Category Archives: bioinformatics

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Presents for the holidays – Plant pathogen genomes

Though a bit cliche, I think the metaphor of “presents under the tree” of some new plant pathogen genomes summarized in 4 recent publications is still too good to resist.  There are 4 papers in this week’s Science that will certainly make a collection of plant pathogen biologists very happy. There are also treats for the general purpose genome biologists with descriptions of next generation/2nd generation sequencing technologies, assembly methods, and comparative genomics. Much more inside these papers than I am summarizing so I urge you to take look if you have access to these pay-for-view articles or contact the authors for reprints to get a copy.


These include the genome of biotrophic oomycete and Arabidopsis pathogen Hyaloperonospora arabidopsidis (Baxter et al). While preserving the health of Arabidopsis is not a major concern of most researchers, this is an excellent model system for studying plant-microbe interaction.  The genome sequence of Hpa provides a look at specialization as a biotroph. The authors found a reduction (relative to other oomycete species) in factors related to host-targeted degrading enzymes and also reduction in necrosis factors suggesting the specialization in biotrophic lifestyle from a necrotrophic ancestor. Hpa also does not make zoospores with flagella like its relatives and sequence searches for 90 flagella-related genes turned up no identifiable homologs.

While the technical aspects of sequencing are less glamourous now the authors used Sanger and Illumina sequencing to complete this genome at 45X sequencing coverage and an estimated genome size fo 80 Mb. To produce the assembly they used Velvet on the paired end Illumina data to produce a 56Mb assembly and PCAP (8X coverage to produce a 70Mb genome) on the Sanger reads to produce two assemblies that were merged with an ad hoc procedure that relied on BLAT to scaffold and link contigs through the two assembled datasets. They used CEGMA and several in-house pipelines to annotate the genes in this assembly. SYNTENY analysis was completed with PHRINGE. A relatively large percentage (17%) of the genome fell into ‘Unknown repetitive sequence’ that is unclassified – larger than P.sojae (12%) but there remain a lot of mystery elements of unknown function in these genomes.  If you jump ahead to the Blumeria genome article you’ll see this is still peanuts compared to that Blumeria’s genome (64%). The largest known transposable element family in Hpa was the LTR/Gypsy element. Of interest to some following oomycete literature is the relative abundance of the RLXR containing proteins which are typically effectors – there were still quite a few (~150 instead of ~500 see in some Phytophora genomes).



A second paper on the genome of the barley powdery mildew Blumeria graminis f.sp. hordei and two close relatives Erysiphe pisi, a pea pathogen, and Golovinomyces orontii, an Arabidopsis thaliana pathogen (Spanu et al).  These are Ascomycetes in the Leotiomycete class where there are only a handful of genomes Overall this paper tells a story told about how obligate biotrophy has shaped the genome. I found most striking was depicted in Figure 1. It shows that typical genome size for (so far sampled) Pezizomycotina Ascomycetes in the ~40-50Mb range whereas these powdery mildew genomes here significantly large genomes in ~120-160 Mb range. These large genomes were primarily comprised of Transposable Elements (TE) with ~65% of the genome containing TE. However the protein coding gene content is still only on the order of ~6000 genes, which is actually quite low for a filamentous Ascomycete, suggesting that despite genome expansion the functional potential shows signs of reduction.  The obligate lifestyle of the powdery mildews suggested that the species had lost some autotrophic genes and the authors further cataloged a set of ~100 genes which are missing in the mildews but are found in the core ascomycete genomes. They also document other genome cataloging results like only a few secondary metabolite genes although these are typically in much higher copy numbers in other filamentous ascomycetes (e.g. Aspergillus).  I still don’t have a clear picture of how this gene content differs from their closest sequenced neighbors, the other Leotiomycetes Botrytis cinerea and Sclerotinia sclerotium, are on the order of 12-14k genes. Since the E. pisi and G. orontii data is not yet available in GenBank or the MPI site it is hard to figure this out just yet – I presume it will be available soon.

More techie details — The authors used Sanger and second generation technologies and utilized the Celera assembler to build the assemblies from 120X coverage sequence from a hybrid of sequencing technologies.  Interestingly, for the E. pisi and G. orontii assemblies the MPI site lists the genome sizes closer to 65Mb in the first drafts of the assembly with 454 data so I guess you can see what happens when the Newbler assembler which overcollapses repeats. They also used a customized automated annotation with some ab intio gene finders (not sure if there was custom training or not for the various gene finders) and estimated the coverage with the CEGMA genes. I do think a Fungal-Specific set of core-conserved genes would be in order here as a better comparison set – some nice data like this already exist in a few databases but would be interesting to see if CEGMA represents a broad enough core-set to estimate genome coverage vs a Fungal-derived CEGMA-like set.


A third paper in this issue covers the genome evolution in the massively successful pathogen Phytophora infestans through resequencing of six genomes of related species to track recent evolutionary history of the pathogen (Raffaele et al). The authors used high throughput Illumina sequencing to sequence genomes of closely related species. They found a variety differences among genes in the pathogen among the findings “genes in repeat-rich regions show[ed] higher rates of structural polymorphisms and positive selection”. They found 14% of the genes experienced positive selection and these included many (300 out of ~800) of the annotated effector genes. P. infestans also showed high rates of change in the repeat rich regions which is also where a lot of the disease implicated genes are locating supporting the hypothesis that the repeat driven expansion of the genome (as described in the 2009 genome paper). The paper generates a lot of very nice data for followup by helping to prioritize the genes with fast rates of evolution or profiles that suggest they have been shaped by recent adaptive evolutionary forces and are candidates for the mechanisms of pathogenecity in this devastating plant pathogen.


A fourth paper describes the genome sequencing of Sporisorium reilianum, a biotrophic pathogen that is closely related species to corn smut Ustilago maydis (Schirawski et al). Both these species both infect maize hosts but while U. maydis induces tumors in the ears, leaves, tassels of corn the S. reilianum infection is limited to tassels and . The authors used comparative biology and genome sequencing to try and tease out what genetic components may be responsible for the phenotypic differences. The comparison revealed a relative syntentic genome but also found 43 regions in U. maydis that represent highly divergent sequence between the species. These regions contained disproportionate number of secreted proteins indicating that these secreted proteins have been evolving at a much faster rate and that they may be important for the distinct differences in the biology. The chromosome ends of U. maydis were also found to contain up to 20 additional genes in the sub-telomeric regions that were unique to U. maydis. Another fantastic finding that this sequencing and comparison revealed is more about the history of the lack of RNAi genes in U. maydis. It was a striking feature from the 2006 genome sequence that the genome lacked a functioning copy of Dicer. However knocking out this gene in S. reilianum failed to show a developmental or virulence phenotype suggesting it is dispensible for those functions so I think there will be some followups to explore (like do either of these species make small RNAs, do they produce any that are translocated to the host, etc).  The rest of the analyses covered in the manuscript identify the specific loci that are different between the two species — interestingly a lot of the identified loci were the same ones found as islands of secreted proteins in the first genome analysis paper so the comparative approach was another way to get to the genes which may be important for the virulence if the two organisms have different phenotypes. This is certainly the approach that has also been take in other plant pathogens (e.g. Mycosphaerella, Fusarium) and animal pathogens (Candida, Cryptococcus, Coccidioides) but requires a sampling species or appropriate distance that that the number of changes haven’t saturated our ability to reconstruct the history either at the gene order/content or codon level.

Without the comparison of an outgroup species it is impossible to determine if U. maydis gained function that relates to the phenotypes observed here through these speculated evolutionary changes involving new genes and newly evolved functions or if S. reilianum lost functionality that was present in their common ancestor. However, this paper is an example of how using a comparative approach can identify testable hypotheses for origins of pathogenecity genes.


Hope everyone has a chance to enjoy holidays and unwrap and spend some time looking at these and other science gems over the coming weeks.


Baxter, L., Tripathy, S., Ishaque, N., Boot, N., Cabral, A., Kemen, E., Thines, M., Ah-Fong, A., Anderson, R., Badejoko, W., Bittner-Eddy, P., Boore, J., Chibucos, M., Coates, M., Dehal, P., Delehaunty, K., Dong, S., Downton, P., Dumas, B., Fabro, G., Fronick, C., Fuerstenberg, S., Fulton, L., Gaulin, E., Govers, F., Hughes, L., Humphray, S., Jiang, R., Judelson, H., Kamoun, S., Kyung, K., Meijer, H., Minx, P., Morris, P., Nelson, J., Phuntumart, V., Qutob, D., Rehmany, A., Rougon-Cardoso, A., Ryden, P., Torto-Alalibo, T., Studholme, D., Wang, Y., Win, J., Wood, J., Clifton, S., Rogers, J., Van den Ackerveken, G., Jones, J., McDowell, J., Beynon, J., & Tyler, B. (2010). Signatures of Adaptation to Obligate Biotrophy in the Hyaloperonospora arabidopsidis Genome Science, 330 (6010), 1549-1551 DOI: 10.1126/science.1195203

Spanu, P., Abbott, J., Amselem, J., Burgis, T., Soanes, D., Stuber, K., Loren van Themaat, E., Brown, J., Butcher, S., Gurr, S., Lebrun, M., Ridout, C., Schulze-Lefert, P., Talbot, N., Ahmadinejad, N., Ametz, C., Barton, G., Benjdia, M., Bidzinski, P., Bindschedler, L., Both, M., Brewer, M., Cadle-Davidson, L., Cadle-Davidson, M., Collemare, J., Cramer, R., Frenkel, O., Godfrey, D., Harriman, J., Hoede, C., King, B., Klages, S., Kleemann, J., Knoll, D., Koti, P., Kreplak, J., Lopez-Ruiz, F., Lu, X., Maekawa, T., Mahanil, S., Micali, C., Milgroom, M., Montana, G., Noir, S., O’Connell, R., Oberhaensli, S., Parlange, F., Pedersen, C., Quesneville, H., Reinhardt, R., Rott, M., Sacristan, S., Schmidt, S., Schon, M., Skamnioti, P., Sommer, H., Stephens, A., Takahara, H., Thordal-Christensen, H., Vigouroux, M., Wessling, R., Wicker, T., & Panstruga, R. (2010). Genome Expansion and Gene Loss in Powdery Mildew Fungi Reveal Tradeoffs in Extreme Parasitism Science, 330 (6010), 1543-1546 DOI: 10.1126/science.1194573

Raffaele, S., Farrer, R., Cano, L., Studholme, D., MacLean, D., Thines, M., Jiang, R., Zody, M., Kunjeti, S., Donofrio, N., Meyers, B., Nusbaum, C., & Kamoun, S. (2010). Genome Evolution Following Host Jumps in the Irish Potato Famine Pathogen Lineage Science, 330 (6010), 1540-1543 DOI: 10.1126/science.1193070

Schirawski, J., Mannhaupt, G., Munch, K., Brefort, T., Schipper, K., Doehlemann, G., Di Stasio, M., Rossel, N., Mendoza-Mendoza, A., Pester, D., Muller, O., Winterberg, B., Meyer, E., Ghareeb, H., Wollenberg, T., Munsterkotter, M., Wong, P., Walter, M., Stukenbrock, E., Guldener, U., & Kahmann, R. (2010). Pathogenicity Determinants in Smut Fungi Revealed by Genome Comparison Science, 330 (6010), 1546-1548 DOI: 10.1126/science.1195330

Distribution of fungal ITS sequences in GenBank

As part of background in preparing a grant I ended up writing a few scripts to see the distribution of fungal species with ITS data in GenBank.  The whole spreadsheet of the data is public and available here and I walk you through the data generation and summary below.

ITS (Internal Transcribed Spacer) is the typically used barcode for identifying fungi at the species level as it works for most (but not all) groups of Fungi. It falls between highly conserved nuclear rDNA genes (18S, 5.8S, 28S) but tends to be hypervariable making it a reasonable locus for identification of species since it tends to be unique between species but fairly unchanged among individuals from the same species. You can see a Map of the amplified region from Tom Brun’s site or info at Rytas Vilgalys’s site among others.

The script to extract these and dump the numbers from GenBank uses Perl, BioPerl, and is plotted in a Google docs table. I queried for all ITS sequences with a pretty simple query – some people use a better more thorough query to get the list of GIs so I separated the GI query from the statistics about taxonomy.

The GI query code uses BioPerl and queries GenBank over the web to dump out a file of GI numbers  The code is in this Perl script.

This generates a file with GI (genbank identifiers) numbers for nucleotide records. This is not cleaned up to remove problematic seqs, but since we’re interested in overall statistics, I don’t think is that important if there are some records with problem.  You might want to do some cleanup of these data and expand the query before using it as a reference ITS database for your BLAST queries. See tools built by Henrik Nilsson and others like Emerencia for some of the cleanup and detection of problems with a dataset like this of ITS.

But given a list of GIs from any query – in our case of ITS sequences – what is the distribution of taxa (based on what is specified by the submitted which is not always correct!)? Of course some aren’t specified to the species level or even to the genus level so the code has to be smart enough to put those in a different category.  But of those specified to a particular taxonomic level – what are they?  This script tallies the information about the phyla and genus and dumps them out – it takes a while to run the first time because it must build a database for all the GI to taxon record links (gi_to_taxa_nucl.dmp file from ncbi taxonomy) so be prepared to wait a while and dedicate several dozen gigabytes to get this all working the first time.

So what is the most abundant deposited genus?  Well according to this analysis it is Fusarium. Which are found everywhere especially in soil. This distribution may have much more to do with the types of places being sampled and the types of questions researchers are working on rather than about relative abundance worldwide so take it as an interesting observation of what is in the databases!  Only in particular environments with dedicated studies to fungal species (for example, the indoor environment or a particular area of a forest or fungi associated with trees in an urban and rural environment or one of many other studies not mentioned) can we really say something. What is important to note also is the massively parallel sequencing studies using 454 are coming online and not necessarily being dumped directly into this particular database at GenBank – these number represent the mainly Sanger clone sequenced data from years past, but it will be a whole new ball game in the next few years as studies start doing 454 sequencing as primary means to identify community structure.





click on image to see this in google docs spreadsheet





So who is generating all that data — well I wrote another version of the script which dumps out the authors for records from a particular taxa by querying the genbank record for the author field of all the records that came from a particular taxa.
The data are in this spreadsheet.

So a few bits of code using queries of GenBank and BioPerl to link things together, hope you see some sense of what is out there and maybe can think of interesting variations on this theme to address other data mining questions.

Yeast population genomics
I have cheered the Sanger-Wellcome SGRP group work to generate multiple Saccharomyces cerevisiae and S. paradoxus strain genome sequences.   The group had previously submitted a version of the manuscript to Nature precedings and it is now published in Nature AOP showing that submitting to a preprint server doesn’t necessarily hurt your manuscript getting published…  The research groups explored the impact of domestication (as was also recently done for the sake and soy sauce worker fungus, Aspergillus oryzae) on the Saccharomyces genome by comparing individuals from wild strains of S. paradoxus.

This paper addressed several challenges including methodology for light genome sequencing for population genomics. This data represents in a way, a pilot project on for genome resequencing projects and using draft genome sequencing with next generation sequencing tools. Of course with the pace of sequencing technology development, any project more than a couple months old will be using outdated technology it seems, but this work represents some important progress.  Tools like MAQ were also developed and tuned as part of the project.  In addition to the methods development it also provided a new look at evolutionary dynamics of a well-studied fungus.

Genome assembly
The authors apply several different quality controls and utilize a new tool called PALAS (Parallel ALignment and ASsembly)  to assemble all the strains at the same time using a graph-based approach that utilized the reference genome sequences for each species. This is different than a full-blown WGA approach like PCAP, Phusion or Arachne because this is deliberately low-coverage sequencing pass.  The authors are trying impute missing sequence via Ancestral Recombination Graphs as implemented in the Margarita system.   They also use MAQ to align sequence from Illumina/Solexa sequencing to these assemblies made by PALAS.

Since this project was on two species of SaccharomycesS. cerevisiae and S. paradoxus they needed good reference assemblies for each of these species. The previously availably S.paradoxus assembly wasn’t complete enough for this study so they did an addition 4.3 X coverage with sanger/ABI sequencing and 80X coverage with Illumina.

Population genomics and domestication

The sequencing data also provided a framework for population genetic investigations. Some simple findings showed that geographic isolates within each species were more genetically similar to each other.  The main geographic regions of samples for S.paradoxus data included the UK, American, and Far East samples, some of which had been analyzed in a very nice study on Chromosome III.  For the S. cerevisiae samples there were individuals from around Europe, at least 10 European wine strains, Malaysian, Sake brewing strains, West Africa, and North America. From these data it was possible to discover that there are several of strains with mosiac genomes meaning that pieces of the genome match best with the sake fermentation strains and other parts from the wine/European samples.

Efforts to detect the effects of natural selection that may be linked to domestication of these strains explored two different approaches. The McDonald-Kreitman test did not identify any loci under positive selection while Tajima’s D was negative in the S.cerevisiae global and wine strain populations indicating an excess of singleton polymorphisms – though they draw little conclusions from that.  The authors also observed a sharper decay of linkage disequilibrium in S.cerevisiae (half maximum of 3kb) than S.paradoxus (half maximum 9kb) suggesting that S.cerevisiae is recombining more, either due to increased opportunities or a great frequency of recombination events when it does.

In context of the paper title and the idea of exploring the effects of domestication on the genome, the authors observe that the standard paradigm that ‘domesticated’ species have lower diversity levels is simply not the case in these samples.  This isn’t to say there isn’t evidence of the selection for fermentation production from these strains based on the stress response conditions they were tested on, but that there is still ample evidence of maintaining diversity within the populations presumably through various amounts of outcrossing.

We are also interested in these results as we apply similar questions to population genomics of the human pathogenic fungus Coccidioides where 14 strains have been sequenced with sanger sequencing technology.  Hopefully some of these lessons will resonate in our analyses and also that this era of population genomics will see ever more extensive collections to address aspects of migration, phylogeography, and local adaptations within populations of fungi and other microbes.

Gianni Liti, David M. Carter, Alan M. Moses, Jonas Warringer, Leopold Parts, Stephen A. James, Robert P. Davey, Ian N. Roberts, Austin Burt, Vassiliki Koufopanou, Isheng J. Tsai, Casey M. Bergman, Douda Bensasson, Michael J. T. O’Kelly, Alexander van Oudenaarden, David B. H. Barton, Elizabeth Bailes, Alex N. Nguyen, Matthew Jones, Michael A. Quail, Ian Goodhead, Sarah Sims, Frances Smith, Anders Blomberg, Richard Durbin, Edward J. Louis (2009). Population genomics of domestic and wild yeasts Nature DOI: 10.1038/nature07743

A few tool updates

I’m working to make more data available in the genome browsers for fungi. One is adding in the Primer information from the Neurospora KO project to the Neurospora browser to indicate the position and primer sequences for all the gene knockouts being (or already) constructed.  At least 60% of the genes have been knocked out and are available from the FGSC.

We’re also integrating SNP data using the HapMap glyphs in which you can see one way to view this information in the Genome Browser for Coccidioides.  Working on other information including PhastCons conservation profiles and other information in our development server and hope to make this public soon.

Lichen genome projects and the power shift prompted by next-gen sequencing

Genome Technology highlights the very cool thing about next-gen sequencing – it puts the power in the hands of the researchers to explore genome sequence and doesn’t limit them to projects only funded through sequencing centers. The Genome Technology piece highlights work at Duke to sequence the genome Cladonia grayi, a lichenized fungus, with 454 technology at Duke’s Institute for Genome Sciences and Policy through their next-gen sequencing program. This is the way of the future where sequencing core facilities will be able to generate sequence only having to wait in the queue at the own university rather than through community sequencing project or sequencing center proposal queues.

This isn’t the only lichen being sequenced. Xanthoria parietina is also in the queue at JGI, but has taken a while to get going because of some logistical problems getting the DNA (and any problems are amplified because it takes a long time to get new material since lichens grow very slow).

The transfer of the power for researchers to be able to quick exploratory whole-genome sequencing with next-gen and eventually, high quality genome sequences from next-gen sequencing is predicted to transform how this kind of science gets done. It means we’ll probably just sequence a mutant strain instead of trying to map the mutation – this is happening already in anecdotal stories in worms and in our work in mushrooms. N.B. this is done after a mutagenized strain has been cleaned up a bit to insure we’re looking for one or only a few mutations based on some crosses – but that is part of standard genetic approaches anyways.

This fast,cheap,whole-genome-sequencing is also the stuff of personal genomics, but for basic research it will also mean that a first pass exploring gene repertoire of an organism will be a multi-week instead of multi-year project. I just hope we’re training enough people who can efficiently extract the information from all this data with solid bioinformatics, computational, data-oriented programming, and statistical skills to support all the labs that will want to take this approach. You’ll need a life-vest to swim in the big data pool for a while until more tools are developed that can be deployed by non-experts.

Gene prediction without training?

A new paper in Genome Research from Borodovsky lab at Georgia Tech provides an improved ab initio gene prediction building on their previous program GeneMark called GeneMark.hmm ES.  This application doesn’t require a training set when building models for gene prediction in fungal genomes and reports to have as good or better sensitivity and specificity than most of the commonly used ab initio programs. They are picking up on proviously described insights about fungal gene structures and introns which is the lack of a necessary branch site and varying degrees of conservation of splice-sites in most intron rich fungi (Schwartz et al, 2008) and that these intron sizes remain short across the fungi (Stajich et al. 2007).

In practice it should simplify the initial genome annotation protocols used and could really streamline the procedures. It doesn’t replace the need to gathering EST sequence data that can also be used generate a training set in an automated fashion.  EST and transcriptional evidence is still very important for identification of UTR and alternative splicing isoforms.

Hopefully these data from the predictions will integrate into the Cryptococcus and Coprinus genome annotations that are undergoing an update at the Broad.  We’ll see how well this performs on a couple of the Chytrid genome sequences we are working on as well.

Papers on our desk

A quick post of some recent comparative genomics papers on our desk that are worth a look.

  • Khaldi N, Wolfe KH (2008) Elusive Origins of the Extra Genes in Aspergillus oryzae. PLoS ONE 3(8): e3036. doi:10.1371/journal.pone.0003036. This was a cool but somewhat controversal finding presented at Fungal Genetics last year.
  • Casselton, LA. Fungal sex genes – searching for the ancestors. doi: 10.1002/bies.20782. A review of recent findings about the Zygomycete MAT locus.
  • Soanes DM, Alam I, Cornell M, Wong HM, Hedeler C, et al. (2008) Comparative Genome Analysis of Filamentous Fungi Reveals Gene Family Expansions Associated with Fungal Pathogenesis. PLoS ONE 3(6): e2300. doi:10.1371/journal.pone.0002300
  • Lee DW, Freitag M, Selker EU, Aramayo R (2008) A Cytosine Methyltransferase Homologue Is Essential for Sexual Development in Aspergillus nidulans. PLoS ONE 3(6): e2531. doi:10.1371/journal.pone.0002531

Fungal genome assembly from short-read sequences

This is a research blog so I though I’d post some quick numbers we are seeing for de novo assembly of the Neurospora crassa genome using Velvet. The genome of N.crassa is about 40Mb and sequencing of several flow cells using Solexa/Illumina technology to see what kind of de novo reconstruction we’d get. I knew that this is probably insufficient for a very good assembly given what has been reported in the literature, but sometimes it is helpful to give it a try on local data.  Mostly this is a project about SNP discovery from the outset. I used a hash size of 21 in velvet with an early (2FC) and later (4FC) dataset. Velvet was run with a hashsize of 21 for these data based on some calculations and running it with different hash sizes to see the optimal N50.  Summary contig size numbers come from the commands using cndtools from Colin Dewey.

  faLen < contigs.fa | stats

2 flowcells (~10M reads @36bp/read or about 10X coverage of 40Mb genome)

            N = 199562
          SUM = 25463251
          MIN = 49
       MEDIAN = 107.0
          MAX = 5371
         MEAN = 127.59568956
          N50 = 130

4 flow cells  (~20M reads @36bp/read; or about 20X coverage of a 40Mb genome)

            N = 102437
          SUM = 38352075
          MIN = 41
 1ST-QUARTILE = 77.0
       MEDIAN = 153
          MAX = 7189
         MEAN = 374.396702363
          N50 = 837

So that’s N50 of 837bp – for those used to seeing N50 on the order or 1.5Mb this is not great.  But from4 FC worth of sequencing which was pretty cheap.  This is a reasonably repeat-limited genome so we should get pretty good recovery if the seq coverage is high enough. Using Maq we can both scaffold the reads and recover a sufficient number of high quality SNPs for the mapping part of the project.

To get a better assembly one would need much deeper coverage as Daniel and Ewan explain in their Velvet paper and shown in Figure 4 (sorry, not open-access for 6 mo). Full credit: This sequence was from unpaired sequence reads from Illumina/Solexa Genomic sequencing done at UCB/QB3 facility on libraries prepared by Charles Hall in the Glass lab.

Chlamy genome investigations

Chlamy coverThis month’s Genetics has a series of articles exploring the genome (published last year & freely available at Science) of the green algae Chlamydomonas reinhardtii. These manuscripts are primarily genome analyses making for a very bioinformatics focused issue of Genetics. Some of the highlights include: