<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34418?offset=310 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/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies Sat, 20 Sep 2025 09:34:04 -0500 https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies <![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]> HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

    panHiTE

    We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

    For detailed instructions, please refer to the panHiTE tutorial.

    Address of the bookmark: https://github.com/CSU-KangHu/HiTE

    ]]>     LEGE     https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads Mon, 20 Aug 2018 14:14:11 -0500 https://bioinformaticsonline.com/bookmarks/view/37561/hercules-a-profile-hmm-based-hybrid-error-correction-algorithm-for-long-reads <![CDATA[Hercules: a profile HMM-based hybrid error correction algorithm for long reads]]> Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

    Hercules source code is available at https://github.com/BilkentCompGen/Hercules

    Address of the bookmark: https://github.com/BilkentCompGen/Hercules

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing Tue, 07 Jul 2015 16:59:25 -0500 https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing <![CDATA[Scaffolding of a bacterial genome using MinION nanopore sequencing]]> Second generation sequencing has revolutionized genomic studies. However, most genomes contain repeated DNA elements that are longer than the read lengths achievable with typical sequencers, so the genomic order of several generated contigs cannot be easily resolved. A new generation of sequencers offering substantially longer reads is emerging, notably the Pacific Biosciences (PacBio) RS II system and the MinION system, released in early 2014 by Oxford Nanopore Technologies through an early access program.

    Address of the bookmark: http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html

    ]]>     Rahul Agarwal     https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore Wed, 25 Apr 2018 07:30:54 -0500 https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore <![CDATA[NGMLR: long-read mapper designed to align PacBio or Oxford Nanopore]]> CoNvex Gap-cost alignMents for Long Reads (ngmlr) is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith-Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments. The gap model allows ngmlr to account for both the sequencing error and real genomic variations at the same time and makes it especially effective at more precisely identifying the position of breakpoints stemming from structural variations. The k-mer search helps to detect and split reads that cannot be aligned linearly, enabling ngmlr to reliably align reads to a wide range of different structural variations including nested SVs (e.g. inversions flanked by deletions).

    Address of the bookmark: https://github.com/philres/ngmlr

    ]]>     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/34396/pore-an-r-package-for-the-visualization-and-analysis-of-nanopore-sequencing-data Thu, 23 Nov 2017 09:55:57 -0600 https://bioinformaticsonline.com/bookmarks/view/34396/pore-an-r-package-for-the-visualization-and-analysis-of-nanopore-sequencing-data <![CDATA[poRe: an R package for the visualization and analysis of nanopore sequencing data]]> Motivation: The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.

    Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

    Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

    Contact:mick.watson@roslin.ed.ac.uk

    Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

    ]]>     Jit