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deveshl — Tue, 20 Feb 2007 05:18:07 CST

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Bio Informatics Companies

deveshl — Tue, 20 Feb 2007 05:15:55 CST

Bioinformatics Companies Bioinformatics Companies Worldwide

3rd Millennium
Aber Genomic Computing
Acero
AgileBio
Allometra
Aneda
ApoCom Genomics
Array Genetics
Artificial Life
Avesthagen
Base4
BioDiscovery Chemical Computing Group
Cimarron Software
Cognia
Compass Bioinformatics
Compass Genomics
CompuDrug
Compugen
Concurrent Pharmaceuticals
Correlogic Systems
Corimbia
CyberGene
Data Unlimited
Decodon
D'Trends
GenTech
Geospiza
iBiomatics
IBM Life Sciences
IDRTech
Incellico
INCOGEN
InCyte
Informagen
InforSense
Ingenovis
Interactiva
Inpharmatica
Insightful Corporation
Insilico Biotechnology
In Silico Labs
Insilico Software
InvenioBioSolutions
LabLogic
Omnigon Technologies
OpenEye
OriGenel
Packard BioChip Technologies
Partek
PharmaDM
Proinformatix
Protein Mechanics
ProSanos
Proteom
QGene
RabbitHutch
Receptor Science
Redstorm Scientific

Bioinformatics Services
Bioinformatics Solutions
BioIT
BioLateral
BioMax Informatics
Biomedical Computing
Biomedical Informatics
BioSoft
BioSolve
Biotique Systems
BioTools
BioWisdom
Caesar Software
Chang Bioscience
Eragen
Fractal Genomics
Gemini Genomics
GeneCodes
GeneData
Gene-IT
GeneticXchange
Geneva Bioinformatics
GenMatrix
Genomica
Genomix
Genops Bioinformatics
GenStruct
Hitachi Life Science Division
LION BioScience
Mantik Bioinformatik
Matrix Science
MDL Information Systems
METabolic EXplorer
MetaWorks
MicroDiscovery
MiraiBio
Molecular Application Group
Molecular Mining
Molecular Networks
MolecularWare
MPI Software Technology
Neomorphic
NetGenics
Net-ID
Ocimum Biosolutions
Rosetta Biosoftware
Science Factory
Scinova
Silicon Graphics
Silico Insights
Simulations Plus
SoftBerry
Southwest Parallel Software
Strand Genomics
Tripos
Turbo Genomics
Virtual Genetics
Visipoint
VizXlabs
Waban Software
WebGenetics
WireWorks West
X-MINE
Xpogen

Bioinformatics Companies in India

Strand Genomics - Bioinformatics software design and development
Ocimum BioSolutions - Bioinformatics software modules and training
Manvish Infotech - Specializes in embedded systems, bioinformatics and content development
BrainWave - Contract based research in bioinformatics and genome studies
Bioinformatics Institute of India - Non-profit research and development centre
Bigtec - Bioinformatics and medical informatics research
Avestha Gengraine - Bioinformatics research and development
AUKBC - Research in communications, networking, mathematics and bioinformatics
Center for DNA Fingerprinting & Diagnostics - DNA, fingerprinting, molecular diagnostics & bioinformatics
Bioinformatics Centre, Madurai Kamaraj University - Research in genetic engineering, molecular biology and biophysics
Bioinformatics Center - Biotechnolgy related information provider
Biotech Companies in India
A.V.Thomas Biotechnolgy - Commercial plant tissue culture laboratory
Amersham Biosciences - Focuses on disease research, and drug development
AstraZeneca - Research on infectious diseases and drugs
Avestha Gengraine - Bioinformatics research and development
Bangalore Bio - Resource on biotech companies, institutes and policies
Bangalore Genei - Manufacturer of reagents and equipment for DNA research
Bigtec - Bioinformatics and medical informatics research
Bio-Instruments - Distributor of biotechnology and agri-research instruments
BioSoft - Application of biotechnology in acqaculture management
Biotech India - Molecular biology, immunology and cell biology research products
Biotech Support Services - Resource on biotechnology industry & services
Biotron Healthcare - Supplier of biotechnology instruments
Esscee Biotech - Synthetic DNA and RNA manufacturer
Genotypic Technologies - Nucleic acids and cDNA library construction services
Greenearth Biotechnologies - Tissue culture research and plants supplier
Growmore Biotech - Commercial plant tissue culture
Harrisons Malayalam - Plant tissue culture & exporter of flowering plants
Hitech Bio-Services - Maintenance services for biotechnology instruments
Honey Bee - Newsletter on grassroot level biodiversity innovations
Indfrag Biosciences - Herbal extracts manufacturer
Metahelix Life Sciences - Research in life science informatics and genomics
Microbax - Producer of lactobacillus sporogenes
Millipore India - Biotechnology research and therapeutic drugs manufacturer
Monsanto India - Developer of biotech products like seeds & herbicides
Novozymes - Enzymes and biocatalysts producer
Organica Biotech - Biotech products for waste water and lake water treatement
Phalada Agro - Biofertilizers and hormones manufacturer
Sartorius Biotech - Biomolecular and microbial separations applications development
Seatech Polymers - Agar polymers for biotechnology, pharmaceutical, microbiology and food industries

U.S Biotech Companies

Arizona

New Mexico

North Carolina

Dialog Corporation (Cary, North Carolina)
Inveresk Research (Incorporating CTBR) (Cary, North Carolina)
inGenium Research, Inc. (Cary, North Carolina)

DarPharma, Inc. (Chapel Hill, North Carolina)
University of North Carolina (UNC) (Chapel Hill, North Carolina)

University of North Carolina - Charlotte (Charlotte, North Carolina)

Bayer Corporation, Biological Products Business Group (Clayton, North Carolina)

Amphora Discovery Corporation (Durham, North Carolina)
ClinForce, Inc. (Durham, North Carolina)
Duke University (Durham, North Carolina)
Duke University Medical Center (Durham, North Carolina)
EMD Pharmaceuticals, Inc. (Durham, North Carolina)
Icagen, Inc. (Durham, North Carolina)
Inspire Pharmaceuticals, Inc. (Durham, North Carolina)
Quintiles Biotechnology Solutions Network - North Carolina (Durham, North Carolina)
Quintiles Transnational Corp. (Durham, North Carolina)
Serenex (Durham, North Carolina)
Trimeris, Inc. (Durham, North Carolina)
Womble Carlyle Sandridge & Rice, PLLC (Durham, North Carolina)

North Carolina Agricultural and Technical State University (Greensboro, North Carolina)
Syngenta Crop Protection USA (Formerly Novartis Crop Protection USA) (Greensboro, North Carolina)
University of North Carolina - Greensboro (Greensboro, North Carolina)

DSM Pharmaceuticals, Inc. (Greenville, North Carolina)

MWG Biotech Inc. (High Point, North Carolina)
PharmaCore, Inc. (High Point, North Carolina)
TransTech Pharma Inc. (TTP) (High Point, North Carolina)

Bertek Pharmaceuticals Inc. (North Carolina) (Morrisville, North Carolina)
Norak Biosciences, Inc. (Morrisville, North Carolina)
PPD Development (Morrisville, North Carolina)
STATPROBE, Inc. (North Carolina) (Morrisville, North Carolina)

Biolex, Inc. (Pittsboro, North Carolina)

Manpower Professional (Raleigh, North Carolina) (Raleigh, North Carolina)

Biogen, Inc. (North Carolina) (Research Triangle Park, North Carolina)
Council for Entrepreneurial Development (Research Triangle Park, North Carolina)
GlaxoSmithKline -- North Carolina, United States (Research Triangle Park, North Carolina)
National Institute of Environmental Health Sciences (NIEHS) (Research Triangle Park, North Carolina)
Nobex Corporation (Research Triangle Park, North Carolina)
North Carolina Biotechnology Center (Research Triangle Park, North Carolina)
Paradigm Genetics, Inc. (Research Triangle Park, North Carolina)
Ribonomics, Inc. (Research Triangle Park, North Carolina)
Syngenta Biotechnology Inc. -- Research (Research Triangle Park, North Carolina)

Wyeth-Vaccines (Sanford, North Carolina)

AAI International, An aaiPharma Company (Wilmington, North Carolina)
PPD, Inc. (Wilmington, North Carolina)
University of North Carolina - Wilmington (Wilmington, North Carolina)

Kucera Pharmaceutical Company (Winston-Salem, North Carolina)
Targacept, Inc. (Winston-Salem, North Carolina)
The MacKenzie Law Firm (Winston-Salem, North Carolina)
Wake Forest University (Winston-Salem, North Carolina)
Wake Forest University Baptist Medical Center (WFUBMC) (Winston-Salem, North Carolina)
Wake Forest University School of Medicine (Winston-Salem, North Carolina)

Ohio

Athersys, Inc. (Cleveland, Ohio)
Case Western Reserve University (Cleveland, Ohio)

The Virtual World Congress - The American College of Chest Physicians (Mason, Ohio)

YSI Incorporated (Life Sciences) (Yellow Springs, Ohio)

Oregon

Oregon State University Center for Gene Research & Biotechnology (Corvallis, Oregon)

University of Oregon (Eugene, Oregon)

Pennsylvania

Locus Discovery, Inc. (Blue Bell, Pennsylvania)

Absorption Systems (Exton, Pennsylvania)

Genactis, Inc. (Fort Washington, Pennsylvania)
Liquent (Fort Washington, Pennsylvania)

Acurian, Inc. (Horsham, Pennsylvania)
Neose Technologies, Inc. (Horsham, Pennsylvania)

Knowledge Express Data Systems, L.C. (King of Prussia, Pennsylvania)

Centocor, Inc. (Malvern, Pennsylvania)
Pennsylvania Biotechnology Association (Malvern, Pennsylvania)

ICON Clinical Research, Inc. (North Wales, Pennsylvania)

Woodcock Washburn Kurtz Mackiewicz & Norris LLP (Philadelphia, Pennsylvania)
Yoh Scientific Headquarters (Philadelphia, Pennsylvania)

Cellomics, Inc. (Pittsburgh, Pennsylvania)
Development Dimensions International (DDI) (Pittsburgh, Pennsylvania)
ImmunoSite, Inc. (Pittsburgh, Pennsylvania)
TissueInformatics.Inc (Pittsburgh, Pennsylvania)

Wyeth (Pennsylvania) (St. Davids, Pennsylvania)

Vincogen Corporation (Trevose, Pennsylvania)

Penn State University (University Park, Pennsylvania)

First Consulting Group (Wayne, Pennsylvania)

Cephalon, Inc. (West Chester, Pennsylvania)

3-Dimensional Pharmaceuticals, Inc. (Yardley, Pennsylvania)

Rhode Island

The Dow Chemical Company (Smithfield, Rhode Island) (Smithfield, Rhode Island)

Amgen Inc. (West Greenwich, Rhode Island) (West Greenwich, Rhode Island)

South Carolina

Pilot Therapeutics (Charleston, South Carolina)
Search Masters International (South Carolina) (Charleston, South Carolina)

Texas

Home

deveshl — Mon, 19 Feb 2007 09:35:41 CST

" Jay Shri Krishna " The Morphon B I O S Y S serves the scientific and educational needs of Bio Informatics practitioners and the general public. We develop and maintain computational resources to facilitate worldwide communications and collaborations between people of all educational and professional levels. We provide and promote open access to the materials and methods required for, and derived from, research, development and education.

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deveshl — Sat, 17 Feb 2007 06:33:30 CST

There is no abstract available for this page revision.

Ignited Min "e" ds are here

deveshl — Sat, 17 Feb 2007 05:59:10 CST

Perfection galore!

Thanks for Visting our page.
And i also like to thanks my team Vikas Somani, Gourav Anand,Anuj Shukla for their technical support and my Friends, my Family for their inspiration.

We are here to carve out a new niche in the arena of Biotechnology.

Give us a chance, and you would find it extremely hard to leave us!

And we would welcome and apperciate suggestions and feedback if any from you. Do feel free to interact. Your suggestions and feed back are pericious to us.

Vivek Ajmera
B.E. Bio Tech
Diagnostic Specialist
Professional Biotech, Pune
morphon.biosys@gmail.com

Sequence analysis is the application of Information Technologies to Molecular Biology

deveshl — Fri, 16 Feb 2007 06:19:51 CST

Sequence analysis is the application of Information Technologies to Molecular Biology. It deals with biological sequences, and processes them to extract significant information that may yield new insights and guidelines in the understanding of biological organisms
Basics for sequence analysis

Proteins

A protein is typically built of a series of basic blocks called amino acids , chained together in a linear sequence of blocks. Amino acids may come in a variety of shapes and properties: they may be small or bulky, hidrophobic or hidrophyllic, electrically charged or neutral, etc... hence allowing for very complex shapes and interactions to be produced.

Amino acids are commonly referred to by name or by an abbreviation, usually in three or one letter. This allows for more efficient descriptions of how they are chained together to build a protein:

Neutral-Nonpolar	3-letter	1-letter
Glycine	Gly	G
L-Alanine	Ala	A
L-Valine	Val	V
L-Isoleucine	Ile	I
L-Leucine	Leu	L
L-Phenylalanine	Phe	F
L-Proline	Pro	P
L-Methionine	Met	M
Neutral-Polar
L-Serine	Ser	S
L-Threonine	Thr	T
L-Tyrosine	Tyr	Y
L-Tryptophan	Trp	W
L-Asparagine	Asn	N
L-Glutamine	Gln	Q
L-Cysteine	Cys	C
Acidic
L-Aspartic	Asp	D
L-Glutamic	Glu	E
Basic
L-Lysine	Lys	K
L-Arginine	Arg	R
L-Histidine	His	H

Nucleic Acids

For them the number of basic building blocks is a lot smaller, each nucleic acid chain being composed of series of only four possible different nucleotides which furthermore provide for a very limited set of interactions.
Nucleic acids come in two flavors: DNA (DeoxyriboNucleic Acid) and RNA (RiboNucleic Acid). Both of them consist of a series of nucleotides that are glued one after the other to constitute the sequence of blocks that make up the functional chain.
Nucleotides are composed of a phosphate group, a sugar (ribose in RNA, and deoxyribose in DNA) and a base which marks the specific difference among nucleotides. The base may be one of guanine, cytosine, adenine and thymine in the case of DNA or guanine, cytosine, adenine or uracil for RNA. They can be referred to by their one letter abbreviations G, C, A, T and U. Interactions are mainly driven by the stablishment of hydrogen bonds, which can only be established among thymine (or uracil) and adenine (two hydrogen bonds) and cytosine and guanine (three hydrogen bonds).
As we said previously, the main role of nucleic acids is to convey all the genetic information needed to make proteins and control the building process. Protein sequences are coded by nucleic acids using groups three of nucleotides that code for a given amino acid: the code is more or less universal with little exceptions, and includes redundancy to increase the fidelity of the reading process when making duplicates or translating the information:

UUU	Phe	UCU	Ser	UAU	Tyr	UGU	Cys
UUC	Phe	UCC	Ser	UAC	Tyr	UGC	Cys
UUA	Leu	UCA	Ser	UAA	Stop	UGA	Stop
UUG	Leu	UCG	Ser	UAG	Stop	UGG	Trp
CUU	Leu	CCU	Pro	CAU	His	CGU	Arg
CUC	Leu	CCC	Pro	CAC	His	CGC	Arg
CUA	Leu	CCA	Pro	CAA	Gln	CGA	Arg
CUG	Leu	CCG	Pro	CAG	Gln	CGG	Arg
AUU	Ile	ACU	Thr	AAU	Asn	AGU	Ser
AUC	Ile	ACC	Thr	AAC	Asn	AGC	Ser
AUA	Ile	ACA	Thr	AAA	Lys	AGA	Arg
AUG	Met	ACG	Thr	AAG	Lys	AGG	Arg
GUU	Val	GCU	Ala	GAU	Asp	GGU	Gly
GUC	Val	GCC	Ala	GAC	Asp	GGC	Gly
GUA	Val	GCA	Ala	GAA	Glu	GGA	Gly
GUG	Val*	GCG	Ala	GAG	Glu	GGG	Gly

* GUG may also code for the initiator Met. This triplet is therefore "ambiguous".

Regulation of expression is encoded as specific patterns that are to be recognized by the translation machinery under appropriate circumstances.
Sequence databases:
For overview of database - click here
For complete sequence database listing - click here
Overview of sequence analysis tools
Sequence Comparison
An alignment is an arrangement of two sequences, which shows where the two sequences are similar, and where they differ. An optimal alignment, of course, is one that exhibits the most similarities, and the least differences. Broadly, there are three categories of methods for sequence comparison.
• Segment methods compare all overlapping segments of a predetermined length (e.g., 10 amino acids) from one sequence to all segments from the other. This is the approach used in dotplots.
• Optimal global alignment methods allow the best overall score for the comparison of the two sequences to be obtained, including a consideration of gaps. These programs align sequences over their whole length.
• Optimal local alignment algorithms seek to identify the best local similarities between two sequences also including explicit consideration of gaps. Alignment may only be over a short span of sequence.

Dotplots

The most intuitive representation of the comparison between two sequences is using dotplots. One sequence is represented on each axis and significant matching regions are distributed along diagonals in the matrix.
There are two different algorithms that are commonly used in creating dotplots. The first method involves matching identical regions of sequence and plotting a dot in these areas. The second involves using "sliding windows" to compare two sequences using a threshold score ` * ' value. A window size is selected as a run of adjacent nucleotide or amino acid residues, and a score chosen to reflect the degree of similarity of sequence required. Each window of sequence A is compared to each window of sequence B, and a dot is only placed in that region if the match scores or exceeds the set threshold level.
Online tool links:
Dotlet Programme
Learn dotlet by example
Sequence alignment
The algorithms we will be using are more rigorous than those used for searching databases; so even if you have retrieved a sequence from a database using something like BLAST. The basic idea behind the sequence alignment programs is to align the two sequences in such a way as to produce the highest score - a scoring matrix is used to add points to the score for each match and subtract them for each mismatch. The matrices commonly used for scoring protein alignments are more complex than the simple match/mismatch matrices used for DNA sequences such as the one we saw earlier; the scores that form the protein matrices are designed to reflect similarity between the different amino acids rather than simply scoring identities. Over time various mutations occur in sequences; the scoring matrices attempt to cope with mutations, but insertions and deletions require some extra parameters to allow the introduction of gaps in the alignment. There are penalties both for the creation of gaps and for the extension of existing ones; the default gap parameters given in alignment programs have been found to be empirically correct with test sequences but you should experiment with different gap penalties.

BLAST

BLAST (Basic Local Alignment Search Tool) is a heuristic method to find the highest scoring locally optimal alignments between a query sequence and a database. Previous versions of BLAST did not allow gapped alignments, but BLAST2 (from the HGMP-RC telnet and www menus) does. A gapped BLAST search allows gaps (deletions and insertions) to be introduced into the alignments that are returned. Allowing gaps means that similar regions are not broken into several segments. The scoring of these gapped alignments tends to reflect biological relationships more closely.
The BLAST algorithm and family of programs rely on work on the statistics of local sequence alignments by Altschul et al[]. The statistics allow us to estimate the probability of obtaining an alignment with a particular score. The BLAST algorithm permits nearly all sequence matches above a cutoff ` * ' to be located efficiently in a database.
The algorithm operates as follows:
• BLAST scans the database for words (typically 3-mers for proteins) that score at least T (a designated threshold value) when aligned with a word in the query sequence - such aligned pairs are called hits.
• If a second non-overlapping hit is found within a distance A of the first and on the same diagonal, the first hit is extended between the database and query sequences in both directions. Extension continues, scoring all the time, until the running score drops below the maximum score seen so far by a value X. The resulting local alignment is called an HSP (high-scoring segment pair) or MSP (maximum scoring segment pair).
• If the alignment score of the HSP exceeds a given value Sg (the gapped score), then a gapped extension of the HSP is initiated.
Earlier versions of BLAST looked only for single hits and extended them all; however, the extensions did not incorporate gaps and thus missed some potentially interesting matches. The gapped extension currently used, takes much longer to execute, but speed is improved overall by the requirement for two non-overlapping close hits before the initial extension is triggered, and the value of Sg is chosen so that only about one extension is triggered per 50 database sequences.
These modifications to BLAST mean that it now runs three times faster than earlier versions and in trials it found more statistically significant alignments than the old BLAST .

BLAST FAMILY OF PROGRAMS

The BLAST family of programs allows all combinations of DNA or protein query sequences with searches against DNA or protein databases. (Most of the time use of these is behind an interface.)
• blastp: compares an amino acid query sequence against a protein sequence database.
• blastn: compares a nucleotide query sequence against a nucleotide sequence database.
• blastx: compares the six-frame conceptual translation products of a nucleotide query sequence (both strands) against a protein sequence database.
• tblastn: compares a protein query sequence against a nucleotide sequence database dynamically translated in all six reading frames (both strands).
• tblastx: compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.
• PSI-Blast: Position-Specific Iterated BLAST . This is potentially a very sensitive method to pull out significant hits in a protein-protein database search. This first performs a gapped BLAST database search and then uses the information from any significant alignments returned to construct a position-specific score matrix, which replaces the query sequence for the next round of database searching. PSI-Blast may be iterated until no new significant alignments are found. We'll look at this tomorrow when we do some protein analysis.
Online tool links:
NCBI - Blast
WU - Blast@ EBI

Global sequence alignment
A global alignment is one that compares the two sequences over their entire lengths, and is appropriate for comparing sequences that are expected to share similarity over the whole length. The alignment maximises regions of similarity and minimises gaps using the scoring matrices and gap parameters provided to the program.
Online tool link:
Clustalw @ EBI
Local sequence alignment
global sequence alignment algorithms align sequences over their entire lengths. You do need to think about whether that type of alignment makes sense for your sequences. For our example, where we expect each exon to be represented in the sequences and in the same order, it has worked well - however, how well do you think this approach would work with, for example, multidomain proteins that share one domain but not others, or sequences where there have been regions of duplication? A second comparison method, local alignment, searches for regions of local similarity and need not include the entire length of the sequences.
Online tool links:
Pairwise alignment at EBI
Pairwise alignment at NCBI

Protein Sequence Analyisis

You can get a variety of clues by looking for patterns and motifs in your sequence:
• These are often derived from multiple sequence alignments.
• Conserved protein domains or regions can be very useful in trying to determine which protein family a sequence belongs to, catalytic sites, carbohydrate binding sites etc.
• Various research groups have created their own databases and search tools; it might be worth using a variety of these.

FIND HOMOLOGOUS ( PARALOGOUS AND ORTHOLOGOUS) SEQUENCES

Using a database similarity search can give you a great deal of information:
• Homologues may be well annotated and their function documented in the literature.
• Simply comparing your sequence with homologues can tell you a lot.
• Phylogenetic analysis may reveal evolutionary relationships between proteins and help you decide which family or super family a protein belongs to.
• N.B. Be aware of convergent evolution.

HAVING SOME IDEA OF STRUCTURE MAY HELP YOU PREDICT POSSIBLE FUNCTIONS

Knowing the protein fold(s) together with conserved domains (or even residues) may tell you what type of functions this protein could have.

Sequence analysis

deveshl — Fri, 16 Feb 2007 06:16:21 CST

Main articles: Sequence alignment and Sequence database Since the Phage Φ -X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases. These data are analyzed to determine genes that code for proteins, as well as regulatory sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenza) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research. Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects--for example, in the use of DNA sequences for protein identification. See also: sequence analysis, sequence profiling tool, sequence motif.

Protein-protein interaction prediction

deveshl — Fri, 16 Feb 2007 06:13:31 CST

Protein-protein interaction prediction is a field combining bioinformatics and structural biology in an attempt to identify and catalog interactions between pairs or groups of proteins. Understanding protein-protein interactions is important in investigating intracellular signaling pathways. Experimentally, interactions between pairs of proteins are inferred from yeast two-hybrid systems, from affinity purification/mass spectrometry assays, or from protein microarrays. In parallel to the experimental determination of the interactome, computational methods are being developed

Methods

Proteins that interact are more likely to co-evolve[1][2][3][4], therefore it is possible to make inferences about interactions between pairs of proteins based on their phylogenetic distances. It has also been observed in some cases that pairs of interacting proteins have fused orthologues in other organisms. In addition, a number of bound protein complexes have been structurally solved and can be used to identify the residues that mediate the interaction so that similar motifs can be located in other organisms. .Phylogenetic profiling This method[5] involves using a sequence search tool such as BLAST for finding homologues of a pair of proteins, then building multiple sequence alignments with alignment tools such as Clustal. From these multiple sequence alignments, phylogenetic distance matrices are calculated for each protein in the hypothesized interacting pair. If the matrices are sufficiently similar (as measured by their Pearson correlation coefficient) they are deemed likely to interact. .Identification of homologous interacting pairs This method [6] consists of searching whether the two sequences have homologues which form a complex in a database of known structures of complexes. The identification of the domains is done by sequence searches against domain databases such as Pfam using BLAST. If more than one complex of Pfam domains is identified, then the query sequences are aligned using a hidden Markov tool called HMMer to the closest identified homologues, whose structures are known. Then the alignments are analysed to check whether the contact residues of the known complex are conserved in the alignment

.Identification of structural patterns

A third method[7][8] builds a library of known protein-protein interfaces from the PDB, where the interfaces are defined as pairs of polypeptide fragments that are below a threshold slightly larger than the Van der Waals radius of the atoms involved. The sequences in the library are then clustered based on structural alignment and redundant sequences are eliminated. The residues that have a high (generally >50%) level of frequency for a given position are considered hotspots[9]. This library is then used to identify potential interactions between pairs of targets, providing that they have a known structure (i.e. present in the PDB).

.Bayesian network modelling

Bayesian methods [10] integrate data from a wide variety of sources, including both experimental results and prior computational predictions, and use these features to assess the likelihood that a particular potential protein interaction is a true positive result. These methods are useful because experimental procedures, particularly the yeast two-hybrid experiments, are extremely noisy and produce many false positives, while the previously mentioned computational methods can only provide circumstantial evidence that a particular pair of proteins might interact.

Relationship to docking methods The field of protein-protein interaction prediction is closely related to the field of protein-protein docking, which attempts to use geometric and steric considerations to fit two proteins of known structure into a bound complex. This is a useful mode of inquiry in cases where both proteins in the pair have known structures and are known (or at least strongly suspected) to interact, but since so many proteins do not have experimentally determined structures, sequence-based interaction prediction methods are especially useful in conjunction with experimental studies of an organism's interactome

Bio Diversity

deveshl — Fri, 16 Feb 2007 06:10:36 CST

In these days of burgeoning human population and concomitant habitant destruction , knowledge of centers of high biodiversity is critical if rational conservation decisions are to be made. The problem is that this information is largely unavailable to the decision makers. The reason for this is twofold. First, although a few groups of organisms such as birds and Mammals and a few geographic areas are well studied and well characterized most groups of organisms are only partly known and the deep ocean have only just begun to be studied in detail. These problems are compound by the fact that there is little incentive for biologists these days to go into the classical fields of taxonomy and systematic. The glamour ( and money ) is in molecular and cell biology. Secondly, although there is an enormous amount of biodiversity information in the world’s museums and universities it is not readily accessible . It is ironic that most of these data are in the great museums which are located in the cool temperate parts of the world whereas; most of the organisms are in the warm humid parts of the world . The data that exist are paper based. Description by collectors and curators, herbarium sheets, diagram and photographs, and of course, pickled and preserved specimens with their labels. If a researcher wishes to consult these data he/ she has to travel to the museum in question and do the work there. For the people who need a breadth of information to make decisions, this is obviously not an option. There are moves afoot to remedy this situation. There are two areas in biology where enormous amounts of information are generated . One is in Molecular Biology which deals with base sequences in DNA and amino acid sequences in proteins, and the other is in biodiversity information crisis. Mathematics and Computers are being used to tackle these problems with procedures which come under the label of Bioinformatics. Attempts are being made at different places to computerize all the data available. This is an enormous and complex task, but this is only part of it. To make the data efficiently available world-wide it has to be accessible via the internet. This is being done by using a relatively new computer language called XML ( Extensible Markup Language ) . Superficially XML looks like the more familiar HTML, but whereas HTML is concerned with the appearance of a document, XML defines the internal structure, the organization of the content of a document and when it is fully implemented will revolutionize the speed and efficiency of access to all types of information over the net. When, and if, we reach a point where most of the information has been accumulated and furthermore, is readily accessible, it will be much easier for concerned individuals and groups to make the case for the preservation of areas that are particularly rich in diversity. They will be able to overwhelm the politicians with actual data that hopefully even they will be able to understand and find difficult to ignore.

Prediction of protein structure

deveshl — Fri, 16 Feb 2007 06:09:56 CST

Main article: Protein structure prediction Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy - aka Mad Cow Disease - prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time. One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modelling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably. One example of this is the similar protein homology between haemoglobin in humans and the haemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes. Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling. See also structural motif and structural domain

Hot Topics

deveshl — Fri, 16 Feb 2007 06:06:46 CST

There is no abstract available for this page revision.

Molecular modelling

deveshl — Fri, 16 Feb 2007 06:04:46 CST

Molecular modelling is a collective term that refers to theoretical methods and computational techniques to model or mimic the behaviour of molecules. The techniques are used in the fields of computational chemistry, computational biology and materials science for studying molecular systems ranging from small chemical systems to large biological molecules and material assemblies. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system. The common feature of molecular modelling techniques is the atomistic level description of the molecular systems; the lowest level of information is individual atoms (or a small group of atoms). This is in contrast to quantum chemistry (also known as electronic structure calculations) where electrons are considered explicitly. The benefit of molecular modelling is that it reduces the complexity of the system, allowing many more particles (atoms) to be considered during simulations. Molecular mechanics is synonymous with molecular modelling, as it is refers to the use of classical mechanics/Newtonian mechanics to describe the physical basis behind the models. Molecular models typically describe atoms (nucleus and electrons collectively) as point charges with an associated mass. The interactions between neighbouring atoms are described by spring-like interactions (representing chemical bonds) and van der Waals forces. The Lennard-Jones potential is commonly used to describe van der Waals forces. The electostatic interactions are computed based on Coulomb's law. Atoms are assigned coordinates in Cartesian space or in internal coordinates, and can also be assigned velocities in dynamical simulations. The atomic velocities are related to the temperature of the system, a macroscopic quantity. The collective mathematical expression is known as a potential function and is related to the system internal energy (U), a thermodynamic quantity equal to the sum of potential and kinetic energies. Methods which minimize the potential energy are known as energy minimization techniques (e.g., steepest descent and conjugate gradient), while methods that model the behaviour of the system with propagation of time are known as molecular dynamics. E = Ebonds + Eangle + Edihedral + Enon − bonded Enon − bonded = Eelectrostatic + EvanderWaals This function, referred to as a potential function, computes the molecular potential energy as a sum of energy terms that describe the deviation of bond lengths, bond angles and torsion angles away from equilibrium values, plus terms for non-bonded pairs of atoms describing van der Waals and electrostatic interactions. The set of parameters consisting of equilibrium bond lengths, bond angles, partial charge values, force constants and van der Waals parameters are collectively known as a force field. Different implementations of molecular mechanics use slightly different mathematical expressions, and therefore, different constants for the potential function. The common force fields in use today have been developed by using high level quantum calculations and/or fitting to experimental data. The technique known as energy minimization is used to find positions of zero gradient for all atoms, in other words, a local energy minimum. Lower energy states are more stable and are commonly investigated because of their role in chemical and biological processes. A molecular dynamics simulation, on the other hand, computes the behaviour of a system as a function of time. It involves solving Newton's laws of motion, principally the second law, F = ma. Integration of Newton's laws of motion, using different integration algorithms, leads to atomic trajectories in space and time. The force on an atom is defined as the negative gradient of the potential energy function. The energy minimization technique is useful for obtaining a static picture for comparing between states of similar systems, while molecular dynamics provides information about the dynamic processes with the intrinsic inclusion of temperature effects. Molecules can be modelled either in vacuum or in the presence of a solvent such as water. Simulations of systems in vacuum are referred to as gas-phase simulations, while those that include the presence of solvent molecules are referred to as explicit solvent simulations. In another type of simulation, the effect of solvent is estimated using an empirical mathematical expression; these are known as implicit solvation simulations.
Molecular modelling methods are now routinely used to investigate the structure, dynamics and thermodynamics of inorganic, biological, and polymeric systems. The types of biological activity that have been investigated using molecular modelling include protein folding, enzyme catalysis, protein stability, conformational changes associated with biomolecular function, and molecular recognition of proteins, DNA, and membrane complexes.

Modeling biological systems

deveshl — Fri, 16 Feb 2007 06:00:34 CST

Main article: Systems biology Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

Measuring biodiversity

deveshl — Fri, 16 Feb 2007 05:57:59 CST

Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth. Databases are used to collect the species names, descriptions, distributions, genetic information, status and size of populations, habitat needs, and how each organism interacts with other species. Specialized software programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation). One very exciting potential of this field is that entire DNA sequences, or genomes of endangered species can be preserved, allowing the results of Nature's genetic experiment to be remembered in silico, and possibly reused in the future, even if that species is eventually lost.

Genome annotation

deveshl — Fri, 16 Feb 2007 05:55:19 CST

Main article: Gene finding In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. Dr. White built a software system to find the genes (places in the DNA sequence that encode a protein), the transfer RNA, and other features, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving

Computational evolutionary biology

deveshl — Fri, 16 Feb 2007 05:52:39 CST

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to: trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone, more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of bacterial speciation factors, build complex computational models of populations to predict the outcome of the system over time track and share information on an increasingly large number of species and organisms Future work endeavours to reconstruct the now more complex tree of life.
The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are unrelated.

Comparitive Genomics

deveshl — Fri, 16 Feb 2007 05:49:36 CST

The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov Chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models. Many of these studies are based on the homology detection and protein families computation. See also comparative genomics, bayesian network and protein family.

Analysis of Mutation in Cancer

deveshl — Fri, 16 Feb 2007 05:47:00 CST

Massive sequencing efforts are currently underway to identify point mutations in a variety of genes in cancer. The sheer volume of data produced requires automated systems to read sequence data, and to compare the sequencing results to the known sequence of the human genome, including known germline polymorphisms. Oligonucleotide microarrays, including comparative genomic hybridization and single nucleotide polymorphism arrays, able to probe simultaneously up to several hundred thousand sites throughout the genome are being used to identify chromosomal gains and losses in cancer. Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes from often noisy data. Further informatics approaches are being developed to understand the implications of lesions found to be recurrent across many tumors. Some modern tools (e.g. Quantum 3.1 ) provide tool for changing the protein sequence at specific sites through alterations to its amino acids and predict changes in the bioactivity after mutations.

Analysis of Gene Expression

deveshl — Fri, 16 Feb 2007 05:43:30 CST

Analysis of gene expression The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells. Analysis of regulation Regulation is the complex orchestration of events starting with an extra-cellular signal and ultimately leading to an increase or decrease in the activity of one or more protein molecules. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the elucidation and study of sequence motifs in the genomic region surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. Analysis of protein expression Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected

Universities/Research Institute

deveshl — Thu, 15 Feb 2007 02:12:37 CST

Bioinformatics courses worldwide
Here is the list Bioinformatics courses offered around the world. Reasons for constructing this site are:

To show the extent of growth of this new discipline
To provide a resource for students, and academics and
To access notes on the subject to allow some brief comparisons between courses.

The graduate courses outlined in the table mainly exist as a coursework component of an MSc or PhD degree. Of course, many institutions will also have pure research programs in Bioinformatics at the Hons, MSc, or PhD level and will not have a 'coursework component'. These research programs may not always reflect the main research program of the institution since the research depends upon the interests of a particular individual or small team within that institution. It is difficult to track these individual research programs. Consequently, we have not included all institutions with a bioinformatics research project. Bioinformatics is an interdisciplinary subject and this is reflected in the fact that the courses are offered by departments or schools as diverse as: computer science, biochemistry, molecular biology, microbiology, engineering, and mathematics. Similarly, bioinformatics may also be known by other names such as: computational biology, biocomputing, molecular informatics, computational molecular biology, biological information theory, molecular phylogenetics, and molecular bioinformatics.