BOL: Related items

piRNA and Bioinformatics: Decoding the Guardians of the Genome

LEGE — Sat, 07 Dec 2024 02:15:11 -0600

In the symphony of small RNAs, PIWI-interacting RNAs (piRNAs) stand out as the protectors of genomic integrity. These small, non-coding RNAs play critical roles in silencing transposable elements, regulating gene expression, and maintaining germline stability. The rise of bioinformatics has revolutionized our understanding of piRNAs, enabling researchers to decipher their biogenesis, functions, and evolutionary significance.

What Are piRNAs?

piRNAs are the largest class of small non-coding RNAs, typically 24–32 nucleotides in length. Unlike microRNAs (miRNAs) and small interfering RNAs (siRNAs), piRNAs do not rely on Dicer enzymes for maturation. Instead, they are processed from long single-stranded precursors and associate with PIWI proteins, a subclass of the Argonaute protein family.

The primary functions of piRNAs include:

Silencing Transposable Elements: By targeting transposons, piRNAs prevent genomic instability, particularly in germline cells.
Regulating Gene Expression: piRNAs modulate gene expression at transcriptional and post-transcriptional levels.
Epigenetic Modulation: They guide epigenetic modifications, such as DNA methylation, to specific genomic loci.

Challenges in piRNA Research

Studying piRNAs is fraught with challenges, including:

Short Length: Their small size complicates sequencing and alignment.
Lack of Sequence Conservation: Unlike miRNAs, piRNAs exhibit limited sequence conservation across species.
Complex Biogenesis: The intricate pathways of piRNA generation require sophisticated computational tools to unravel.

Bioinformatics: Illuminating the World of piRNAs

Bioinformatics has emerged as an indispensable tool for studying piRNAs, facilitating their discovery, annotation, and functional analysis. Here's how bioinformatics is transforming piRNA research:

1. Identification and Annotation

The discovery of piRNAs relies on next-generation sequencing (NGS) data. Bioinformatics tools such as piRNApredictor and Piano identify piRNA clusters and predict potential targets. Databases like piRBase and piRNAdb curate information about known piRNAs, their sequences, and associated proteins.

2. Mapping and Alignment

piRNAs often originate from repetitive regions, making their alignment challenging. Tools like Bowtie and STAR handle the unique mapping requirements of piRNAs, enabling accurate identification of piRNA clusters in genomes.

3. Functional Analysis

Bioinformatics approaches predict piRNA functions by analyzing their interactions with transposons, genes, and epigenetic marks. Algorithms such as TargetFinder and RIblast explore piRNA-mRNA interactions, shedding light on regulatory networks.

4. Evolutionary Studies

piRNAs are evolutionarily diverse, reflecting their roles in species-specific genomic defense. Comparative genomics tools help trace the evolution of piRNA clusters and their associated PIWI proteins across species.

5. Epigenomic Insights

piRNAs are key players in epigenetic regulation. Bioinformatics pipelines integrate piRNA data with chromatin immunoprecipitation sequencing (ChIP-seq) and DNA methylation data to uncover their role in shaping the epigenome.

Case Study: piRNAs in Germline Integrity

One of the hallmark functions of piRNAs is the suppression of transposable elements in the germline. For example, in Drosophila melanogaster, piRNAs target retrotransposons like gypsy and copia. Bioinformatics analyses revealed that these piRNAs guide PIWI proteins to transposon-derived RNA, ensuring genome stability during gametogenesis.

Clinical Relevance of piRNAs

Recent studies suggest that piRNAs may serve as biomarkers for diseases such as cancer, infertility, and neurodegenerative disorders. For instance:

Cancer: Dysregulated piRNA expression has been linked to tumorigenesis, making them potential targets for cancer therapies.
Infertility: Aberrant piRNA pathways are implicated in male infertility due to their role in spermatogenesis.
Neurodegeneration: piRNAs may regulate neuronal gene expression, highlighting their potential in neurological research.

Future Directions

The integration of bioinformatics with emerging technologies offers exciting opportunities for piRNA research:

Single-Cell Sequencing: Unveiling cell-specific piRNA expression and function.
Machine Learning: Predicting piRNA functions and targets with greater accuracy.
CRISPR-Based Tools: Editing piRNA clusters to explore their roles in vivo.

Conclusion

piRNAs are the unsung guardians of the genome, safeguarding genetic material from transposable elements and contributing to gene regulation and epigenetic programming. Bioinformatics has opened the floodgates of discovery, unraveling the complexities of piRNAs and their myriad roles in biology and disease.

As we continue to decode the piRNA landscape, these small RNAs promise to unveil big secrets about genome stability, evolution, and human health, cementing their place as a fascinating frontier in molecular biology.

Genomic architecture surrounding the fusion site of human chromosome 2

LEGE — Tue, 04 Mar 2025 12:26:29 -0600

The article "Genomic Structure and Evolution of the Ancestral Chromosome Fusion Site in 2q13–2q14.1 and Paralogous Regions on Other Human Chromosomes (https://pmc.ncbi.nlm.nih.gov/articles/PMC187548/)" explores the genomic architecture surrounding the fusion site of human chromosome 2. This fusion event is a key evolutionary marker distinguishing humans from other great apes, as humans have 46 chromosomes while chimpanzees, gorillas, and orangutans possess 48. The fusion occurred through an end-to-end joining of two ancestral chromosomes, which remain separate in nonhuman primates.

Key Findings:

Chromosomal Fusion and Its Molecular Signature:
- The fusion site is located at 2q13–2q14.1 and is characterized by degenerate telomeric sequences appearing interstitially, indicating the historical head-to-head joining of ancestral chromosomes.
- Despite being a signature of a past fusion event, these telomeric repeats are no longer functional and have undergone sequence degradation over time.
Extensive Duplications in the Surrounding Genomic Region:
- The study identifies large-scale segmental duplications flanking the fusion site, with several of these regions duplicated and scattered across multiple chromosomes.
- These duplications are predominantly located in subtelomeric and pericentromeric regions, suggesting their role in genomic instability and chromosomal evolution.
Paralogous Regions and Their Evolutionary Relationships:
- A 168-kilobase (kb) segment near the fusion site has 98%–99% sequence identity with three regions on chromosome 9 (9pter, 9p11.2, and 9q13).
- Another 67-kb region distal to the fusion site shows a high degree of homology to sequences in chromosome 22qter.
- Additionally, a 100-kb segment exhibits 96% sequence identity with a region in chromosome 2q11.2.
Comparative Genomics and Evolutionary Implications:
- By comparing the duplicated sequences and their arrangement in primates, the researchers traced the order of duplication events leading to their present distribution.
- The presence of specific repetitive elements within these duplicated segments serves as evolutionary markers that help infer their historical rearrangements.
- Some of these duplicated regions are associated with chromosomal inversion breakpoints, potentially contributing to evolutionary changes in primates.
- Recurrent structural rearrangements in these regions have been linked to human chromosomal disorders.

Conclusions and Implications:

The findings provide valuable insights into the structural evolution of human chromosome 2, which played a crucial role in human speciation.
Understanding these segmental duplications and their evolutionary trajectories sheds light on genomic instability, which may contribute to human genetic diseases.
The study highlights how large-scale chromosomal rearrangements, such as fusion and duplication, have influenced the evolutionary divergence of humans from other primates.

This research advances our understanding of human genome evolution and offers a foundation for studying the effects of structural variants in genetic disorders.

NanoSim: nanopore sequence read simulator based on statistical characterization.

Jit — Mon, 18 Dec 2017 04:16:31 -0600

NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

https://github.com/bcgsc/NanoSim

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

Interview Mark Sansom (U. Oxford): Simulations of Membrane Proteins

Mon, 07 Oct 2013 14:34:13 -0500

Workshop in Bioinformatics, 4/June/2012 Campus Vida's Research Centers organize in Santiago de Compostela the Workshop in Bioinformatics. This event addressed issues such as structural bioinformatics, biological modelling and mining bioinformatics data. Professor Mark Sansom (University of Oxford), belonging to the Structural Bioinformatics and Computational Biochemistr Unit, opened the sessions with the lecture "Multiscale Simulations of Membrane Proteins: Lipid Interactions and Signalling".

orthodotter: Synteny plots (oxford grid)

Jit — Wed, 09 Aug 2017 07:16:16 -0500

orthodotter -h
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orthodotter - Plot orthologous genes on an oxford grid.
       -f      : input file, containing orthologous genes, default is stdin
                       species chr-name start end species chr-name start end
       -toPlot  : give the x and y sets and the color separated by double-dots,
                       for example set1:set2:red will plot set1 on x, set2 on y with
                       red points. Could give several -toPlot arguments.
                       To launch the clustering of dots, use extra-option 1=dist,min_nb_genes
                       where dist is the minimal distance (euclidian) between two points and min_nb_genes the minimal
                       number of genes in a cluster to be valid.
       -o      : output file, default is stdout
       -x       : resolution of x axis, default is 600
       -y       : resolution on y axis, default is 600
       -r       : radius of circle representing orthologous genes
       -format       : could be png, gif, jpg, pdf or ps. Default is png.
       -fg           : foreground color, default is black
       -bg           : background color, default is transparent
       -fSize   : fontSize, default is 1
       -filter       : check chromosome names
       -h            : help
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orthodotter -f Vigne_Banane.ortho -toPlot Vigne:Banane:black:1=10,5 -x 1200 -y 1200 -bg white -o Vigne_vs_Banane.png > Vigne_vs_Banane.clusters
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Address of the bookmark: https://github.com/institut-de-genomique/orthodotter

Should you get sequenced? Not all bad genes predict disease

Rahul Agarwal — Thu, 29 Aug 2013 15:10:53 -0500

“What we really don’t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful”

“As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you’re going to get the disease. That’s only true in a very few cases.”

Source:Today Health

Address of the bookmark: http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154

Two major breakthrough!!

Rahul Agarwal — Mon, 02 Sep 2013 10:18:11 -0500

"Scientists in Uruguay in colloboration with European partners sequenced the genome of the high-value Tannat grape, from which "the most healthy of red wines" are fermented.

A quick, $1 syphilis test in development by researchers from UNU-BIOLAC."

Source:

http://www.sciencedaily.com/releases/2013/09/130902101846.htm

http://www.eurekalert.org/pub_releases/2013-09/tca-ssg082613.php

Encode sequencing data freely available to download and use for academic means

Rahul Agarwal — Thu, 13 Mar 2014 18:18:08 -0500

In Encode, regulatory elements investigated via DNA hypersensitivity assays, assays of DNA methylation, and chromatin immunoprecipitation (ChIP) of proteins that interact with DNA, including modified histones and transcription factors, followed by sequencing (ChIP-Seq).

More information:

https://genome.ucsc.edu/ENCODE/pilot.html

Address of the bookmark: https://genome.ucsc.edu/ENCODE/

Tsetse Fly Genome sequenced

Rahul Agarwal — Fri, 25 Apr 2014 10:48:35 -0500

As it reported online today in Science, the team used several sequencing approaches to tackle the tsetse fly's 366 million base genome.

The current study, and companion articles slated to appear in PLOS One, PLOS Genetics, and PLOS Neglected Tropic Diseases, are the result of nearly 150 researchers based in 18 countries.

Source:

http://www.genomeweb.com/sequencing/international-team-sequences-tsetse-fly-genome

Science for Life Laboratory (SciLifeLab)-Sweden

Sat, 10 May 2014 06:22:30 -0500

Science for Life Laboratory (SciLifeLab) is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource and a collaboration between four universities: Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University.

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/