Tutorial Series

I will be visiting basics of computational biology in a series of tutorials that I will be preparing based on my knowledge of the subject. These will include sequence analysis methods, microRNAs: the biology, predictions and functions, modeling and simulation methods, basic genomics and much more... So keep reading and please feel free to post any comments...

miRNA companies

Asuragen Launches New Company Focused on miRNAs. Read more here.

Columbia University Medical Center and Rosetta Genomics Announce Columbia University's Submission of the First Cancer Diagnostic Test Based on Rosetta Genomics Proprietary MicroRNA Technology for Approval to the New York State Department of Health Clinical Laboratory Evaluation Program

new softwares for bioinformatics

RNACompress: A novel way to compress RNA sequence and secondary structure

Background
With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression.

Results
RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective.

Conclusions
A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

in the news...

• Merck & Co. Inc. (MRK) said two studies showed genetic susceptibility to obesity involves changes in entire networks of genes, not just mutations in several specific genes.Visit here for more.
• First study to show that microRNAs may also play a role in synaptic plasticity and the modulation of translation.Michael Greenberg's group at Harvard Medical School and Austrian colleagues hypothesized that microRNAs are involved in the regulation of protein synthesis in neuronal dendrites. To test this, they overexpressed a hippocampal microRNA, miR-134, and found that it reduced the size of dendritic spines by inhibiting a protein kinase that induces spine development.Read the paper here.
  

today in microRNA Research...

microRNAs play an important role in limb(fin) regeneration.
When the zebrafish is injured, the level of microRNA miR-133 drops and regeneration begins. In uninjured zebrafish, the level of this microRNA is quite high. This research was performed by researchers at Duke University and will feature in the 15th March issue of Genes & Development.
Read more here

events coming up...

Developmental Biology of the Sea Urchin XVIII
April 23-26, 2008
Marine Biological Laboratory, Woods Hole, MA
Click here for more information and to register

One day conference on sleep regulation and role of gene susceptibility in sleep disorders.
Jun 20, 2008 • 8:30 AM - 7:30 PM
The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St. at Barclay St., 40th fl.
Click here for more information and to register

hiatus...

Its been almost a year since I posted. Life moves fast and progress in Science, faster...

I am currently in my first year PhD Computational Biology at Carnegie Mellon University. You can check out what I work in RIGHT HERE.

You can come back daily for latest in the field and some interesting posts from me.

broadcast of the day:

Macaque Genome Analysis Will Help Find Human Disease Genes
The rhesus macaque (Macaca mulatta) is physiologically similar to humans. Its genome was sequenced in 2005 (2.9 billion DNA base pairs). The humans and chimpanzees are so closely related(6 million years) that a comaparative genomic study is not as informative as using the macaque. The different studies involve studying the common genes between these 3 genomes, differences between the Indian and Chinese macaques (for example, Chinese macaques develop AIDS-like symptoms more slowly than Indian macaques).

Full Article
Medicinal leeches have been misclassified for centuries
Until now, the leeches were assumed to be the species Hirudo medicinalis, but new research reveals they are actually a closely related but genetically distinct species, Hirudo verbana. Wild European medicinal leeches are at least three distinct species, not one.
Full Article

Human sperm made from bone marrow

Stem cells from the bone marrow have been used to create immature sperm cells. It is expected that this research can be be used in the future to find a cure for male infertility. Currently, mature sperms have not been created. Of course, with the bans, moral, ethical issues involved in stem cell research in addition to the scientific fact that manipulating stem cells can cause lasting genetic changes that may not all be desirable, its too early to jump to any conclusions.

paper of the day:

"A Systems Biology Dynamical Model of Mammalian G1 Cell Cycle Progression"
Thomas Haberichter, Britta Mädge, Renee A Christopher, Naohisa Yoshioka, Anjali Dhiman, Robert Miller, Rina Gendelman, Sergej V Aksenov, Iya G Khalil1 & Steven F Dowdy

The paper describes a combined experimental and computational approach used to understand progression of the mammalian G1 cell cycle, one of the phases in mammalian cell reproduction and tumor growth.
The GNS software was used to quantitatively model the cell cycle progression and then experimentally verified using cultured cells. An excellent example to demonstrate the power of the combinatorial approach.

broadcast of the day

University of Pittsburgh School of Medicine and Children's Hospital have recently made a startling discovery. Female stem cells are more able to regenerate muscle, that is, make muscle cells than male cells.
Advantages of this finding:
- influence treatment approaches for Duchenne muscular dystrophy (genetic condition found in boys causing progressive weakening of muscles)
- maybe provide an explanation for why some therapies work better on women than men
- make scientists more aware and consider whether stem cells are collected from or injected into males/females

GLOSSARY: from Wikipedia
1. Stem cells: primal cells common to all multi-cellular organisms that retain the ability to renew themselves through cell division and can differentiate into a wide range of specialized cell types. (more on stem cells to follow in future posts)

broadcast of the day:

• Genome of streptococcus sanguinis (2.4m bp) has been sequenced. This bacteria lives in healthy human mouth but can cause deadly heart infection (bacterial endocarditis) if it enters the bloodstream (through minor cut or wound). It also plays a role in formation of dental plaque.

• Symbiosis of the fungus Rhizopus microsporus and Burkholderia bacteria that live within its cells: The two species effectively team up to break down young rice plants for their nutrients, causing a plant disease known as rice seedling blight. Latest research shows that reproduction (spore formation) of the fungus is dependent on the bacteria, which lives inside its cytoplasm.

Software Tool: GENIUS

GENIUS: a new tool for gene networks visualization
Paolo Ciccarese, Stefano Mazzocchi, Fulvia Ferrazzi, Lucia Sacchi

Methods for gene network reconstruction based on : (Reverse engineering methods)

• Boolean networks
• Bayesian networks
• Differential Equations

INPUT:
For n genes in the network, an nXn matrix such that
aij = 1 if connection between genes i and j
aij = 0 if no connection between genes i and j

GENIUS visualizes
Genes = nodes
connections = edges

Two types of visualizatiobs:

AGORA STYLE
Algorithm used assumes that every individual can be treated exactly the same.
Simulation paradigm: "PRIVATE SPACE"
This mathematical model uses a repulsive force field and a basic attractive force field.
- Repulsive force field --> Infinity
as
distace between objects --> 0
- Then
Repulsive force field rapidly decreases to 0 on a short distance.
- Attractive force field starts with 0 and increases to infinity.

The Agora view tool has been extended so that a connection between two genes is directed such that the 'from node' is the regulator and the 'to node' is the regulated gene.

TOUCHGRAPH STYLE
This view is useful to show relationships between nodes characterized by maximum level of the number of edges in the minimum-length path connecting these nodes in the graph.

This paper then examines the network visualization of cDNA microarray data set analyzed in [1] and then analyzing temporal profiles relative to the 517 genes using the Reveal algorithm described in [2].Data set available here.

Brief description of Reveal algorithm
- For every gene x,
find set of regulators(minimal set of input genes that can univocally explain behavior of output gene x)
- Based on use of Entropy and Mutual Information scores:
if for 2 genes x and y,
Mutual Information(x,y) = Entropy(x)
then
y univocally determines x

Use of Reveal in GENIUS
They extend the algorithm to include 3 discretization data levels instead of 2.
-1 : under-expression
0 : equal expression
+1 : over-expression
of serum stimulated cell genes w.r.t. expression values of same genes measured using non-stimulated cells.

179 groups(pseudo-genes) recognized and extended algorithms was applied to them.

You can check out the example given in the paper to see how the output looks.

REFERENCES
1. Iyer V. R. et al. (1999): The transcriptional program in the response of human fibroblasts to serum. Science: 283: 83-87
2. Liang S, Fuhrman S, Somogyi R. REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symp. Biocomp. 1998: 98 (3):18-29.

Paper summarized - Principles of microRNA regulation of a human cellular signaling network

Principles of microRNA regulation of a human cellular signaling network
Qinghua Cui, Zhenbao Yu, Enrico O Purisima and Edwin Wang

What are microRNAs?

• ~22nucleotide long non-coding RNAs
• responsible for RNA-based gene regulation
• act as post transcriptional and translational regulators
• base-pair with target mRNAs
• ~1% of predicted genes in human genome
• BUT Regulate 10–30% of genes
• Targets
  
  • signaling proteins
  • enzymes
  • transcription factors

It is unclear if and how miRNAs might orchestrate their regulation of cellular signaling networks and how regulation of these networks might contribute to the biological functions of miRNAs.

What are signalling networks?
These make decisions about whether to grow, differentiate, move or die. Their components are Proteins. They are represented as graphs where the nodes represent the proteins and the links between the nodes represent the interactions between the proteins.

Hypothesis paper is based on
Role of miRNAs in strength and specificity of signaling networks through direct control of proteins at post-transcriptional and translational levels.

Signaling network used:
Signal transduction processes from multiple cell surface receptors to various cellular machines in a mammalian hippocampal CA1 neuron consisting of:
540 nodes
1258 links
-689 activating (positive) links
-306 inhibitory (negative) links
-263 neutral (protein interactions)

Results stated (Glossary for the terms given below)

• MiRNAs more frequently target network downstream signaling components than ligands and cell surface receptors



• MiRNAs preferentially target the downstream components of the adaptors, which have potential to recruit more downstream components


• MiRNAs more frequently target positively linked network motifs


• MiRNAs avoid targeting common components of cellular machines in the network

Glossary

1. adaptor proteins: The function of these proteins is recruiting downstream signaling components to the vicinity of receptors. It invloves no enzyme activity – they physically interact with upstream and downstream signaling proteins
   
2. network motif: A complex signaling network can be broken down into distinct regulatory patterns, or network motifs, typically comprised of three to four interacting components capable of signal processing. The function of a motif also depends on whether the links are positive or negative.
3. scaffold proteins: Unlike adaptors, scaffold proteins do not directly activate or inhibit other proteins but provide regional organization for activation or inhibition between other proteins.
4. functional modules: represent a set of proteins that are always present in various cellular conditions.