<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/8265?offset=90 https://bioinformaticsonline.com/researchlabs/view/44226/rotifers-lab Wed, 08 Mar 2023 23:23:14 -0600 <![CDATA[Rotifers Lab]]> For scientists in the MBL’s Gribble Lab, the rotifer (Brachionus manjavacas) is used as a model organism to study evolution, stress responses, the biology of aging, and maternal effects. Rotifers are small, easy to grow in the lab, have a short lifespan, and share many of their genes with humans. That makes them ideal specimens in which to address questions relevant to human health as well as understand basic biological and evolutionary processes. Brachionus rotifers produces eggs that can be completely dried and frozen for decades, then hatch within a day when exposed to water and light.

https://www.mbl.edu/research/research-organisms/rotifer
https://gribblebiolab.org/

]]> https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau Mon, 30 Sep 2013 11:34:09 -0500 https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau <![CDATA[Bioinformatics Algorithms (Part 1) with Pavel Pevzner, Phillip E. C. Compeau,]]>

The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.

]]> https://bioinformaticsonline.com/opportunity/view/5255/walk-in-interview-indian-agricultural-statistics-research-institute Wed, 02 Oct 2013 15:40:17 -0500 <![CDATA[Walk-in-Interview @ Indian Agricultural Statistics Research Institute]]> Indian Agricultural Statistics Research Institute
Library Avenue, Pusa, New Delhi – 110012

Walk-in-Interview

Walk-in-interview will be held on October 5, 2013 at 10:00 A.M. at IASRI, New Delhi for a project “A New Distributed Computing Framework for Data Mining” funded by Department of Electronics and Information Technology, Government of India for the following posts. The appointment will be on contractual basis upto 14th October, 2015 or till the termination of the project whichever is earlier and the incumbent shall not have any claim for regular appointment under ICAR.

Research Associate

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or

Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 1st Division and at least two years of research experience

 Knowledge of Statistical Analysis /Bioinformatics tools for computational genomics.

 Knowledge of R/Perl programming language

Research Associate

Ph.D. in Computer Science/ Computer Application / Bioinformatics/ Agricultural
Statistics/ Statistics or equivalent or

Post-Graduation in Computer Science/ Computer Application /Bioinformatics/ Agricultural Statistics/ Statistics or equivalent with 1st Division and at least two years of research experience

 Expertise in Java programming.
 Knowledge of system administration and networking under Linux environment.
 Knowledge of parallel programming and cluster computing.

Emoluments for Research Associate: Consolidated Rs:24000/- per month + HRA (for Ph.D. Degree holders) and Rs:23000/- per month + HRA (for Master’s Degree holders)

Age Limit: Age should be not more than 40 years (5 years relaxation for SC/ST/women candidates and 3 years for OBC candidates as on date of interview).

Interested candidates are requested to appear for Walk-in-Interview on the date and time as specified above in Room No. 106, Training Cum Administrative Block of the Institute along with their application giving bio-data with attested copies of certificates, degrees, testimonials, etc. and one passport size photograph.

Original certificates/ Degrees are needed to be produced at the time of interview.

No T.A. /D.A. will be paid for appearing in the interview.

Advertisement: http://www.iasri.res.in/employment/employment.htm

]]> https://bioinformaticsonline.com/opportunity/view/44727/postdoctoral-scholar-in-bacterial-evolution-at-pathogen-and-microbiome-institute-at-northern-arizona-university Fri, 13 Dec 2024 12:49:16 -0600 <![CDATA[Postdoctoral Scholar in Bacterial Evolution at Pathogen and Microbiome Institute at Northern Arizona University]]> We are pleased to announce a Postdoctoral Scholar position to study
bacterial evolution at the Pathogen and Microbiome Institute at
Northern Arizona University with Professor Paul Keim. The scholar
will have the opportunity also work with Professor Sam Sheppard at
The University of Oxford on joint projects. See our recent paper
on interspecific gene flow in Campylobacter. (DOI:
https://doi.org/10.1128/mbio.00581-24)

The job description: "This research position focuses on the science
of bacterial evolution. It will consist of researching theoretical
principles, but could include translational applications. Phylogenomic
and bioinformatic analysis of bacterial populations in nature or
in laboratory experiments will be a key component of the work. Prior
experience is an asset though training will be possible at PMI.
Likewise, laboratory microbiological, molecular, and biochemical
skills are an asset though not essential. Communication and critical
thinking skills are essential for performing the work and for
communicating to the local and international scientific communities.
Participating in team or independent grant writing to obtain research
funding will be required. Student mentoring is a part of the NAU
mission and is a partial expectation."

https://hr.peoplesoft.nau.edu/psp/ph92prta/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_APP_SCHJOB.GBL?Page=HRS_APP_JBPST&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=608024&PostingSeq=1

Northern Arizona University is located in Flagstaff, Arizona, a
beautiful mountain town with a surprisingly vibrant restaurant
scene. Located a little over an hour from the Grand Canyon and ~45
min from Sedona, Flagstaff is a hiker's paradise. In fact, the city
of Flagstaff operates more than 50 miles of unpaved trails and there
are, on average, 266 sunny days per year with which to enjoy them.
At 7000 ft in elevation, Flagstaff experiences all four seasons,
but thesummers are mild and, in the winter, you can be on the ski
slopes within 30 min! https://www.flagstaffarizona.org/

As mentioned, joint projects with Professor Sheppard at Oxford
University are possible, including travel to his laboratory in the
United Kingdom. https://www.biology.ox.ac.uk/people/samuel-sheppard

Contact Information:
Paul.Keim@nau.edu

Paul S. Keim, Ph.D.
Regents Professor, &
Cowden Endowed Chair of Microbiology
Northern Arizona University
Flagstaff, AZ 86011-4073

Paul S Keim

]]> https://bioinformaticsonline.com/researchlabs/view/5422/shendure-lab Wed, 09 Oct 2013 14:21:58 -0500 <![CDATA[Shendure Lab]]> The Shendure Lab is part of the Department of Genome Sciences at the University of Washington (Seattle, WA). The mission of the lab is to develop and apply new technologies in genomics and molecular biology. Most projects in the lab exploit new DNA sequencing technologies (Shendure et al., Nature Reviews Genetics 2004; Shendure & Ji, Nature Biotechnology 2008; Shendure & Lieberman Aiden, Nature Biotechnology 2012), and generally fall into one of six areas: 1) next-generation human genetics; 2) genome contiguity & completeness; 3) massively parallel functional analysis; 4) molecular tagging; 5) synthetic biology; 6) translational genomics. Our interests in each of these areas are outlined briefly below, and a full list of publications is available via PubMed. http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=shendure
More http://krishna.gs.washington.edu/research.html

Lab page @ http://krishna.gs.washington.edu/index.html

]]> https://bioinformaticsonline.com/researchlabs/view/5661/shankar-lab Wed, 16 Oct 2013 07:02:22 -0500 <![CDATA[Shankar Lab]]> Research Interest:

(A) Regulatory System Analysis with respect to microRNAs

(B) Computational Epigenomics & Regulomics:

(C) Computational issues with Next Generation Sequencing:

Department of Biotechnology,
Institute of Himalyan Bioresources Technology
CSIR, Palampur(Himachal Pradesh), India.
Email: ravishihbt.res.in; ravish9gmail.com

More @ http://scbb.ihbt.res.in/SCBB_dept/Lab_Member.php

]]> 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/researchlabs/view/5747/dbbrowser-attwood-lab Fri, 18 Oct 2013 10:48:19 -0500 <![CDATA[DbBrowser: Attwood Lab]]> DbBrowser: Attwood Lab research concerns protein sequence analysis, primarily using the method of protein 'fingerprinting'. DbBrowser: Attwood Lab maintain a diagnostic fingerprint database (PRINTS), one of the founding partner of InterPro. We also design software to display sequence and structural data in visually-striking ways (e.g., Ambrosia, CINEMA); DbBrowser: Attwood Lab are building re-usable software components to create semantically integrated bioinformatics applications through UTOPIA, including a 'smart' PDF reader that links bioinformatics databases and tools directly with scientific articles (Utopia Documents); and have developed a number of tools for automatic annotation and text mining (e.g., MINOTAUR, PRECIS, METIS).

More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php

]]> 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/opportunity/view/5946/bioinformatics-tata-memorial-centre-navi-mumbai Mon, 28 Oct 2013 10:40:25 -0500 <![CDATA[Bioinformatics @ TATA MEMORIAL CENTRE, NAVI MUMBAI]]> TATA MEMORIAL CENTRE
ADVANCED CENTRE FOR TREATMENT, RESEARCH AND EDUCATION IN CANCER
KHARGHAR, NAVI MUMBAI – 410210

No. ACTREC/Advt./ 72 /2013

WALK IN INTERVIEW

1. JRF*
Genome-wide RNAi screen with human pooled tyrosine kinase shRNA libraries in head and neck squamous call carcinoma (HNSCC) cell lines
DBT A/C No. 3071, Dr. Amit Dutt

2. JRF
IRB Project ACTREC Funds
Dr. Amit Dutt

3. RA
Defining the cancer genome of Head and Neck Squamous Cell Carcinoma (HNSCC) with SNP arrays and next generation sequencing technology
A/C No. 2895, Dr. Amit Dutt

Duration of the Project: One year from the date of appointment, or as and when project terminates.

Consolidated Salary: RA : Rs. 40,000/- p.m.
JRF* (DBT): Rs. 20,800/- p.m.
JRF: Rs. 16,000/- p.m.
Date & Time: 6th November, 2013, at 10.00 a.m.

Venue: Conference Room

Minimum Qualifications and Experience:

RA: The ideal applicant should have a PhD in a relevant field. He/she should have a strong computational biology background, with demonstrated experience in coding using Perl, Python, Java or C++. He/she should be familiar with working in unix enviromnent, devising computational algorithms for data analysis, statistical data analysis in R and matlab and database programming using MySQL. Hands on experience in analyzing high throughput data would be an added advantage.

JRF* (DBT project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Valid UGC-CSIR/DBT/ICMR JRF qualification and laboratory experience in molecular biology. Previous experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team would be desirable.

JRF (ACTREC project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Minimum 2 yrs experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team is essential.

*M.Sc. degree obtained after a one year course will not be considered.

Candidates fulfilling above requirements should send their application by e-mail to
‘careers.duttlab@gmail.com. in the format given below so as to reach on or before
4th November, 2013.

Advertisement:

http://www.actrec.gov.in/data%20files/2013/AD-RA-JR-TECHN-6-NOV.pdf

]]>