Getting Genetics Done: Shameless plug – I write a blog highlighting literature of interest, new tools, and occasionally tutorials in genetics, statistics, and bioinformatics. I recently wrote this post about how to stay current in bioinformatics & genomics.Titus Brown's list of bioinformatics courses: Includes a few others not listed here (also see the comments).GMOD Training and Outreach: GMOD is the Generic Model Organism Database project, a collection of open source software tools for creating and managing genome-scale biological databases. This page links out to tutorials on GMOD Components such as Apollo, BioMart, Galaxy, GBrowse, MAKER, and others.Seqanswers.com: A discussion forum for anything related to Bioinformatics, including Q&A, paper discussions, new software announcements, protocols, and more.Biostars.org: Similar to SEQanswers, but more strictly a Q&A site.BioConductor Mailing list: A very active mailing list for getting help with Bioconductor packages. Make sure you do some Google searching yourself first before posting to this list.Bioconductor Events: List of upcoming and prior Bioconductor training and events worldwide.Learn Galaxy: Screencasts and tutorials for learning to use Galaxy.Galaxy Event Horizon: Worldwide Galaxy-related events (workshops, training, user meetings) are listed here.Galaxy RNA-Seq Exercise: Run through a small RNA-seq study from start to finish using Galaxy.Rafael Irizarry's Youtube Channel: Several statistics and bioinformatics video lectures.PLoS Comp Bio Online Bioinformatics Curriculum: A perspective paper by David B Searls outlining a series of free online learning initiatives for beginning to advanced training in biology, biochemistry, genetics, computational biology, genomics, math, statistics, computer science, programming, web development, databases, parallel computing, image processing, AI, NLP, and more.NGS Conferences: A comprehensive list of next-generation sequencing conferences.I want to learn bioinformatics!: A guide by Nick Loman for helping a non-computer-literate person to learn bioinformaticsBioinformatics not something you are taught; it's a way of life: Blog post by Mick Watson about how one doesn't simply learn bioinformatics in a week-long workshop. It takes years of practice and hands-on doing to learn how to solve problems in bioinformatics, and this post gives some great suggestions about how to get started.So you want to be a computational biologist?: Commentary article in Nature Biotechnology (2013, 31:996-998) by Nick Loman and Mick Watson, motivated in part by the blog posts above.
How much is 1% of your time? Five minutes each day, two hours each month or two days each year. At the UK Plant Science Federation meeting, I made the case for each scientist to commit 1% of his or her time to outreach.
Funders, politicians, school children and voters all need to hear about the roles of plants and plant science in their lives, and plant scientists are the ones who have to say it!
I had an interesting meeting last week about using online resources and tools for student engagement, and to what extent any classroom activity could be done virtually. Another discussion point was to what extent MOOCs can provide meaningful learning experiences.
This nice video about the JGI's 1000 fungal genome project suggests a good non-traditional lesson plan. In the four minute video, scientists introduce fungal diversity and what can be learned from fungal genomes. I'd use this as a launching-off point to have the class investigate fungal diversity through individual and group projects, with the "goal" being to select a species as a class mascot. (The goal is also to set up a need-to-know environment so students are motivated to read and learn).
Outside of class time, have students individually 1) watch the video, 2) read through the information on the 1000 Fungal genomes project (http://genome.jgi.doe.gov/programs/fungi/1000fungalgenomes.jsf), select a fungal species of interest, and prepare a 3-slide overview about it. (If you like, you can tell them what kinds of information must be included, such where it fits on the fungal tree of life, what kind of interactions it has with plants, and what might be learned from its genome).
Then, in class (or online) groups of students would review the 3-slide summaries of the members, and choose the best-of-group. The group then builds on the summary to produce a 3-minute YouTube video that makes the case for why their species is the most interesting and deserving of recognition. If your class meets physically, you can show the videos during class time, If not, the video viewing and voting can take place online. Finally, the class votes to select the species to adopt, with a small prize going to the winning group.
Posting the videos publically motivates the students to make the content broadly accessible and the overall quality high. Google documents and Dropboxes provide the students with shared workspaces, and by having the groups make these working materials accessible to you, you can assess the contributions of the group members.
If you haven't yet explored the learning opportunities available to students through online resources, I'd encourage you to give it a try.
How and why to use your PhD as leverage to get into teaching. Pros include job security, the joy of teaching, and (somewhat) shorter hours. I'm not sure about the summers off claim - most teachers I know don't really sit by the pool all summer ....
Oh dear! This baby would come in hand for a couple screenings in mind. Shaking, temperature control, multiple readings, injectors... All I might need to test lots of mutants. Don't even want to know the price, but opinions are welcome!!
"Problem solvers, shelf stockers, bench scientists, record keepers, machine fixers, weekend warriors, den mothers, old hands, fresh eyes, mentors, managers. Every research lab has behind-the-scenes specialists without whom modern science could not get done."
Yet another excellent resource from Sense about Science. This one is produced with the help of early career scientists, and written to help early career researchers understand how the peer review process works, some of the limitations of peer review, and the role of peer review in society. Free PDF.
Twin cynomolgus monkeys born in China are the first with mutations in specific target genes. This is an important milestone for targeted gene-editing technology, which in turn promises better models for human diseases.
The ultimate potential of precision gene-editing techniques is beginning to be realized. Today, researchers in China report the first monkeys engineered with targeted mutations1, an achievement that could be a stepping stone to making more realistic research models of human diseases.
Xingxu Huang, a geneticist at the Model Animal Research Center of Nanjing University in China, and his colleagues successfully engineered twin cynomolgus monkeys (Macaca fascicularis) with two targeted mutations using the CRISPR/Cas9 system — a technology that has taken the field of genetic engineering by storm in the past year. Researchers have leveraged the technique to disrupt genes in mice and rats2, 3, but until now none had succeeded in primates.
Transgenic mice have long dominated as models for human diseases, in part because scientists have honed a gene-editing method for the animals that uses homologous recombination — rare, spontaneous DNA-swapping events — to introduce mutations. The strategy works because mice reproduce quickly and in large numbers, but the low rates of homologous recombination make such a method unfeasible in creatures such as monkeys, which reproduce slowly.
"We need some non-human primate models," says Hideyuki Okano, a stem-cell biologist at Keio University in Tokyo. Human neuropsychiatric disorders can be particularly difficult to replicate in the simple nervous systems of mice, he says.
Stem-cell researcher Rudolf Jaenisch of the Whitehead Institute for Biomedical Research in Cambridge, Massachusetts, calls the result an interesting demonstration, but says that it offers little scientific insight. "The next step is to see if we can learn anything from it," says Jaenisch, who pioneered the use of transgenic mice in the 1970s.
The combined mutations in Ppar-γ and Rag1 do not represent a particular disease syndrome, says Huang, although each gene is associated with human disorders.The group has yet to fully analyze the monkeys' condition, and must run further tests to assess whether the mutations occurred in all of the animals' cells."Our first aim was to get it done, to get it to work," Huang says. But the finding suggests that researchers could one day model other human conditions involving multiple mutations.
The race is already on to create more CRISPR-modified monkeys, and with greater reliability. Zhang and his colleagues are working to optimize the technology for primate cells, in order to boost mutation efficiency. Okano's team is analyzing unpublished results from monkey models of autism and immune dysfunction, recently created with older gene-editing technologies; they, too, are now trying their luck with CRISPR. And Huang's group is expecting results from eight other pending pregnancies. "There are a lot more things to do," says Huang.
Scooped from: Science 26 April 2013: Vol. 340 no. 6131 pp. 446-447 DOI: 10.1126/science.340.6131.446
"Scientists may begin their studies with an eye on a tenure-track position, but these coveted jobs have been vanishing under a glut of science degrees and university budget cuts. As researchers found in a 2012 study in the journal PLoS One, jobs in academia become less attractive to U.S. doctoral candidates as they progress through their studies. "
The Harvard School of Engineering and Applied Sciences (SEAS) and the Alícia Foundation developed a General Education science course, "Science and Cooking: From Haute Cuisine to the Science of Soft Matter," which debuted in the fall of 2010. The course uses food and cooking to explicate fundamental principles in applied physics and engineering. (Watch a video about the course.)
Limited to currently enrolled Harvard undergraduates, the class brings together eminent Harvard researchers and world-class chefs.
I recently discovered this amazing PODcast bridging Science and Culinary with a series of scientifically relevant and fun talks. Highly recomended for scientists who enjoy cooking and thinkings out of the box.
Background: RNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.
Findings: When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, respectively identified 11,995 and 11,317 differentially expressed genes from an RNA-seq dataset generated from soybean leaf tissue grown in elevated O3. However, the number of genes in common between these two methods was only 10,535, resulting in 2,242 genes determined to be differentially expressed by only one method. Upon analysis of the non-significant genes, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.
Conclusions: Because of the high variability between methods for determining differential expression of RNA-Seq data, we suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. We also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.
This is a good article for young scientists. Sometimes the young and naive don't get the credit they deserve. Be prepared to argue for the credit you are due, and discuss authorship early in the project.
"Committee on Publication Ethics (COPE) recommends that researchers decide who will be an author and what order they will be listed in before they even conduct experiments, and that the group revisits the author list as a project evolves. A handshake isn't enough to seal the deal — researchers should keep author agreements in writing."
Anxiety buids up as Europe prepares itself to face the US scientific investment. Will we dare? Will we be able to take that step? Hope so...
"Tens of billions of euros are at stake as negotiations ramp up to shape Europe’s next seven-year research programme. The discussions will cover familiar divisions over applied versus basic research and conflicting national agendas, but the continent’s ongoing financial problems will add an extra measure of anxiety"
Good advice for young scientists on why they should get training in bioinformatics or computational biology, from Casey Bergman, at the University of Manchester. How about #5 "you will publish more papers"!
This story is still hard to believe. Nevertheless worth reading
"For a female scientist, particularly talking to a male colleague, if she thinks it's possible he might hold this stereotype, a piece of her mind is spent monitoring the conversation and monitoring what it is she is saying, and wondering whether or not she is saying the right thing, and wondering whether or not she is sounding competent, and wondering whether or not she is confirming the stereotype," Schmader said.
All this worrying is distracting. It uses up brainpower. The worst part?
"By merely worrying about that more, one ends up sounding more incompetent," Schmader said.
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