BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data. It integrates enriched genomic information with metadata accessible via a flexible search-engine.

Key features

• Assembly statistics: ensure data quality with genome-based key metrics
• Taxonomic classification: robust, purely genome-based classifications (GTDB)
• MLST: subtyping for deeper insights into genetic variation
• Annotation: comprehensive & taxonomy-independent (Bakta)
• Metadata: full original submission records

Address of the bookmark: https://bakrep.computational.bio/

Led by David Haussler (UCSC) and Richard Durbin (Sanger Institute), the Data Working Group (DWG) of the Global Alliance brings together the leading Genome Institutes and Centers with IT industry leaders to create global standards and tools for the secure, privacy respecting and interoperable sharing of Genomic data.

More at http://ga4gh.org/#/

Address of the bookmark: http://ga4gh.org/#/

Tutorial https://github.com/andyrimmer/Platypus/blob/master/misc/README.txt

Address of the bookmark: http://www.well.ox.ac.uk/platypus

Address of the bookmark: https://github.com/yjx1217/LRSDAY

In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.

With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.

Ref @ A comparative genomics multitool for scientific discovery and conservation https://www.nature.com/articles/s41586-020-2876-6

 Data at http://zoonomiaproject.org/

Address of the bookmark: https://github.com/sanger-pathogens/ariba/wiki/Task%3A-getref

So, What Is Data Science?
At its core, data science is the interdisciplinary field that extracts knowledge and insights from data using programming, statistics, and domain expertise. In bioinformatics, data science enables us to turn gigabytes of sequence data into biological meaning.

Imagine trying to understand gene regulation in cancer by analyzing thousands of RNA-seq samples, or predicting antibiotic resistance from bacterial genomes—these challenges are not solvable through wet lab experiments alone. They require data-driven thinking.

Data Science Meets Bioinformatics
Bioinformatics is inherently a data science domain. From genomics to systems biology, every field in modern biology relies on data science techniques to:

Clean and process massive datasets

Discover patterns in high-dimensional data

Build predictive models (e.g., for disease classification)

Visualize complex biological networks and trends

Integrate diverse data types (e.g., transcriptomic + epigenomic data)

The Bioinformatics Toolkit
Here’s what data science typically looks like in bioinformatics:

Task Data Science Role
Sequence alignment Efficient algorithms, indexing, parallel processing
Gene expression analysis Statistical modeling (e.g., DESeq2, limma)
Variant calling Data filtering, probabilistic models
Clustering of cells in single-cell data Unsupervised learning
Protein structure prediction Deep learning models (e.g., AlphaFold)
Metagenomics Data integration, classification, dimensionality reduction

Common tools include Python, R, Bioconductor, scikit-learn, Pandas, Seurat, and TensorFlow—often working together in reproducible workflows.

It's Not Just About Coding
A common misconception is that bioinformatics is just programming or scripting. But being a data scientist in bioinformatics also means:

Understanding experimental design

Asking biologically meaningful questions

Choosing the right statistical or machine learning models

Communicating findings effectively (e.g., plots, dashboards, papers)

In other words, data science in bioinformatics is where biology, statistics, and computer science converge.

Why It Matters
The real power of data science in bioinformatics is its ability to scale discovery.

Instead of studying one gene, we can study thousands.

Instead of analyzing one species, we can explore entire ecosystems.

Instead of waiting months for lab results, we can generate hypotheses in days.

From personalized medicine and cancer diagnostics to agricultural genomics and pandemic surveillance, data science is at the heart of the bioinformatics revolution.

Final Thoughts
If you’re a biologist who’s curious about code, or a data enthusiast fascinated by life sciences, bioinformatics is your playground—and data science is your toolkit.

In bioinformatics, data science isn’t just useful. It’s essential.

What Are Chromosome Rearrangements?

Chromosome rearrangements are structural changes that alter the normal configuration of chromosomes. These changes can involve large segments of DNA — from thousands to millions of base pairs — and can occur spontaneously, be inherited, or result from exposure to mutagens (like radiation or chemicals).

Common Types of Rearrangements:

1. Deletions – Loss of a chromosome segment

2. Duplications – Repetition of a segment

3. Inversions – A segment breaks off, flips, and reattaches

4. Translocations – Segments exchange places between non-homologous chromosomes

5. Insertions – A segment is inserted into another part of the genome

These changes can disrupt genes directly or affect gene regulation, leading to disease.

How Do Chromosome Rearrangements Cause Disease?

The impact of a rearrangement depends on which genes are involved, how much DNA is affected, and when the rearrangement occurs (in development vs. adulthood). Here are some key mechanisms:

• Gene disruption: Breaking a gene can lead to loss of function or the creation of a non-functional protein.

• Gene fusion: Joining parts of two genes may form a novel hybrid gene with new functions (common in cancer).

• Dosage effects: Extra or missing gene copies can disturb the balance of gene expression.

• Position effects: Moving a gene to a new regulatory environment may silence or over-activate it.

Chromosome Rearrangements in Human Disease

1. Developmental Disorders

• Cri-du-chat syndrome: Caused by a deletion on chromosome 5p. Affected infants often have a high-pitched cry and intellectual disability.

• Williams syndrome: Results from a microdeletion on chromosome 7q, affecting genes related to cardiovascular and cognitive function.

2. Cancer

Cancer is perhaps the most striking example of disease caused by chromosome rearrangements.

• Chronic Myeloid Leukemia (CML): Caused by a translocation between chromosomes 9 and 22, forming the Philadelphia chromosome. This creates the BCR-ABL fusion gene, which drives uncontrolled cell growth.

• Burkitt lymphoma: Involves translocation of the MYC gene, leading to excessive cell division.

• Ewing sarcoma: A fusion of EWSR1 and FLI1 genes through translocation promotes tumor development.

3. Infertility and Miscarriages

Balanced rearrangements (like inversions or translocations) in carriers may not cause disease directly but can result in:

• Recurrent miscarriages

• Infertility

• Birth defects in offspring

Detecting Rearrangements

Thanks to modern genomics, chromosome rearrangements can now be detected with high precision using:

• Karyotyping – Classic method for detecting large rearrangements

• FISH (Fluorescence In Situ Hybridization) – Uses fluorescent probes to target specific DNA sequences

• Array CGH (Comparative Genomic Hybridization) – Detects copy number changes across the genome

• Whole Genome Sequencing (WGS) – Identifies even small or complex rearrangements at base-pair resolution

Looking Forward: The Future of Chromosome Medicine

Understanding chromosome rearrangements is now central to:

• Personalized medicine

• Genetic counseling

• Targeted therapies, especially in cancer (e.g., tyrosine kinase inhibitors for BCR-ABL fusion)

With the rise of long-read sequencing and single-cell genomics, even previously “invisible” rearrangements are being uncovered, offering new insights into both rare diseases and common conditions.

Final Thoughts

Chromosome rearrangements remind us that genetics isn't just about which genes we have — but where they are, how they're arranged, and when they're active. As our tools grow sharper, so does our ability to diagnose, understand, and treat diseases rooted in genomic architecture.

In a way, the genome is like a book not just defined by its words, but also by how the chapters are ordered. Rearranging them can create a new story — sometimes harmful, sometimes insightful — and understanding these changes is key to writing a healthier future.

The ongoing 2014 West Africa Ebola outbreak is considered to be the largest and longest outbreak ever recorded of Ebola, killing at least 932 people and infecting more than 1,700 till date since March in Sierra Leone, Guinea, Nigeria and Liberia.

Hence, the World Health Organisation (WHO) on 8 August, 2014 declared the killer Ebola epidemic ravaging parts of West Africa an international health emergency.

Causes

EVD is caused by infection with a virus of the family Filoviridae, genus Ebolavirus. While there are five identified sub-species of Ebolavirus, four viruses cause disease in humans. They are Bundibugyo virus (BDBV), Ebola virus (EBOV), Sudan virus (SUDV), Taï Forest virus (TAFV).

The fifth virus, Reston virus (RESTV), is not considered to be disease-causing in humans.

According to WHO, EVD first appeared in 1976 in two simultaneous outbreaks, in Nzara, Sudan, and in Yambuku, Democratic Republic of Congo. The latter was in a village situated near the Ebola River from which the disease takes its name.

How does it spread?

It is still unclear how Ebola spreads. However, it is believed that the first pateint becomes infected through contact with an infected animal's body fluids.

Human-to-human transmission can occur through direct contact with blood, organs or other body fluids of infected people or exposure to objects such as needles and syringes that have been contaminated with infected secretions.

Ebola can also be transmitted from men who have recovered from the disease through semen as it is infectious for up to 7 weeks.

Infected dead bodies can spread Ebola as they are still infectious. So mourners who have direct contact with the body of deceased person can also get the disease.

Who is most at risk?

Health-care workers who do not wear appropriate protective clothing and family members who are in close contact with infected people or deceased patients.

Signs and symptoms:

Symptoms may occur between 2 and 21 days after contracting the infection. Common signs of Ebola include:

Fever

Headache

Muscle, abdominal and joint pain

Sore throat

Weakness

Diarrhea

Vomit or cough up blood

Chest pain

Difficulty in breathing and swallowing

Rash

Hiccups

Bleeding inside and outside the body

Prevention

Currently there is no vaccine available for humans. But the infection can be controlled through the use of recommended protective measures such as:

Avoid contacting infected blood or secretions, including from those who are dead .

Using standard precautions for all patients in the healthcare setting.

Sterilizing equipment, and wearing protective clothing including masks, gloves, gowns and goggles.

Washing your hands with soaps or detergents.

Disinfecting your surroundings.

Isolate people who have Ebola symptoms.

Culling of infected animals, with close supervision of burial or incineration of carcasses.

Yet, not travelling to the areas or countries where the virus is found is the best way to avoid Ebola.

More at https://iubmb.onlinelibrary.wiley.com/doi/full/10.1002/bmb.21480

Address of the bookmark: https://covid.postera.ai/covid