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.

• 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
  

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.

[...] the addition of GS FLX Paired End reads vastly improved the capability of 454 pyrosequencing by enabling the assembly of contigs into large scaffolds. Indeed, in terms of the number of scaffolds produced, the GS FLX assembly that included the combined shotgun and paired end reads was comparable to the Sanger assembly. Moreover, the order of the GS FLX scaffolds could be established from information from BAC-end sequences and the Atlantic salmon physical map. However, numerous gaps remained within the scaffolds, which is undesirable when a complete or reference genome sequence is one of the goals. Currently, if the Atlantic salmon genome is to provide a reference sequence for all salmonids, then a substantial proportion of the sequencing will have to be carried out using Sanger technology.