Búsqueda de ancentros comunes entre genomas de diferentes especies

La cerca de similituds entre regions de diferents genomes ofereix molta informació sobre les relaciones entre les especies d’aquest genomes. Es molt útil per a l’estudi de la conservació de gens d’una especia a un altre, de com les propietats d’un gen son assignades a un altre gen o de com es creen variacions en genomes diferents durant l’evolució d’aquestes especies. La finalitat d’aquest projecte es la creació d’una eina per a la cerca d’ancestres comuns de diferents especies basada en la comparació de la conservació entre regions dels genomes d’aquestes especies. Per a una comparació entre genomes mes eficaç una part important del projecte es destinarà a la creació d’una nova unitat de comparació. Aquestes noves unitats seran superestructures basades en agrupació dels MUMs existent per la mateixa comparació que anomenarem superMUMs. La aplicació final estarà disponible al servidor: http://revolutionresearch.uab.es

Optimització d'una aplicació bioinformàtica d'aliniament de seqüències executada en processadors many-core (GPUs)

Las herramientas de análisis de secuencias genómicas permiten a los biólogos identificar y entender regiones fundamentales que tienen implicación en enfermedades genéticas. Actualmente existe una necesidad de dotar al ámbito científico de herramientas de análisis eficientes. Este proyecto lleva a cabo una caracterización y análisis del rendimiento de algoritmos utilizados en la comparación de secuencias genómicas completas, y ejecutadas en arquitecturas MultiCore y ManyCore. A partir del análisis se evalúa la idoneidad de este tipo de arquitecturas para resolver el problema de comparar secuencias genómicas. Finalmente se propone una serie de modificaciones en las implementaciones de estos algoritmos con el objetivo de mejorar el rendimiento.

Optimització d'una aplicacio bioinformàtica d'alineament de seqüències executada en processadors multi-core i many-core (GPUs)

Las aplicaciones de alineamiento de secuencias son una herramienta importante para la comunidad científica. Estas aplicaciones bioinformáticas son usadas en muchos campos distintos como pueden ser la medicina, la biología, la farmacología, la genética, etc. A día de hoy los algoritmos de alineamiento de secuencias tienen una complejidad elevada y cada día tienen que manejar un volumen de datos más grande. Por esta razón se deben buscar alternativas para que estas aplicaciones sean capaces de manejar el aumento de tamaño que los bancos de secuencias están sufriendo día a día. En este proyecto se estudian y se investigan mejoras en este tipo de aplicaciones como puede ser el uso de sistemas paralelos que pueden mejorar el rendimiento notablemente.

Disseminació, integració i visualització de dades biològiques

La recent revolució en les tècniques de generació de dades genòmiques ha portat a una situació de creixement exponencial de la quantitat de dades generades i fa més necessari que mai el treball en la optimització de la gestió i maneig d'aquesta informació. En aquest treball s'han atacat tres vessants del problema: la disseminació de la informació, la integració de dades de diverses fonts i finalment la seva visualització. Basant-nos en el Sistema d'Anotacions Distribuides, DAS, hem creat un aplicatiu per a la creació automatitzada de noves fonts de dades en format estandaritzat i accessible programàticament a partir de fitxers de dades simples. Aquest progrtamari, easyDAS, està en funcionament a l'Institut Europeu de Bioinformàtica. Aquest sistema facilita i encoratja la compartició i disseminació de dades genòmiques en formats usables. jsDAS és una llibreria client de DAS que permet incorporar dades DAS en qualsevol aplicatiu web de manera senzilla i ràpida. Aprofitant els avantatges que ofereix DAS és capaç d'integrar dades de múltiples fonts de manera coherent i robusta. GenExp és el prototip de navegador genòmic basat en web altament interactiu i que facilita l'exploració dels genomes en temps real. És capaç d'integrar dades de quansevol font DAS i crear-ne una representació en client usant els últims avenços en tecnologies web.

Genome Biology and Evolution of the Animal Kingdom

Estudi realitzat a partir d’una estada a la Institut J.W. Jenkinson Laboratory for Evolution and Development of the University of Oxford, Regne Unit, entre 2010 i 2012. He estat membre del laboratori del Professor Peter W.H. Holland com a becari post-doctoral Beatriu de Pinós des de setembre de 2010 al setembre de 2012. El nostre projecte de recerca se centra en l'anàlisi genòmic comparatiu del Regne Animal, tot explorant el contingut dels genomes a través de totes les branques de l'arbre dels animals. Totes les referències a les meves publicacions durant aquest post-doc es poden trobar a http://about.me/jordi_paps. Crec que el nombre i la qualitat dels resultats del meu post-doc, un total de 8 publicacions incloent dos articles a la prestigiosa revista Nature, són prova de l'èxit d'aquest post-doc. Prof Peter W. H. Holland (Departament de Zoologia de la Universitat d'Oxford) i jo som coautors de tres articles de genòmica comparativa, resultats directes d'aquest projecte: 1) comparació de families gèniques entre vertebrats invertebrats (Briefings in Functional Genomics), 2) el genoma de l'ostra (publicat a la revista Nature), i 3) els genomes de 6 platihelmints paràsits (acceptat també a Nature). A més, tenim altres 2 treballs en preparació. Un d'ells analitza l'evolució, expressió i funció dels gens Hox al a la tènia Hymenolepis. El perfil fi d'aquests gens clau del desenvolupament esclareix els canvis d'estil de vida dels organismes. A més, durant aquest últim post-doc he participat en diverses col•laboracions, incloent anàlisi de gens d'envelliment a cucs plans, un estudi sobre la filogènia del grup Gastrotricha, una revisió de l'evolució phylum Platyhelminthes, així com un capítol d'un llibre sobre l'evolució dels animals bilaterals. Finalment, gràcies a la beca Beatriu de Pinós, el Prof. Peter W.H. Holland m'ha convidat a formar part del seu equip com un investigador post-doctoral en el seu projecte ERC Advance actual sobre duplicacions genòmiques.

Pseudogenes in the ENCODE regions: consensus annotation, analysis of transcription, and evolution

Arising from either retrotransposition or genomic duplication of functional genes, pseudogenes are “genomic fossils” valuable for exploring the dynamics and evolution of genes and genomes. Pseudogene identification is an important problem in computational genomics, and is also critical for obtaining an accurate picture of a genome’s structure and function. However, no consensus computational scheme for defining and detecting pseudogenes has been developed thus far. As part of the ENCyclopedia Of DNA Elements (ENCODE) project, we have compared several distinct pseudogene annotation strategies and found that different approaches and parameters often resulted in rather distinct sets of pseudogenes. We subsequently developed a consensus approach for annotating pseudogenes (derived from protein coding genes) in the ENCODE regions, resulting in 201 pseudogenes, two-thirds of which originated from retrotransposition. A survey of orthologs for these pseudogenes in 28 vertebrate genomes showed that a significant fraction (∼80%) of the processed pseudogenes are primate-specific sequences, highlighting the increasing retrotransposition activity in primates. Analysis of sequence conservation and variation also demonstrated that most pseudogenes evolve neutrally, and processed pseudogenes appear to have lost their coding potential immediately or soon after their emergence. In order to explore the functional implication of pseudogene prevalence, we have extensively examined the transcriptional activity of the ENCODE pseudogenes. We performed systematic series of pseudogene-specific RACE analyses. These, together with complementary evidence derived from tiling microarrays and high throughput sequencing, demonstrated that at least a fifth of the 201 pseudogenes are transcribed in one or more cell lines or tissues.

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

Comparative gene prediction in human and mouse

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.

A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library

The construction of metagenomic libraries has permitted the study of microorganisms resistant to isolation and the analysis of 16S rDNA sequences has been used for over two decades to examine bacterial biodiversity. Here, we show that the analysis of random sequence reads (RSRs) instead of 16S is a suitable shortcut to estimate the biodiversity of a bacterial community from metagenomic libraries. We generated 10,010 RSRs from a metagenomic library of microorganisms found in human faecal samples. Then searched them using the program BLASTN against a prokaryotic sequence database to assign a taxon to each RSR. The results were compared with those obtained by screening and analysing the clones containing 16S rDNA sequences in the whole library. We found that the biodiversity observed by RSR analysis is consistent with that obtained by 16S rDNA. We also show that RSRs are suitable to compare the biodiversity between different metagenomic libraries. RSRs can thus provide a good estimate of the biodiversity of a metagenomic library and, as an alternative to 16S, this approach is both faster and cheaper.

EGASP: the human ENCODE Genome Annotation Assessment Project

Background: We present the results of EGASP, a community experiment to assess the state-ofthe-art in genome annotation within the ENCODE regions, which span 1% of the human genomesequence. The experiment had two major goals: the assessment of the accuracy of computationalmethods to predict protein coding genes; and the overall assessment of the completeness of thecurrent human genome annotations as represented in the ENCODE regions. For thecomputational prediction assessment, eighteen groups contributed gene predictions. Weevaluated these submissions against each other based on a ‘reference set’ of annotationsgenerated as part of the GENCODE project. These annotations were not available to theprediction groups prior to the submission deadline, so that their predictions were blind and anexternal advisory committee could perform a fair assessment.Results: The best methods had at least one gene transcript correctly predicted for close to 70%of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into accountalternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotidelevel, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programsrelying on mRNA and protein sequences were the most accurate in reproducing the manuallycurated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could beverified.Conclusions: This is the first such experiment in human DNA, and we have followed thestandards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe theresults presented here contribute to the value of ongoing large-scale annotation projects and shouldguide further experimental methods when being scaled up to the entire human genome sequence.

Comparative gene finding in chicken indicates that we are closing in on the set of multi-exonic widely-expressed human genes

The recent availability of the chicken genome sequence poses the question of whether there are human protein-coding genes conserved in chicken that are currently not included in the human gene catalog. Here, we show, using comparative gene finding followed by experimental verification of exon pairs by RT–PCR, that the addition to the multi-exonic subset of this catalog could be as little as 0.2%, suggesting that we may be closing in on the human gene set. Our protocol, however, has two shortcomings: (i) the bioinformatic screening of the predicted genes, applied to filter out false positives, cannot handle intronless genes; and (ii) the experimental verification could fail to identify expression at a specific developmental time. This highlights the importance of developing methods that could provide a reliable estimate of the number of these two types of genes.

Nematode selenoproteome: the use of the selenocysteine insertion system to decode one codon in an animal genome?

Selenocysteine (Sec) is co-translationally inserted into selenoproteins in response to codon UGA with the help of the selenocysteine insertion sequence (SECIS) element. The number of selenoproteins in animals varies, with humans having 25 and mice having 24 selenoproteins. To date, however, only one selenoprotein, thioredoxin reductase, has been detected in Caenorhabditis elegans, and this enzyme contains only one Sec. Here, we characterize the selenoproteomes of C.elegans and Caenorhabditis briggsae with three independent algorithms, one searching for pairs of homologous nematode SECIS elements, another searching for Cys- or Sec-containing homologs of potential nematode selenoprotein genes and the third identifying Sec-containing homologs of annotated nematode proteins. These methods suggest that thioredoxin reductase is the only Sec-containing protein in the C.elegans and C.briggsae genomes. In contrast, we identified additional selenoproteins in other nematodes. Assuming that Sec insertion mechanisms are conserved between nematodes and other eukaryotes, the data suggest that nematode selenoproteomes were reduced during evolution, and that in an extreme reduction case Sec insertion systems probably decode only a single UGA codon in C.elegans and C.briggsae genomes. In addition, all detected genes had a rare form of SECIS element containing a guanosine in place of a conserved adenosine present in most other SECIS structures, suggesting that in organisms with small selenoproteomes SECIS elements may change rapidly.

Gene finding in the chicken genome

Background: Despite the continuous production of genome sequence for a number of organisms,reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularlytrue for genomes for which there is not a large collection of known gene sequences, such as therecently published chicken genome. We used the chicken sequence to test comparative andhomology-based gene-finding methods followed by experimental validation as an effective genomeannotation method.Results: We performed experimental evaluation by RT-PCR of three different computational genefinders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram wascomputed and each component of it was evaluated. The results showed that de novo comparativemethods can identify up to about 700 chicken genes with no previous evidence of expression, andcan correctly extend about 40% of homology-based predictions at the 5' end.Conclusions: De novo comparative gene prediction followed by experimental verification iseffective at enhancing the annotation of the newly sequenced genomes provided by standardhomology-based methods.

Deciphering 3'ss selection in the yeast genome reveals an RNA thermosensor that mediates alternative splicing

Poor understanding of the spliceosomal mechanisms to select intronic 3' ends (3'ss) is a major obstacle to deciphering eukaryotic genomes. Here, we discern the rules for global 3'ss selection in yeast. We show that, in contrast to the uniformity of yeast splicing, the spliceosome uses all available 3'ss within a distance window from the intronic branch site (BS), and that in 70% of all possible 3'ss this is likely to be mediated by pre-mRNA structures. Our results reveal that one of these RNA folds acts as an RNA thermosensor, modulating alternative splicing in response to heat shock by controlling alternate 3'ss availability. Thus, our data point to a deeper role for the pre-mRNA in the control of its own fate, and to a simple mechanism for some alternative splicing.