Absent corneal

Acute cardiac allograft rejection

Acute lymphoblastic leukemia

Acute myeloid leukemia

AIDS

Allergic diseases

Allergic inflammatory diseases

Allergic pathology

Allergic rhinitis

Allergic skin disorders

Alzheimer’s disease

Angiogenesis

Anhidrosis

Antley-Bixler syndrome (ABS)

Apert syndrome (APRS)

Arthritis

Asthma

Atherosclerosis

Autoimmune diseases

autoimmune skin diseases

Beare-Stevenson cutis gyrata syndrome (BSCGS)

Bladder cancer

Bone marrow disorders

Bowing of the lower limbs

Cancer

Cancer metastasis

Cancer progression

Cardiovascular disease

Carotid Intimal medial thickness

CATSHL syndrome

Celiac disease

Cervical cancer

Chronic inflammatory diseases

Chronic myelomonocytic leukemia (CMML)

Chronic rhinosinusitis

Chronic viral infections

Congenital amegakaryocytic thrombocytopenia (CAMT)

Congenital insensitivity to pain

Conjunctival itching.

Crouzon syndrome

Cutaneous T-cell lymphoma

Diabetes

Diabetes mellitus insulin-dependent type 22 (IDDM22)

Diabetic retinopathy

Endocrine disorders

Epstein-Barr virus infection

Erythroleukemia

Excessive Nasal secretion

Familial erythrocytosis.

Familial partial lipodystrophies

Familial scaphocephaly syndrome (FSPC)

Fibrosis.

Glioma type 1 Atherosclerosis

HBV induced hepatitis

HBV induced hepatitis

HCV-induced hepatitis

Heart disease

hemangioma capillary infantile

Hematological diseases

Hepatitis

Hepatocellular carcinoma

Hepatosplenomegaly

hereditary papillary renal carcinoma (HPRC)

HIV

HIV1 Infection

Hodgkin’s disease

Human gliomas

Hypercytokinemia

hyper-IgM immunodeficiency syndrome type 3 (HIGM3)

Hyperproliferative skin diseases

Hyperproliferative skin diseases

Hypertension

Hypochondroplasia (HCH)

Idiopathic hypogonadotropic hypogonadism (IHH)

Immune

Inflammatory  Skin diseases

Inflammatory bowel disease type 17 (IBD17)

Inflammatory bowel diseases

Inflammatory diseases

Inflammatory infections

insulin resistance

Invasion

IRAN type A

Jackson-Weiss syndrome (JWS)

Japanese cedar pollinosis

Juvenile hemangioma

Kallmann syndrome type 2 (KAL2)

Keratinocytic non-epidermolytic nevus

Keratosis seborrheic (KERSEB)

Lacrimo-auriculo-dento-digital syndrome (LADDS)

Leprechaunism

Leukemia

Lung cancer

Lymphadenopathy

lymphedema hereditary type 1A (LMPH1A)

Lympho proliferative syndrome type 1A (ALPS1A)

Lymphoma

malignant melanoma

medulloblastoma

Melanoma

Mendelian susceptibility to mycobacterial disease (MSMD)

Mendenhall syndrome

Mental retardation

Metabolic diseases

Metabolic disorders

Metabolic syndrome

Metastasis

Microphthalmia syndromic type 9

Mood disorders

Muenke syndrome

Multiple Myeloma

Multiple sclerosis

Multiple sclerosis type 3 (MS3)

Myalgia

Myeloid malignancy

myeloproliferative disorder chronic with eosinophilia (MPE)

Nasal congestion

Neoplasm

Nephrotoxic Nephritis

Neuroblastomas

Neurodegenerative disorders

Neurodegenerative Disorders

Neuromuscular diseases

Non insulin-dependent diabetes mellitus (NIDDM)

Non-syndromic trigonocephaly

Obesity

Osteoglophonic dysplasia (OGD)

Osteopetrosis autosomal recessive type 2 (OPTB2)

osteoporosis and Rheumatoid Arthritis

Patellar reflexes

Pathological states related to neural cell death.

Pathology of Atopy

Pfeiffer syndrome (PS)

Progressive scoliosis

Prostate carcinoma

Psoriasis

Psoriasis type 7 (PSORS7)

Pulmonary fibrosis

Pulmonary hypoplasia or aplasia

Pulmonary surfactant metabolism dysfunction type 4 (SMDP4)

Rheumatoid Arthritis

Rheumatoid Arthritis

Self-mutilating behavior

Serositis

Severe combined immunodeficiency (SCID)

severe congenital neutropenia (SCN)

Sneezing

Spontaneous fractures

Stueve-Wiedemann syndrome

Systemic Autoimmune disease

Systemic lupus Erythematosus

systemic lupus erythematosus

Temperature instability

Th1-associated granulomatous diseases

Thanatophoric dysplasia type

Tumor

Tumor angiogenesis

Tumor growth

Tumor Metastasasis

Tumor metastasis

Tumorigenesis

Tumors

Type 2 diabetes

Type M4 acute myeloblastic leukemia

Ulcerative colitis

Uveitis

Viral diseases

Viral infections

Visceral heterotaxy autosomal type 4 (HTX4)

WHIM syndrome

List of Ligands (Alphabetical order)

BCA-1(BLC)

Brain Derived Neurotrophic Factor (BDNF)

cardiotrophin-1

IL29

MCP3

4-1BBL

abc

Adiponectin

Annexin II

CCL1 (SCYA1)

CCL11

CCL17

CCL19,CCL21

CCL20 (LARC)

CCL20 (LARC)

CCL22

CCL25 (SCYA25)

CCL26

CCL27

CCL27

CCL28

CD27L (CD70L)

CD30L

CD40L

Chemerin

Ciliary neurotrophic factor

CXCL1

CXCL10

CXCL11,

CXCL12

CXCL12

CXCL13,

CXCL16

CXCL2

CXCL3

CXCL3

CXCL4

CXCL5

CXCL6

CXCL6,

CXCL7

CXCL8

CXCL8

CXCL9

EGF,

eotaxin2

Epo

FGF acidic,

FGF basic

Flt3

G-CSF

Ghrelin

GM-CSF

GM-CSF

HGF

HGFL

hippocampus cells

IFNalpha

IFNbeta

IFNgamma

IL11

IL12

IL12

IL13

IL13

IL13

IL16

IL17

IL17

IL17,

IL18

IL1A

IL1B

IL1RA

IL2

IL2

IL21

IL22

IL23

IL27

IL28

IL28,

IL3

IL3,

IL31

IL-34

IL4

IL4

IL4,

IL5

IL5

IL6

IL6

IL-6

IL7

IL7

IL8

IL8

IL8

IL9

IRS-1

IRS-2

IRS-3

IRS-4

Leptin

LIF

LIGHT

Ly-6E/A

lymphotoxin A

MCP1

MCP2

MCP-2

MCP3,

MCP4

M-CSF

M-CSF

MIF

MIF

MIP1 alpha,

MIP1 beta

MPIF1

Myostatin

Nerve Growth Factor (NGF)

Neurons

Neurotrophin1

Neurotrophin3

Neurotrophin-3 (NT-3)

Neurotrophin-4 (NT-4)

Oncostatin M

OX40L

PAI-1

PAI-2

PDGF(PDGF-A;PDGF-B; PDGF-C;PDGF-D)

PDGF(PDGF-A;PDGF-B;PDGF-C;PDGF-D)

PGF

RANK-L

RANTES

RANTES

RANTES

RBP4

Resistin

TGF-alpha

TGF-beta

TNF Alpha

TNF Alpha

TNFSF18/GITRL

tPA

TPO

TRAIL:CD253

TSLP

TWEAK

uPAR

VEGF

View

vitronectin

Chemokine

Interleukin

Colony StimulatingFactor Receptor

Interferon

Tumor Necrosis Factor

Adipokine

MIF

The main objective of CytReD (Cytokine Receptor Database) is to provide an user-friendly access for a quick review of the cytokine receptors, of their ligands, of their involvement in diseases and of their use in clinical treatments.In particular, CytReD can be used by researchers as well as physicians and clinicians to identify what cytokines are reported in the literature as significant in a given disease and in its clinical treatment.

CytReD is part of a broader project aimed to develop tools and portals able to be useful supports for a reliable predictive medicine. In fact, CytReD is also correlated with Clinical Data Mining Software (CDMS) that was recently developed in our research group to collect cytokine profiles evaluated both on healthy subjects and on patients by multiplex immunoassays and annotated with their clinical and laboratory story.

The user can search in CytReD by selecting “Cytokine Receptor Name”, “Ligands”, “Cytokine Family” or “Disease”:

• PhyloD
  • Pathogens live and reproduce inside the human host, whose immune system continually tries to rid the body of these pathogens. This leads to a tug-of-war between the pathogen and the human host, where the pathogen tries to adapt so as to “escape” the immune system, while the immune system learns to recognize and eliminate new foreign pathogens. A set of key players for the immune system are the HLA proteins, each of which can recognize specific short fragments of foreign (e.g. HIV) proteins, called epitopes, in infected cells and then alert the immune system to their presence. For rapidly evolving pathogens like HIV, a key defense mechanism is to evolve mutations that prevent the HLA proteins from recognizing the viral DNA. This evolution takes place anew in each patient, as each patient has a different set of HLA proteins that recognize different epitopes. PhyloD is a statistical tool that can identify HIV mutations that defeat the function of the HLA proteins in certain patients, thereby allowing the virus to escape elimination by the immune system. By applying this tool to large studies of infected patients, researchers are now able to start decoding the complex rules that govern the HIV mutations, in the hope of one day creating a vaccine to which the virus is unable to develop resistance.
• Epitope Prediction
  • This tool computes the probability that a given kmer is a T-cell epitope restricted to a given HLA allele. The tool can scan for 8, 9, 10, and 11mer epitopes and over all common HLA alleles.
• HLA Completion
  • HLA sequence typing sometimes yields uncertain results. For example, an allele may be identified as A6801/6802 or simply A02. This tool takes as input HLA typing data (loci A,B,C) and probabilistically resolves the typing ambiguities (i.e., probabilistically “completes” the data to 4-digit resolution).
• HLA Assignment
  • One way to find epitopes is to do lab studies such as ELISPOT. One problem with this approach is that, if you see a reaction in a patient, you don’t know which of the patient’s HLA genes is responsible for the reaction. This tool takes lab data from a series of patients and determines (probabilistically) which HLA genes are responsible for the reaction.
• Create Epitome
  • This tool takes, as input, a weighted list of amino acid sequences. It creates epitomes of all lengths.
• False Discovery Rate
  • Estimate the false discovery rate for 2X2 contingency tables, based on Fisher’s statistics

source : Codeplex

However,  India lacks the records of clinical information about the patients, sequence data without clinical information will have little meaning. And hence partnership with clinicians is essential. The real money is in discovering new drugs for ourselves and not in supplying genetic information and data to the foreign companies, who would then use this information to discover new molecules .

The genomic data provides information about the sequences, but it doesn’t give information about the functions. It is still not possible to predict the actual 3-D structure of proteins. This is a key area of work as tools to predict correct folding patterns of proteins will help drug design research substantially.

Looking at this biotech and pharma companies need tremendous software support. Software expertise is required to write algorithms, develop software for existing algorithms, manage databases, and in final process of drug discovery.

Some major opportunity areas for IT companies include:

*  Improving utility& content of  databases

*  Tools for data generation, capture, and annotation

*  For comprehensive functional studies tools and databases

*  Representing and analyzing sequence similarity and variation

* Creating mechanisms to support effective approaches for producing robust, software that can be widely shared.

Indian IT companies have a great business opportunity to offer complete database solutions to major pharmaceutical and genome-based biotech companies in the world.

Pure cost benefits for the biotech companies will definitely drive the bioinformatics industry in the country. The biotech industry in 2000 has spent an estimated 36 percent on R & D. Success for many will mean a drastic reduction in R&D costs. Thus biotech companies will be forced to outsource software rather than developing propriety software like in the past. Since the cost of programs for handling this data is extremely high in the west, Indian IT companies have a great business opportunity to offer complete database solutions to major pharmaceutical and genome-based biotech companies in the world.

The IT industry can also focus more on genomic’s through different levels of participation areas such as hardware, database product and packages, implementation and customization of software, and functionality enhancement of database.

Abraham Thomas, managing director, IBM India Ltd, says, “the alignment of a vast pool of scientific talent, a world-class IT industry, a vigorous generic pharmaceutical sector and government initiatives in establishment of public sector infrastructure and research labs are positioning India to emerge as a significant participant on the global biotech map.”

With an objective to help and rise bioinformatics sector to the world map the Bioinformatics Society of India (Inbios) has been working since August 2001. The Inbios already has over 270 members in a short span of one and half years. It has become a common informal platform for the younger generation to learn and contribute to this sun rising field in India.

Problems in the sector

The major issue for India is its transition from a recognized global leader in software development to areas of real strength upon which it can capitalize in the biosciences. The identifiable areas are in computation biology and bioinformatics, where a substantial level of development skills are required to develop custom applications to knot together and integrate disparate databases (usually from several global locations), simulations, molecular images, docking programs etc.

The industry people, meanwhile, say that the mushrooming of Bioinformatics institutes is creating a problem of finding talented and trained individuals in this industry. While many of them has superficial knowledge and a certificate, India lacks true professionals in this area.

Most people, who opt for bioinformatics are from the life sciences areas that do not have exposure to IT side of bioinformatics, which is very important. Another issue is that some companies face shortage of funds and infrastructure. The turn around time for an average biotech industry to brakemen would be around three to five years.

Most of the venture capitals and other sources of funding would not be very supportive, especially if the company is not part of a larger group venture. It would help if the government would take an active role in building infrastructure and funding small and medium entrepreneurs.

source : http://www.geocities.com/bioinformaticsweb/bioindia.html

Scenario of Bioinformatics in India for Entry level student ?

1. Where is  the  opportunities in field of Bioinformatics in commercial acumen in India?

According to me, there is opportunities of Bioinformatics in India are  in research institutes and projects undergoing government funding. In India there is not much of opportunities in Pharmaceutical companies and there R&d section.

2. Does Research insti’s and universities favoring Bioinformatician for there specialization or just taking any one who just have passed NET/GATE or other eligibility test for PhD?

There are lots of  Research institutes and Universities employing or enrolling candidates from different streams of life sciences like Zoology, botany, genomics , Biotechnology and other related streams of life Sciences for PhD and jobs in  Bioinformatics irrespective as they have opted for Bioinformatics as last chance to get employment in research field. There are lots of places where  work in Bioinformatics is going on and they don’t have even single  Bachelor or Master’s student of Bioinformatics to do work or to pursue Further studies. As a bioinformatics student I really feel that passing Net/Gate or other eligibility test is a task for Bioinformatics or pharmacoinformatics student as compared to other students pursuing studies in other disciple of life sciences as he / she is having 4-6 programming languages, Mathematics , Statistics and Bioinformatics, and molecular Biology in academic curriculum in spite of  having Chemistry,Physics,Zoology and Botany.

Future aspects of Bioinformatics :

As discussed in earlier post about CUDA (source:  http:// www.nvidia.com ). Bioinformatics is emerging with a new concept of accelerating its application ultimately leading to genome analysis of millions of base pairs in a few seconds on your Notepad or Personal computer.So There are lots of opportunities coming our way.

Related information and Discussions are welcome. send it to ankushsharma85@aol.com

source : http:// www.nvidia.com

Compute Unified Device Architecture is a parallel computing architecture developed by NVIDIA. CUDA is the computing engine in NVIDIA graphics processing units or GPUs that is accessible to software developers through industry standard programming languages. Programmers use ‘C for CUDA’ (C with NVIDIA extensions), compiled through a PathScale Open64 C compiler, to code algorithms for execution on the GPU. CUDA architecture supports a range of computational interfaces including OpenCL and DirectX Compute. Third party wrappers are also available for Python, Fortran, Java and Matlab.

Sequencing and protein docking

Sequencing and protein docking are very compute-intensive tasks that see a large performance benefit by using a CUDA-enabled GPU. There is quite a bit of ongoing work on using GPUs for a range of bio-informatics and life sciences codes.

Accelerating HMMER using GPUsScalable Informatics	MUMmerGPU: High-through DNA sequence

Molecular dynamics

Molecular dynamics applications are extremely amenable to the massively parallel architecture of NVIDIA’s GPUs. In the charts below, we highlight work done on VMD and also molecular dynamics software packages such as NAMD and HOOMD.

Ion Placement in VMDStone, Phillips, Hardy, Schulten	HOOMD on 1 NVIDIA GPUoutperforms 16 CPU cores running LAMMPs Anderson, Lorenz, Travesset