BOL: Related items

Ebola virus disease (EVD)or Ebola haemorrhagic fever !!!

Rahul Nayak — Sun, 10 Aug 2014 13:08:13 -0500

Ebola virus disease (EVD)or Ebola haemorrhagic fever is a severe and often deadly illness in humans, caused by the Ebola virus. The disease has high mortality rate, killing upto 90% of people who are infected.

The ongoing 2014 West Africa Ebola outbreak is considered to be the largest and longest outbreak ever recorded of Ebola, killing at least 932 people and infecting more than 1,700 till date since March in Sierra Leone, Guinea, Nigeria and Liberia.

Hence, the World Health Organisation (WHO) on 8 August, 2014 declared the killer Ebola epidemic ravaging parts of West Africa an international health emergency.

Causes

EVD is caused by infection with a virus of the family Filoviridae, genus Ebolavirus. While there are five identified sub-species of Ebolavirus, four viruses cause disease in humans. They are Bundibugyo virus (BDBV), Ebola virus (EBOV), Sudan virus (SUDV), Taï Forest virus (TAFV).

The fifth virus, Reston virus (RESTV), is not considered to be disease-causing in humans.

According to WHO, EVD first appeared in 1976 in two simultaneous outbreaks, in Nzara, Sudan, and in Yambuku, Democratic Republic of Congo. The latter was in a village situated near the Ebola River from which the disease takes its name.

How does it spread?

It is still unclear how Ebola spreads. However, it is believed that the first pateint becomes infected through contact with an infected animal's body fluids.

Human-to-human transmission can occur through direct contact with blood, organs or other body fluids of infected people or exposure to objects such as needles and syringes that have been contaminated with infected secretions.

Ebola can also be transmitted from men who have recovered from the disease through semen as it is infectious for up to 7 weeks.

Infected dead bodies can spread Ebola as they are still infectious. So mourners who have direct contact with the body of deceased person can also get the disease.

Who is most at risk?

Health-care workers who do not wear appropriate protective clothing and family members who are in close contact with infected people or deceased patients.

Signs and symptoms:

Symptoms may occur between 2 and 21 days after contracting the infection. Common signs of Ebola include:

Fever

Headache

Muscle, abdominal and joint pain

Sore throat

Weakness

Diarrhea

Vomit or cough up blood

Chest pain

Difficulty in breathing and swallowing

Rash

Hiccups

Bleeding inside and outside the body

Prevention

Currently there is no vaccine available for humans. But the infection can be controlled through the use of recommended protective measures such as:

Avoid contacting infected blood or secretions, including from those who are dead .

Using standard precautions for all patients in the healthcare setting.

Sterilizing equipment, and wearing protective clothing including masks, gloves, gowns and goggles.

Washing your hands with soaps or detergents.

Disinfecting your surroundings.

Isolate people who have Ebola symptoms.

Culling of infected animals, with close supervision of burial or incineration of carcasses.

Yet, not travelling to the areas or countries where the virus is found is the best way to avoid Ebola.

NGS Online Training

Sat, 27 Sep 2014 07:42:29 -0500

ArrayGen Technologies announces to provide online NGS training through out the globe. Now analyze your own NGS datasets from anywhere.For more information contact us at training@arraygen.com

Please visit our site at www.arraygen.com

Big genomic data on Google Cloud Platform

Fri, 17 Oct 2014 02:16:00 -0500

As the cost of DNA sequencing has dropped, the volume of data produced has risen into the petabytes. Google is working with the genomics community to define a standard API for working with big genomic data sets in the cloud. Building on Google Cloud Platform, we show how to store, process, explore and share genomic data using technologies like BigQuery, AppEngine MapReduce, R and more.

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html

Zhang Lab

Sun, 28 Dec 2014 12:43:08 -0600

We develop and use integrative bioinformatics approaches to extract biological meanings from experimental data and generate hypotheses for experimental validation. Please explore our website to learn more about our people and our research.

More at http://bioinfo.vanderbilt.edu/zhanglab/

Narcis Fernandez-Fuentes Lab

Mon, 25 May 2015 07:30:00 -0500

Welcome to our web-site compiling all the research-related activities of the group. Our research interests relate to a number of areas within Bioinformatics. We have a long-standing interest in protein structure prediction and structure-to-function relationships. We work in the study of biomolecular interactions, modeling of protein complexes, the study and characterization of protein-protein interactions, peptide design, modeling of genetic variation, structure-based protein design and different aspects of Plant Bioinformatics. Take a look at the our databases and servers and the list of publications for more information.

More at http://www.bioinsilico.org/

X. Shirley Liu Lab

Tue, 26 May 2015 17:28:23 -0500

The research in our laboratories are focused on the following three areas:

Bioinformatics
Cancer
Epigenetics

More at http://liulab.dfci.harvard.edu/

BISR Jaipur

Tue, 07 Jul 2015 23:12:26 -0500

The Bioinformatics Centre at BISR has created an infrastructure for providing facilities to the users working in the field of Biological Sciences. The users of Rajasthan, Jaipur in particular, are using facilities available at the Bioinformatics Centre extensively. The centre has leased line Internet connection as well latest Bioinformatics software for sequence and structure analysis. The centre provides the following services:

Bioinformatics supports to researchers
Customized training in Bioinformatics for researchers and faculty members
Support in Installing, implementing and maintaining software on computer.
Create awareness for taking preventive measure against data security
Organize workshops on thrust ares of Bioinformatics
Research Training to students of Biotechnology and Bioinformatics

More at http://bioinfo.bisr.res.in/index.php

liftover

Jitendra Narayan — Mon, 08 Feb 2016 15:45:03 -0600

Convenient conversions between genome assemblie. The liftover package makes it easy to remap genomic coordinates to a different genome assembly.

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

SATSUMA : Highly sensitive whole-genome synteny alignments.

Jit — Fri, 13 May 2016 05:25:26 -0500

Satsuma is a whole-genome synteny alignment program. It takes two genomes, computes alignments, and then keeps only the parts that are orthologous, i.e. following the conserved order and orientation of features, such as protein coding genes, non-coding genes, or neutral sequences. Satsuma does not require any pre-processing, such as repeat masking, since it will automatically detect ambiguous mappings.

Satsuma has parallelization built-in and is designed to run on multi-core architectures. The run-time for aligning two bird-size genomes (~1.2 Gb) is around two days on 24 CPUs.

You can find the manual here.
Download the latest source code from here.
Stable versions can also be downloaded from the Broad Institute's web site.

An incomplete list of questions and answers (yes, these have really been asked by our users! Please feel free to add your own by e-mailing us) is here.

If you use Satsuma in your research, please cite:
Grabherr, M. G., Russell, P., Meyer, M., Mauceli, E., Alföldi, J., Di Palma, F., & Lindblad-Toh, K. (2010). Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics, 26(9), 1145-51.

Tutorial at http://evomics.org/learning/genomics/satsuma/

Address of the bookmark: http://satsuma.sourceforge.net/