<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36373?offset=130 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/26999/discovar Mon, 18 Apr 2016 11:59:16 -0500 https://bioinformaticsonline.com/bookmarks/view/26999/discovar <![CDATA[DISCOVAR]]> DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/23253/resolving-the-complexity-of-the-human-genome-using-single-molecule-sequencing Sat, 11 Jul 2015 12:47:34 -0500 https://bioinformaticsonline.com/bookmarks/view/23253/resolving-the-complexity-of-the-human-genome-using-single-molecule-sequencing <![CDATA[Resolving the complexity of the human genome using single-molecule sequencing]]> The human genome is arguably the most complete mammalian reference assembly yet more than 160 euchromatic gaps remain and aspects of its structural variation remain poorly understood ten years after its completion. The results in this paper https://www.genomeweb.com/sequencing/team-uses-single-molecule-sequencing-close-gaps-chart-complexity-human-reference suggest a greater complexity of the human genome in the form of variation of longer and more complex repetitive DNA that can now be largely resolved with the application of this longer-read sequencing technology.

 

Address of the bookmark: http://www.nature.com/nature/journal/v517/n7536/full/nature13907.html

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data Fri, 04 Jan 2019 10:10:48 -0600 https://bioinformaticsonline.com/bookmarks/view/38593/excavator-detecting-copy-number-variants-from-whole-exome-sequencing-data <![CDATA[EXCAVATOR: detecting copy number variants from whole-exome sequencing data]]> EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.


EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.
EXCAVATOR has been published on Genome Biology (http://genomebiology.com/2013/14/10/R120/abstract).

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

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data Tue, 28 Jan 2020 03:44:43 -0600 https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data <![CDATA[vt: a variant tool set that discovers short variants from Next Generation Sequencing data.]]> vt is a variant tool set that discovers short variants from Next Generation Sequencing data.

https://genome.sph.umich.edu/wiki/Vt

https://github.com/atks/vt

Address of the bookmark: https://genome.sph.umich.edu/wiki/Vt

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43904/jasmine-jointly-accurate-sv-merging-with-intersample-network-edges Sat, 02 Jul 2022 11:41:53 -0500 https://bioinformaticsonline.com/bookmarks/view/43904/jasmine-jointly-accurate-sv-merging-with-intersample-network-edges <![CDATA[JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges]]> This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.

Address of the bookmark: https://github.com/mkirsche/Jasmine

]]> Shruti Paniwala