<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43260?offset=60 https://bioinformaticsonline.com/bookmarks/view/43791/comparative-genomics-visualisation-tools Thu, 17 Feb 2022 05:37:55 -0600 https://bioinformaticsonline.com/bookmarks/view/43791/comparative-genomics-visualisation-tools <![CDATA[Comparative genomics visualisation tools !]]> Comparative genomics visualisation tools !

Address of the bookmark: https://cmdcolin.github.io/awesome-genome-visualization/?latest=true&selected=%23BRIG&tag=Comparative

]]> Neel https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr Sat, 08 Jun 2024 15:59:46 -0500 https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr <![CDATA[Bioinformatic tools for pathogens informatics at CVR]]>

Novel sequencing and analytical approaches focused on studying viruses and virus-host interactions. Below you will find summaries and links to a number of bioinformatic tools that have been developed @ CVR.

The database-integrated genome-screening (DIGS) tool provides a framework for implementing automated in silico screening of sequence databases using BLAST in combination with a relational database (MySQL).

DisCVR is a Diagnostic tool for detecting known human viruses in clinical samples from Next-Generation Sequencing (NGS) data. The tool uses a simple and straightforward Graphical User Interface and is optimized on Windows OS without compromising speed and accuracy.

DiversiTools is a computational tool that is specifically tailored towards viral HTS data sets and the analysis of the underlying viral populations that they represent. It was initially developed in collaboration with a number of virologists interested in characterising the intra-host diversity of viral populations and studying their evolution across transmission chains at the micro-evolutionary scale.

GLUE is a flexible data-centric bioinformatics environment for virus sequence data, with a focus on virus evolution and genomic variation. GLUE has been applied to a range of viruses. A GLUE-based resource focused on Hepatitis C virus is HCV-GLUE.

Tanoti is a BLAST guided reference based short read aligner. It is developed for maximising alignment in highly variable next generation sequence data sets (Illumina).

ViCTree is a bioinformatic framework that automatically selects new candidate virus sequences from GenBank, generates multiple sequence alignments, calculates a maximum likelihood phylogeny and integrates the sequences into the existing phylogenetic trees. For more information click here.

Viral Host Predictor provides a fast and simple way to predict the hosts and vectors of RNA viruses from viral sequences.

GRACy is a bioinformatic tool designed for the analysis of Illumina data originated from Human cytomegalovirus samples. GRACy can be used to perform read quality filtering, genotyping, de novo assembly, variant detection, annotation and data submission to public database.

LoReTTA (Long Read Template Targeted Assembler) is a reference assisted de novo assembler specifically designed to deal with PacBio reads generated from viral genomes. 

BingleSeq is a R-package enables the user-friendly analysis of count tables obtained by both Bulk RNA-Seq and single-cell RNA-Seq protocols. The development of BingleSeq focused on providing a flexible and intuitive user experience.

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment Thu, 06 Sep 2018 16:27:35 -0500 https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment <![CDATA[LSC: Improving PacBio Long Read Accuracy by Short Read Alignment]]>
  • Added Command line argument support.
  • Multi-stage execution modes.
  • Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
  • Better compressed intermediate files.
  • Added utilities folder.
  • Added support for multiple short read files.
  • Removed use of configuration file.

    • Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads Wed, 07 Jun 2017 04:18:16 -0500 https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads <![CDATA[GraphMap - A highly sensitive and accurate mapper for long, error-prone reads]]> GraphMap - A highly sensitive and accurate mapper for long, error-prone reads http://www.nature.com/ncomms/2016/160415/ncomms11307/full/ncomms11307.html

    Features

        Mapping position agnostic to alignment parameters.
        Consistently very high sensitivity and precision across different error profiles, rates and sequencing technologies even with default parameters.
        Circular genome handling to resolve coverage drops near ends of the genome.
        E-value.
        Meaningful mapping quality.
        Various alignment strategies (semiglobal bit-vector and Gotoh, anchored).
        Overlapping of reads for de novo assembly.
        Transcriptome mapping through internal construction of a transcriptome from a given genomic reference and a GTF file.
        ...and much more.

    GraphMap is also used as an overlapper in a new de novo genome assembly project called Ra (https://github.com/mariokostelac/ra-integrate).
    Ra attempts to create de novo assemblies from raw nanopore and PacBio reads without requiring error correction, for which a highly sensitive overlapper is required.

    Currently, development of a new spliced-alignment mode for mapping RNA-seq reads is under way.
    Description of the current effort as well as how to reach the experimental implementation can be found here: doc/rnaseq.md.

    Address of the bookmark: https://github.com/isovic/graphmap

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes Tue, 12 Dec 2017 17:23:31 -0600 https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes <![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]> MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a k-mer based Jaccard similarity using a combination of Winnowing and MinHash. This is then converted to an estimate of sequence identity using the Mash distance. An appropriate k-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.

    Address of the bookmark: https://github.com/marbl/MashMap

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/36456/alpaca-a-hybrid-strategy-for-assembly-of-genomic-dna-shotgun-sequencing-reads Mon, 30 Apr 2018 04:38:40 -0500 https://bioinformaticsonline.com/bookmarks/view/36456/alpaca-a-hybrid-strategy-for-assembly-of-genomic-dna-shotgun-sequencing-reads <![CDATA[ALPACA: A hybrid strategy for assembly of genomic DNA shotgun sequencing reads.]]> ALPACA requires Celera Assembler 8.3 or later. It is recommended to build Celera Assembler from source. (Why? The pre-built binaries CA_8.3rc1 and CA8.3rc2 will work for any large data set. 

    Detail paper at https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3927-8

    Address of the bookmark: https://github.com/VicugnaPacos/ALPACA

    ]]>     Seema Singh     https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads Tue, 15 May 2018 02:53:11 -0500 https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads <![CDATA[TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads]]> TAndem REpeat ANalyzer -TAREAN – is a computational pipeline for unsupervised identification of satellite repeats from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.

    How to use TAREAN:

    Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

    References

    Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

    Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean

    ]]>     Surabhi Chaudhary     https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application Wed, 23 May 2018 06:54:32 -0500 https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application <![CDATA[BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,]]> BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.

    Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The “target.fasta.sa” is the suffix array from “target.fasta” generated by sawriter.

    blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15

    the output format option “-m 4″ generate the alignment coordinate. Not fully documented, but I can explain that to you. 

    I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.

    Address of the bookmark: http://bix.ucsd.edu/projects/blasr/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads Tue, 29 May 2018 07:33:44 -0500 https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads <![CDATA[Porechop: tool for finding and removing adapters from Oxford Nanopore reads]]> Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.

    Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

    Address of the bookmark: https://github.com/rrwick/Porechop

    ]]>     Rahul Nayak     https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads Mon, 11 Jun 2018 05:14:57 -0500 https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads <![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]> Address of the bookmark: http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html

    ]]>     Shruti Paniwala