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Showing posts with label research. Show all posts
Showing posts with label research. Show all posts









Welcome to ß-Lactamase
Beta-lactamases are enzymes  produced by some bacteria and are responsible for their resistance to beta-lactam antibiotics like penicillins, cephamycins, and carbapenems (ertapenem) (Cephalosporins are relatively resistant to beta-lactamase). These antibiotics have a common element in their molecular structure: a four-atom ring known as a beta-lactam.







In the last ten years, the open source R statistics language has exploded in popularity and functionality, emerging as the data scientist's tool of choice. Today, R is used by over 2 million analysts worldwide, many having been introduced to its elegance and power in academia. Users around the world have embraced R to solve their most challenging problems in fields ranging from computational biology to quantitative finance, and to train their students in these same fields. The result has been an explosion of R analysts and applications, leading to enthusiastic adoption by premier analytics-driven companies like Google, Facebook, and and the New York Times.









Why Use Consed?

  1. Supports Illumina, 454, other Next-Gen and Sanger Reads and allows mixtures of these read types
  2. Consed now includes BamScape which can view bam files with unlimited numbers of reads. BamScape can bring up consed to edit reads and the reference sequence in targetted regions.
  3. Consed is compatible with Newbler, Cross_match, Phrap, MIRA, Velvet and PCAP output.
  4. Quickly takes the user to each variant site for viewing (also available as an automated report)
  5. Overview of assembly can help detect and fix misassemblies
  6. Consed is licensed to over 4000 sites and climbing. In *active* use at over 230 sites in 36 countries including biotech, chemical, pharmaceutical, and agricultural companies, major genome centers, small academic labs, and government labs
  7. Editing time reduced by the program's ability to pin-point problem areas
  8. Editing is guided by error probabilities
  9. Consed is able to pick primers very successfully (98 out of 98 in a controlled study). Labs that use it are quite happy with it.
  10. Able to pick PCR primers to amplify a region, even if you only have a fasta file for the region










originally published in

published by:Simon Harold 

The low cost computing hardware Raspberry Pi is now being used to train the next generation of computational biologists, and is proving to be a low-cost alternative to more traditional methods of learning.
“Bioinformatics is great and shouldn’t be limited to one small module” was the reaction of one enthusiastic undergraduate at the University of St Andrews (UK), following the 7 week teaching course entitled 4273 π Bioinformatics for Biologists.

They are of course correct on both counts.
Bioinformatics, or some variant of computational biology, arguably underpins a majority of modern basic biological analysis, and is working its way steadily into the realms of clinical and translational science. Think of the software you use to align your DNA sequences, infer genetic structure in your populations, or model the conformations of your newly crystallized protein. All are made possible because someone, somewhere, coded them into existence. Yet how many of us could code even a basic program of this type?

Fig1 Barker et al BMC Bioinfo (2013) 14, 243

One difficulty that contributes to this issue is teaching. Few university courses exist that offer dedicated training in bioinformatics, with researchers coming to the subject either as biologists with an interest in computation, or computational scientists with an interest in biology. Although there may not be a problem with bioinformaticians coming to the field through either of these routes, training the average bench biologist or early-career researcher to have basic skills in the field can be difficult. This is partly down to the diversity of subject areas to which computational skills need to be applied.

Speaking to Biome magazine, Ian Korf, Associate Director of Bioinformatics at the Genome Center at University of California, Davis, sees teaching such diversity as a real issue for university courses “One of the greatest obstacles to teaching bioinformatics is the teachers themselves. Bioinformatics is an eclectic field drawing from molecular biology, statistics, computer science, mathematics and other disciplines. Not many teachers have such a diverse education.”
Another problem is infrastructure. Whilst some universities may have access to vast computing power for researchers, gaining access to servers that allow students to experience administrative privileges, or simply give them the time to experiment with basic computational architecture, can often be problematic.
Now, an open access, open learning method developed by Daniel Barker and colleagues aims to strip this teaching back to basics by using the newly-developed Raspberry Pi computing system to let students experience full administrator rights and gain valuable insights into real-world bioinformatics. The low-costs involved (each computer typically costs around £30/$40/€35) also means that large-scale teaching may be achieved without university costing departments having to worry about whether their laptops will be returned in full working order at the end of the semester.

What’s in the Pi?
Raspberry Pi Model B Rev 2_Tors_Wikimedia commons cc
The hardware costs stay so low because the Raspberry Pi strips computing back to its basic elements. This credit-card sized computer eschews the modern movement toward bigger, faster processing by using a basic single-board device running an open-source operating system, without the usual hardware features like disk-drives and keyboards. Developed by a non-profit, British-based company, it is now being hailed as a revolutionary tool in facilitating mass-participation in home programming.
As well as some of the more frivolous uses to which the device can be applied, it is hoped that this low cost could not only pique a new generation’s interest in the anatomy of computing, but could also have much broader implications for access to teaching computation in the developing world. Barker feels that key to this is empowering the bench-scientist to lose their fear of the motherboard:
“Many would-be bioinformaticians get scared away because of the arcane syntax of the command line, their lack of computer programming experience, or a feeling that their mathematics skills are insufficient. These are just fears. If you hold their hand for a little while they can get through the scary bits, they will emerge on the other side self-empowered and with a new perspective on problem solving. It will impact everything from grocery shopping to genome analysis.”
A key part of this will be openness. Although developed specifically to run the bioinformatics teaching course at the University of St Andrews, Barker and colleagues acknowledge that thephilosophy of openness encouraged by this new hardware also needs to be translated into teaching, and have made the course fully available to anyone wanting to have a go if they wished: full course details can be downloaded as an additional file from their article in BMC Bioinformatics.








A new computational method for working out in advance whether a chemical will be toxic will be reporting in a forthcoming issue of the International Journal of Data Mining and Bioinformatics.
There is increasing pressure on the chemical and related industries to ensure that their products comply with increasing numbers of safety regulations. Providing regulators, intermediary users and consumers with all the necessary information to allow them to make informed choices with respect to use, disposal, recycling, environmental issues and human health issues is critical. Now, Meenakshi Mishra, Hongliang Fei and Jun Huan of the University of Kansas, in Lawrence, have developed a computational technique that could allow the industry to predict whether a given compound will be toxic even at a low dose and thus allow alternatives to be found when necessary.
Toxicity is almost always an issue of availability and dosage. Whether or not a compound is natural or synthetic it can be toxic from snake venom and jellyfish stings to petrochemicals and pesticides. However, some chemicals are more toxic than others, exposure to a lower dose will cause health problems or potentially be lethal. It is very important to find a way to determine whether a newly discovered synthetic or natural chemical might cause toxicity problems.
The team also points out that the US Environmental Protection Agency (EPA) and the Office of Toxic Substances (OTS) in the USA had listed 70,000 industrial chemicals in the 1990s, with 1000 chemicals added each year for which even simple toxicological experiments had not been carried out. This is largely a problem of logistics and costs as well as the ethical question of whether so many tests, which would have to be carried out on laboratoryanimals, should be done at all.
Now, Huan and colleagues in the Department of Electrical Engineering and Computer Science at Kansas, have successfully tested a statistical algorithm against more than 300 chemicals for which the toxicity profile is already known. Their technique offers a computational method of screening a large number of compounds for obvious toxicity very quickly and might preclude the need for animal testing of the compounds, provided regulators don’t insist on such “in vivo” data from the latter.
The research builds on well-established principles from the pharmaceutical industry known as Quantitative structure-activity relationships (QSARs) in which the type of atoms and how they are connected together can be correlated with the activity of a drug molecule. Certain molecular shapes and types are soluble in water, for instance, or interact in a certain way with different enzymes and other proteins in the body, leading to their overall activity. Different molecular features will make a similar molecule behave in a different way – more or less soluble, stronger or weaker acting. The team has now turned the QSAR around so that instead of searching for the features in a molecule that make it of benefit in medicine they look for the atomic groups and the type of bonds that hold them together to find associations with toxicity.
The team points out that few earlier attempts at predicting toxicity of chemicals have proved successful, most approaches are no better than random guessing. The team’s new statistical approach combines “Random Forest” selection with “Naïve Bayes” statistical analysis to boost the predictions well beyond random. They team saw prediction accuracy in 2 out of 3 chemicals tested. Given that there are around 100,000 industrial chemicals that need toxicity profiling, this result should allow the industry and regulators to focus on a large number of the most pressing of those, the ones predicted to have greatest toxicity and leave the less likely until additional resources are available.
The researchers are now tuning the algorithm to work faster and with greater precision so that it ignores common molecular features now known not to contribute to toxicity characteristics in the chemicals they have studied so far.
As Britney Spears asked in her song: “Don’t you know that you’re toxic?” Well, we do now.
“Computational prediction of toxicity” in Int. J. Data Mining and Bioinformatics, 2013, 8, 338-348
Source of the article 












Infographics animated video simplifying the role of Systems Bilogy in biological research. produced for the Weizmann Institute of Science.









Installation

$ go get code.google.com/p/biogo/...

Documentation

Core packages

See sub-packages below.

Mailing list

Overview

bíogo is a bioinformatics library for the Go language.

The Purpose of bíogo

bíogo stems from the need to address the size and structure of modern genomic and metagenomic data sets. These properties enforce requirements on the libraries and languages used for analysis:
  • speed - size of data sets
  • concurrency - problems often embarrassingly parallelisable
In addition to the computational burden of massive data set sizes in modern genomics there is an increasing need for complex pipelines to resolve questions in tightening problem space and also a developing need to be able to develop new algorithms to allow novel approaches to interesting questions. These issues suggest the need for a simplicity in syntax to facilitate:
  • ease of coding
  • checking for correctness in development and particularly in peer review
These ideas are more fully discussed in this paper.
Related to the second issue is the reluctance of some researchers to release code because of quality concerns ("Publish your computer code: it is good enough. Nature 2010.").
The issue of code release is the first of the principles formalised in the Science Code Manifesto.

A language with a simple, yet expressive, syntax should facilitate development of higher quality code and thus help reduce this barrier to research code release. The Go language design satisfies these requirements.

If you use bíogo for work that you subsequently publish, please include a note in the paper linking to this site - and let us know.

Yet Another Bioinformatics Library

It seems that nearly every language has it own bioinformatics library, some of which are very mature, for example BioPerl andBioPython. Why add another one?
The different libraries excel in different fields, acting as scripting glue for applications in a pipeline (much of [1], [2] and [3]) and interacting with external hosts¹, ², ⁴, ⁵, wrapping lower level high performance languages with more user friendly syntax¹, ², ³, ⁴ or providing bioinformatics functions for high performance languages⁵, ⁶.
The intended niche for bíogo lies somewhere between the scripting libraries and high performance language libraries in being easy to use for both small and large projects while having reasonable performance with computationally intensive tasks.
The intent is to reduce the level of investment required to develop new research software for computationally intensive tasks.
  1. BioPerl


  1. BioPython
http://bioinformatics.oxfordjournals.org/content/25/11/1422
  1. BioRuby
http://bioinformatics.oxfordjournals.org/content/26/20/2617
  1. PyCogent
http://genomebiology.com/2007/8/8/R171
  1. BioJava
http://bioinformatics.oxfordjournals.org/content/24/18/2096
  1. SeqAn
http://www.biomedcentral.com/1471-2105/9/11












Biology Questions and Answers is a website that discusses all branches and subjects of Biology. You can learn everything about Biology here!

This site was specially written and organized to make Biology learning easier. More than 1800 questions and answers are available to help you study Biology in the easiest way possible.

Discover how you can build your biological knowledge step-by-step through intelligent sequences of questions and answers.

The content is divided into all Biology branches: biochemistry, cell biology, microbiology, zoology, physiology, embryology, botany, genetics, evolution, ecology and diseases. Each of these branches are then subdivided into specific subjects, as listed below.

You will enjoy studying Biology with Biology Questions and Answers! 






Posted on  behalf of Professor Nadia Rosenthal of EMBL Australia.

Last month, sixty first and second year PhD students from around Australia came to Melbourne to take part in the inaugural EMBL Australia PhD Course.

Over the two weeks of the course, I watched as these young researchers were inspired by new scientific ideas, connected with other students as well as the speakers, and came into their own as scientists. The speakers, invited from around Australia as well as from EMBL in Europe, also enjoyed the experience and the atmosphere. It felt like I was back at EMBL, with the same excitement and buzz and level of excellence that we strive for there.

One of the students asked me what’s in it for EMBL Australia to hold a course for PhD students. It’s a good question, and one I was happy to answer. Quite simply, we are investing in the future of Australian science, growing future leaders and imbuing them with an international outlook.

When the students heard about the annual EMBL PhD Symposium, which is organised by the first year PhD students at EMBL, I was asked why Australia couldn’t have a conference for PhD students. I’m delighted to announce that the students attending the 2013 PhD course have volunteered to organise their own conference, with the support of EMBL Australia. They plan to invite students from EMBL to attend the conference, just as students from Australia are able to attend the EMBL PhD Symposium through our student grants program.

I’d like to extend my thanks once again to the University of Melbourne, Doug Hilton and the Walter and Eliza Hall Institute for hosting the course, the speakers and the students for your input and enthusiasm, and finally the staff at EMBL Australia, for making the first EMBL Australia PhD Course a resounding success.

Two international visitors will be visiting Australia this month. Mike Hucka will present a series of talks in Melbourne and Sydney about open standards in systems biology research, while Bob Kuhn is bringing the USCS Genome Browser Workshop Roadshow to Brisbane, Sydney, Canberra and Melbourne. They’ll be followed by a series of visits by Japanese science leaders in September and October. More details can be found in the events listings.

Finally, later this month, I’ll be heading off to the 2013 International Conference on Systems Biology (ICSB) with Silvio Tiziani as well as Sarah Boyd from SBI Australia. While there, we’ll be talking about next year’s conference in Melbourne and catching up with some of the scientists working with us to plan the program. I look forward to bringing you more news about the conference in coming months.

First EMBL Australia PhD course a roaring success..Read more








Scientists said Monday they had used a new-generation gene sequencing technique to select a viable embryo for in-vitro fertilization (IVF) that yielded a healthy baby boy.

IVF, the process whereby a human egg is fertilised with sperm in the laboratory, is a hit-and-miss affair, with only about 30 percent of fertilised embryos resulting in pregnancy after implantation.

The reason for the high failure rate is not clear but genetic defects are the prime suspects, according to the authors of the paper presented Monday at a meeting in London of the European Society of Human Reproduction and Embryology (ESHRE).

The new method, known as next generation sequencing or NGS, uses updated technology to sequence an entire genome — revealing inherited genetic disorders, chromosome abnormalities and mutations.




Source:Posted by Biome on 12th August 2013

Gaining an understanding of bioinformatics is now par for the course when it comes to training undergraduate students in biology. However, facilitating a comprehensive course in bioinformatics is often hampered by the constraints of a typical classroom environment, specifically the software and hardware requirements. To grasp more in-depth concepts, beyond what is offered from basic tools available online, Daniel Barker from the University of St Andrews, UK and colleagues propose the re-creation of a bioinformatics research environment. To this end they developed the 4273π platform for teaching bioinformatics, on the inexpensive Raspberry Pi computer, modified for the classroom setting and including their Open Access bioinformatics course – as explained in their recent study published in BMC Bioinformatics. Barker and leading bioinformatician Ian Korf from University of California, Davis, USA discuss the importance of bioinformatics training for biologists, and the potential for 4273π to improve the way this is implemented.

Why is teaching bioinformatics so important now?
IK The main reason is that biology is becoming much more quantitative. Sequencing and other high-throughput technologies are revolutionizing many biology-related industries. In the past, people would focus on a few genes, for example, but today it’s possible to look at all genes. That’s a very powerful paradigm, but you need a different ‘microscope’ when looking at genomic data, and it takes some training to understand how to handle and analyze large data sets.
Biologists are adaptable people in general, and I find that the typical biologist can become a good bioinformatician. Many would-be bioinformaticians get scared away because of the arcane syntax of the command line, their lack of computer programming experience, or a feeling that their mathematics skills are insufficient. These are just fears. If you hold their hand for a little while they can get through the scary bits, they will emerge on the other side self-empowered and with a new perspective on problem solving. It will impact everything from grocery shopping to genome analysis.

What are the challenges in organizing an undergraduate bioinformatics course at a university?
IK Most universities have plenty of computers, but they aren’t typically configured to analyze genomic data. Most bioinformatics takes place in a Linux/Unix/Mac environment yet most labs have Windows boxes. It’s easy enough to install Linux on a PC either as a second OS or in a virtual machine, but the University might not want to do that and may have policies against it. So one solution is to have a separate set of Linux machines for bioinformatics. That can get expensive. Once you have the computers, you have to set them up with an appropriate bioinformatics environment too, and that can be complicated by the ever changing nature of the field.
DB At my university, the computer classrooms run Windows, which is one of the least useful operating systems for bioinformatics. A few years ago we used the Windows computers only to log in to a remote Linux server, but this still has limitations and isn’t much fun. Students are denied administrator access – so they are more dependent on staff, to install software and so on, than would be likely in a subsequent career.
I have not found costs to be a big problem. Laboratory practical classes in other areas of the curriculum can be expensive, requiring consumables and technical preparation every time they run. For bioinformatics, equipment has to be purchased every few years but the intervening years are much cheaper.
IK One of the greatest obstacles to teaching bioinformatics is the teachers themselves. Bioinformatics is an eclectic field drawing from molecular biology, statistics, computer science, mathematics and other disciplines. Not many teachers have such a diverse education.
DB Another problem has been textbooks. There are plenty, but it has been hard to find anything with the right balance. I was very happy when ‘UNIX and Perl to the Rescue’ came out (Keith Bradnam and Ian Korf, 2012). It covers the underlying skills well. Still, it is not intended to be a complete bioinformatics textbook.
The Cancer Genome Atlas (TCGA) — a collaborative effort funded by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) — has opened up the new field of “precision medicine” (AKA “genetically informed medicine” or “personalized medicine”) — phrases that describe how the availability of genetic information about a person’s disease can be used to diagnoses and treatment informed by and tailored to an individual’s specific genetic makeup and that particular disease variant.
A National Cancer Institute article entitled: “Cancer Genome Atlas, Impact of Cancer Genomics on Precision Medicine for the Treatment of Cancer” outlines how since cancer is a disease of the genome, as more is learned about cancer tumors, the more scientists are finding that each tumor has its own set of genetic changes. This greater understanding of genetic changes in are in cancer cells is facilitating development of more effective treatment strategies tailored specifically to the genetic profile of each individual patient’s cancer.
The article outlines how the relatively new field of cancer genomics research is focused on advancing personalized medicine through the DNA sequencing and analysis of patient tumors to find new genetic alterations associated with specific cancers, and that providing researchers with comprehensive catalogs of the key genomic changes that take place in various major types and subtypes of cancer will support advances in developing more effective ways to diagnose, treat and prevent the disease.
Examples cited of how genomic information has already helped shape development and utilization of some of the newest cancer treatments include the drug imatinib (Gleevec), which was designed to inhibit an altered enzyme produced by a fused version of two genes found in chronic myelogenous leukemia. Another instance is the breast cancer drug trastuzumab (Herceptin), which works only for women whose tumors have a particular genetic profile called HER-2 positive. Studies have also found that lung cancer patients whose tumors are positive for EGFR mutations respond to the drugs gefitinib (Iressa) and erlotinib (Tarceva) which target this mutation.
On the other hand, it’s been determined that colon cancer patients whose tumors have a mutation in a gene called KRAS derive little benefit from the drugs cetuximab (Erbitux) and panitumumab (Vectibix). The type of genomic information generated by TCGA and other cancer genomics projects will drive research to develop similar treatment strategies that will be most effective for a given set of genomic changes.
In June, investigators in The Cancer Genome Atlas (TCGA) Research Network reported discovery of a connection between how tumor cells use energy from metabolic processes and the aggressiveness of the most common form of kidney cancer, clear cell renal cell carcinoma (ccRCC), demonstrating that normal metabolism is altered in ccRCC tumor cells, and involves a shift from using one metabolic pathway to another. This change – termed a metabolic shift – correlates with tumor stage and severity in some cases.
The scientists also found mutations in a pathway that may cause increased aggressiveness in this cancer — their findings providing new insight into underlying disease mechanisms and potential treatments as well as better understanding of how some cancer cells can shift from using normal metabolic pathways to alternative pathways, thereby providing a growth advantage to tumor cells.
W. Marston Linehan, M.D., chief of the NCI Urologic Oncology Branch and one of the study’s leaders, quoted in a recent TCGA release, sees several implications from the results. “The finding of a metabolic shift in the aggressive tumors could provide the foundation for the development of a number of novel approaches to therapy for patients with advanced kidney cancer,” said Dr. Linehan. The results of this study were published online June 23, 2013, in Nature.
Memorial Sloan Kettering Cancer Center PRGs
In a TCGA article “The New Backbone of Clinical Trial Design,”Dr. Douglas A. Levine, head of the Gynecology Research Laboratory at the Memorial Sloan-Kettering Cancer Center in New York observes that most scientists would now agree that The Cancer Genome Atlas (TCGA) is a transformative program in cancer biology, at least insofar as defining the genomic landscape for a variety of malignancies in a reliable and robust manner. Dr.Levine notes that research begets research, and TCGA is no exception, with even the most nascent scientists able to imagine countless additional outstanding analyses that can be performed with TCGA data.

“A major contribution that TCGA has made to date,” says Dr. Levine, “is in the design of molecularly targeted clinical trials. Prior to the widespread availability of reference genomic data, clinical trials of targeted therapeutics were based on a comprehensive review of the limited literature, generally indicating specific genomic events identified in a relatively small and sometimes non-uniform patient population. With TCGA’s data, now the starting point is often a survey of relevant events in the specified disease. As characterizations of more cancer types are completed, similarities and differences in event types for a given pathway or target can be highlighted across varied tumors types.”
JohnHaymac
“It’s likely that more than half of tumors have some alteration we can target with a drug,” says John V. Heymach, an Associate Professor and lung-cancer specialist at Houston’s MD Anderson Cancer Center, told the Wall Street Journal this week. “They may not all have the same success, but we know that in many cases, a targeted agent will work very well.”

The central focus at the Heymach laboratory is to conduct translational and basic research that advances the development of targeted therapeutic agents, particularly angiogenesis inhibitors, for non-small cell lung cancer (NSCLC) and other solid tumors, increase understanding of how oncogenic pathways can lead to metastatic spread and resistance to anticancer therapies, and to develop predictive markers for identifying which patients are likely to respond or develop resistance to targeted agents.
Dr. Heymach notes that they have taken a systems biology approach to develop proteomic, genomic, and gene expression profiles in NSCLC cell lines and tumors and have used this to identify key pathways or processes driving tumor progression and drug resistance. This includes extensive profiling of more than 100 cell lines and tumor specimens which the researchers can then correlate with clinical outcomes.
Cancer genome sequencing is allowing the NCI to focus on ushering in the era when tailored prevention and treatment strategies, based on the unique characteristics of each person and tumor, become standard practice in research-based clinics and community settings. Understanding of the unique characteristics of cancer cells and how they are different from normal cells can result in treatments targeted to specific types of cancer cells rather than at all of the cells within the body, which for example should diminish the sometimes devastating side effects of chemotherapy. The promise of genetically informed or precision medicine points to patients receiving treatments in their local communities that target the unique characteristics of their specific tumors, resulting in fewer side effects, and allowing patients to experience a higher quality of life during treatment.
Toward achieving this end, the NCI says it is spearheading an innovative platform of activities to enhance the full spectrum of cancer research and accelerate the translation of scientific discoveries in the laboratory into better treatments in the clinic.
This NCI graphic provides an overview with more information about these activities.
NCIgeninf
For more information, visit:

In a PLOS Genetics Open Access research article entitled “High-Precision, Whole-Genome Sequencing of Laboratory Strains Facilitates Genetic Studies,” co-authors Anjana Srivatsan, Yi Han, Jianlan Peng, Ashley K. Tehranchi, Richard Gibbs, Jue D. Wang, and Rui Chen — all scientists at the Baylor College of Medicine Department of Molecular and Human Genetics in Houston — note that completion of the whole-genome sequencing of many organisms, ranging from bacteria to humans, has transformed the way in which biological research is conducted, observing that genome sequencing is mostly used as a resource to obtain the reference sequence information of laboratory species, and its full applications in genetic research remain unexplored, due to its time-consuming and expensive nature.

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