Estudi de l'evolució de la formació professional agrària a Catalunya

El projecte és una anàlisi profunda sobre l'evolució de la formació professional agrària a Catalunya, sobretot en els darrers vint-i-cinc anys. Parteix de la recollida i recopilació de totes les dades disponibles respecte a la formació, tan reglada o inicial, com la contínua; amb la corresponent anàlisi de les dades. A partir d'aquesta anàlisi, i de l'experiència viscuda i la visió des del lloc de treball que ocupa l'autor en l'actualitat -cap de servei de formació agrària-, s'arriba a plantejaments sobre els desajustaments que s'hi veuen en la formació agrària, i les possibles perspectives de futur de la mateixa

Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications

Genome-wide mapping and functional analysis of copy number variation in the human genome

Copy Number Variation (CNV) has emerged as a major source of human genomic variation comprising benign and pathological variants. These deletions and duplications of genomic regions form a size continuum from small indels to whole chromosomal aneuploidies, and have been mapped by dozens of studies employing diverse methods. Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. There are several microarray-based technologies for mapping CNVs utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping and combination platforms. We developed methods to refine the mapping of CNVs in the human genome using high-resolution and high-throughput microarray technologies. We developed several aCGH platforms targeted to specific genomic regions in order to map CNVs in these regions of interest with high breakpoint accuracy. We also attempted to develop a multiplexed aCGH protocol to allow four different samples to be hybridized to the same array, thereby increasing the array CNV mapping efficiency. Alongside our efforts, several commercial array- based CNV detection platforms became available. We quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878 (Haraksingh et al. 2011). We found significant differences in performance and that sensitivity, total number, size range, and breakpoint resolution of CNV calls were highest for CNV focused arrays. Despite the rapidly growing appreciation for the extent of CNV in the human genome, evidence for their functional consequences remains limited. It is clear, that CNVs are theoretically capable of reorganizing functional elements of the genome by altering gene dosage, coding segments, and regulatory regions. Recently, several association studies have suggested that CNVs significantly impact certain disease phenotypes. Performing CNV-phenotype association studies requires cost-effective, unbiased, genome-wide, high-resolution mapping of common and rare CNVs. We used some of the best performing array-based technologies from our comparison to investigate the association of CNVs with various phenotypes; the NimbleGen 2.1 M CNV array for hereditary hearing loss, our custom NimbleGen Functional Elements and Variable Regions (FEVR) array for melanoma, our custom NimbleGen lexinome array for dyslexia, and the NimbleGen 2.1 M WG array for basal cell carcinoma. We found a relatively strong association between a deletion on chromosome 16 and hearing loss (Odds Ratio = 3.41). In addition, we investigated whether certain pathways were enriched for CNVs in cases versus controls, and whether the cases had a higher CNV load than the controls. Both of these analyses showed no difference in CNV load between the cases and controls. We found several other CNVs in genes already known to be associated with hearing loss, indicating the existence of multiple causative alleles in this sample set. We also found weaker CNV associations to melanoma and basal cell carcinoma. Finally, we attempted to measure the direct effects of CNVs on transcription using RNA-seq on 42 lymphoblastoid cell lines each containing one of three large CNVs known to be associated with Schizophrenia. We found that copy number within these large CNVs is generally not predictive of transcriptional activity indicating that complex dosage compensation mechanisms may exist. This work highlights the importance of high-resolution mapping of CNVs to understand their role in human genomic variation and their biological relevance

Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

Abstract Background High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4–489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0–86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies

Additional file 3: Figure S2. of Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

Cumulative frequencies of sizes of validated and non-validated CNV calls using platform specific algorithm. Cumulative frequencies of the sizes of validated CNVs are shown in red. Cumulative frequencies of sizes of non-validated CNVs are shown in green. CNV size is plotted on a log scale. Plots are shown for all arrays with more than 50 validated CNVs called using the platform specific algorithm. P-values were computed using a Mann–Whitney U test that corrects for ties and uses a continuity correction. The p-values correspond to a one-sided hypothesis. (PDF 88 kb

Additional file 5: Spreadsheet 1. of Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

Gold standard and array CNV chromosomal coordinates. Spreadsheet 1 lists the chromosomal coordinates of the gold standard CNVs and the CNVs called by the arrays. These data were used to compare the CNVs called by the arrays with those in the gold standard using the overlap criteria described in the methods. (XLSX 339 kb

Additional file 6: Table 3. of Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

Manifest and cluster files used in Genome Studio analysis of Illumina arrays. Lists the Illumina-supplied manifest and cluster files for each array that were used in Genome Studio analysis. These files were downloaded from http://support.illumina.com/array/downloads.html . (DOCX 57 kb

Prevalence of CYP2C19*2 and CYP2C19*3 Allelic Variants and Clopidogrel Use in Patients with Cardiovascular Disease in Trinidad & Tobago

The above slide deck represents the opinions of the authors. For a full list of declarations, including funding and author disclosure statements, and copyright information, please see the full text online. (see “read the peer-reviewed publication” opposite). </p

Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes