Bio::Blogs #8
The eighth edition of Bio::Blogs is coming up soon. It is going to be published here on Public Rambling tomorrow night. I almost forgot that this month is shorter than usual :). There are currently 3 submissions sent in. Pick something from your blogs that is bioinformatic related and sent it in (bioblogs at gmail) or leave the link in the comments. Please also let me know if you mind that I create a PDF file including your submission for offline reading. If you don;t have a blog start one and let me know or just send in a link to something that you particularly liked.
Tuesday, February 27, 2007
The future impact of genome synthesis
The synthesis blog pointed to a detailed report discussing the economical importance of impending advances in biological engineering. The study, supported by DOE, DuPont Corporation and The Berkley Nanosciences Nanoengineering Institute tries to cover the main driving forces for biotechnology innovation, it's possible future applications and economical impact. The last chapter is dedicated to envisioning future scenarios for synthetic biology based on different assumptions about important factors that could determine the progress of this technology.
While the scenarios described in the end of the report might be useful to track the speed and mode of evolution of this emerging technology, the most relevant section for life scientists is arguably the one discussing possible applications of genome synthesis.
There are three main applications listed:
Chemicals: Engineering new production pathways and creating new products
Energy: Opening new biological routes for energy transformation
Synthetic Vaccines: Opportunities for rapid-response biosecurity
The best examples of synthetic biology research have consisted up to now mostly of simple toy examples. Usually simple circuits are created and studied to detail but few have obvious immediate practical applications. Are we currently at the inflection point, were synthetic biology research will produce more practical applications or is the complexity of living systems still too large a barrier ?
One example of the use of synthetic biology in chemical production is the work of Dae-Kyun Ro and colleagues in the Keasling lab (free PDF). They re-engineered S. cerevisiae to produce artemisinic acid, a precursor of the malaria drug Artemisinin.
Keasling is also one of the researchers involved in the Helios project. An effort directed at developing technology for solar fuel generation (in the form of biofuel). The project is also headed by Nobel prize laureate Steve Chu that explains the project in this video presentation.
The synthesis blog pointed to a detailed report discussing the economical importance of impending advances in biological engineering. The study, supported by DOE, DuPont Corporation and The Berkley Nanosciences Nanoengineering Institute tries to cover the main driving forces for biotechnology innovation, it's possible future applications and economical impact. The last chapter is dedicated to envisioning future scenarios for synthetic biology based on different assumptions about important factors that could determine the progress of this technology.
While the scenarios described in the end of the report might be useful to track the speed and mode of evolution of this emerging technology, the most relevant section for life scientists is arguably the one discussing possible applications of genome synthesis.
There are three main applications listed:
Chemicals: Engineering new production pathways and creating new products
Energy: Opening new biological routes for energy transformation
Synthetic Vaccines: Opportunities for rapid-response biosecurity
The best examples of synthetic biology research have consisted up to now mostly of simple toy examples. Usually simple circuits are created and studied to detail but few have obvious immediate practical applications. Are we currently at the inflection point, were synthetic biology research will produce more practical applications or is the complexity of living systems still too large a barrier ?One example of the use of synthetic biology in chemical production is the work of Dae-Kyun Ro and colleagues in the Keasling lab (free PDF). They re-engineered S. cerevisiae to produce artemisinic acid, a precursor of the malaria drug Artemisinin.
Keasling is also one of the researchers involved in the Helios project. An effort directed at developing technology for solar fuel generation (in the form of biofuel). The project is also headed by Nobel prize laureate Steve Chu that explains the project in this video presentation.
Friday, February 23, 2007
Traveling around (Boston, San Francisco)
I have been traveling during the past week. I have been in Boston and I am now in San Francisco (labs: Marc Vidal, Wendel Lim, Adam Arkin).It would be interesting to be able to talk about some of the nice projects I have heard about but I guess it is really not up to me to make this public. Some of it is on their webpages. That leaves very little to say about the trip in respect to science. So instead, here is a picture I took in San Francisco :)
I am looking for a place to start a postdoc after the summer time. Even if I don't move to the states it is very unlikely that I will stay in Germany. This will be my 3rd country and 7th city. It is funny that so many of the grants that are currently available in Europe for postdocs are incentives to increase mobility. Isn't it time to also create some incentives to settling down ? I am 28 and this will be my first postdoc but eventually I will get tired of moving around. I hope by then it will be easier to stay.
I have been traveling during the past week. I have been in Boston and I am now in San Francisco (labs: Marc Vidal, Wendel Lim, Adam Arkin).It would be interesting to be able to talk about some of the nice projects I have heard about but I guess it is really not up to me to make this public. Some of it is on their webpages. That leaves very little to say about the trip in respect to science. So instead, here is a picture I took in San Francisco :)
I am looking for a place to start a postdoc after the summer time. Even if I don't move to the states it is very unlikely that I will stay in Germany. This will be my 3rd country and 7th city. It is funny that so many of the grants that are currently available in Europe for postdocs are incentives to increase mobility. Isn't it time to also create some incentives to settling down ? I am 28 and this will be my first postdoc but eventually I will get tired of moving around. I hope by then it will be easier to stay.
Wednesday, February 07, 2007
in sillico network reconstruction (using expression data)
In my last post I commented on a paper that tried to find the best mathematical model for a cellular pathway. In that paper they used information on known and predicted protein interactions. This time I want to mention a paper, published in Nature Mol. Systems Biology, that attempts to reconstruct gene regulatory networks from gene expression data and Chip-chip data.
The authors were interested in determining how/when transcription factors regulate their target genes over time. One novelty introduced in this work was the focus on bifurcation events in gene expression. They tried to look for cases where a groups of genes clearly bifurcated into two groups at a particular time point. Combining these patterns of bifurcation with experimental binding data for transcription factors they tried to predict what transcription factors regulate these group of genes. There is a simple example shown in figure 1, reproduced below.
In this toy example there is a bifurcation event at 1 h and another at the 2h time point. All of the genes are assigned to a gene expression path. In this case, the red genes are those that are very likely to show a down regulation in between the 1st and 2nd hour and stay at the same level of expression from then on. Once the genes have been assigned it is possible to search for transcription factors that are significantly associate to each gene expression path. For example in this case, TF A is strongly associated to the pink trajectory. This means that many of the genes in the pink group have a known binding site for TF A in their promoter region.
To test their approach, the authors studied the amino-acid starvation in S. cerevisiae. In figure 2 they summarize the reconstructed dynamic map. The result is the association of TFs to groups of genes and the changes in expression of these genes over time during amino acid starvation.
One interesting finding from this map was that Ino4 activates a group of genes related to lipid metabolism starting at the 2h time point. Since Ino4 binding sites had only been profiled by Chip-chip in YPD media and not in a.a. starvation, this is a novel result obtained using their method.
To further test the significance of their observation they performed Chip-chip assays of Ino4 in amino acid starvation. They confirmed that Ino4 binds many more promoters during amino acid starvation as compared to synthetic complete glucose media. Out of 207 genes bound by Ino4 (specifically during AA starvation) 34 were also among the genes assigned to the Ino4 gene path obtained from their approach.
This results confirmed the usefulness of this computational approach to reconstruct gene regulatory networks from gene expression data and TF binding site information.
The authors then go on to study the regulation of other conditions.
For anyone curious enough about the method, this was done using Hidden Markov Models (see here for available primer on HMMs).
In my last post I commented on a paper that tried to find the best mathematical model for a cellular pathway. In that paper they used information on known and predicted protein interactions. This time I want to mention a paper, published in Nature Mol. Systems Biology, that attempts to reconstruct gene regulatory networks from gene expression data and Chip-chip data.
The authors were interested in determining how/when transcription factors regulate their target genes over time. One novelty introduced in this work was the focus on bifurcation events in gene expression. They tried to look for cases where a groups of genes clearly bifurcated into two groups at a particular time point. Combining these patterns of bifurcation with experimental binding data for transcription factors they tried to predict what transcription factors regulate these group of genes. There is a simple example shown in figure 1, reproduced below.
In this toy example there is a bifurcation event at 1 h and another at the 2h time point. All of the genes are assigned to a gene expression path. In this case, the red genes are those that are very likely to show a down regulation in between the 1st and 2nd hour and stay at the same level of expression from then on. Once the genes have been assigned it is possible to search for transcription factors that are significantly associate to each gene expression path. For example in this case, TF A is strongly associated to the pink trajectory. This means that many of the genes in the pink group have a known binding site for TF A in their promoter region.
To test their approach, the authors studied the amino-acid starvation in S. cerevisiae. In figure 2 they summarize the reconstructed dynamic map. The result is the association of TFs to groups of genes and the changes in expression of these genes over time during amino acid starvation.
One interesting finding from this map was that Ino4 activates a group of genes related to lipid metabolism starting at the 2h time point. Since Ino4 binding sites had only been profiled by Chip-chip in YPD media and not in a.a. starvation, this is a novel result obtained using their method.
To further test the significance of their observation they performed Chip-chip assays of Ino4 in amino acid starvation. They confirmed that Ino4 binds many more promoters during amino acid starvation as compared to synthetic complete glucose media. Out of 207 genes bound by Ino4 (specifically during AA starvation) 34 were also among the genes assigned to the Ino4 gene path obtained from their approach.
This results confirmed the usefulness of this computational approach to reconstruct gene regulatory networks from gene expression data and TF binding site information.
The authors then go on to study the regulation of other conditions.
For anyone curious enough about the method, this was done using Hidden Markov Models (see here for available primer on HMMs).
Tuesday, February 06, 2007
In silico network reconstruction
It is day one of Just Science week and I want to tell you about a recent paper that was published in BMC Systems Biology by Rui Alves and Albert Sorribas. It is about a general approach to integrate information to come up with models for cellular pathways. What does this mean and why is this important ?
Increasingly the scientific knowledge is being stored in databases (literature, protein structures, gene expression, protein-protein interactions, protein-DNA interactions, etc). The general idea behind the work described is that we should be able to use the accumulated information about cellular pathways to extract models of how the cell's components interact to preform their functions. By models I mean a formal representation that can tell us how the components' concentrations and activities change with time.
There are several works already dealing with this problem of trying to reconstruct cellular networks from large data sources but I found this article particularly interesting because it uses so many of these methods.
To give you an idea I reproduce below figure 4 of the paper with a diagram of the method (click to zoom in):
The authors have pulled in experimentally known interactions and combined them with putative interactions obtained from docking and phylogenetic based predictions. These predicted networks are then converted to several possible mathematical models that are examined under different parameter conditions and compared with known experimental values.
This method should be particularly suited for a case when some of the genes in the pathway are known and there are experimental measured outputs for the pathway that can be compared with the predictions from the putative pathway models.
Ideally this whole procedure would be fully converted into an automatic pipeline that could be used by people that are not so familiar with the tools.
I will try to stick with the same theme during the week, hopefully covering different methods to achieve the same thing.
It is day one of Just Science week and I want to tell you about a recent paper that was published in BMC Systems Biology by Rui Alves and Albert Sorribas. It is about a general approach to integrate information to come up with models for cellular pathways. What does this mean and why is this important ?
Increasingly the scientific knowledge is being stored in databases (literature, protein structures, gene expression, protein-protein interactions, protein-DNA interactions, etc). The general idea behind the work described is that we should be able to use the accumulated information about cellular pathways to extract models of how the cell's components interact to preform their functions. By models I mean a formal representation that can tell us how the components' concentrations and activities change with time.
There are several works already dealing with this problem of trying to reconstruct cellular networks from large data sources but I found this article particularly interesting because it uses so many of these methods.
To give you an idea I reproduce below figure 4 of the paper with a diagram of the method (click to zoom in):
The authors have pulled in experimentally known interactions and combined them with putative interactions obtained from docking and phylogenetic based predictions. These predicted networks are then converted to several possible mathematical models that are examined under different parameter conditions and compared with known experimental values.
This method should be particularly suited for a case when some of the genes in the pathway are known and there are experimental measured outputs for the pathway that can be compared with the predictions from the putative pathway models.
Ideally this whole procedure would be fully converted into an automatic pipeline that could be used by people that are not so familiar with the tools.
I will try to stick with the same theme during the week, hopefully covering different methods to achieve the same thing.
Sunday, February 04, 2007
Publishing greasemonkey scripts (update)
A while ago I asked if greasemonkey scripts should be published in peer reviewed journals or if blogs could be a more suitable way of distributing these tools. The blog post was triggered by the publication of iHOPerator, mentioned also by Deepak.
I would like to thank one of the authors, Benjamin Good, and a BMC editor, Matt Hodgkinson, for taking the time to post their opinion in the comments. In summary they both argue that this publication helps raise awareness to greasemonkey and related technologies.
For me this exchange in the comments exemplifies the usefulness of the web for discussing science. The comments on this paper are aggregated in this Postgenomic entry and anyone could, in principle, participate no matter where they are.
This also reminded me of a discussion I had with someone here at EMBL recently. If web based discussions like this take off then authors might have a higher work load in trying to keep up with what is being said about their works. If a misinterpretation occurs it has a higher potential for spreading online. On the other hand, these sorts of web discussion help to level the playing field for manuscripts. In the near future it might not matter so much were the paper is published but if attracted the attention of the people in the field.
A while ago I asked if greasemonkey scripts should be published in peer reviewed journals or if blogs could be a more suitable way of distributing these tools. The blog post was triggered by the publication of iHOPerator, mentioned also by Deepak.
I would like to thank one of the authors, Benjamin Good, and a BMC editor, Matt Hodgkinson, for taking the time to post their opinion in the comments. In summary they both argue that this publication helps raise awareness to greasemonkey and related technologies.
For me this exchange in the comments exemplifies the usefulness of the web for discussing science. The comments on this paper are aggregated in this Postgenomic entry and anyone could, in principle, participate no matter where they are.
This also reminded me of a discussion I had with someone here at EMBL recently. If web based discussions like this take off then authors might have a higher work load in trying to keep up with what is being said about their works. If a misinterpretation occurs it has a higher potential for spreading online. On the other hand, these sorts of web discussion help to level the playing field for manuscripts. In the near future it might not matter so much were the paper is published but if attracted the attention of the people in the field.
Friday, February 02, 2007
Just Science
(Via RPM, Razib, Chris, Arunn) Next week is Just Science week. I will try to review recent papers on cellular networks, systems/synthetic biology and evolution that I found interesting.
(Via RPM, Razib, Chris, Arunn) Next week is Just Science week. I will try to review recent papers on cellular networks, systems/synthetic biology and evolution that I found interesting.
Bio::Blogs #7
The February edition of Bio::Blogs was just published in BioHacking. Thanks to Paras for editing it. He highlighted some blogs related to synthetic biology and some of the recent bioinformatics posts from Neil, Sandra and Pierre.
The 8th edition will be coming back here. The participation has been generally going down so Bio::Blogs might, in the near future, morph to something else.
Just to give it a little twist I thought that it could be interesting to add a PDF version of Bio::Blogs (with the permission of the authors of course). So, for the next month entries I will be asking if the authors concede that the blog posts be compiled into a printable document.
Entries can be submitted until the end of February to bioblogs at gmail.
The February edition of Bio::Blogs was just published in BioHacking. Thanks to Paras for editing it. He highlighted some blogs related to synthetic biology and some of the recent bioinformatics posts from Neil, Sandra and Pierre.
The 8th edition will be coming back here. The participation has been generally going down so Bio::Blogs might, in the near future, morph to something else.
Just to give it a little twist I thought that it could be interesting to add a PDF version of Bio::Blogs (with the permission of the authors of course). So, for the next month entries I will be asking if the authors concede that the blog posts be compiled into a printable document.
Entries can be submitted until the end of February to bioblogs at gmail.
Monday, January 29, 2007
Bio::Blogs #7 final call
As Deepak mentioned in his blog, the February edition of Bio::Blogs (the bioinformatics blog journal) is due soon. It will be up on the 2nd of February at BioHacking, so get your blogging pens working. It has been 2 months since the last call. Go have a look at the past two months, pick up one of your posts or a post you liked, related to bioinformatics and send it to the usual bioblogs at gmail.
The 8th edition will be back here on this blog. If anyone is interested in hosting future editions let me know in the comments or by email and I can make a list (including previous editors :).
Let's see how many posts highlighted in Bio::Blogs make it to the next years Science Blogging Anthology ;).
As Deepak mentioned in his blog, the February edition of Bio::Blogs (the bioinformatics blog journal) is due soon. It will be up on the 2nd of February at BioHacking, so get your blogging pens working. It has been 2 months since the last call. Go have a look at the past two months, pick up one of your posts or a post you liked, related to bioinformatics and send it to the usual bioblogs at gmail.
The 8th edition will be back here on this blog. If anyone is interested in hosting future editions let me know in the comments or by email and I can make a list (including previous editors :).
Let's see how many posts highlighted in Bio::Blogs make it to the next years Science Blogging Anthology ;).
Friday, January 26, 2007
Not so silent mutations
DNA mutations that do not change the coding amino-acid are many times referred to as "silent mutations", or synonymous mutations, because it is less likely that they will result in a change in function. Synonymous mutations are often considered to be evolutionary neutral and the ratio of non-synonymous substitutions (Ka) to synonymous substitutions (Ks) is used to study sequence evolution. It can be used for example to search for DNA regions targeted by selection (see review and a practical application).
In the last issue of Science Kimchi-Sarfaty and colleagues found a synonymous mutation in a transport protein that has an effect on the protein function. They have shown, at least in cell-lines, that the mutation does not affect mRNA levels nor the produced protein sequence. Finally the authors showed that the mutation might change the protein's conformation by comparing the sensibility of wild type and mutated sequence to trypsin digestion.
The authors speculate that the usage of that particular codon, even if not affecting the coding region, might change the translation rate and folding of the protein. It had already been shown in E. coli that synonymous mutations can affect the in vivo folding of a protein. Here the authors have shown a case where a silent mutation can change the substrate specificity of a transporter.
Because of these codon preferences it is important to adjust for codon selection pressures when studying synonymous substitutions. The codon preferences are usually considered to be due to differences in the pool of the cognate tRNA but other studies have shown that codon bias might arise also by codon context. In E. coli, codon pair preferences, were observed to affect their in vivo translation. Also, these codon pair preferences are species specific and are, at least in part, influenced by nucleotide positions within A-site tRNA sequences.
Hypothesis: If codon pairs can be selected due to tRNA structural constrains on the ribosome P and A sites then it might be necessary to correct for these codon preferences when studying synonymous mutations.
DNA mutations that do not change the coding amino-acid are many times referred to as "silent mutations", or synonymous mutations, because it is less likely that they will result in a change in function. Synonymous mutations are often considered to be evolutionary neutral and the ratio of non-synonymous substitutions (Ka) to synonymous substitutions (Ks) is used to study sequence evolution. It can be used for example to search for DNA regions targeted by selection (see review and a practical application).
In the last issue of Science Kimchi-Sarfaty and colleagues found a synonymous mutation in a transport protein that has an effect on the protein function. They have shown, at least in cell-lines, that the mutation does not affect mRNA levels nor the produced protein sequence. Finally the authors showed that the mutation might change the protein's conformation by comparing the sensibility of wild type and mutated sequence to trypsin digestion.
The authors speculate that the usage of that particular codon, even if not affecting the coding region, might change the translation rate and folding of the protein. It had already been shown in E. coli that synonymous mutations can affect the in vivo folding of a protein. Here the authors have shown a case where a silent mutation can change the substrate specificity of a transporter.
Because of these codon preferences it is important to adjust for codon selection pressures when studying synonymous substitutions. The codon preferences are usually considered to be due to differences in the pool of the cognate tRNA but other studies have shown that codon bias might arise also by codon context. In E. coli, codon pair preferences, were observed to affect their in vivo translation. Also, these codon pair preferences are species specific and are, at least in part, influenced by nucleotide positions within A-site tRNA sequences.
Hypothesis: If codon pairs can be selected due to tRNA structural constrains on the ribosome P and A sites then it might be necessary to correct for these codon preferences when studying synonymous mutations.
Tuesday, January 23, 2007
System Biology quick links
(via Pierre) BMC System Biology has published their first papers. More or less at the same time the new Systems and Synthetic Biology (published by Springer Netherlands) has started publishing papers. These two journals join IEE Systems Biology and Molecular Systems Biology (Nature/EMBO) as forums to publish works on Systems and Synthetic Biology. All journals (with the exception of IEE Systems Biology) publish in open access or at least (in the case of Systems and Synthetic Biology) offer an open access option.
Some of the talks from the BioSysBio conference are online in Goggle Video.
Here is a nice talk from Alfonso Valencia talking about species co-evolution and a very promising improvement to a sequence based method to predict protein-protein interactions:
(via Pierre) BMC System Biology has published their first papers. More or less at the same time the new Systems and Synthetic Biology (published by Springer Netherlands) has started publishing papers. These two journals join IEE Systems Biology and Molecular Systems Biology (Nature/EMBO) as forums to publish works on Systems and Synthetic Biology. All journals (with the exception of IEE Systems Biology) publish in open access or at least (in the case of Systems and Synthetic Biology) offer an open access option.
Some of the talks from the BioSysBio conference are online in Goggle Video.
Here is a nice talk from Alfonso Valencia talking about species co-evolution and a very promising improvement to a sequence based method to predict protein-protein interactions:
Monday, January 22, 2007
Social gene annotation in Connotea
There has been a lot of excitement over the recent web technological developments. Time magazine has recognized this by announcing that instead of profiling an individual in their annual issue of Person of Year they decided to select You as the most influential group of last year. This "you" refers to everyone that is out there on the web building, interacting, blogging, uploading their videos and pictures for the world to see. As with almost every rising meme, the backlash is inevitable. Some see this web euphoria as little more than global narcissism.
This social web holds some powerful promises of more efficient collaboration but clear examples might still be lacking. Scientists, given our need to communicate and collaborate, are a group of individuals that could do more to take advantage of these tools. Unfortunately we seem to be too unaware and too slow to pick them up.
I have shown before that the accumulating body of knowledge in Connotea, in the form of simple tagging of science papers, can in principle be used to highlight papers of higher impact.
I tough that it could also be possible to mine Connotea to retrieve gene annotations. I tested if manuscripts tagged as "cell-cycle" and "yeast" would contain, in their abstracts, mostly genes names related to cell cycle in yeast. There are currently 38 papers in Connotea tagged as cell-cycle and yeast with an associated Pubmed ID. I used a dictionary of S. cerevisiae gene names obtained from SGD and retrieved the abstracts for the 38 manuscripts using eUtils.
Within these abstracts there were 38 gene names associated by a simple pattern match. To evaluate the performance of this social gene annotation I took from the SGD's slim GO mapping the function and processes associated to these genes. I also included the gene description from gene name registry.
Table 1 - Known GO process/function annotations and gene function description associated to the genes predicted to participate in cell-cycle in yeast by social annotations.
From the 38 genes, 14 (~37%) are annotated in the slim GO annotation as participating in cell-cycle,meiosis or cytokinesis. From the remaining, 15 (39%) have a described function associated to the cell-cycle (ex. G1 cyclin involved in cell cycle progression, expression restricted to mother cells in late G1 as controlled by Swi4p-Swi6p, Swi5p and Ash1p,etc). In total roughly 76% of the gene names obtained are associated to cell-cycle in S. cerevisiae.
This simple test highlights the potential usefulness of social bookmarking of science papers. However it was limited to a very specific field and to a very small number of annotated manuscripts. Hopefully someone can come up with a better way of testing this :).
There has been a lot of excitement over the recent web technological developments. Time magazine has recognized this by announcing that instead of profiling an individual in their annual issue of Person of Year they decided to select You as the most influential group of last year. This "you" refers to everyone that is out there on the web building, interacting, blogging, uploading their videos and pictures for the world to see. As with almost every rising meme, the backlash is inevitable. Some see this web euphoria as little more than global narcissism.
This social web holds some powerful promises of more efficient collaboration but clear examples might still be lacking. Scientists, given our need to communicate and collaborate, are a group of individuals that could do more to take advantage of these tools. Unfortunately we seem to be too unaware and too slow to pick them up.
I have shown before that the accumulating body of knowledge in Connotea, in the form of simple tagging of science papers, can in principle be used to highlight papers of higher impact.
I tough that it could also be possible to mine Connotea to retrieve gene annotations. I tested if manuscripts tagged as "cell-cycle" and "yeast" would contain, in their abstracts, mostly genes names related to cell cycle in yeast. There are currently 38 papers in Connotea tagged as cell-cycle and yeast with an associated Pubmed ID. I used a dictionary of S. cerevisiae gene names obtained from SGD and retrieved the abstracts for the 38 manuscripts using eUtils.
Within these abstracts there were 38 gene names associated by a simple pattern match. To evaluate the performance of this social gene annotation I took from the SGD's slim GO mapping the function and processes associated to these genes. I also included the gene description from gene name registry.
Table 1 - Known GO process/function annotations and gene function description associated to the genes predicted to participate in cell-cycle in yeast by social annotations.
From the 38 genes, 14 (~37%) are annotated in the slim GO annotation as participating in cell-cycle,meiosis or cytokinesis. From the remaining, 15 (39%) have a described function associated to the cell-cycle (ex. G1 cyclin involved in cell cycle progression, expression restricted to mother cells in late G1 as controlled by Swi4p-Swi6p, Swi5p and Ash1p,etc). In total roughly 76% of the gene names obtained are associated to cell-cycle in S. cerevisiae.
This simple test highlights the potential usefulness of social bookmarking of science papers. However it was limited to a very specific field and to a very small number of annotated manuscripts. Hopefully someone can come up with a better way of testing this :).
Thursday, January 18, 2007
Petition for guaranteed public access
(via PLoS publishing blog):
"A group of European organisations - JISC (Joint Information Systems Committee, UK), SURF (Netherlands), SPARC Europe, DFG (Deutsches Forschungsgemeinschaft, Germany), DEFF (Denmark's Electronic Research Library) - have posted a petition to encourage the EC to formally endorse the open access recommendations."
This petition recommends that "any potential 'embargo' on free access should be set at no more than six months following publication" for any EC funded research.
Have a look and sign the petition if you are for it.
(via PLoS publishing blog):
"A group of European organisations - JISC (Joint Information Systems Committee, UK), SURF (Netherlands), SPARC Europe, DFG (Deutsches Forschungsgemeinschaft, Germany), DEFF (Denmark's Electronic Research Library) - have posted a petition to encourage the EC to formally endorse the open access recommendations."
This petition recommends that "any potential 'embargo' on free access should be set at no more than six months following publication" for any EC funded research.
Have a look and sign the petition if you are for it.
Sunday, January 14, 2007
Bio::Blogs# 7 and some quick links
The bioinformatics blog journal Bio::Blogs will have it's 7th edition on the 1st of February. We skipped the January edition because of the holidays. It will be hosted by Paras Chopra on BioHacking blog. Anyone can submit the link to their posts on paras1987 {at} gmail or bioblogs {at} gmail until the end of this month.
Some quick links:
Paras released the source code of a Python program for protein structure prediction.
(via Gerstein' blog) Yale university has a podcast. I wish I could convince EMBL's press office to start blogging and/or a podcast.
(via Deepak) The Science Commons blog announced that the three journals published by EMBO and NPG (EMBO reports, EMBO journal and Molecular Systems Biolgoy) will soon start publishing with a creative commons license. More information on the subject can be found in the EMBO site. In the case of Molecular Systems Biology all articles are published in open access but for EMBO Journal and EMBO reports it looks like the author will decide if they wish to pay an extra fee (2000 euros) to publish in open access. Only the articles published in open access will be published with the creative commons license. Adopting the creative commons license will make re-using their papers much easier, hopefully increasing the usefulness of their content.
(disclaimer: I am currently working for Molecular Systems Biology. All opinions expressed in this blog are my own)
Speaking of re-using content. Alf has set up a mirror site for PLoS One. He called it PLoS Too :) and he is using it to try out some ideas on layout, microformats and features like rating. This is one funny thing about the creative commons license. As long as you give credit to the source you are free to re-use the content. Nothing stops a group of people from setting up a new journal, based on those that are published in creative commons, with a different editorial line. For this particular license you can even try to make some money from re-using the content :).
The bioinformatics blog journal Bio::Blogs will have it's 7th edition on the 1st of February. We skipped the January edition because of the holidays. It will be hosted by Paras Chopra on BioHacking blog. Anyone can submit the link to their posts on paras1987 {at} gmail or bioblogs {at} gmail until the end of this month.
Some quick links:
Paras released the source code of a Python program for protein structure prediction.
(via Gerstein' blog) Yale university has a podcast. I wish I could convince EMBL's press office to start blogging and/or a podcast.
(via Deepak) The Science Commons blog announced that the three journals published by EMBO and NPG (EMBO reports, EMBO journal and Molecular Systems Biolgoy) will soon start publishing with a creative commons license. More information on the subject can be found in the EMBO site. In the case of Molecular Systems Biology all articles are published in open access but for EMBO Journal and EMBO reports it looks like the author will decide if they wish to pay an extra fee (2000 euros) to publish in open access. Only the articles published in open access will be published with the creative commons license. Adopting the creative commons license will make re-using their papers much easier, hopefully increasing the usefulness of their content.
(disclaimer: I am currently working for Molecular Systems Biology. All opinions expressed in this blog are my own)
Speaking of re-using content. Alf has set up a mirror site for PLoS One. He called it PLoS Too :) and he is using it to try out some ideas on layout, microformats and features like rating. This is one funny thing about the creative commons license. As long as you give credit to the source you are free to re-use the content. Nothing stops a group of people from setting up a new journal, based on those that are published in creative commons, with a different editorial line. For this particular license you can even try to make some money from re-using the content :).
Thursday, January 11, 2007
Scientific Journals blog
Blogs have been around for some time. From the wikepedia:
Blogs in science, on the other hand, have only recently become popular. The first two traditional science news journals to pick up the raising interest in scientific blogging were The-Scientist in August 2005, and then Nature in December 2005. Since then many more scientist have picked up blogging for a variety of purposes (see review by Coturnix). The science journals have been slowly reacting. Here is a current list of blogs from science journal blogs or publishing groups. If anyone knows more please leave a comment and I will add them to the list.
Blogs have been around for some time. From the wikepedia:
The term "weblog" was coined by Jorn Barger on 17 December 1997. The short form, "blog," was coined by Peter Merholz, who jokingly broke the word weblog into the phrase we blog in the sidebar of his blog Peterme.com in April or May of 1999.
Blogs in science, on the other hand, have only recently become popular. The first two traditional science news journals to pick up the raising interest in scientific blogging were The-Scientist in August 2005, and then Nature in December 2005. Since then many more scientist have picked up blogging for a variety of purposes (see review by Coturnix). The science journals have been slowly reacting. Here is a current list of blogs from science journal blogs or publishing groups. If anyone knows more please leave a comment and I will add them to the list.
| Journal | Blog |
| The Scientist | |
| Scientific American | |
| Nature publishing group | |
| Nature Journals | |
| Nature Genetics | |
| Nature Neuroscience | |
| Nature Methods | |
| Nature Medicine | |
| Nature News | |
| Chemistry at Nature (portal not journal) | |
| Nature publishing group | |
| Nature publishing group | |
| Nature publishing group | |
| Heredity | |
| Public Library of Science | |
| PLoS Blogs | |
| PLoS publishing | |
| PLoS Technology | |
| PLoS Medicine | |
| The Lancet | |
| Science | The Weblog of Science Magazine"s (stopped) |
Wednesday, January 10, 2007
Science Blogging Anthology 2006
I mentioned before that Coturnix was getting ready a list of blog posts that would go into a science blogging anthology of 2006. Twelve judges have selected 50 blog posts that will be put together in a book. The book will then be published by lulu. The judges were nice enough to select one of my posts to go in the book :)
Opening up the scientific process
I mentioned before that Coturnix was getting ready a list of blog posts that would go into a science blogging anthology of 2006. Twelve judges have selected 50 blog posts that will be put together in a book. The book will then be published by lulu. The judges were nice enough to select one of my posts to go in the book :)
Opening up the scientific process
Saturday, January 06, 2007
Science Blogging Anthology
Coturnix from a Blog Around the Clock is organizing a science blogging anthology. I missed it during the Christmas holidays but the results are due in a couple of days. Here is the list of posts that got nominated and are now being evaluated. One of my posts made the nomination list :) cool.
Last month Roland Krause said that this type of vanity posts (like blog carnivals) are similar to spam blogs. I actually think that there is value in carnivals and other equivalent content promotion activities. They create a cheap reward system that motivates people to produce more and better content. They also provide with a layer of quality rating even if, in the case of carnivals, the posts that are submitted are self contributed. Bloggers tend to submit their best content to the carnivals.
On a related note but with a very different opinion, here is a rant on Web 2.0 And Narcissism (via Rough Type):
Coturnix from a Blog Around the Clock is organizing a science blogging anthology. I missed it during the Christmas holidays but the results are due in a couple of days. Here is the list of posts that got nominated and are now being evaluated. One of my posts made the nomination list :) cool.
Last month Roland Krause said that this type of vanity posts (like blog carnivals) are similar to spam blogs. I actually think that there is value in carnivals and other equivalent content promotion activities. They create a cheap reward system that motivates people to produce more and better content. They also provide with a layer of quality rating even if, in the case of carnivals, the posts that are submitted are self contributed. Bloggers tend to submit their best content to the carnivals.
On a related note but with a very different opinion, here is a rant on Web 2.0 And Narcissism (via Rough Type):
"What he's getting at is that this whole Web 2.0, social networking, virtual community business is essentially a pornography of the self—a projected, fictionalized self that is then worshipped by the slightly less-perfect self."
Thursday, January 04, 2007
Specificity and Evolvability in Protein Interaction Networks
I finally have the opportunity to blog about some of what I have worked on during last year. It has been published in PLoS Computational Biology and is freely available here in the early online release format (still in the original ugly format :). One way to use our blogs may be to add some depth to the papers that we published. Something like the extras we get when we buy the DVD of a movie ;)
Main conclusions
- Protein interactions can change at a fast rate of 1E-5 interactions per protein pair per million years
- Binding specificity is a strong determining of binding specificity with more promiscuous binding proteins having a higher rate of change of interactions.
- Human proteins involved in immune response, transport and establishment of localization, show signs of positive selection for change of interactions.
The making of
We had been using comparative genomics to search for conserved putative protein binding sites. These very conserved putative target sites were very likely to be experimentally known target sites but many other known binding sites seamed not to be so conserved. This was what got us started thinking about the evolution of these protein interactions and what might determine the rate at which interactions are gained and lost during evolution. The analysis was mostly inspired on the nice work of Andreas Wagner that first proposed a rate for the addition of new interactions in S. cerevisiae. We have tried to build on this by analyzing different species and determining also what protein properties might determine the rate at which interactions are gained and lost in evolution.
More than nodes and edges
One of the main conclusions from this work was that binding specificity also determines the rate of change of interactions during evolution. More promiscuous binding proteins not only have many binding partners but they also tend to change partners faster during evolution. To establish a proxy for binding specificity we have used structural information from the iPFAM database. In essence we have considered that protein domains that have been seen in contact with many different other domains would be more promiscuous. In general we observed that proteins containing these promiscuous domains had a high rate of change of interactions (see figure below).
This highlights something that I had stressed before, that it is important to consider protein interaction networks as more than nodes and edges. Another recent paper has also shown that it is possible and useful to use the accumulating structural information available in the PDB to obtain a more accurate representation of protein interaction networks. Philip M. Kim and co workers from the Gerstein lab (blog, webpage) published a study in Science were they have used also the iPFAM database to curate all the S. cerevisie interactions and to discriminate between interactions that use the same or different binding interfaces. With this information the authors distinguish between hubs that tend to interact with their partners mostly trough one interface or trough many interfaces. They have shown that the multi interface hubs are more restricted in evolution and more likely to be essential than single interface hubs. They have a website with presentations and additional data for this paper.
In the pipeline
To be submitted soon (hopefully), are some collaborations on how to use structural information to predict protein-protein binding specificity. With these collaborations I finish my thesis (still waiting for the defense). During the next couple of months I am off to search for a lab to work as a postdoc.
I finally have the opportunity to blog about some of what I have worked on during last year. It has been published in PLoS Computational Biology and is freely available here in the early online release format (still in the original ugly format :). One way to use our blogs may be to add some depth to the papers that we published. Something like the extras we get when we buy the DVD of a movie ;)
Main conclusions
- Protein interactions can change at a fast rate of 1E-5 interactions per protein pair per million years
- Binding specificity is a strong determining of binding specificity with more promiscuous binding proteins having a higher rate of change of interactions.
- Human proteins involved in immune response, transport and establishment of localization, show signs of positive selection for change of interactions.
The making of
We had been using comparative genomics to search for conserved putative protein binding sites. These very conserved putative target sites were very likely to be experimentally known target sites but many other known binding sites seamed not to be so conserved. This was what got us started thinking about the evolution of these protein interactions and what might determine the rate at which interactions are gained and lost during evolution. The analysis was mostly inspired on the nice work of Andreas Wagner that first proposed a rate for the addition of new interactions in S. cerevisiae. We have tried to build on this by analyzing different species and determining also what protein properties might determine the rate at which interactions are gained and lost in evolution.
More than nodes and edges
One of the main conclusions from this work was that binding specificity also determines the rate of change of interactions during evolution. More promiscuous binding proteins not only have many binding partners but they also tend to change partners faster during evolution. To establish a proxy for binding specificity we have used structural information from the iPFAM database. In essence we have considered that protein domains that have been seen in contact with many different other domains would be more promiscuous. In general we observed that proteins containing these promiscuous domains had a high rate of change of interactions (see figure below).
This highlights something that I had stressed before, that it is important to consider protein interaction networks as more than nodes and edges. Another recent paper has also shown that it is possible and useful to use the accumulating structural information available in the PDB to obtain a more accurate representation of protein interaction networks. Philip M. Kim and co workers from the Gerstein lab (blog, webpage) published a study in Science were they have used also the iPFAM database to curate all the S. cerevisie interactions and to discriminate between interactions that use the same or different binding interfaces. With this information the authors distinguish between hubs that tend to interact with their partners mostly trough one interface or trough many interfaces. They have shown that the multi interface hubs are more restricted in evolution and more likely to be essential than single interface hubs. They have a website with presentations and additional data for this paper.
In the pipeline
To be submitted soon (hopefully), are some collaborations on how to use structural information to predict protein-protein binding specificity. With these collaborations I finish my thesis (still waiting for the defense). During the next couple of months I am off to search for a lab to work as a postdoc.
Tags:
Monday, December 25, 2006
Publish your GreaseMonkey scripts
I knew it was possible to use GreaseMonkey scripts to change webpages to better suit our needs. I tried it once to get blog comments about scientific papers to show up in journal websites. Pierre and Stew have been creating several interesting scripts for postgenomic and connotea. What I did not know was that one could actually publish these scripts. I am all for publishing of smaller and finner grained scientific content but I have to say that this paper seemed like little more than a big blog post. Should we try to publish this type of work ?
Anyway :) ho ho ho , merry xmas everyone
I knew it was possible to use GreaseMonkey scripts to change webpages to better suit our needs. I tried it once to get blog comments about scientific papers to show up in journal websites. Pierre and Stew have been creating several interesting scripts for postgenomic and connotea. What I did not know was that one could actually publish these scripts. I am all for publishing of smaller and finner grained scientific content but I have to say that this paper seemed like little more than a big blog post. Should we try to publish this type of work ?
Anyway :) ho ho ho , merry xmas everyone
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