<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42485?offset=60 https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database Wed, 26 Jul 2017 07:49:29 -0500 https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database <![CDATA[GOLD:Genomes Online Database]]> GOLD:Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.

https://gold.jgi.doe.gov/

Address of the bookmark: https://gold.jgi.doe.gov/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data Wed, 27 Dec 2017 20:36:54 -0600 https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data <![CDATA[Ra assembler - a de novo DNA assembler for third generation sequencing data]]> Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

]]> biogeek https://bioinformaticsonline.com/bookmarks/view/36806/manta-rapid-detection-of-structural-variants-and-indels-for-germline-and-cancer-sequencing-applications Mon, 28 May 2018 09:41:39 -0500 https://bioinformaticsonline.com/bookmarks/view/36806/manta-rapid-detection-of-structural-variants-and-indels-for-germline-and-cancer-sequencing-applications <![CDATA[Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.]]> Address of the bookmark: https://github.com/Illumina/manta

]]> Jit 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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity Thu, 20 Sep 2018 08:38:29 -0500 https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity <![CDATA[d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity]]> d2Tools are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the d2-style(d2/d2*/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.

Manual at https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki

Address of the bookmark: https://code.google.com/archive/p/d2-tools/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic Tue, 13 Nov 2018 07:25:38 -0600 https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic <![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]> Required python modules

  • biopython
  • intervaltree
  • numpy
  • scipy
  • tqdm
  • PyYAML

Address of the bookmark: https://github.com/cerebis/sim3C

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data Tue, 22 Jan 2019 06:26:55 -0600 https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data <![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data]]>
  • Run a pipeline processing fast5s to a consensus in a single command.
  • Recommended fixed "standard" and "fast" pipelines.
  • Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
  • Seemless distribution of tasks over local or distributed compute.
  • Highly configurable.
  • Open source (Mozilla Public License 2.0).

    • Documentation can be found at https://nanoporetech.github.io/katuali/.

    Address of the bookmark: https://github.com/nanoporetech/katuali

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization Fri, 24 Jan 2020 04:09:15 -0600 https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization <![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]> MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

    https://academic.oup.com/nar/article/47/11/e63/5377471

    Address of the bookmark: https://github.com/linzhi2013/MitoZ

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/42271/mcclintock-meta-pipeline-to-identify-transposable-element-insertions-using-next-generation-sequencing-data Tue, 27 Oct 2020 00:21:18 -0500 https://bioinformaticsonline.com/bookmarks/view/42271/mcclintock-meta-pipeline-to-identify-transposable-element-insertions-using-next-generation-sequencing-data <![CDATA[McClintock: Meta-pipeline to identify transposable element insertions using next generation sequencing data]]> an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae. 

    Address of the bookmark: https://github.com/bergmanlab/mcclintock

    ]]>     BioStar     https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data Fri, 04 Oct 2024 02:45:06 -0500 https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data <![CDATA[Libraries or management tools for high throughput sequencing data]]>
  • GATB Library. The Genome Analysis Toolbox with de-Bruijn graph. A large part of tools developed by the GenScale team are based on this library.
    These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes). Among them are (the full is available here: https://gatb.inria.fr/software/):
  • LRez: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.

    • Variant calling and/or genotyping

    • DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
    • MindTheGap: Detection and assembly of large insertion variants
    • TakeABreak: reference-free inversion discovery tool
    • SVJedi: Structural Variant genotyper with long read data
    • SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

    Sequence assembly

    • MinYS: reference-guided genome assembly in metagenomics data
    • MTG-link: local assembly tool for linked-read data
    • Minia: De novo short read assembler
    • de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
    • Mapsembler2: Targeted assembly (not maintained)

    Managing k-mers & indexation

    • findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
      • fimpera extends findere adding the abundance information.
    • kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
    • kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
    • back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
    • Backpack Quotient Filter: k-mer indexing data structure with abundance
    • short read connector: Detect similar reads from potentially large read set
    • DSK: Count K-mer in sequences

    Pangenome graph manipulation

    • Pancat: Pangenome Comparison and Analysis Toolkit
    • GFAGraphs: a Python library to handle pangenome graph files in GFA format.

    Comparative metagenomics with k-mers

    Species and bacterial strains identification

    • ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
    • StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

    General-purpose sequencing data manipulation

    • GASSST: long read mapper
    • Leon: short read compressor (now included in GATB-core)
    • Bloocoo: short read corrector
    • BCALM: Construct compacted de Bruijn graphs (unitigs)

     Protein Structure

    • A_Purva: Contact Map Overlap solver
    • MD-Jeep: Distance Geometry solver
    • CSA: Comparative Structural Alignment

    Workflow

    • SLICEE: parallel execution of bioinformatics workflows

    Comparative Genomics

    • CASSIS: detection of rearrangement breakpoints
    • PLAST: intensive bank-to-bank sequence comparison
    • DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments
    ]]>     LEGE