Category Archives: genome sequencing

NSF Poststdoc opportunity for Research using biological collections

Earlier this year the NSF released a postdoc opportunity for research to use Biological Collections. In particular these can be strain collections and stock collections. The US Culture Collection Network is a Research Coordination Network which brings together many collaborating culture collections. You can find many of the U.S. living collections there include fungal centers like the Phaff Yeast Collection and Fungal Genetics Stock Center. The Gilbertson Mycological Herbarium at U Arizona under Elizabeth Arnold‘s leadership has developed a rich collection of endophyte fungi which would be another excellent environment to work with these resources. Kyria Boundy-Mills who is the curator of the Phaff collection has also expressed interest in either hosting or helping working with a postdoc on this. There is tremendous biodiversity of the fungi available in these and other culture collections so seems like a great chance to tap into these.
This would be a great opportunity to link work in the 1000 Fungal genomes project and sampling from culture collections (not just sequencing, but growing and characterizing growth, carbon source utilization and integrating that with predictions made from genome comparisons). If this is something interesting to you – do get in touch with some of the curators at these collections, but also my lab and I expect many other labs would be interested hosting someone to work on these questions that take advantage of these living collections of fungi.
Proposals are to be submitted by potential post docs. Submitter must be a US citizen or US permanent resident. The next deadline is November 3, 2015Funding total for the program is $8 million, 40 awards anticipated, up to two years. Here’s some key text from the solicitation:

Competitive Area 2. Postdoctoral Research Fellowships Using Biological Collections.

Biological research collections represent the documented scientific history of life on Earth, and the U.S. museum community alone curates over a billion specimens ranging from bacteria to plants, insects and vertebrates, as well as fossils. Across the globe, collections represent critical infrastructure and support essential research activities in biology and its related fields. Scientists, government agencies, industry and citizens utilize collections to document and understand evolution and biodiversity, study global change, formulate advice on conservation planning, educate the general public, improve interactions between sciences, and devise new practical applications from science to every day life. New technologies supported by NSF in digitization, such as the Advancing Digitization of Biodiversity Collections (ADBC) program, are making collections and their associated data, whether they are physical specimens, text, images, sounds, or data tables, searchable in online databases. Despite this clear progress in improving access to physical specimens and their associated metadata, collections remain under-utilized for answering contemporary questions about fundamental aspects of biological processes. Thus, collections are poised to become a critical resource for developing transformative approaches to address key questions in biology and potentially develop applications that extend biology to physical, mathematical, engineering and social sciences. This postdoctoral track seeks transformative approaches that use biological collections in highly innovative ways to address grand challenges in biology. Priority may be given to applicants who integrate biological collections and associated resources with other types of data in an effort to forge new insight into areas traditionally funded by BIO. Examples of key questions in biology of interest include, but are not limited to, links between genotype and phenotype, evolutionary developmental biology, comparative approaches in functional and developmental neurobiology, and the biophysics of nanostructures. Using collections as a resource for grand challenge questions in biology is expected to present new opportunities to advance understanding of biological processes and systems, inspiring new discoveries in areas with relevance to other disciplines with overlapping interests in biological systems. Applicants must document access to the selected collection(s) in the research and training plan.

Some recent fungal and oomycete genome papers A few papers covering some published genomes you should definitely read if you have the chance.

  • Youssef NH, Couger MB, Struchtemeyer CG, Liggenstoffer AS, Prade RA, Najar FZ, Atiyeh HK, Wilkins MR, & Elshahed MS (2013). The Genome of the Anaerobic Fungus Orpinomyces sp. Strain C1A Reveals the Unique Evolutionary History of a Remarkable Plant Biomass Degrader. Applied and environmental microbiology, 79 (15), 4620-34 PMID: 23709508
    Describes first published genome of a Neocallimastigomycota fungus that resides within the rumen gut. Cool findings related to lignocellulolytic degradation pathways and basic biology about early diverging fungi which have intact flagellar apparatus.
  • Bushley KE, Raja R, Jaiswal P, Cumbie JS, Nonogaki M, Boyd AE, Owensby CA, Knaus BJ, Elser J, Miller D, Di Y, McPhail KL, & Spatafora JW (2013). The Genome of Tolypocladium inflatum: Evolution, Organization, and Expression of the Cyclosporin Biosynthetic Gene Cluster. PLoS Genetics, 9 (6) PMID: 23818858Describes the genome of a pathogen of beetle larvae (and related to Cordyceps). This fungus is important as it produces the immunosuppresive drug cyclosporin as a secondary metabolite. Analysis of the complete secondary metabolite pathways in the genome help shed light on the origin of this and other secondary metabolite gene clusters.
  • Schardl CL, Young CA, Hesse U, Amyotte SG, Andreeva K, Calie PJ, Fleetwood DJ, Haws DC, Moore N, Oeser B, Panaccione DG, Schweri KK, Voisey CR, Farman ML, Jaromczyk JW, Roe BA, O’Sullivan DM, Scott B, Tudzynski P, An Z, Arnaoudova EG, Bullock CT, Charlton ND, Chen L, Cox M, Dinkins RD, Florea S, Glenn AE, Gordon A, Güldener U, Harris DR, Hollin W, Jaromczyk J, Johnson RD, Khan AK, Leistner E, Leuchtmann A, Li C, Liu J, Liu J, Liu M, Mace W, Machado C, Nagabhyru P, Pan J, Schmid J, Sugawara K, Steiner U, Takach JE, Tanaka E, Webb JS, Wilson EV, Wiseman JL, Yoshida R, & Zeng Z (2013). Plant-symbiotic fungi as chemical engineers: multi-genome analysis of the clavicipitaceae reveals dynamics of alkaloid loci. PLoS Genetics, 9 (2) PMID: 23468653 

    A very rich and detailed paper, this presents a gold mine of complete genome data of 15 species and secondary metabolite profiling. The data include genomes of 10 epichloae fungi that are endophytes of grasses, three Claviceps species (ergot fungi), a morning-glory symbiont and a bamboo pathogen. The analyses of the genes from pathway analyses of the genomes along with profiling alkaloid productions the authors were able to link clusters to products in many cases. This is a rich and useful paper for anyone working in this field of secondary metabolites and sets the standard for a how a biological question can be answered by genome sequencing of a clade of related species.

  • Wicker T, Oberhaensli S, Parlange F, Buchmann JP, Shatalina M, Roffler S, Ben-David R, Doležel J, Simková H, Schulze-Lefert P, Spanu PD, Bruggmann R, Amselem J, Quesneville H, van Themaat EV, Paape T, Shimizu KK, & Keller B (2013). The wheat powdery mildew genome shows the unique evolution of an obligate biotroph. Nature Genetics PMID: 23852167

    Genome of wheat pathogen Blumeria graminis f.sp. tritici.This paper includes an identification and analysis of effector genes and dating the emergence of the pathogen relative the domestication and diversification of wheat.
  • Jiang RH, de Bruijn I, Haas BJ, Belmonte R, Löbach L, Christie J, van den Ackerveken G, Bottin A, Bulone V, Díaz-Moreno SM, Dumas B, Fan L, Gaulin E, Govers F, Grenville-Briggs LJ, Horner NR, Levin JZ, Mammella M, Meijer HJ, Morris P, Nusbaum C, Oome S, Phillips AJ, van Rooyen D, Rzeszutek E, Saraiva M, Secombes CJ, Seidl MF, Snel B, Stassen JH, Sykes S, Tripathy S, van den Berg H, Vega-Arreguin JC, Wawra S, Young SK, Zeng Q, Dieguez-Uribeondo J, Russ C, Tyler BM, & van West P (2013). Distinctive Expansion of Potential Virulence Genes in the Genome of the Oomycete Fish Pathogen Saprolegnia parasitica. PLoS Genetics, 9 (6) PMID: 23785293

    Genome of the fish pathogen and Oomycete Saprolegnia provide additional perspective on this diverse group organisms, evolution of metabolism and host-associated lifestyles.
  • Aylward FO, Burnum-Johnson KE, Tringe SG, Teiling C, Tremmel DM, Moeller JA, Scott JJ, Barry KW, Piehowski PD, Nicora CD, Malfatti SA, Monroe ME, Purvine SO, Goodwin LA, Smith RD, Weinstock GM, Gerardo NM, Suen G, Lipton MS, & Currie CR (2013). Leucoagaricus gongylophorus produces diverse enzymes for the degradation of recalcitrant plant polymers in leaf-cutter ant fungus gardens. Applied and environmental microbiology, 79 (12), 3770-8 PMID: 23584789Genome of the ant farmed fungus Leucoagaricus. This paper presents a draft genome assembly a useful step in understanding the fascinating symbiosis between ants and their cultivated fungi.

Reference ITSdb for QIIME released

Good news!

An alpha version of the ITS reference database for use with QIIME was released this week as part of the QIIME team development. There are more details on the release and how to obtain it from the project’s post here.

Please note that this is an Alpha release and may not be completely consistent, but the team wants to make something available now to give people a starting DB for use of QIIME and ITS data.  Parameters will need to be modified from the defaults, so watch the QIIME space, and we are working on a best practices document in the lab here to help ease the training in this.

All the data are also in a github repository and this is built starting from the database provided and curated by the UNITE team. I love that the data are getting version controlled here so it is easy to look at versions and revisions.

Schizophyllum genome update

Robin Ohm at the JGI has announced the release of version 2 of the Schizophyllum commune genome. This is great news on the heels of the announcement that one of the funded 2012 CSPs will include detailed functional genomics experiments in this mushroom.

I am pleased to announce the public release of the JGI annotation and portal for the improved assembly of Schizophyllum commune.  Annotations of the assembly are now publicly visible at .  Annotation and editing privileges remain password-protected but all other tools are now available to the general public.

A detailed set of statistics on the assembly and annotation can be found on the Info page of that portal:


Microsporidia genomes on the way

New genomes from Microsporidia are on the way from the Broad Institute and other groups, and will be a boon to those working on these fascinating creatures. Microsporidia are obligate intracellular parasites of eukaryotic cells and many can cause serious disease in humans. Some parasitize worms and insects too. The evolutionary placement of these species in the fungi is still debated with recent evidence placing them as derived members of the Mucormycotina based on shared synteny (conserved gene order), in particular around the mating type locus.  There is still some debate as to where this group belongs in the Fungal kingdom, with their highly derived characteristics and long branches they are still make them hard to place.  The synteny-based evidence was another way to find a phylogenetic placement for them but it would be helpful to have additional support in the form of additional shared derived characteristics that group Mucormycotina and Microsporidia. There is hope that increased number of genome sequences and phylogenomic approaches can help resolve the placement and more further understand the evolution of the group.

For data analysis, a new genome database for comparing these genomes is online called MicrosporidiaDB. This project has begun incorporating the available genomes and providing a data mining interface that extends from the EuPathDB project.

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.

White nose syndrome genome released

The Broad Institute released their sequence of the genome of Geomyces destructans implicated in the White Nose Syndrome that is causing a massive die-offs of bats. The researchers sequenced a North America isolated strain in this project which is part of an epidemic spreading across the Northeastern United States. This is just the assembly of the genome not the annotation which will be forthcoming.

Genome sequence of mushroom Schizophyllum commune

Schizophyllum CommuneI am excited to announce the publication of another mushroom genome this week. The mushroom Schizophyllum commune is an important model system for mushroom biology, development of genome was sequenced as part of efforts at the Joint Genome Institute and a collection of international researchers.  The data and analyses from these efforts are presented in a publication appearing in Nature Biotechnology today.

Studies in mushrooms can have important impact on other research areas.  They can be useful in biotechnology as protein biosynthesis factories for producing compounds or even as an edible delivery mechanism for new drugs.  What we found in the analysis of this genome include clues to mechanisms of how white rotting fungi degrade lignin through analysis of enzyme families.  We also saw evidence for extensive antisense transcription during different developmental stages suggesting some important clues as to how some gene regulation could impact or control developmental progression.  Through gene expression comparison (by MPSS) a large number of transcription factors were shown to be differentially regulated during sexual development.  A knockout out two of these (fst3 and fst4) resulting in changes in ability to form mushrooms (fst4) or smaller mushrooms (fst3).

Several more interesting findings in this work that I hope to add back to this post when there is a little more time –

Ohm, R., de Jong, J., Lugones, L., Aerts, A., Kothe, E., Stajich, J., de Vries, R., Record, E., Levasseur, A., Baker, S., Bartholomew, K., Coutinho, P., Erdmann, S., Fowler, T., Gathman, A., Lombard, V., Henrissat, B., Knabe, N., Kües, U., Lilly, W., Lindquist, E., Lucas, S., Magnuson, J., Piumi, F., Raudaskoski, M., Salamov, A., Schmutz, J., Schwarze, F., vanKuyk, P., Horton, J., Grigoriev, I., & Wösten, H. (2010). Genome sequence of the model mushroom Schizophyllum commune Nature Biotechnology DOI: 10.1038/nbt.1643