BOL: Related items

Ensembl 27

Jit — Tue, 16 Jun 2015 16:10:36 -0500

What is new?

Expansion of Protists and Fungi with hundreds of annotated genomes
Variation data for bread wheat, rice, Aedes aegypti, and Ixodes scapularis
Whole genome alignments for O. longistaminata and T. cacao
Non-coding RNA gene models in Bacteria
New assembly of tomato (version 2.50)
Full support for UCSC Track Hub format for hosting your own data in Ensembl

More at http://www.ensembl.info/blog/2015/06/16/ensembl-genomes-release-27-is-out/

Pattern Searching in a Single Genome

Aasha — Mon, 07 Mar 2016 05:02:51 -0600

Pattern searching holds much importance for biologists , for the understanding of DNA ( and its functionality) can be more than a matter of satisfying curiosity , but also give answers to many issuess uchas medical conditions . However,there are a number of ways of searching with in a single chromosome.

Address of the bookmark: https://www.stats.ox.ac.uk/__data/assets/pdf_file/0018/5373/LintonFinalReport.pdf

MEDEA: Comparative Genomic Visualization with Adobe Flash

Jit — Tue, 26 Apr 2016 12:15:16 -0500

As the number of sequence and annotated genomes grows larger, the need to understand, compare, and contrast the data becomes increasingly important. Using the power of the human visual system to detect trends and spot outliers is necessary in such large and complex data sets.

More at http://www.broadinstitute.org/annotation/medea/

Address of the bookmark: http://www.broadinstitute.org/annotation/medea/

Bergman Lab

Thu, 03 Oct 2013 17:20:09 -0500

Broad area of research:

Genome Annotation and Functional Genomics

Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences. Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.

Genome and Molecular Evolution
Text and Data Mining

More @ http://bergmanlab.smith.man.ac.uk/

When Chromosomes Shift: Understanding Chromosome Rearrangement and Human Disease

BioStar — Fri, 11 Apr 2025 01:07:17 -0500

In the vast and complex world of genetics, our chromosomes are like carefully arranged bookshelves — each holding critical information that defines who we are. But what happens when those books are shuffled, inverted, or swapped? The answer lies in a phenomenon known as chromosome rearrangement, a powerful force behind many human diseases, from developmental disorders to cancer.

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:

Deletions – Loss of a chromosome segment
Duplications – Repetition of a segment
Inversions – A segment breaks off, flips, and reattaches
Translocations – Segments exchange places between non-homologous chromosomes
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.

Comparative Genomics in Ensembl

Wed, 21 Jan 2015 08:31:11 -0600

The Ensembl browser provides viewable whole-genome alignments, homologues and phylogenetic gene trees, protein families, and ancestral sequences. Learn how to view and export these data in this video.

ChromEvol

Jit — Sun, 25 Jan 2015 00:33:11 -0600

Chromosome number is a remarkably dynamic feature of eukaryotic evolution. Chromosome numbers can change by a duplication of the whole genome (a process termed polyploidy), or by single chromosome changes (ascending dysploidy via, e.g., chromosome fission or descending dysploidy via, e.g., chromosome fusion).
Of the various mechanisms of chromosome number change, polyploidy has received significant attention because of the impact such an event may have on the organism.
ChromEvol implements a series of likelihood models for the evolution of chromosome numbers. By comparing the fit of the different models to biological data, it may be possible to gain insight regarding the pathways by which the evolution of chromosome number proceeds. For each model, the program estimates the rates for the possible transitions assumed by the model, infers the set of ancestral chromosome numbers, and estimates the location along the tree for which polyploidy events (and other chromosome number changes) occurred. For further methodological details, see the publications and manual on the Downloads page.

http://www.tau.ac.il/~itaymay/cp/chromEvol/about.html

Address of the bookmark: http://www.tau.ac.il/~itaymay/cp/chromEvol/downloads.html

Ryan E. Mills Lab

Tue, 26 May 2015 09:29:24 -0500

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.

We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.

More at http://millslab.ccmb.med.umich.edu/index.html

Raphael Lab

Sat, 04 Jul 2015 19:05:29 -0500

Raphael Lab research is focused on Bioinformatics and Computational Biology.

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

GenomeRing: alignment visualization based on SuperGenome coordinates

Neel — Wed, 18 Jan 2017 10:24:10 -0600

The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.

More at https://academic.oup.com/bioinformatics/article/28/12/i7/268598/GenomeRing-alignment-visualization-based-on

Address of the bookmark: http://it.informatik.uni-tuebingen.de/?page_id=185