<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/22793?offset=940 https://bioinformaticsonline.com/bookmarks/view/41691/genobuntu-package-for-next-generation-sequencing-and-genome-assembly Mon, 18 May 2020 16:47:56 -0500 https://bioinformaticsonline.com/bookmarks/view/41691/genobuntu-package-for-next-generation-sequencing-and-genome-assembly <![CDATA[Genobuntu: Package for Next Generation Sequencing and Genome Assembly]]>

Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.

Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one’s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.

Therefore, Genobuntu offers a well-tailored environment for both novices and experts working in the field of genome assembly.

Features

  • Velvet
  • MiB
  • SSAKE
  • EULER
  • VCAKE
  • ABySS
  • ALLPATHS
  • Celera
  • SHARCGS
  • Allpaths
  • IDBA
  • TAIPAN
  • Edena
  • SOAPdenovo
  • Maq
  • IDBA-UD
  • No. of Reads present in the Ref. Seq.
  • ART NGS Reads Simulator
  • HiTEC, FASTQC
  • Minimum Description Length
  • SOAPaligner
  • Sequencing Read Archive Toolkit

Address of the bookmark: https://sourceforge.net/projects/genobuntu/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/44366/mitofinder Tue, 29 Aug 2023 02:13:01 -0500 https://bioinformaticsonline.com/bookmarks/view/44366/mitofinder <![CDATA[MitoFinder]]> Allio, R., Schomaker-Bastos, A., Romiguier, J., Prosdocimi, F., Nabholz, B., & Delsuc, F. (2020) Mol Ecol Resour. 20, 892-905. (publication link)

Drawing

Mitofinder is a pipeline to assemble mitochondrial genomes and annotate mitochondrial genes from trimmed read sequencing data.

MitoFinder is also designed to find and annotate mitochondrial sequences in existing genomic assemblies (generated from Hifi/PacBio/Nanopore/Illumina sequencing data...)

MitoFinder is distributed under the license.

Address of the bookmark: https://github.com/RemiAllio/MitoFinder

]]> Neel https://bioinformaticsonline.com/bookmarks/view/43374/reference-sequence-resource Wed, 15 Sep 2021 21:15:22 -0500 https://bioinformaticsonline.com/bookmarks/view/43374/reference-sequence-resource <![CDATA[Reference Sequence Resource!]]> The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. T

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit Thu, 16 Dec 2021 02:50:19 -0600 https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit <![CDATA[Peregrine & SHIMMER Genome Assembly Toolkit]]> Peregrine is a fast genome assembler for accurate long reads (length > 10kb, accuracy > 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.

Address of the bookmark: https://github.com/cschin/Peregrine

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/43725/comparative-genomics-workshops Tue, 25 Jan 2022 20:39:58 -0600 https://bioinformaticsonline.com/bookmarks/view/43725/comparative-genomics-workshops <![CDATA[Comparative Genomics Workshops !]]> This meeting's objective was to obtain a big picture look at the current state of the field of comparative genomics with a focus on commonalities across genomic investigations into humans, model organisms (both traditional and non-traditional), agricultural species, wildlife species and microbes.

https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator Wed, 08 Mar 2023 01:39:33 -0600 https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator <![CDATA[ALE: Assembly Likelihood Estimator]]> Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly.  Depending on your genome size you may want to convert the .wig files to the BigWig format.

Address of the bookmark: https://github.com/sc932/ALE

]]> BioStar 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/researchlabs/view/22402/alessandra-carbone-lab Tue, 26 May 2015 08:54:34 -0500 <![CDATA[Alessandra Carbone Lab]]> Our group works on various problems connected with the functioning and evolution of biological systems. We use mathematical tools, coming from statistics and combinatorics, algorithmic tools and molecular physics tools to study basic principles of cellular functioning starting from genomic data. We run several projects in parallel, all aiming at understanding the basic principles of evolution and co-evolution of molecular structures in the cell. They are intimately linked to each other.

Our main research themes are:

Domain annotation and metagenomics
Transcriptomics and sequence analysis
Protein evolution and interactions
Protein conformational dynamics

More at http://www.lcqb.upmc.fr/AnalGenom/home.html

]]> https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome Thu, 08 Aug 2024 23:31:18 -0500 https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome <![CDATA[Tools to access the quality of your assembled genome !]]>
  • FASTA VALIDATOR + SEQKIT RMDUP: FASTA validation
  • GENOMETOOLS GT GFF3VALIDATOR: GFF3 validation
  • ASSEMBLATHON STATS: Assembly statistics
  • GENOMETOOLS GT STAT: Annotation statistics
  • NCBI FCS ADAPTOR: Adaptor contamination pass/fail
  • NCBI FCS GX: Foreign organism contamination pass/fail
  • BUSCO: Gene-space completeness estimation
  • TIDK: Telomere repeat identification
  • LAI: Continuity of repetitive sequences
  • KRAKEN2: Taxonomy classification
  • HIC CONTACT MAP: Alignment and visualisation of HiC data
  • MUMMER → CIRCOS + DOTPLOT & MINIMAP2 → PLOTSR: Synteny analysis
  • MERQURY: K-mer completeness, consensus quality and phasing assessment
    • ]]>     LEGE     https://bioinformaticsonline.com/researchlabs/view/22414/x-shirley-liu-lab Tue, 26 May 2015 17:28:23 -0500 <![CDATA[X. Shirley Liu Lab]]> The research in our laboratories are focused on the following three areas:

    Bioinformatics
    Cancer
    Epigenetics

    More at http://liulab.dfci.harvard.edu/

    ]]>