castanyes blaves

The skills necessary within the Facility include the following.

1.

An intimate knowledge of UNIX-based operating systems.
2.

Understanding of a scripting language such as Perl.
3.

An understanding of parallel computing environments for UNIX clusters.
4.

Knowledge of network-based data storage.
5.

General knowledge of biology and genome sciences.
6.

Ability to derive data analysis and software requirements from investigators who do not have a sophisticated understanding of information technology.
7.

Ability to develop software encapsulating new analysis methods.
8.

Understanding of relational databases and database architecture.
9.

Ability to seek out and test novel bioinformatics software and analysis routines.

Amemiya and his co-workers became suspicious that the lamprey's genome structure and composition was changing during development when they heard rumors lamprey genome sequences efforts were being complicated by genome fragmentation. They speculated that this might be due to genome rearrangements similar to those described for the hagfish, a chordate and superficially similar organism.

To test this, the researchers compared germ line and somatic tissues from sea lamprey caught in Lake Michigan.

Indeed, they found that the genome was larger in sperm (germ line cells) than in adult blood nuclei (somatic cells), even within the same individual. The sperm cells also contained more DNA than kidney and liver cells, which both had similar DNA content to red blood cells. Overall, the researchers noted, sperm genomes contained some 20 percent more DNA than adult cells such as red blood cells.

• New interesting cDNAs that don't have hits against existing public cDNAs -- What do they do?
  
• Expression patterns -- Are these different to the patterns in other close species?
  
• Protein coding evolution -- run pairwise dNdS against closest genome or tree-based dNdS against an existing phylogeny [1,2] -- Does anything show up in a Gene Ontology enrichment analysis?

• Transcriptome coverage is hugely biased to the 3' end (or 5' depending on library preparation). A lot more than I suspected.
• Would be great to do queries for phylogenetic subclades: e.g. my human sequence against all SRA data for primates.
• A lot of the 454 data has homopolymer issues, mostly TTT[...]TTTs but also some others:
  

Query  465  GGGCCTTGACAAAGTGTAAACCGCATGGATGGGCTTCCCC-AAGGATTTATTGACATTGC  523<br /><font color="#ff0000"><b>Sbjct</b></font>  249  ........................................<font color="#ff0000"><b>C</b></font>...................  190<br /><br /></pre><ul><li>Some of these (unless they are real variations) get picked up as mismatches, some as indels:<pre>Query  1    CGGCAAGGTATGTGCGTGATTTTGGGCCCACGTGTATTTCCATTAATTTT-AAGCCGTAA  59<br /><font color="#ff0000"><b>Sbjct</b></font>  224  ..................................................<font color="#ff0000"><b>T</b></font>.........  165<br /><br />Query  60   TTGTCGTTTTTGGCGGTTTCGAGTTGAACTGCGTTAGTCCGTGCGCTGTTCGCAAGTGTG  119<br /><font color="#ff0000"><b>Sbjct</b></font>  164  ..........<font color="#ff0000"><b>C</b></font>.................................................  105<br /></pre></li></ul><pre><br />Query  61   TGTCGTTTTTGGCGGTTTCGAGTTGAACTGCGTTAGTCCGTGCGCTGTTCGCAAGTGTGC  120<br /><font color="#ff0000"><b>Sbjct</b></font>  118  .....<font color="#ff0000"><b>C</b></font>......................................................  177<br />Query  61   TGTCG-TTTTTGGCGGTTTCGAGTTGAACTGCGTTAGTCCGTGCGCTGTTCGCAAGTGTG  119<br /><font color="#ff0000"><b>Sbjct</b></font>  160  .....<font color="#ff0000"><b>T</b></font>...............................<font color="#ff0000"><b>-</b></font>......................  102<br />Query  61   TTAATTTTAAGCCGTAATTGTCGTTTTTGGCGGTTTCGAGTTGAACTGCGTTAGTCCGTG  120<br /><font color="#ff0000"><b>Sbjct</b></font>  181  ...........................<font color="#ff0000"><b>C</b></font>................................  122<br /><br /><br /><br />

• Detected sequencing error, correction needed for the reference
• Detected alternative haplotype
  

1. UniprotKB/Swiss-Prot
2. NCBI RefSeq proteins and mRNAs
3. UniprotKB/TrEMBL
4. EMBL Nucleotide Sequence Archive

• perfect
• added start
• longer region
• missing start
• non-matching start
• non-matching region
• shorter region
• ...

The Tammar Wallaby (Macropus eugenii), also known as the Dama Wallaby or Darma Wallaby, is a small member of the kangaroo family and is the type species for research on kangaroos and marsupials.

It is found on offshore islands on the South Australian and Western Australian coast. It is classified as vermin on Kangaroo Island, where it seasonally breeds in large numbers and damages the echidna habitat on the island.

• HGNC has made great progress in solving nomenclatures for 130 cases where the community has a diversity of opinions and it's difficult to agree on something. Very good point in saying that in the Internet era it's better to give a gene a name that is distinctive to common words that would clobber your Google search results. There is now a forum set up for different communities to use in discussions for gene family names.
• Havana now has started using RNAseq data to confirm new genes found in human and zebrafish that didn't have evidence before. One new feature is a "confirmed intron" for when paired Solexa reads bridge two exons, with an associated score for read depth. Confident this type of data will bring out many interesting new annotations that couldn't be found before, e.g. genes expressed in a given tissue during a lapse of a few hours in the development. There are already a few examples in zebrafish.
• Wormbase has been working hard on compiling more data from the modENCODE. Small but cool infrastructure achievement in having VMware images running for old releases that investigators can just pull and bring up on demand.
• Ensembl Genomes has been successfully testing the beta sites for Bacteria, Protists and the first Metazoa build. Another Metazoa build is in progress, with all the phylogenetics goodness of the 12 Drosophila genomes plus the vectors plus C.elegans and a few other outgroups. The modENCODE project is about to complete the re-annotation of gene models using CAGE data that will bring more precise gene starts for melano and elegans. Ensembl Genomes is still working together with Manchester and now the US to put together an Aspergillus resource that provides the best value for money to researchers. PombBase is also being pursued, lots of labs interested in having it Ensemblified and ready to use.

The ape package written by Emmanuel Paradis is the foundation for phylogenetic analyses in R. Yesterday, Paradis and his coauthors posted a new version (3.2) on the CRAN archive yesterday. There don't seem to be too many new functions, but there are some important bug fixes. One these - preventing calculation of negative state probabilities when reconstructing discrete character states - solves one of the more vexing problems I've had with the ace function. You should definitely get the update if you're doing ancestral reconstruction of discretely coded traits! Now we just need to hope the April 17th upgrade to R 2.9 goes smoothly...

Sugarcane genes associated with sucrose content.

Papini-Terzi FS, Rocha FR, Vencio RZ, Felix JM, Branco DS, Waclawovsky AJ, Del Bem LE, Lembke CG, Costa MD, Nishiyama MY Jr, Vicentini R, Vincentz MG, Ulian EC, Menossi M, Souza GM.

ABSTRACT: BACKGROUND: Sucrose content is a highly desirable trait in sugarcane as the worldwide demand for cost-effective biofuels surges. Sugarcane cultivars differ in their capacity to accumulate sucrose and breeding programs routinely perform crosses to identify genotypes able to produce more sucrose. Sucrose content in the mature internodes reach around 20% of the culms dry weight. Genotypes in the populations reflect their genetic program and may display contrasting growth, development, and physiology, all of which affect carbohydrate metabolism. Few studies have profiled gene expression related to sugarcanes sugar content. The identification of signal transduction components and transcription factors that might regulate sugar accumulation is highly desirable if we are to improve this characteristic of sugarcane plants. RESULTS: We have evaluated thirty genotypes that have different Brix (sugar) levels and identified genes differentially expressed in internodes using cDNA microarrays. These genes were compared to existing gene expression data for sugarcane plants subjected to diverse stress and hormone treatments. The comparisons revealed a strong overlap between the drought and sucrose-content datasets and a limited overlap with ABA signaling. Genes associated with sucrose content were extensively validated by qRT-PCR, which highlighted several protein kinases and transcription factors that are likely to be regulators of sucrose accumulation. The data also indicate that aquaporins, as well as lignin biosynthesis and cell wall metabolism genes, are strongly related to sucrose accumulation. Moreover, sucrose-associated genes were shown to be directly responsive to short term sucrose stimuli, confirming their role in sugar-related pathways. CONCLUSION: Gene expression analysis of sugarcane populations contrasting for sucrose content indicated a possible overlap with drought and cell wall metabolism processes and suggested signaling and transcriptional regulators to be used as molecular markers in breeding programs. Transgenic research is necessary to further clarify the role of the genes and define targets useful for sugarcane improvement programs based on transgenic plants.

As more and more data is being poured into the public sequence databases, an increasingly detailed map is being drawn that relates sequences from different individuals or different species, mainly into what has been known in the field as protein or genomic (DNA) alignments. This is what one can call twenty-first century molecular cartography.

All references to molecular evolution this year should be accompanied with an analogy to Darwin's work, so here is how it works in this case: Darwin's next generation machine, the Beagle, went on a journey to accumulate an enormous variety of specimens that, when compared all together, allowed Darwin to draw the first phylogenetic tree.

Contrary to what one would think, alignments with more sequences are easier to resolve than ones with less sequences, at least when the phylogenetic tree relating the sequences increases in detail, which is almost always. And this is allowing researchers to generate genomic alignments for phylogenetically dense groups of genomes while, in parallel, the protein alignments for the corresponding protein coding genes in these genomes are combined together with more distantly related species. This dense taxon sampling is making the distinction between protein alignments and genomic alignments less and less obvious.

As an example, one can use the highly conserved protein coding exons to anchor the points in the different chromosomes that define stretches of conserved synteny among the genomes, and then align these DNA stretches all together with a genomic aligner. At the same time, one can use the exon boundaries defined in the DNA sequences of the coding genes to help infer the right protein alignment at the aminoacidic level.

A new opportunity is now arising in exploiting the information that is contained separately in the genomic and protein alignments to combine them into a single object representing both. New methods are being developed that will exploit the landmarks that both genomic and protein alignments have correctly place to converge into a single intertwined alignment object. This new type of alignment has in a way already been represented in closely related prokaryotic genomes. But prokaryotic genomes are less interesting for some topics, like alternative splicing, repetitive elements or recombination hotspots. Combined genomic and protein alignments will bring new elements of detail together that have been scattered so far for researchers to study and hopefully some new and brilliant mechanistic explanations of the innards of molecular evolution will arise from them, in the same way that Darwin did two centuries ago.

So a deluge of sequencing data is not really a problem but an opportunity.