tools and databases make virulence prediction accessible to the scientific community:

  • VFanalyzer – For identifying virulence genes based on VFDB.

  • PathoFact – Predicts virulence, antimicrobial resistance (AMR), and toxin genes from metagenomic data.

  • Pangenome-based models – Identify virulence-associated gene clusters across strains.

  • Machine learning models – Use features like GC content, codon usage bias, or protein domains to predict pathogenicity.

Emerging tools now integrate multi-omic data—including transcriptomics, proteomics, and metabolomics—to understand virulence in a systems biology framework.

Applications in the Real World

Virulence prediction has major implications across public health and research sectors:

  • Epidemic preparedness: Early identification of virulent strains in outbreak samples.

  • AMR surveillance: Linking virulence profiles with antibiotic resistance determinants.

  • Environmental monitoring: Predicting pathogenic potential of soil or waterborne microbes.

  • Clinical diagnostics: Supporting personalized treatment through pathogen profiling.

For instance, integrating virulence prediction pipelines into national surveillance networks could enable faster risk assessment and response to infectious outbreaks.

The Road Ahead

As machine learning and genomics advance, virulence prediction will evolve from simple gene-based detection to dynamic, context-aware models that account for host–pathogen interactions, environmental signals, and evolutionary adaptation.

Future tools may predict not just if a strain is virulent, but under what conditions it expresses that virulence—bridging the gap between genotype and phenotype.

In Summary

Virulence prediction is redefining how we understand and anticipate infectious diseases. By coupling genomic insights with computational intelligence, researchers can identify potential threats earlier, design smarter interventions, and ultimately, strengthen our preparedness against emerging pathogens.

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system Mon, 19 Aug 2013 15:24:26 -0500 https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system <![CDATA[What Junk DNA? It’s an Operating System]]> The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as “junk DNA,” is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/

]]> Rahul Agarwal https://bioinformaticsonline.com/researchlabs/view/6130/rna-bioinformatics-and-high-throughput-analysis-jena Sat, 09 Nov 2013 20:03:56 -0600 <![CDATA[RNA Bioinformatics and High Throughput Analysis Jena]]> Research Topics:

High Throughput Sequencing Analysis
Comparative Genomics
Identification and Annotation of Non-coding RNAs
Bioinformatic Analysis and System Biology of Viruses
Coevolution of Proteins and RNAs
Algorithmic Bioinformatics
Phylogenetic Analysis

http://www.rna.uni-jena.de/index.php

]]> https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers Wed, 02 Apr 2014 06:52:28 -0500 https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers <![CDATA[List of bioinformatics companies and genomics service providers]]> Plz check out link for bioinformatics and genomics companies. 

Address of the bookmark: http://grouthbio.com/Genome_Software_Service.php

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/17946/7th-international-conference-on-bioinformatics-and-computational-biology-bicob Mon, 06 Oct 2014 16:19:36 -0500 https://bioinformaticsonline.com/bookmarks/view/17946/7th-international-conference-on-bioinformatics-and-computational-biology-bicob <![CDATA[7th International Conference on Bioinformatics and Computational Biology (BICoB)]]> In recent years, computational biology and medical informatics have seen significant advances driven by computational techniques in bioinformatics making bioinformatics and computational biology among the most vibrant research areas. The 7th international conference on Bioinformatics and Computational Biology (BICoB-2015) provides an excellent venue for researchers and practitioners in the fields of bioinformatics and computational biology to present and publish their research results and techniques. The BICoB conference seeks original and high quality papers in the fields of bioinformatics, computational biology, systems biology, medical informatics and the related disciplines. We also encourage work in progress and research results in the emerging and evolutionary computational areas. Computational techniques have already enabled unprecedented advances in modern biology and medicine. Work in the computational methods related to, or with application in, bioinformatics is also encouraged including: data mining, text mining, machine learning, modeling and simulation, pattern recognition, data visualization, biostatistics, .etc. The topics of interest include (and are not limited to): 
Genome analysis: Genome assembly, genome annotation, gene finding, alternative splicing, EST analysis and comparative genomics. 
Sequence analysis: Multiple sequence alignment, sequence search and clustering, function prediction, motif discovery, functional site recognition in protein, RNA and DNA sequences. 
Phylogenetics: Phylogeny estimation, models of evolution, comparative biological methods, population genetics. 
Structural Bioinformatics: Structure matching, prediction, analysis and comparison; methods and tools for docking; protein design 
Analysis of high-throughput biological data: Microarrays (nucleic acid, protein, array CGH, genome tiling, and other arrays), EST, SAGE, MPSS, proteomics, mass spectrometry. 
Genetics and population analysis: Linkage analysis, association analysis, population simulation, haplotyping, marker discovery, genotype calling. 
Systems biology: Systems approaches to molecular biology, multiscale modeling, pathways,gene networks. 
Computational Proteomics: Filtering and indexing sequence databases, Peptide quantification and identification, Genome annotations via mass spectrometry, Identification of post-translational modifications, Structural genomics via mass spectrometry, Protein-protein interactions, Computational approaches to analysis of large scale Mass spectrometry data, Exploration and visualization of proteomic data, Data models and integration for proteomics and genomics, Querying and retrieval of proteomics and genomics data etc.

Authors of selected high quality papers in BICoB-2015 will be invited to submit extended version of their papers for possible publication in bioinformatics journals (Journal of Bioinformatics and Computational Biology JBCB).

Deadlines:

Paper Submission Deadline October 24, 2014
Notification of Acceptance December 15, 2014
Camera-Ready Manuscript January 16, 2015

Address of the bookmark: http://www.cs.umb.edu/bicob/

]]> Rahul Agarwal https://bioinformaticsonline.com/videolist/watch/7288/critical-to-discoveries-in-bioinformatics Mon, 16 Dec 2013 17:13:24 -0600 https://bioinformaticsonline.com/videolist/watch/7288/critical-to-discoveries-in-bioinformatics <![CDATA[Critical to discoveries in bioinformatics]]> EMBL-EBI distributes datasets worldwide using the Janet network. This biological data enables the discovery of new drugs, new diagnostics and increasingly new agro-chemicals. Their work, which includes the 1000-genome project, has generated petabytes of data and this growth is showing no signs of abating. On-demand bandwidth over Janet will therefore be critical to their ongoing work.]]> https://bioinformaticsonline.com/opportunity/view/22179/marie-curie-phd-position-available-immediately Fri, 24 Apr 2015 09:23:57 -0500 <![CDATA[Marie Curie PhD position available immediately]]> Sub-project 10: Development of bioinformatic tools for the analysis of MACE data
Host Organizations GenXPRO (Germany)
Objectives : The ESR will be in charge of standardising pipelines that will be used for RNA-seq and MACE analyses by all the participants. He will be involved in performing next generation sequencing to characterise environmental adaptation. A single pipeline to analyse listerial transcriptomic and proteomic data will be developed and implemented by each partner for the sake of uniformity of all the data produced within List_MAPS. The ESR will be involved in the interpretation of transcriptomic and proteomic data for which pathway analyses and good data visualization will be required. A cytoscape app will be developed as visualization tool.
Expected Results: MACE analysis pipeline. Database. Transcriptome comparisons in selected habitats. Data visualization tool.
Duration (months) 24
Contact Dr. Bjorn ROTTER: rotter@genxpro.de

11. Development of innovative tools for rapid phenotypic characterisation of intraspecific diversity of Listeria monocytogenes (Joint supervision PhD)
Host Organizations BioFilm Control (France) and GenXPRO (Germany)
Objectives
1. The ESR will develop an assay to test biofilm phenotype in a large array of food processing-related environmental conditions (salt, acides, disinfectants, preservatives) in BFC facilities. He will be in charge of the development and validation of an in silico virulence assay. This assay will target specific mRNAs in order to estimate the virulence potential of strains of L. monocytogenes. Transcript targets will be selected and tested by qPCR in GXP premises. In the process of validation, virulence results of several strains collected in a humanised mouse model will be compared with the in silico analysis. Once these innovative tools will be validated, intraspecific phenotypic diversity (biofilm and virulence) will be assessed on a collection of environmental and clinical isolates of L. monocytogenes. Genotypic diversity will be assessed under the supervision of GPX.
Expected Results : Adaptation of the BioFilm Ring test R to test food processing environmental conditions. Development of an innovative in silico virulence assay surrogate to animal models. Diversity results will inform stakeholders on the level of health hazard according to the strain. This in turn will help secure food safety all along the shelf life of foodstuff.
Duration (months) 36
Contact : Dr. Thierry BERNARDI: thbe@biofilmcontrol.com
Dr. Bjorn ROTTER: rotter@genxpro.de
ELIGIBLE CRITERIA of Marie Sklokowska Curie actions:
Researchers may be of any nationality
Candidates shall at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers. Full-time equivalent research experience is measured from the date when a researcher obtained the degree which would formally entitle him or her to embark on a doctorate, either in the co

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