<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/10741?offset=1020 https://bioinformaticsonline.com/researchlabs/view/9868/raghavas-group Tue, 15 Apr 2014 23:59:48 -0500 <![CDATA[Raghava's Group]]> Raghava's group is known for developing open source software or web servers. Group have developed large number of web-based services.

Find more at http://www.imtech.res.in/raghava/

]]> https://bioinformaticsonline.com/bookmarks/view/43243/interactive-bioinformatics-resources Thu, 12 Aug 2021 00:09:00 -0500 https://bioinformaticsonline.com/bookmarks/view/43243/interactive-bioinformatics-resources <![CDATA[Interactive Bioinformatics Resources !]]> Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44279/bioinformatics-training-material Sat, 18 Mar 2023 11:26:18 -0500 https://bioinformaticsonline.com/bookmarks/view/44279/bioinformatics-training-material <![CDATA[Bioinformatics Training Material !]]> Glittr is a curated list of bioinformatics training material.
All material is:

  • In a GitHub or GitLab repository
  • Free to use
  • Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

]]> BioStar https://bioinformaticsonline.com/blog/view/44516/16srna-database-download Wed, 24 Apr 2024 04:33:15 -0500 https://bioinformaticsonline.com/blog/view/44516/16srna-database-download <![CDATA[16sRNA Database Download]]> Downloading 16S rRNA databases can be crucial for various bioinformatics analyses, especially in microbiome research. However, it's important to note that databases can vary based on your specific needs, such as the taxonomic coverage you require or the type of analysis you're performing. Here's a general guideline on how you can obtain 16S rRNA databases:

  1. NCBI (National Center for Biotechnology Information):

    • NCBI provides various databases related to genetic information, including 16S rRNA sequences.
    • You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
    • Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
    • You can download sequences individually or in batches using the provided tools.

  2. GreenGenes:

    • GreenGenes is a widely used 16S rRNA gene sequence database.
    • You can access it at http://greengenes.secondgenome.com/.
    • GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.

  3. SILVA:

    • SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
    • It covers not only 16S rRNA but also other ribosomal RNA sequences.
    • SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.

  4. Ribosomal Database Project (RDP):

    • RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
    • It provides tools for sequence analysis and classification.
    • You can download sequences and taxonomy information from their website.

  5. QIIME (Quantitative Insights Into Microbial Ecology):

    • QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
    • It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
    • QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

 

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

]]> LEGE https://bioinformaticsonline.com/researchlabs/view/852/queensland-centre-for-medical-genomics-grimmond-lab Sun, 14 Jul 2013 11:58:34 -0500 <![CDATA[Queensland Centre for Medical Genomics, Grimmond Lab]]> Queensland Centre for Medical Genomics

Research Area:
pancreatic cancer; ovarian cancer; prostate cancer; bowel cancer; brain cancer; endometrial cancer; breast cancer; personalised medicine; high-throughput genomics

Link @ http://www.imb.uq.edu.au/sean-grimmond

]]> https://bioinformaticsonline.com/opportunity/view/4725/complex-systems-from-physics-to-biology-october-15-16-2013-at-jnu-convention-center Mon, 23 Sep 2013 10:17:17 -0500 <![CDATA[Complex Systems: From Physics to Biology October 15-16 2013 at JNU Convention Center]]> The symposium intents to focus on complex systems arising in a variety of settings in physics and biology. In particular, applications of the concepts of physics to biological sciences will be the major theme of this meeting.

Selected Topics:

Cluster Dynamics
Non-equilibrium Statistical Mechanics
Forced Systems
Hamiltonian Dynamics
Synchronization & Control
Genomics & Systems Biology
Computational Neuroscience
Econophysics

More @ http://www.jnu.ac.in/Conference/SCS2013/

]]> https://bioinformaticsonline.com/researchlabs/view/863/rolland-lagan-lab Sun, 14 Jul 2013 12:57:57 -0500 <![CDATA[Rolland-Lagan lab]]> The Rolland-Lagan lab at the University of Ottawa is specializing in computational and developmental biology. We use a combination of experimental work, microscopy, image analysis and computer simulations to explore developmental mechanisms in two and three dimensions.

Research Area

Developmental biology, Computational biology, Simulation modeling, Image data analysis

Link @ http://mysite.science.uottawa.ca/arolland/index.html

]]> https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data Mon, 13 Nov 2017 05:10:23 -0600 https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data <![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]> Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

Address of the bookmark: https://github.com/OpenGene/AfterQC

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data Sun, 03 Dec 2017 15:19:18 -0600 https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data <![CDATA[MinION_GC: An R script to do some QC on MinION data]]> Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data Mon, 06 Aug 2018 17:24:53 -0500 https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data <![CDATA[NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data]]> NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.

 

 

Address of the bookmark: https://github.com/Nextomics/NextSV

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