OK, more updates 2017

Well, the last post was about updates, but it was more about 2016 than 2017. Here a couple graphs for your delight. One before filtering, the other after, with the counts of prokaryotic genomes in each NCBI category as of July 2017.

The four categories are: Complete, Chromosome, Scaffold, and Contigs. My filtering used to include redundant TaxIDs, but I learned that TaxIDs wasn’t a good idea. Now I filter only by strain, substrain, etc, as provided by the NCBI list of features. Not perfect, but I seem to keep most genomes.



Updates 2017

You might already know, but if you didn’t, NCBI changed the organization of its genome database. They used to have a BACTERIA directory containing all the complete genomes (with a few caveats), and a DRAFT_BACTERIA containing, well, draft genomes. Today, the genomes are scattered and organized somewhat taxonomically, so you have to look at some files to figure out if the genomes are drafty or not so drafty. Now they have four categories: Complete, Chromosome, Scaffold, and Contig. I think that’s the order of completeness, though I’m still not sure how Chromosome


Growth of genome data at NCBI

differs from Complete, but I suspect that’s what used to be the caveats (maybe only one replicon, of many, was sequenced). Anyway, last December I finished some BLASTP comparisons of a Complete genomes dataset that I downloaded by August (2016). The dataset contains 4085 complete prokaryotic genomes (I eliminated genomes from the same strains or the same taxid). Updates are thus starting to appear in the data I offer through this web site and my server at Laurier. Check frequently if you need newer data than what you found previously.

Happy new year!

Undergrad theses!

This term I have three students working on their undergrad theses, plus one working on directed studies. I am very proud of these students. Lots of initiative, reading articles, trying the computer (except for one, they hadn’t worked under unix before!), now having lots of success running their commands, and looking at results!

What are they doing? Two of them are working with protein domains in transporter proteins (from the TCDB), one on sorting prokaryotic genomes into taxonomically-coherent groups, one more on the divergence of orthologs and paralogs.


Half Sabbatical 2015!

I spent four great months working with Milton Saier at UCSD. Milton built a very useful database on transporter proteins, The Transporter Classification database (TCDB), and his lab has developed several pieces of software to play and analyze the database looking for such things as homologs that have diverged beyond the limits of detection by common sequence comparison tools. It was my privilege and honor to help Milton’s lab update and improve some of these tools, and develop a couple new ones. The tasks also gave me a lesson about sharing software, no matter how complex or simple.

In any event,  I’m still working on some specific projects that we started during my visit, and feel full of new ideas, for example about detection of protein domains. I expect that these ideas will complement work that’s been going on in my lab on assignment of functions to homologs with highly divergent sequences.

In short, this was a sabbatical as they should be. I learned a lot and got inspiration for new projects that I would have never thought about before this visit.


The whole 2015 Spring/Summer group

lab-photo-2015-reducedHere the whole group in the lab of Computational conSequences during the Spring/Summer of 2015. I’d say that this is the best group ever.

Gustavo leaves today, going back to Michoacán, Mexico after spending his sabbatical here. Julie left a few weeks ago, also back to Michoacán. She might come back for the Spring/Summer 2016.

The only locals are Brigitte, Kissa, Thomas, César and me. Brigitte and Kissa being honorary members who have been in the lab for collaborative reasons, but work for their M.Sc. degrees with other faculty members at Laurier (Michael Suits and Geoff Horsman, respectively).

We’ve been working on phages, plant-growth promoting bacteria, 16S rRNA gene analyses, metabolic annotations, gene neighborhoods, predicting gene functions, and predicting metabolism and transcriptional regulation networks. Lots of fun.

Summer 2015 group

group-2015-reducedThis is [most of] the group in the lab of Computational conSequences this summer. Several visitors from Mexico! Julie, Gustavo, and Ramiro from Michoacán, and Adrián from Mexico City.

What we’re doing?

In no particular order:

  • Julie is working with 16S rRNA genes
  • Gustavo is on sabbatical doing all kinds of reviews and such on plant growth promoting bacteria
  • Ramiro is working on the genome of a plant growth promoting bacteria
  • Adrián is working on Phage
  • Kissa is working with adjacent genes (gene neighborhoods)
  • Harold is working on genome annotations
  • Thomas is working on predicted functions (metabolism and such)
  • César is working on regulatory networks in prokaryotes, and on metagenome annotations

What’s true for E. coli is true of an elephant

The quote by Jacque Monod in the title celebrates our recent publication of an article suggesting that our previous results in Escherichia coli hold true for most other prokaryotes:

  • del Grande, M., & Moreno-Hagelsieb, G. (2014). The loose evolutionary relationships between transcription factors and other gene products across prokaryotes. BMC Research Notes, 7, 928. doi:10.1186/1756-0500-7-928

This article expands on the part about transcriptions factors presented in our previous study comparing the conservation of different kinds of functional associations:

  • Moreno-Hagelsieb, G., & Jokic, P. (2012). The evolutionary dynamics of functional modules and the extraordinary plasticity of regulons: the Escherichia coli perspective. Nucleic Acids Research, 40(15), 7104–7112. doi:10.1093/nar/gks443

The earlier article dealt with several experimentally-confirmed functional interactions determined in Escherichia coli: genes in operons, genes whose products physically interact, genes regulated by the same transcription factor (regulons), and genes coding for transcription factors and their regulated genes. In that study we found that the associations involving transcription factors tend to be much less conserved than any of the other associations studied. Our work is not the first to suggest this lack of conservation, but is the first to compare conservation across different kinds of associations, and thus show that those mediated by transcriptional regulation are the least conserved.

The most recent article was an expansion of the association between genes coding for transcription factors and other genes. The idea being to extend the study towards as many other prokaryotes as possible. But how could we determine conservation between genes coding for transcription factors and other genes without experimentally-determined interactions? We knew that at least some transcription factors could be predicted from their possessing a DNA binding domain. But what about their associations? Our prior experience has been that target genes are hard to predict even when there’s information on some characterized binding sites (sites that we like calling operators for tradition’s sake). So what to do if we have only the transcription factors? Well, to answer that we should first explain how we measured relative evolutionary conservation.

To measure evolutionary conservation we used a measure of co-occurrence called mutual information. For any two genes, the higher the mutual information, the less the observed co-occurrence looks random. Since we obtained mutual information scores for all gene pairs in the genomes we analyzed, we decided that instead of something as hard as predicting operators, and matching them to predicted transcription factors, we could use top scoring gene pairs as representatives of the most conserved interaction between our predicted transcription factors and anything else. This allowed us to compare the most conserved interactions involving transcription factors against the conservation of other interactions. Our findings suggest that interactions involving transcription factors evolve quickly in most-if-not-all of the genomes analyzed.

Please read the articles for more details and information.


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