<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26306?offset=1300 https://bioinformaticsonline.com/bookmarks/view/38441/genome-sequence-based-sub-species-delineation Wed, 12 Dec 2018 08:31:14 -0600 https://bioinformaticsonline.com/bookmarks/view/38441/genome-sequence-based-sub-species-delineation <![CDATA[Genome sequence-based (sub-)species delineation.]]> The GGDC web service reports digital DDH for a universal and accurate delineation of prokaryotic (sub-)species without inheriting the pitfalls of classic DDH, and also calculates differences in genomic G+C content.

http://ggdc.dsmz.de/ggdc_background.php#

Genome-to-Genome Distance Calculator 2.1

http://ggdc.dsmz.de/ggdc.php

 

Address of the bookmark: http://ggdc.dsmz.de/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/39250/darwin-wga-a-co-processor-provides-increased-sensitivity-in-whole-genome-alignments-with-high-speedup Sat, 13 Apr 2019 08:55:31 -0500 https://bioinformaticsonline.com/bookmarks/view/39250/darwin-wga-a-co-processor-provides-increased-sensitivity-in-whole-genome-alignments-with-high-speedup <![CDATA[Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup]]> Darwin-WGA, is the first hardware accelerator for whole genome alignment and accelerates the gapped filtering stage. Darwin-WGA also employs GACT-X, a novel algorithm used in the extension stage to align arbitrarily long genome sequences using a small on-chip memory, that provides better quality alignments at 2× improvement in memory and speed over the previously published GACT algorithm. Implemented on an FPGA, Darwin-WGA provides up to 24× improvement (performance/$) in WGA over iso-sensitive software.

https://stanford.edu/~yatisht/pubs/darwin-wga.pdf

Address of the bookmark: https://github.com/gsneha26/Darwin-WGA

]]> Jit https://bioinformaticsonline.com/researchlabs/view/40609/genome-informatics-section-lab Sat, 25 Jan 2020 06:38:23 -0600 <![CDATA[Genome Informatics Section Lab !]]> Our section develops and applies computational methods for the analysis of massive genomics datasets, focusing on the challenges of genome sequencing and comparative genomics. We aim to improve such foundational processes and translate emerging genomic technologies into practice.

The Genome Informatics Section is hiring! Come join our outstanding team at the NIH’s National Human Genome Research Institute and contribute to the development of new reference genomes and computational methods for DNA sequencing and analysis. Both postdoc and PhD students positions are available. More information and application instructions follow below.

More at https://genomeinformatics.github.io/

]]> https://bioinformaticsonline.com/bookmarks/view/42415/sneakysnake-a-fast-and-accurate-universal-genome-pre-alignment-filter-for-cpus-gpus-and-fpgas Sun, 20 Dec 2020 01:39:54 -0600 https://bioinformaticsonline.com/bookmarks/view/42415/sneakysnake-a-fast-and-accurate-universal-genome-pre-alignment-filter-for-cpus-gpus-and-fpgas <![CDATA[SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs]]> The first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. SneakySnake greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short (Illumina) and long (ONT and PacBio) reads. Described by Alser et al. (preliminary version at https://arxiv.org/abs/1910.09020).

Address of the bookmark: https://github.com/CMU-SAFARI/SneakySnake

]]> Neel https://bioinformaticsonline.com/bookmarks/view/43427/ogdraw-draw-organelle-genome-maps Tue, 05 Oct 2021 03:34:35 -0500 https://bioinformaticsonline.com/bookmarks/view/43427/ogdraw-draw-organelle-genome-maps <![CDATA[OGDRAW - Draw Organelle Genome Maps]]> OrganellarGenomeDRAW converts annotations in the GenBank or EMBL/ENA format into graphical maps. The input has to be a GenBank or EMBL/ENA flat file wherase the output can vary among several types of files. The application is optimized to create detailed high-quality maps of organellar genomes (plastid and mitochondria). Nevertheless, you can upload most database entries.

 

Please take a look at our FAQ section and do not hesitate to report bugs or suggestions for improvements by email.

Address of the bookmark: https://chlorobox.mpimp-golm.mpg.de/OGDraw.html

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/43714/hiv-genome-database Fri, 21 Jan 2022 05:40:15 -0600 https://bioinformaticsonline.com/bookmarks/view/43714/hiv-genome-database <![CDATA[HIV genome database !]]> HIV resources

https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html

Address of the bookmark: https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43909/human-complete-genome Wed, 06 Jul 2022 06:42:55 -0500 https://bioinformaticsonline.com/bookmarks/view/43909/human-complete-genome <![CDATA[Human Complete Genome]]> Telomere-to-telomere consortium

We have sequenced the CHM13hTERT human cell line with a number of technologies. Human genomic DNA was extracted from the cultured cell line. As the DNA is native, modified bases will be preserved. The data includes 30x PacBio HiFi, 120x coverage of Oxford Nanopore, 70x PacBio CLR, 50x 10X Genomics, as well as BioNano DLS and Arima Genomics HiC. Most raw data is available from this site, with the exception of the PacBio data which was generated by the University of Washington/PacBio and is available from NCBI SRA.

A UCSC browser is available for v2.0 (as well as legacy v1.0 and v1.1 versions). An interactive dotplot visualization of all genomic repeats is also available from resgen.io. Known issues identified in the assembly are tracked at CHM13 issues.

 

MORE at https://github.com/marbl/CHM13

Address of the bookmark: https://www.science.org/doi/10.1126/science.abj6987

]]> Shruti Paniwala https://bioinformaticsonline.com/opportunity/view/17515/ngs-online-training Sat, 27 Sep 2014 07:42:29 -0500 <![CDATA[NGS Online Training]]> ArrayGen Technologies announces to provide online NGS training through out the globe. Now analyze your own NGS datasets from anywhere.For more information contact us at training@arraygen.com

Please visit our site at www.arraygen.com

]]> https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome Thu, 31 Aug 2023 02:43:28 -0500 https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome <![CDATA[Steps to find all the repeats in the genome !]]>

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

  1. Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website[0].
  1. Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".

./RepeatMasker -pa <number_of_processors> -nolow -norna -no_is -div <divergence_value> -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species <species_name> -dir <output_directory> -length <min_length>-<max_length> genome.fasta

Replace the following placeholders with appropriate values:

  • <number_of_processors>: The number of processors/threads you want to use for parallel processing.
  • <divergence_value>: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation[0].
  • <species_name>: The name of the species you are analyzing.
  • <output_directory>: The directory where you want the output files to be saved.
  • <min_length> and <max_length>: The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).
  1. Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files[0]. You can download it from the source provided.
  1. Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:

perl one_code_to_find_them_all.pl --rm <RepeatMasker_out_file> --length <length_file>

 

Replace <RepeatMasker_out_file> with the path to your RepeatMasker .out file, and <length_file> with the path to a file containing the lengths of the reference elements.

This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.

Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.

]]> Neel https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment Mon, 05 Aug 2024 23:01:29 -0500 https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment <![CDATA[UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment]]> UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

  • Using state of the art tools, easily extended for other viruses

  • Tool and database updates for critical components via Conda

  • Built using modern design patterns with Conda and Snakemake

  • Extensible and easy to customize

  • Submission Ready Genomes

  • Customizable reporting with comprehensive visualization

https://ikim-essen.github.io/uncovar/

Github https://github.com/IKIM-Essen/uncovar

 

 

Address of the bookmark: https://ikim-essen.github.io/uncovar/

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