Synthetic Biology Open Language (SBOL) is an open-source standard for in silico representation of genetic designs. SBOL is designed to:
Allow synthetic biologists and genetic engineers to electronically exchange designs
Send and receive genetic designs to and from biofabrication centers
Facilitate storage of genetic designs in repositories
Embed genetic designs in publications
SBOL is built around the idea of a core that is used to unambiguously specify the design of a DNA molecule. Around the core are extensions that are used to increase the kind and amount of information transmitted by the language. There are six extensionsunder development. For example, one extension includes data and information on the performance of DNA components.
The adoption of SBOL offers many benefits, including: (1) enabling the use of multiple tools without rewriting designs for each tool, (2) enabling designs to be shared and published in a form other researchers can use even in a different software environment, and (3) ensuring the survival of design (and the intellectual effort put into them) beyond the lifetime of the software or the reseachers that were used to create them.
MicroRNAs are small pieces of genetic material similar to the messenger RNA that carries protein-encoding recipes from a cell's genome out to the protein-building machinery in the cytoplasm. Only microRNAs don't encode proteins. So, for many years, scientists dismissed the regions of the genome that encode these small, non-protein coding RNAs as "junk."
We now know that microRNAs are far from junk. They may not encode their own proteins, but they do bind messenger RNA, preventing their encoded proteins from being constructed. In this way, microRNAs play important roles in determining which proteins are produced (or not produced) at a given time.
MicroRNAs are increasingly recognized as an important part of both normal cellular function and the development of human disease.
Today, we are beginning a new phase of Rosalind with the publication of 17 new problems. If you have taken a break from the site, we want you back. And… we will be publishing new problems on Friday at 7 PM GMT from now on.
From what we're seeing of a next-generation analyzer due in 2014, those expectations are more likely to be met. The new version puts the full DNA extraction, amplification and separation processes on a newer chip that meets NEC's original goal of producing output in 25 minutes -- faster than a short cop drama, if you include the commercial breaks.
The book is intended for Python programmers who need to learn about algorithmic problem-solving, or who need a refresher. Students of computer science, or similar programming-related topics, such as bioinformatics, may also find the book to be quite useful.
This is my Rosalind problem set running platform. A Raspberry Pi 256M . Today I solve two problem. One is n-1 another is n-2 . I have spend a whole day to figure it out. There problem are all well document in the Computer Algorithm Book. Hope you can find the solution in the book or by yourself.
The problem set is getting harder and harder. The major issue is sample dataset is too small to verify the program. And download dataset has no answer just told you Correct or not. Make the debug very hard.
Trying to solve the Shortest Superstring problem .
This week, the ENCODE project released the results of its latest attempt to catalog all the activities associated with the human genome. Although we've had the sequence of bases that comprise the genome for over a decade, there were still many questions about what a lot of those bases do when inside a cell. ENCODE is a large consortium of labs dedicated to helping sort that out by identifying everything they can about the genome: what proteins stick to it and where, which pieces interact, what bases pick up chemical modifications, and so on. What the studies can't generally do, however, is figure out the biological consequences of these activities, which will require additional work.
Yet the third sentence of the lead ENCODE paper contains an eye-catching figure that ended up being reported widely: "These data enabled us to assign biochemical functions for 80 percent of the genome." Unfortunately, the significance of that statement hinged on a much less widely reported item: the definition of "biochemical function" used by the authors.
This was more than a matter of semantics. Many press reports that resulted painted an entirely fictitious history of biology's past, along with a misleading picture of its present. As a result, the public that relied on those press reports now has a completely mistaken view of our current state of knowledge (this happens to be the exact opposite of what journalism is intended to accomplish). But you can't entirely blame the press in this case. They were egged on by the journals and university press offices that promoted the work—and, in some cases, the scientists themselves.
ENCODE was designed to pick up where the Human Genome Project left off. Although that massive effort revealed the blueprint of human biology, it quickly became clear that the instruction manual for reading the blueprint was sketchy at best. Researchers could identify in its 3 billion letters many of the regions that code for proteins, but those make up little more than 1% of the genome, contained in around 20,000 genes — a few familiar objects in an otherwise stark and unrecognizable landscape. Many biologists suspected that the information responsible for the wondrous complexity of humans lay somewhere in the ‘deserts’ between the genes. ENCODE, which started in 2003, is a massive data-collection effort designed to populate this terrain. The aim is to catalogue the ‘functional’ DNA sequences that lurk there, learn when and in which cells they are active and trace their effects on how the genome is packaged, regulated and read.
Our Android App currently supports several other inexpensive water tests, including thePortable Microbiology Laboratory, which is available from Professor Robert Metcalf. One of the components in this kit, 3M Petrifilms, can detect E. coli and other bacteria in water. The tests can be incubated at room temperature in many locations or they can be worn in a pouch to make use of body heat for incubation. The mWater Android app can use the onboard camera to automatically count the bacteria on a Petrifilm and calculate the risk to the user.
In the near future, we will add simple test strips for Free Chlorine (important in piped water systems), Nitrate (commonly found in contaminated wells), and pH. We envision a complete low-cost laboratory suite for communities, health workers, utilities and emergency workers, that is connected to our cloud-based reporting system for instant sharing and mapping of data.
"The more and more data you produce faster and cheaper, the more the bottleneck—which used to be the DNA sequencing itself—is actually now the data management," he says.
Sundquist sees his company as an instant online genomics center, offering clients immediate access to vast stores of DNA data and to analysis tools so they can make sense of it all—and potentially come up with better treatments for cancer and genetic diseases, as well as identify genetic links to diseases like autism and alcoholism.
Let’s assume we have a species of bacteria that is part of the normal millions of ‘good’ bacteria living on and inside healthy human beings; we’ll call this Bacteria X.0. One day Bacteria X started making people very ill. What happened to Bacteria X.0 to make it become the harmful Bacteria X.1? Let’s see how we could answer this question using bioinformatics, along the way gaining insight into the wonderful world of bioinformatics.
Using traditional molecular biology techniques, we isolate Bacteria X and extract its DNA. Then we “sequence” this DNA. Cue the first link in the bioinformatics chain: acquiring data! Acquiring data is the process of generating useable data from a biological sample. In our case, deriving and determining the DNA sequence of the Bacteria X genome.
The next link in the chain is storing this sequence data. While bacterial genomes are typically small, other genomes, such as those of human beings, can produce terabytes (1000 gigabytes) of data.
Now we analyze this sequence data. There are people who specialize in developing computational tools to analyze and visualize data, versus people who actually analyze the information. A typical analysis for our sample case might be to first graphically visualize and compare the genome of the original, harmless Bacteria X.0 with the genome of the new, harmful Bacteria X.1. A scientist might observe a segment of DNA in Bacteria X.1 which is not present in the original Bacteria X.0. This new region of DNA may be responsible for the harmful effects, so the next analysis steps might be to drill down deeper into this region and see what genes lie there, what the function of those genes are, where they may have come from, etc.
[Remember: all assumptions made and conclusions drawn in this example are hypothetical and for illustrative purposes only.]
In this example, we encountered at least 4 different specialized areas within the field of bioinformatics:
1) Acquiring of data (working with machines and equipment, sequencing DNA) 2) Storing data (typically working with databases) 3) Developing tools to analyze and visualize data (programming) 4) Analyzing data (statistics, analysis)
Typically, individuals will specialize in one particular area rather than working simultaneously across all these fields. That, combined with all the different applications of bioinformatics, means you could ask 100 different “bioinformaticians” what they do and get 100 very different answers!
The group plans to sell its so-called "Bina Box" preloaded with software that can reduce the 300 gigabytes or so of raw data from a human genome into a few hundred megabytes of genetic information. The box will upload the compressed dataset to Bina's cloud service for storage, sharing, and further analysis. The Bina Box can do the initial heavy lifting and make the data small enough to send to the cloud, the company says.
Bina Technologies says its system does this initial processing of genomic data at speeds that are orders of magnitude faster than tools made available by the Broad Institute, the MIT-Harvard joint genome center. What takes about a week using the Broad's genome variation analysis pipeline on a high-end eight-core machine on Amazon's cloud can be done in about two hours on a Bina Box, says Bani Asadi. The company expects to publish a full description of its comparison to other analysis pipelines in the coming months.
The cost of sequencing human genomes is plunging—in the most advanced genomics centers, it's falling five times faster than the cost of computing. Increasingly, people are getting their DNA sequenced by companies and research labs in a search for clues about genetic variation and disease.
But the industry must figure out how to cheaply store all the resulting data. Each of the 3.2 billion DNA base pairs in a human genome can be encoded by two bits—800 megabytes for the entire genome. But considerable data about each base is usually collected, and genes are often sequenced many times to ensure accuracy, so it's common to save around 100 gigabytes when sequencing a human genome with a machine made by industry leader Illumina. Keeping this much data about every person on the planet would require about as much digital storage as was available in the whole world in 2010.
The trick, then, will be to save less. Harvard geneticist George Church says that eventually only the differences between a newly sequenced genome and a reference genome will need to be stored. That information could be encoded in as little as four megabytes. Then your genome might be just another e-mail attachment.