Bioinformatics : https://www.linkedin.com/jobs/view/2681785372/

Engineer: https://www.linkedin.com/jobs/view/2681796993/

1. SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.

2. ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.

3. Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.

4. SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.

5. MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.

6. Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.

7. Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.

8. SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.

9. SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.

10. Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.

11. Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.

12. wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.

13. Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.

14. Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.

15. CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.

16. Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

Address of the bookmark: https://biokit.readthedocs.io/en/latest/index.html

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Pine Biotech, Inc., a US-based startup working with the Tauber Bioinformatics Research Center is offering a full curriculum online preparing students without any technical background for real-life challenges with large scale biomedical data. Workshops on processing, analysis and biomedical interpretation of Next Generation Sequencing data cover important up-to-date algorithms and machine learning approaches. The most important thing is that there are virtually no pre-requisites such as coding, biostatistics or advanced medical skills. If you know what gene is and how the genes are expressed, you are ready to take the courses or join our workshops. Learn more: https://edu.t-bio.info/workshops/

C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039

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.

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. 

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods. 

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various  Markov decoder , and assignment of the membrane helix boundaries was apparent.