JW numbers in the Keio

Hirotada Mori is one of the top five names* in E. coli genomics as his group built the Keio and the ASKA collections. Yet the strains from the Keio and ASKA do not start with HM, but JW. Here is why...

Promiscuously hitchhiking on a pathway

In the seminal 1976 paper on enzyme evolution Roy Jensen first pointed out that the TCA cycle, the ketoadipate route to lysine, pantothenate, isoleucine, valine and leucine biosynthesis all operated via the same mechanistic steps (condensation with an acyl-CoA, rearrangement, oxidation and elimination of a carbon) and conjectured that they descent from a common primordial pathway.
Promiscuity is generally studied with a single enzyme as a model. A few paper tip-toe around it, but I am not sure that there are any that deal specifically with a pathway where the each enzyme along a pathway shows substrate ambiguity towards the promiscuous product of the previous reaction. I mentioned in another post that the branched chain amino acid pathway can produce norvaline, norleucine and homonorleucine when certain enzymes are overexpressed. Each enzyme in the pathway shows substrate ambiguity, so the whole pathway possess substrate ambiguity.
It is not a feature of the oxaloacetate-to-ketoglutarate–like pathways, but can be found in other pathways.

PrettyFastaJS

I wrote a small script to allow one to easily and prettily embed both nucleic acid and protein FASTA sequences on a webpage (it guesses based on EFILPQ which are not degerate bases, but residues). It was a mix of "I need this" and "I want to play with colours". The latter was rather painful. But the result is a JS modified and CSS coloured fasta files.
The files can be found in my GitHub repository for PrettyFastaJS and so are the explanations of how to use it. As a demo, here are two curious sequences. Two consecutive genes in the same operon that encode each a full length glycine dehydrogenases, which in other species is a homodimer, which suggests this may be a cool heterodimer.

>Gthg02641 Glycine dehydrogenase (EC 1.4.4.2) [Geobacillus thermoglucosidasius TM242] MLKNWGIAMHKDQPLIFELSKPGRIGYSLPELDVPAVRVEEVVPADYIRTEEPELPEVSELDIMRHYTALSKRNHGVDSGFYPLGSCTMKYNPKINENVARLAGFAHIHPLQPEETVQGALELMYDLQEHLKEITGMDAVTLQPAAGAHGEWTGLMMIRAYHEANGDFQRTKVIVPDSAHGTNPASATVAGFETITVKSTEDGLVDLEDLKRVVGPDTAALMLTNPNTLGLFEENILEMAKIVHDAGGKLYYDGANLNAVLSKARPGDMGFDVVHLNLHKTFTGPHGGGGPGSGPVGVKADLIPFLPKPVVEKGENGYYLDDDRPQSIGRVKPFYGNFAINVRAYTYIRSMGPEGLKAVAEYAVLNANYMMRRLAEYYDLPYDRHCKHEFVLSGKRQKKLGVRTLDIAKRLLDFGFHPPTVYFPLIVEECMMIEPTETESKETLDAFIDAMIQIAKEAEENPEIVQEAPHTTVVKRLDETKAARKPILRYQKQ >Gthg02642 Glycine dehydrogenase (EC 1.4.4.2) [Geobacillus thermoglucosidasius TM242] MLHRYLPMTEEDKREMLNVIGVDSIDDLFADIPESVRFRGELNIKKAKSEVELWKELSALAAKNADVKKYTSFLGAGVYDHYIPAIVDHVISRSEFYTAYTPYQPEISQGELQAIFEFQTMICELTGMDVANSSMYDGGTALAEAMLLSAAHTKKKKVLLAKTVHPEYRGVVKTYAKGQRLHVVEIPFQHGVTDLEALKAEMDDEVACVIVQYPNFFGQIEPLKDIEPVAHSCKSMFVVASNPLALGILAPPGKFGADIVVGDVQPFGIPMQFGGPHCGYFAVKAELMRKIPGRLVGQTTDEEGRRGFVLTLQAREQHIRRDKATSNICSNQALNALAASVAMTALGKKGIKEMATMNIQKAQYAKNELVKHGFAVPFTGPFFNEFVVCLAKPVAEVNKQLLRKGIIGGYDVGRDYPELQNHMLIAVTELRTKEEIDMFVKELGDCHA

Publication-driven complexification

"Any sufficiently advanced technology is indistinguishable from magic."

—A. Clarke's Third Law

I like knowing what I am doing, it helps me figure out if something is wrong. However, there is a trend for accepted operations to become "mathemagical" either out of necessity... or out of publication race.  

RNASeq is often cheered for being absolute reads and thus requiring less normalisation than microarrays. Anyone that has tried to understand Loess or Lowess takes that as a blessing.
However, the situation is not that simple as different samples have different RNA compositions and sequencing depths and, due to publications trying to out do the other it quickly gets complicated, where acceptant nodding is the best option lest one want to spend an evening reading a method's section and the appendix on a paper.
An arithmetic average is discouraged as the highly expressed genes will wreak havoc with other genes. On those lines, Mortazavi et al. 2008 (PMID: 18516045) introduced RPKM: reads per kilobase of exon per million mapped reads. The two normalisations account for transcript length and sequencing depth; this is not a per-mille scale. The underlying logic is clear and for a while it was popular. RPKM got ousted by Anders and Huber 2010 (PMID:20979621) and Robinson et al. 2010 (PMID:19910308) with DESeq and edgeR, which do many calculations. The normalisation in DESeq is done by taking the median for each sample of each of the ratios of the gene count for that samples over the geometric mean of the values of that genes across the samples. That makes sense, albeit cumbersome to grasp. For the test of significance, the improvement on a negative binomial GLM spans a page or two and works by magic, or more correctly mathemagic, maths that is so advanced it might as well be magic —Google shows the word is taken by some weird mail order learn maths course or somesuch, but shhh! The sequel to DESeq, DESeq2 does everything automatically and in 3 lines everything is done. It is really good although I do like to know what I am doing.
Long story aside, I did in parallel the analysis with DESeq methods in MatLab, using their agonising tutorial, which was tedious, but good to see how it everything works  —to quote Richard Feynman: "what I cannot create I cannot understand". It wants a true waste of time as it made me realise what graphs would be helpful as the various protocols and co. try and out do each in terms of graph complexity. It really bugged me that an obvious graph, double-log plot of each replicate against its counterpart with a Pearson's ρ thrown in, was omitted everywhere in favour of graphs, such as the empirical cumulative function of the distribution of the variance against the χ-squared of the variance estimates —a great way of inspecting if there are any oddities in a distribution, but really not the simplest graph. It is used because it is sophisticated, which doesn't necessarily mean better (incorrect assumptions can go a long way in distorting data), but it means more publishable.
Curiously, once the alignment, normalisation and significance steps are done, it is cowboy territory especially for bacteria. For example, for bacterial operon composition there is either a program, Rockhopper, which does not like my genome (I had to submit for realignment each replicon on different runs to get operons for the plasmids and refuses to do the chromosome) and another paper where the supplementary zip file was labelled .docx and the docx was labelled zip, which goes to indicate the scarce interest.
The main use of differential expression is functional enrichment, which depends on annotations that are rough as nails...

I am in awe at the mathematics involved in the calculation, but I cannot help, but feel annoyed at the fact that they are embarrassingly more sophisticated than they ought to be, especially since the complexity gives somewhat marginal improvements and the created dataset will be badly mishandled with clustering based on wildy guessed functions.