Tag Archives: transcription

A word about databases

Logo for fungal GenomesReport concludes that a fungal genome database is of “the highest priority”.

This is the title as listed in PubMed for this article from Future Medicine about the AAM report on charting future needs and avenues of research on the fungal kingdom.

The need for a comprehensive database for information about fungi, starting at least with systematic collections of genomic and transcript data, is highlighted as a major need.  Really and sort of new database effort should strive to be more comprehensive and include genetic and population data (alleles, strains) and information like protein-protein, protein-nucleic acid interactions (as Pedro mentioned). But on top of that it, it needs to be comparative so that information from systems that serve as great models can be transferred to other fungal systems that are being studied for their role as pathogens or interacting in the environmental.

Affordable next-gen sequencing will allow us to obtain genome and transcript sequence for basically all species or strains of interest.  Researchers with no bioinformatics support in their lab will likely be able to outsource this to a company or campus core facility.  But how can they easily map in the collective information about genes, proteins, and pathways onto this new data?  And have it be a dynamic system that can update as new information is published and curated in other systems.

I think this has to be the future beyond setting up a SGD, CGD, etc for every system.  The individual databases are useful for a large enough community where there are curators (and funding), but we will have to move to a more modular system in the future (aspects of which are in GMOD) that can have both an individual focus on a specific species/clade and a more comprehensive view of the that is comparable across the kingdom.  There are 100+ fungal genomes, but the community size for some of them are in the dozens of labs or less. How can they take advantage of the new resources without an existing infrastructure of curators?  Their systems serve an important need in a research aim, but how can discoveries there make its way back into the datastream of othe systems?

I see it as there are several ways one would interact with a system that provided single-genome tools as well as a framework for comparative information.  At a gene level, one might be looking for all information about a specific gene, based on sequence similarity searches, or starting with a cloned gene in one species. Something akin to Phylofacts or precomputed Orthogroups for defining a Gene but with more linking information about function by linking in information from all sources.  So a comparative resource, but also tapping into curated andliterature mined data.

At a genome level, one might want to do whole genome comparisons of gene content from evolutionarily defined families genes (gene family size change) or at a functional level.  To start out with, each gene/protein would already need a systematic functional mapping.  This could be as simple as running InterProScan on every protein, expanded to find Orthogroups (or OrthoMCL orthologs) and transfer function from model systems, and finally even more advanced, do further classified better with tools like SIFTER.

Interlinked with these orthologous and paralogous gene sets would be anchors for analyses of chromosomal synteny and even comparative assembly including tools like Mercator.  Certainly things like all of this exist but making it more pluggable for different sets of species would be an important additional component.

At a utility level, the gene annotation and functional mapping of all this information should be possible. I would imagine a researcher could upload the sequence assembly they received from the core facility and the system can generate multiple gene predictions, annotate the genes, and link these genes within the known orthogroups of the system (preserving their privacy for these genes if desired).  Presumably this sort of thing would be easier as a standalone in-house for the researcher, but web services could also be the place for this.

For fungal-sized genomes this amount of data is not too extereme.  Things like Genome Browser, BLAST, etc should all be rolled out of the box based on the basic builds.

On the DIY and community annotation front, there would also need to be a layer of community derived annotation that could be layered on all these systems.  I would imagine this both to be for gene structure annotation (genome annotation) and functional annotation (protein X does Y based on experiment Z, here is the journal reference).  I think aspects of this would be visible, auditable (tracked), but maybe not blessed as official until a curator could oversee these inputs. In my mind, whether or not this is in a Wiki per se or just new system that allows community input is less important to me than having it be a) structured (not a bunch of free text) b) tracked and versionable c) easy for researchers to input so that the knowledge is captured, even if it has to be reorganized later on.

Seems like a lot of work to be done, but really many of these things already exist through what  the GMOD project has built.  Many loose ends and software that doesn’t fully meet up to these needs, but I think the important concept is these are all general solutions that will be of benefit to most communities, not just the fungal ones.  One lingering question I always have when approaching genomic datas

that will be dynamic, what if any of this makes its way into GenBank?  How is this sort of thing banked so that it can be captured, and does the improved functional or gene structure annotation ever make its way into the repository databases to correct and improve what has already been submitted there?

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:

Aspergillus comparative transcriptional profiling


Researchers from Technical University of Denmark published some interesting results from comparing expression across the very distinct Aspergillus species.

Kudos also goes to making it Open Access. I am posting a few key figures below the fold because I can! They grew the fungi in bioreactors fermenting glucose or xylose. After calibrating the growth curves they were able to sample the appropriate time points for comparison of gene expression across these three species. They found a set of genes commonly expressed.

Continue reading Aspergillus comparative transcriptional profiling

B. dendrobatidis strain JAM81 released

B.dendrobatidis zoosporeThe following is an announcement to the B.dendrobatidis and fungal community at large from Alan Kuo at JGI. This is the JAM81 strain (Jess Morgan collected from a frog in the California Sierra Nevada). The JEL423 (Joyce Longcore, collected in Panama) strain genome sequence and annotation is available from the Broad Institute.

Please do contact me if you would like to contribute to assigning functions to the annotation. We’re in the last round of analyses for some of the genome work, but if there are particular questions you want to contribute to, we’re open to collaborators and can outline the basis of our work to see how other work can complement it.

From Alan Kuo at JGI:

The JGI Batrachochytrium annotation portal is now on the public JGI website. As it is public, no password is required.

For those of you who have not yet registered to be an annotator, go to this new link to register.As before, please choose a username that is personal, so that other annotators may be able to recognize it as yours. A derivative of your personal name would be best.

Those of you who are already registered, you do not need to do anything. Your old pre-release username and password are valid on the new public portal too.

As always, please direct all questions and problems to me. Use email or phone: Cheers, Alan.

Some information about the assembly and annotation:

The first annotation of the 127 scaffolds and 24 Mbp of JGI’s 8.74X assembly of the Batrachochytrim dendrobatidis JAM81 genome. We predict 8732 genes, with the following average properties:

Gene length 1825.16 nt
Transcript length 1407.29 nt
Protein length 450.56 aa
Exon frequency 4.29 exons/gene
Exon length 328.37 nt
Intron length 129.18 nt
Gene density 359.1 genes/Mbp scaffold

The genes were found by the following methods:
Total models 8732 (100%)
Jason’s models 3214 (37%)
cDNAs and ESTs 518 (6%)
Similarity to nr 1928 (22%)
ab initio 3072 (35%)

The genes were validated by the following evidence:
start+stop codons 7990 (92%)
EST support 2488 (28%)
nr hit 6787 (78%)
Pfam hit 4329 (50%)

Candida White-Opaque switching

Blogging on Peer-Reviewed ResearchA paper in PLoS Biology from Sandy Johnson’s lab entitled “Interlocking Transcriptional Feedback Loops Control White-Opaque Switching in Candida albicans discusses phenotype switching in the human pathogenic fungus Candida albicans. Why is the important?

“White-opaque switching is an epigenetic phenomenon, where genetically identical cells can exist in two distinctive cell types, white and opaque. Each cell type is stably inherited for many generations, and switching between the two types of cells occurs stochastically and rarely—roughly one switch in 10^4 cell divisions”

white-opaque coloniesThere is also a review by Kira O’Day to discuss the implications of the findings. Understanding this sort of developmental and epigenetic signaling is important to better know how fungi adjust and interact with their environment. However, the authors do conclude that White-Opaque switching is exclusive to Candida albicans so aspects of this research only directly applicable to studies in this system. Phenotype switching is an active area of research for Candida biologists – some nice micrographs and SEM of the different cell morphologies can be seen at Prof. Joachim Morschhäuser’s page (and linked to the right).

Continue reading Candida White-Opaque switching

Fungal Genetics 2007 details

I’m including a recapping as many of the talks as I remember. There were 6 concurrent sessions each afternoon so you have to miss a lot of talks. The conference was bursting at the seams as it was- at least 140 people had to be turned away beyond the 750 who attended.

If there was any theme in the conference it was “Hey we are all using these genome sequences we’ve been talking about getting”. I only found the overview talks that solely describe the genome solely a little dry as compared to those more focused on particular questions. I guess my genome palate is becoming refined.

Continue reading Fungal Genetics 2007 details

Deeper and Deeper, Down the Transcriptome-hole We Fall

Your eye contains the same genetic content as your fingernail, but these two tissues look nothing alike. One significant cause of this difference is the tissue specific regulation of the genes in the genome. In some tissues in your body, a gene may be expressed (transcribed) while that same gene may be silent in another tissue type. A great deal of modern biological research explores the regulation of expression of all the genes in a genome, collectively known as the transcriptome. Such studies are, for example, aimed at understanding which genetic regulation events account for the differences between an eye and a fingernail.

However, the effectiveness of this research is predicated upon actually knowing which parts of the genome are capable of being expressed and, subsequently, regulated. Conventionally, researchers extract RNA from an organism grown in various conditions (or, as in the case of our example, various tissues from an organism) and clone and sequence the RNA to identify at least a subset of genes that are expressed (Ebbole 2004*). Such Expressed Sequence Tags (ESTs) have proven vital to our understanding of gene and gene structure annotation as they frequently provide evidence of intron splice sites. While this method has facilitated a robust understanding of gene regulation, it is expensive, time consuming, and provides a relatively low coverage of the transcriptome. If our goal is to understand everything that is expressed, then we need a superior tool.

Enter SAGE (serial analysis of gene expression) and MPSS (massively parallel signature sequencing) [Irie 2003*, Harbers 2005*]. Both methods sequence short tags of a transcript’s 3′ end. SAGE uses conventional sequencing technology while MPSS uses Solexa, Inc.’s novel bead-based hybridization technology. One of the massive advantages of these technologies is the number of sequences they provide: large EST databases are on the order of several tens of thousands, while SAGE generally provides 100,000 to 200,00 tags and MPSS can provide over a million signatures. That being said, there are still questions regarding the sensitivity of the depth of coverage of the transcriptome. It may well be that despite a lower total sequence count, ESTs provide more information about what parts of the genome are expressed.

Fortunately, Gowda et al put all three methods to work as well as an RNA microarray (which doesn’t provide sequence, but enables its inference through hybridization) in their recent study of the Magnaporthe grisea transcriptome [Gowda 2006]. M. grisea is the causative agent of rice blast, a devastating disease that results in tremendous crop yield loss. The researchers evaluated two tissues types: the non-pathogenic mycelium and the invasive, plant penetrating appressorium.

Interestingly, 40% of the MPSS tags and 55% of the SAGE tags identified represent novel genes as they had no matches in the existing M. grisea JGI EST collection. Additionally, the authors found that no one method could identify the majority of the transcripts, but that a two-way combination of array data, MPSS or SAGE could provide over 80% of the total unique transcripts all of the methods identified. One additional suprise was that roughly a quarter of the genes identified also produced an antisense RNA, possibly for siRNA regulation of the gene.

The long story short appears to be that there is, as of yet, no magic bullet of a method. To adequately cover the transcriptome, multiple techniques are required.

*These references are, unfortunately, not located in an open access journal.

Whole genome tiling arrays

A recent paper describes the discovery of 9 new introns in Saccharomyces cerevisiae by Ron Davis’s group at Stanford, using high density tiling arrays from Affymetrix. The arrays are designed for both strands allow the detection of transcripts transcribed from both strands. The arrays were also put to work by the Davis and Steinmetz labs to create a high density map of transcription in yeast and for polymorphism mapping from the Kruglyak lab.

PNAS Yeast Transcriptional map

Whole genome tiling arrays have also been employed in other fungi. For example, Anita Sil’s group at UCSF constructed a random tiling array for Histoplasma capsulatum and used it to identify genes responding to reactive nitrogen species. A similar approach was used in Cryptococcus neoformans to investigate temperature regulated genes using random sequencing clones.

As the technology has become cheaper, it may become sensible to use a tiling array to detect transcripts rather than ESTs when attempting to annotate a genome. In the Histoplasma work transcriptional units could be identified from hybridization alone. Some of the algorithms will need some work to correct incorporate this information, and the sensitivity and density of the array will influence this. These techniques can be part of a resequencing approaches or fast genotyping progeny from QTL experiments when the sequence from both parents is known (or at least enough of the polymorphims for the genetic map).

What is superior about the current Affymetrix yeast tiling array is the inclusion of both strands. This allows detection of transcripts from both strands. Several anti-sense transcripts in yeast have been discovered recently including in the IME4 locus through more classical approaches, but perhaps many more await discovery with high resolution transcriptional data from whole genome tiling arrays.