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a domain for bioinformatics lovers...

Updated: 2016-09-08T09:27:16.273+05:00


fMRI studies help to discover more about grief


I read this very interesting article on studies performed by clinical psychologists on 23 women who were divided into 2 groups based on their ability to handle grief. All of them had lost a loved one (mother/sister) to breast cancer and while some were able to accept it and move on, others were still in grief (state called 'complicated grief'). They were shown photos of their loved one or a stranger along with a word that was either grief-related or otherwise. The normal group, when shown the pic of their loved one, had activity in the region of the brain known to process pain. While the surprise was that the group belonging to 'complicated grief' not only had activity in those regions but also in the nucleus accumbens, the region of the brain related to pleasure. Mary-Frances O'Connor of the University of California, Los Angeles, who conducted this study, said this would help find a cure for people with this condition. Read more about it here.

In the news...


Latest lead in finding cure for Parkinson's
Researchers at Griffith University have recently published a study in the Journal Stem Cells, which could be a major step in finding the cure for Parkinson's disease. They created lesions in the brains of lab mice, similar to those found in Parkinson's patients due to the disease. They then planted adult stem cells from the olfactory nerves of patients in the brains of the mice and were able to see an improvement in the behavior of the mice, like regaining the ability to run in a straight line. The stem cells were able to differentiate into dopamine producing brain cells. These cells are damaged in the brains of Parkinson's patients.

Breaking News: Oldest mother found


Researchers in Melbourne, Australia have found, what they call, the oldest pregnant mother. This placoderm fossil is said to be 380 million years old and bears an embryo with an umbilical cord. This has proved that vertebrates have been giving birth (exhibiting viviparity) for at least 380 million years. To read the full story Click Here.

In the news: Parkinsons's Disease


  • Study Links Parkinson's Disease With Chemical Exposure
Scientists have now discovered additional evidence of a connection between Parkinson's disease and long-term exposure to pesticides. Parkinson's disease strikes movements like walking, talking and writing. Symptoms of the disease first tend to appear in patients over 50. These symptoms may include tremors and muscle rigidity.

A study of over 300 people with Parkinson's found that sufferers were more than two times as likely to have had heavy exposure to pesticides over their lifetime as other members of their family without the disease.The new research observed the lifetime pesticide exposure of over 300 Parkinson's patients. Over 200 of their healthy relatives were also included in the study as a control group. The results showed that patients with Parkinson's were 1.6 times more likely to report an exposure to pesticides in their lifetimes compared with the control group.Additionally, people with Parkinson's were 2.4 times more likely than healthy people to report heavy exposure to pesticides. Heavy exposure is defined as more than 215 days over a lifetime.

A change in several genes has been identified as a precursor to the disease, however these variations are somewhat unusual and they only account for a small number of the incidence of the disease. The majority of cases are believed to be the result of a reaction between genes and the environment. Pesticides may be contributing to nerve cell death in some people who have Parkinson's. It is unlikely to be the only cause of the disease, however.

Read the article here

  • Medical Breakthrough -- Tango For Parkinson's Disease
A new study is showing how a popular dance may help patients fight some of the effects. Researchers at Washington University are studying a unique treatment for those afflicted with Parkinson's -- the tango.

"It seemed to be a good fit because several of the movements that tango incorporates might specifically target some of the difficulties that people with Parkinson's disease have."

To tango, patients have to balance, turn, initiate steps, dance at different speeds and walk backwards.Researchers say it's these moves that help improve symptoms. Dr. Gammon Earhart says "given the challenges that they're facing on a day-to-day basis, but they come in here with such energy and enthusiasm, and they're so very, very appreciative." Researchers followed Parkinson's patients who attended 20 tango classes. The participants saw much more dramatic improvements in balance and mobility than those who did traditional exercise.
Study participants ranged from their mid-forties to age eighty-two. Researchers are now testing whether a more intense two-week dance course could benefit patients even more. The study was funded by the American Parkinson Association.

Read the article here

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


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.

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.


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



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
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:

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
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.

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)
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.

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 networkQinghua Cui, Zhenbao Yu, Enrico O Purisima and Edwin WangWhat are microRNAs?~22nucleotide long non-coding RNAsresponsible for RNA-based gene regulationact as post transcriptional and translational regulatorsbase-pair with target mRNAs~1% of predicted genes in human genome BUT Regulate 10–30% of genesTargetssignaling proteinsenzymestranscription factorsIt 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 onRole 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 nodes1258 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 componentsMiRNAs more frequently target positively linked network motifsMiRNAs avoid targeting common components of cellular machines in the networkGlossaryadaptor 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 proteinsnetwork 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. 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.functional modules: represent a set of proteins that are always present in various cellular conditions.[...]

Sequence Alignment - An introduction


Sequence alignment is one of the most important and basic concepts in computational genomics. Given 2 sequences, what is the best way to arrange the letters of the sequences one below the other so that there are maximum matches between them? Putting it another way, given 2 sequences s and t, find s' and t' such that
1. by removing the gaps from s' and t', we can retrieve s & t.
2. there is no i such that s[i] and t[i] are gaps.
3. length(s') = length(t') >= max(length(s),length(t)) and <= sum(length(s),length(t)) In the above definition, s[i] means the letter in sequence s at the ith position. length(s') means length of sequence s' max(length(s),length(t)) means maximum of length of s and length of t sum(length(s),length(t)) means sum of the lengths of s and t I think things will get absolutely clear with some examples here. Mind you, I am trying to explain keeping a layman in mind. Some of the terms used might sound too technical to some while to others my explanations might seem too kiddish. I hope to attain the right balance. Let us assume we have 2 DNA sequences s = ACCT and t = ACGT. For those who dont know about DNA click here.

When we try to align these 2 sequences,

| | : |

The A,C,T match whereas there is a mismatch at position 3. Naturally, there are many alignments possible for any such pair. (Real data would consist of much longer sequences). The alignment chosen is the one with the highest alignment score.

The score is calculated adding the scores for the matches and mismatches(usually negative) and penalizing for gaps. For example, if match(M) = +1, mismatch(m) = -1 and gap penalty(g) = 2,

A C C - T
| | |
A C - G T

"-" are gaps. The alignment scores for the 2 alignments are

1. M+M+m+M = 1+1-1+1 = 2
2. M+M-g-g+M = 1+1-2-2+1 = -1

Clearly, the first option is preferable.

There are 2 types of alignments based on number of sequences to align:
1. Pairwise alignments
2. Multiple Sequence Alignments

There are 2 types of alignments based on parts of the sequences aligned:
1. Global alignments - align entire sequences
2. Local Alignments - align regions of the sequences

I will cover each of these types in latter posts.

References and Further Reading:
1. Bioinformatics - Sequence Analysis by David Mount
2. Needleman, S.B. and Wunsch, C.D. (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48:443-453

today's video


This is an amazing short video and article on

Diagnosing Alzheimer's Early

Helpful Animations


Following are some really helpful animation links to help you understand some biological phenomena better:



I know this post has come much later but I hope it helps anyways.
So what's DNA?
Two strands of linked nucleotides with one of the four bases adenine (A), thymine
(T), guanine (G) and cytosine (C).
Every cell in an organism has chromosomes in its nucleus which is made up of DNA strands coiled together with very high density. Think of this DNA as a string of letters from an alphabet. The english alphabet has 26 letters. Similarly, the DNA alphabet has 4 letters: A,C,T,G. Just like english has sentences made of words which are made of the 26 letters, exactly the same way, DNA strands have strings made of "codons"(explained in a moment) which are made of the 4 letters.

What are these codons?
Codons are equivalent to the words in English. But English has words of different lengths . Heres where codons differ. They ONLY consist of 3 nucleotides(A,C,T,G). Each codon then after transcription (conversion of DNA to RNA) gets translated by a complex mechanism to amino acids. Again using analogies from English language, like every word has a specific meaning (stupid eg. you cant use "man" when you mean "car"), every codon can be translated only to a specific amino acid. Strings of amino acids form proteins. Again, since there are 4 nucleotides and 3 letter long codons, there are 4^3 = 64 possible codons. But there are only 20 amino acids(more about them later) thus there is some ambiguity. Like man, male, fellow refer to the same thing, many codons code for same amino acid. Apart from the 20 amino acids, there is one START codon and 3 STOP codons. The codon table is given below (taken from [1])


What is transcription?
Transcription is the process of converting DNA to RNA. RNA has the 4 letters A,C,G,U in its alphabet. More about transcription later.

Fig: from Bioinformatics: from data to biological knowledge by Dena Leshkowitz


Further Reading:
1. Molecular Biology by Robert Weaver for details on transcription and translation.

microRNA target recognition - 2


Experimental identification of miRNA targets is not an easy task, especially using conventional tools. The principal challenge in target recognition of miRNAs is based on the small size of their targets (18-24 nucleotides (nts)). Also, every human miRNA has hundreds of targets with limited complementarity, unlike plant miRNAs. The affinity and specificity required for their recognition requires highly precise tools as the difference between a true target and a false positive might be a single base.
There has been an explosion in computational biology algorithms for human miRNA target prediction. We propose their identification and verification for human miRNAs using a combinatorial approach involving computational and molecular biology. We intend to make extensions to well-established tools and techniques to verify the miRNA targets predicted using the computational methods.
Prediction of miRNA targets provides an alternative approach to assign biological functions. This is simpler in plants due to their high complementarity and limited targets per miRNA but functional duplexes can be more variable in structure in humans [1]. Thus, we propose the use of more than one method to verify these targets.
For accurate and sensitive means to measure the expression levels of miRNAs without need for RNA size fractionation and/or RNA amplification, we intend to optimize RNA preparation protocols, as well as labeling and hybridization protocols.
A Harvard University researcher and pioneer of miRNA research, Gary Ruvkun has called miRNAs "the biological equivalent of dark matter, all around us but almost escaping detection." It has been well-established that miRNAs have a role in cancer development and tissue differentiation. They regulate almost one third of the genes in the human genome [2] although, it is still not known why miRNAs regulate some genes and not others. Some of their other functions include cell proliferation, apoptosis, oncogenesis and anti-viral defense. These previously considered “junk” RNA have implications for the treatment of cancer, diabetes and brain disorders.
The necessity to study these tiny pieces of mRNA also stems from the fact that they comprise 1% of the genes in animals and are highly conserved across the species. The understanding of miRNA function is very limited, which makes even target prediction an extremely challenging task.
Once miRNA targets are known, it might help understand complicated gene regulation, especially in gene networks. It has been shown that genes with higher cis-regulation complexity are more coordinately regulated by transacting factors at the transcriptional level and by miRNAs at the post-transcriptional level [3]. Thus, understanding the miRNA regulation pattern might fill gaps in the studies of gene networks and regulation.

1. Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA–target recognition. PLoS Biol 3(3): e85.
2. Lewis BP, Burge CB, Bartel DP (2005) Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell 120: 15–20.
3. Cui Q, Yu Z, Pan Y, Purisima EO, Wang E; MicroRNAs preferentially target the genes with high transcriptional regulation complexity; Biochem Biophys Res Commun. 2006 Nov 27

microRNA target recognition - 1


This is part of an original proposal I came up with for one of my courses. I will be following up on it soon with another project and hopefully get some answers some day :D

This article will be posted in parts. Today, I will be giving an introduction to miRNAs.

MicroRNAs are small (~22nucleotide long) non-coding RNAs that form part of a highly conserved system of RNA-based gene regulation in eukaryotes. Mature miRNAs are found in cytoplasm where they act as post transcriptional regulators of gene expression by base-pairing with target mRNAs.

miRNAs are transcribed as regions of longer RNA molecules, that are processed in the nucleus into hairpin RNAs (70-100nt) by the dsRNA-specific ribonuclease Drosha. The hairpin RNAs are transported to the cytoplasm via an exportin-5 dependent mechanism where they are digested by a second dsRNA specific ribonuclease called Dicer. The resulting ~22mer is bound to a complex called RNA-induced Silencing Complex (RISC). RISC is responsible for RNAi. These miRNAs bind to mRNA through limited complementarity in humans and thus, cause reduced/blocked gene expression, through mechanisms not yet understood completely. miRNAs are bound to proteins that belong to the Argonaute family and, in humans, may also assemble with other proteins, including the Gemin3 and Gemin4 proteins, to form micro-ribonucleoprotein complexes[1]

The first miRNAs, lin-4 were discovered in Caenorhabditis elegans in 1993 (Lee et al. 1993) in a genetic focused on identifying genes involved in the heterochronic pathway. For almost a decade, they were considered relatively unimportant. But they have stirred much enthusiasm in the biological and medical communities since 2004 when their function of stifling the production of proteins , contrary to their close relatives, mRNA was highlighted through the work of many laboratories and their roles in brain development, HIV resistance, blood cell development, obstruction of genes causing certain types of cancer etc. were discovered.

1. 9. Kiriakidou M, Nelson PT, Kouranov A, et al; A combined computational-experimental approach predicts human microRNA targets. Genes Dev. 2004 May 15;18(10):1165-78
Fig. Biogenesis of miRNAs



In case you are wondering what do Bioinformatics experts do.

Biological questions can be explored through wetlab experimental work - the traditional arena of biologists - or through modeling and simulation in virtual environments, also known as drylab research or computational biology. The later is more generally the domain of mathematicians and algorithm researchers. Of course wetlab research is used to develop better models to describe our understanding of biology, while drylab research needs to validate its' results through wetlab experimentation. Thus wet~ and drylab biology is closely related.

Bioinformatics is about improving the methods and technologies for the management and manipulation of data used by people trying to answer biological questions.

So what do I do or what do I intend to do? I intend to be a computational biologist who not only models systems computationally but also verifies those results experimentally in the laboratories. I dont think there can be a more satisfying feeling for a computational designer.



I started this blog as a hobby, posted some very basic bioinformatics posts and stopped... Well, today I am a Computational Biologist and am restarting this wonderful journey of blogging again... I want to share my knowledge, my thoughts and facts... I hope you enjoy reading this blog as much as I love writing here... Its a slow start so give me a few days to catch up and revamp this site...