BOL: Related items

Early Genome Screening: The New Health Horoscope!

LEGE — Thu, 02 Jan 2025 19:44:36 -0600

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:

Accuracy and Interpretation: Genetic predisposition does not guarantee disease. Misinterpretation of results can lead to unnecessary anxiety or unwarranted medical interventions.
Privacy and Data Security: Genetic data is highly sensitive. Ensuring robust data protection measures is crucial to prevent misuse.
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.

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies

LEGE — Sat, 20 Sep 2025 09:34:04 -0500

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

Illumina Smartphone Chip !!!

Robert M Willioms — Tue, 30 Dec 2014 23:19:54 -0600

Illumina, the company that claims it brought human genome sequencing down to $1000 prices, has now turned its attention to a consumer product - a chip that you can plug into your smartphone and have it read your genetic information.

The biggest challenge ahead of Illumina is simplifying the process of genetic sequencing. Currently, Illumina’s DNA sequencers are gigantic machines that use techinques like colorimetry to work, but while the core technology is computational, it takes some 30 steps to extract genetic data and run it through. This process will likely have to be hugely simplified on mobile devices, given the fact that some studies require extracting 10 mililiters of blood. Illumina researchers are also working on finding the optimal technology for this on-chip DNA sequencing - be it electrical, optical, or other.

Illumina is one of the most prominent names in genetics, often said to be the Intel of genetic sequencing, as just like Intel it provides the algorithms, the processing brain that runs a DNA reading task.

In other recent smartphone-related biotech news, drug company Pfizer launched its REMOTE project, a new type of clinical trial that does not require going to a hospital for checks - targeted at patients with overactive bladder problems, the FDA-approved REMOTE project allowed to gather data from patients from over 10 states remotely, via mobile devices.

This is indeed the Illumina answer to Apple's Health app, HealthBook, Google HealthFit.

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads

Jit — Wed, 07 Jun 2017 04:18:16 -0500

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

HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads

Rahul Nayak — Tue, 08 May 2018 04:27:22 -0500

HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

minialign: fast and accurate alignment tool for PacBio and Nanopore long reads

Jit — Thu, 24 May 2018 08:33:26 -0500

Minialign is a little bit fast and moderately accurate nucleotide sequence alignment tool designed for PacBio and Nanopore long reads. It is built on three key algorithms, minimizer-based index of the minimap overlapper, array-based seed chaining, and SIMD-parallel Smith-Waterman-Gotoh extension.

Address of the bookmark: https://github.com/ocxtal/minialign

npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads

Shruti Paniwala — Mon, 11 Jun 2018 05:14:57 -0500

npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.

Address of the bookmark: http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

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

LoRMA: A tool for correcting sequencing errors in long reads

Abhimanyu Singh — Thu, 06 Sep 2018 16:21:01 -0500

An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher.

conda install -c atgc-montpellier lorma

Address of the bookmark: https://gite.lirmm.fr/lorma/lorma-releases/wikis/home