<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43736?offset=230 https://bioinformaticsonline.com/bookmarks/view/43690/ucsc-sars-cov-2-genome-browser Thu, 06 Jan 2022 06:48:40 -0600 https://bioinformaticsonline.com/bookmarks/view/43690/ucsc-sars-cov-2-genome-browser <![CDATA[UCSC SARS-CoV-2 Genome Browser]]> The UCSC SARS-CoV-2 Genome Browser (https://genome.ucsc.edu/covid19.html) is an adaptation of our popular genome-browser visualization tool for this virus, containing many annotation tracks and new features, including conservation with similar viruses, immune epitopes, RT–PCR and sequencing primers and CRISPR guides. We invite all investigators to contribute to this resource to accelerate research and development activities globally.

Address of the bookmark: https://www.nature.com/articles/s41588-020-0700-8

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants Wed, 27 Apr 2022 04:34:12 -0500 https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants <![CDATA[MUM&Co is a simple bash script that uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants.]]> MUM&Co is able to detect:
Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)
Inversions (>=1kb) and translocations (>=10kb)

Address of the bookmark: https://github.com/SAMtoBAM/MUMandCo

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44322/genome-context-viewer-gcv Sun, 21 May 2023 19:33:43 -0500 https://bioinformaticsonline.com/bookmarks/view/44322/genome-context-viewer-gcv <![CDATA[Genome Context Viewer (GCV)]]> The Genome Context Viewer (GCV) is a web-app that visualizes genomic context data provided by third party services. Specifically, it uses functional annotations as a unit of search and comparison. By adopting a common set of annotations, data-store operators can deploy federated instances of GCV, allowing users to compare genomes from different providers in a single interface.

Address of the bookmark: https://github.com/legumeinfo/gcv

]]> LEGE 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/blog/view/44754/early-genome-screening-the-new-health-horoscope Thu, 02 Jan 2025 19:44:36 -0600 https://bioinformaticsonline.com/blog/view/44754/early-genome-screening-the-new-health-horoscope <![CDATA[Early Genome Screening: The New Health Horoscope!]]> In an era where precision medicine is reshaping healthcare, genome screening is emerging as the modern equivalent of a health horoscope. It offers insights into our biological "stars," unraveling predispositions to various conditions and empowering individuals with knowledge to navigate their health journeys proactively. But how reliable is this "horoscope," and how does it impact our lives?

    Understanding Genome Screening

    Genome screening involves analyzing an individual's DNA to identify genetic variations that may influence health and disease susceptibility. This can range from simple single-gene tests to comprehensive whole-genome sequencing. By peering into our genetic blueprint, we can uncover risks for conditions like cancer, diabetes, cardiovascular diseases, and even rare genetic disorders.

    The process is straightforward: a saliva or blood sample is collected, and advanced sequencing technologies decipher the genetic code. The results provide a personalized health map, guiding lifestyle modifications, preventive measures, or medical interventions.

    A Shift from Reactive to Proactive Healthcare

    Traditional healthcare often focuses on treating diseases after they manifest. Genome screening flips this model on its head, enabling a shift toward prevention and early intervention. For instance:

    • Cancer Risk Management: Individuals with BRCA1 or BRCA2 gene mutations can opt for enhanced screening programs or preventive surgeries to mitigate their risk of breast and ovarian cancers.

    • Cardiovascular Health: Genetic predispositions to conditions like familial hypercholesterolemia can prompt early cholesterol monitoring and lifestyle adjustments.

    • Rare Diseases: Identifying carriers of genetic disorders can aid in family planning and reduce the incidence of inherited conditions.

    The Ethical and Practical Concerns

    While genome screening offers incredible promise, it is not without challenges:

    1. Accuracy and Interpretation: Genetic predisposition does not guarantee disease. Misinterpretation of results can lead to unnecessary anxiety or unwarranted medical interventions.

    2. Privacy and Data Security: Genetic data is highly sensitive. Ensuring robust data protection measures is crucial to prevent misuse.

    3. Accessibility and Equity: High costs and limited availability may restrict access to genome screening, exacerbating health disparities.

    Balancing Science and Pseudoscience

    The comparison of genome screening to horoscopes isn’t entirely unfounded. Both offer predictive insights, but the scientific foundation of genome screening distinguishes it from astrology. Unlike the alignment of celestial bodies, genetic predictions are based on rigorous data and evidence. However, the probabilistic nature of genetic predispositions underscores the importance of interpreting results in conjunction with clinical and lifestyle factors.

    The Road Ahead

    As genome screening becomes more affordable and integrated into routine healthcare, its potential to transform lives is immense. Policymakers, healthcare providers, and genetic counselors must collaborate to ensure ethical implementation, public awareness, and equitable access.

    Imagine a future where your genetic "horoscope" is a trusted guide, not just a prediction. Early genome screening could help chart a healthier path for generations, making it a cornerstone of personalized medicine. After all, our genes might just hold the key to unlocking a future of better health and well-being.

     

    ]]>     LEGE     https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite Fri, 28 Oct 2016 07:59:54 -0500 https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite <![CDATA[Graph Genome Suite]]> Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the Graph Genome Suite — the most advanced population genomics tools in the world.

    Address of the bookmark: https://www.sbgenomics.com/graph/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/35896/phylographer-graph-visualization-tool Wed, 07 Mar 2018 18:11:25 -0600 https://bioinformaticsonline.com/bookmarks/view/35896/phylographer-graph-visualization-tool <![CDATA[PhyloGrapher - Graph Visualization Tool]]> PhyloGrapher is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements). PhyloGrapher is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use PhyloGrapher to visualize any type of relations between elements. 

    https://www.youtube.com/watch?v=WgufqYMHCvM

    Address of the bookmark: http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/42198/pggb-the-pangenome-graph-builder Sun, 13 Sep 2020 20:54:20 -0500 https://bioinformaticsonline.com/bookmarks/view/42198/pggb-the-pangenome-graph-builder <![CDATA[pggb: the pangenome graph builder]]> This pangenome graph construction pipeline renders a collection of sequences into a pangenome graph (in the variation graph model). Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for the interpretation, visualization, and reuse of pangenome variation graphs.

    image

    Address of the bookmark: https://github.com/pangenome/pggb

    ]]>     biogeek     https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network Wed, 27 Mar 2024 20:51:21 -0500 https://bioinformaticsonline.com/bookmarks/view/44497/graphpath-a-graph-attention-model-for-molecular-stratification-with-interpretability-based-on-the-pathway-pathway-interaction-network <![CDATA[GraphPath: A graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network]]> Achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. In this paper, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling.

    image

    Address of the bookmark: https://github.com/amazingma/GraphPath

    ]]>     LEGE     https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline Mon, 10 Jul 2017 05:56:07 -0500 https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline <![CDATA[Omega2: metagenome assembly pipeline]]> Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.

    Address of the bookmark: http://omega.omicsbio.org/

    ]]>     Jit