<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43112?offset=140 https://bioinformaticsonline.com/bookmarks/view/44489/proksee Wed, 27 Mar 2024 11:11:54 -0500 https://bioinformaticsonline.com/bookmarks/view/44489/proksee <![CDATA[Proksee]]> Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.

Please Cite the Following
Grant JR, Enns E, Marinier E, Mandal A, Herman EK, Chen C, Graham M, Van Domselaar G, and Stothard P
Nucleic Acids Research, 2023, gkad326, https://doi.org/10.1093/nar/gkad326

Address of the bookmark: https://proksee.ca/

]]> 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/opportunity/view/1720/postdoctoral-associate-bioinformatics-at-duke-university-medical-center Sat, 10 Aug 2013 18:38:38 -0500 <![CDATA[Postdoctoral Associate - Bioinformatics at Duke University Medical Center]]> The Department of Biostatistics and Bioinformatics at Duke University Medical Center is seeking a Postdoctoral Associate for a one year appointment to work on several high-dimensional research projects. The specific goals of the project are to identify genes or molecular markers that are predictive of clinical outcomes in renal and prostate cancer.

Candidates must have: a PhD degree in statistics, biostatistics or bioinformatics, extensive experience in analyzing high-dimensional data (microarray, SNP, CNVs) and of validation approaches. In addition, experience in penalized regression methods, data base manipulation; and strong programming skills in order to conduct Monte Carlo studies and applications (R). Candidate must have excellent communication skills (verbal, written and presentation), a strong proficiency in Linux system.

This position is available immediately and will be filled as soon as possible. Appointment could be extended beyond the first year based on additional funding.

For more information about the Department of Biostatistics and Bioinformatics, please visit our website: http://www.biostat.duke.edu.

For more info: http://biostat.duke.edu/sites/biostat.duke.edu/files/Halabi%20-%20Postdoc%20Job%20Posting%202013%20updated.pdf

Duke University is an Equal Opportunity/Affirmative Action Employer.

]]> https://bioinformaticsonline.com/news/view/6302/a-allele-of-slc24a5-gene-is-found-to-be-responsible-for-variation-in-skin-color-of-south-east-asians-and-europeans Tue, 12 Nov 2013 21:02:27 -0600 https://bioinformaticsonline.com/news/view/6302/a-allele-of-slc24a5-gene-is-found-to-be-responsible-for-variation-in-skin-color-of-south-east-asians-and-europeans <![CDATA[A-allele of SLC24A5 gene is found to be responsible for variation in skin color of South-East Asians and Europeans]]> Key finding:

  1. rs1426654 SNP of SLC24A5 gene is decider of skin pigmentation variation in South Asia
  2. rs1426654-A allele is widely spread throughout the Indian subcontinent 
  3. Skin pigmentation is also account by the combination of processes like selection and demographic history of populations affected by their language and origin
  4. Sign of positive selection in Europeans, Middle East, Pakistan, Central Asia and North India but not in South India
  5. In European , A-allele is almost reached to fixation

Paper:

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003912

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data Thu, 19 Apr 2018 08:06:03 -0500 https://bioinformaticsonline.com/bookmarks/view/36271/heap-a-highly-sensitive-and-accurate-snp-detection-tool-for-low-coverage-high-throughput-sequencing-data <![CDATA[Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data]]> Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

Address of the bookmark: https://github.com/meiji-bioinf/heap

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model Fri, 28 Feb 2020 23:27:37 -0600 https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model <![CDATA[RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)]]> RAINBOWR(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of GWAS as follows.

  • Single-SNP GWAS with RGWAS.normal function
  • SNP-set (or gene set) GWAS with RGWAS.multisnp function (which tests multiple SNPs at the same time)
  • Check epistatic (SNP-set x SNP-set interaction) effects with RGWAS.epistasis (very slow and less reliable)

https://github.com/KosukeHamazaki/RAINBOWR

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663

https://cran.r-project.org/web/packages/RAINBOWR/index.html

Address of the bookmark: https://github.com/KosukeHamazaki/RAINBOWR

]]> Surabhi Chaudhary https://bioinformaticsonline.com/bookmarks/view/39720/snakemake-workflow-dna-seq-gatk-variant-calling Thu, 25 Jul 2019 12:55:07 -0500 https://bioinformaticsonline.com/bookmarks/view/39720/snakemake-workflow-dna-seq-gatk-variant-calling <![CDATA[Snakemake workflow: dna-seq-gatk-variant-calling]]> This Snakemake pipeline implements the GATK best-practices workflow for calling small genomic variants.

Address of the bookmark: https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling

]]> Jit https://bioinformaticsonline.com/pages/view/923/phylogenetic-for-bioinformatics Tue, 16 Jul 2013 03:50:30 -0500 https://bioinformaticsonline.com/pages/view/923/phylogenetic-for-bioinformatics <![CDATA[Phylogenetic for Bioinformatics]]> Biologists estimate that there are about 5 to 100 million species of organisms living on Earth today. Evidence from morphological, biochemical, and gene sequence data suggests that all organisms on Earth are genetically related, and the genealogical relationships of living things can be represented by a vast evolutionary tree, the Tree of Life. The Tree of Life then represents the phylogeny of organisms, i. e., the history of organismal lineages as they change through time.
Every living organism contains DNA, RNA, and proteins. Closely related organisms generally have a high degree of agreement in the molecular structure of these substances, while the molecules of organisms distantly related usually show a pattern of dissimilarity. Molecular phylogeny uses such data to build a "relationship tree" that shows the probable evolution of various organisms. Not until recent decades, however, has it been possible to isolate and identify these molecular structures. 
phylogenetics is the study of evolutionary relatedness among various groups of organisms (for example, species or populations), which is discovered through molecular sequencing data and morphological data matrices. In other word, Phylogenetics, the science of phylogeny, is one part of the larger field of systematics, which also includes taxonomy. Taxonomy is the science of naming and classifying the diversity of organisms Molecular phylogeny is the use of the structure of molecules to gain information on an organism's evolutionary relationships. The result of a molecular phylogenetic analysis is expressed in a so-called phylogenetic tree.

The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms. 
Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.
The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.

For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.

Phylogeny and Reconstructing Phylogenetic Trees: http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html
the CBRG and Department of Statistics Phylogeny tutorial: http://www.compbio.ox.ac.uk/tutorials/phylogeny/
TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html

PHYLIP: http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html
An Introduction to Molecular Phylogeny: http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf

How to make a phylogenetic tree: http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Treetutorial.html
Phylogenetic Trees: http://cnx.org/content/m11052/latest/
Phylogeny by Ron Shamir: http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf
Introduction to Phylogeny: http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm
Lecturer notes on Phylogeny: http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf
Principles and Practice of Phylogenetic Systematics:http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm

Inferring phylogenetic trees: http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf

Lecture Notes

Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:http://academic.emporia.edu/mooredwi/nathist/chap1.htm

Algorithms for Phylogenetic Reconstructions:http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail : http://www.phylogeny.fr/version2_cgi/index.cgi

A Brief Tutorial on Phylogenetics
http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf

A Brief Tutorial on Phylogenetics Human Rabbit Chicken
http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf

Phylogenetic Tree Computation Tutorial Overview
http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf

MrBayes: A program for the Bayesian inference of phylogeny
http://golab.unl.edu/teaching/SBseminar/manual.pdf

Web sites providing software for the construction of phylogenetic trees

  • TNT-Tree Analysis Using New Technology
  • Tree View-Taxonomy and Systematics Group at Glasgow
]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/3013/python-and-biopython-tutorial Fri, 23 Aug 2013 06:47:40 -0500 https://bioinformaticsonline.com/bookmarks/view/3013/python-and-biopython-tutorial <![CDATA[Python and BioPython Tutorial]]> A quickstart tutorial that allows to become familiar with the Python language. The exercises expect knowledge of basic concepts of programming. A group of 2nd year computer science students with no previous Python knowledge required 60'-90' to complete the exercises. With about 3 hours time, the exercise is suitable for non-programmers as well.

Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

]]> Manshi Raghubanshi