Data analysis methods for copy number discovery and interpretation

Copy number variation (CNV) is an important type of genetic variation that can give rise to a wide variety of phenotypic traits. Differences in copy number are thought to play major roles in processes that involve dosage sensitive genes, providing beneficial, deleterious or neutral modifications to individual phenotypes. Copy number analysis has long been a standard in clinical cytogenetic laboratories. Gene deletions and duplications can often be linked with genetic Syndromes such as: the 7q11.23 deletion of Williams-­‐Bueren Syndrome, the 22q11 deletion of DiGeorge syndrome and the 17q11.2 duplication of Potocki-­‐Lupski syndrome. Interestingly, copy number based genomic disorders often display reciprocal deletion / duplication syndromes, with the latter frequently exhibiting milder symptoms. Moreover, the study of chromosomal imbalances plays a key role in cancer research. The datasets used for the development of analysis methods during this project are generated as part of the cutting-­‐edge translational project, Deciphering Developmental Disorders (DDD). This project, the DDD, is the first of its kind and will directly apply state of the art technologies, in the form of ultra-­‐high resolution microarray and next generation sequencing (NGS), to real-­‐time genetic clinical practice. It is collaboration between the Wellcome Trust Sanger Institute (WTSI) and the National Health Service (NHS) involving the 24 regional genetic services across the UK and Ireland. Although the application of DNA microarrays for the detection of CNVs is well established, individual change point detection algorithms often display variable performances. The definition of an optimal set of parameters for achieving a certain level of performance is rarely straightforward, especially where data qualities vary ... [cont.]

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Positive selection of hearing loss candidate genes,based on multiple microarray platforms experiments and data mining

2006/2007Secondo le stime del World Health Organization, le perdite uditive colpiscono circa 278 milioni di persone in tutto il mondo. Approssimativamente 1 bambino ogni 100, nasce con problemi d’udito. Nonostante l’identificazione negli ultimi 10 anni di più di 100 loci genetici associati a fenotipi di perdita uditiva, non tutti i corrispettivi geni causativi sono stati identificati. Normalmente utilizzando un approccio sperimentale di linkage tradizionale non è sempre possibile identificare un intervallo genomico sufficientemente corto da essere analizzato per la ricerca di mutazioni. Il lavoro presentato in questa tesi ha lo scopo di selezionare un set limitato di geni potenzialmente coinvolti nelle perdite uditive non sindromiche, utilizzando la combinazione di un approccio biologico e bioinformatico. Il punto di partenza dell’analisi è stato il gene GJB2. Il gene GJB2 codifica la Connessina 26, proteina coinvolta nella formazione delle gap junction tra le cellule, ma anche implicata in più del 50% dei casi di perdite uditive non sindromiche. Per questa ragione è stato suggerito un ruolo chiave nella biologia dell’orecchio, che va oltre la sua funzione di proteina canale. In questa tesi è stato esaminato il profilo d’espressione genica di cellule HeLa transfettate con la forma naturale e con delle forme mutate della Connessina26. Le analisi dei dati hanno identificato numerosi geni differenzialmente espressi e si è quindi deciso di passare ad un approccio informatico per ridurne il numero. Questa analisi ha permesso di identificare 19 geni in 11 loci privi di geni causativi selezionandoli in base alla loro espressione rispetto librerie di cDNA prodotte da orecchio. Sono stati quindi identificati i geni omologhi in topo per 5 dei 19 geni, con lo scopo di verificare la loro rilevanza con la perdita uditiva. Per tutti questi 5 geni è stata confermata l’espressione nell’organo di corti in topo e con Real-time RT-PCR nelle linee cellulari transfettate impiegate negli esperimenti di microarray. Il progetto proseguirà ora con lo screening di mutazioni nei geni candidati in famiglie di pazienti selezionate.According to WHO estimates hearing impairment affects 278 million people worldwide. Approximately 1/1000 children are born with a significant hearing impairment. To date approximately 100 genetic loci involved in deafness have been described. Despite the fact that such a large number of genetic locations associated with deafness phenotypes are known, not all the genes involved have been identified yet. Using a traditional linkage approach, however, it is not always possible to map a locus to intervals short enough to be amenable for costly mutation analysis. So far no more than 40 deafness genes have been identified and these encode very heterogeneous proteins. The work presented in this thesis aims to identify a limited set of candidate genes with high potential to be involved in Non-Syndromic Hearing Loss using a combination of biological and bioinformatics approaches. The starting point of the analysis was the GJB2 gene. The GJB2 gene encodes for the gap junction protein Connexin26 and is responsible for more than half of the non-syndromic hearing loss cases. For this reason it has been proposed that this protein might play a wider role in the biology of the ear, beyond its mere channel function. I therefore performed whole genome expression profiles of HeLa cells transfected with the wild type form of the GJB2 gene and compared them to that of cells transfected with mutant forms of this gene to shed light on its function. Initially this experiment yielded a bewildering number of differentially expressed genes (4,984). Thus I devised an in silico strategy to narrow down this number, focusing on genes which were positionally linked to specific non-syndromic hereditary hearing loss conditions, as well as found within human ear cDNA libraries, thus potentially causative of the disease. This further analysis yielded 19 genes within 11 loci. In order to assess their relevance to hearing loss, the mouse homologs of these genes were identified for 5 of them and indeed they were all found to be expressed in the mouse organ of corti. These five genes were also validated by Real-time RT-PCR in the human cell line used for the microarray experiments.197

Bioinformatics protocols for analysis of functional genomics data applied to neuropathy microarray datasets

Microarray technology allows the simultaneous measurement of the abundance of thousands of transcripts in living cells. The high-throughput nature of microarray technology means that automatic analytical procedures are required to handle the sheer amount of data, typically generated in a single microarray experiment. Along these lines, this work presents a contribution to the automatic analysis of microarray data by attempting to construct protocols for the validation of publicly available methods for microarray. At the experimental level, an evaluation of amplification of RNA targets prior to hybridisation with the physical array was undertaken. This had the important consequence of revealing the extent to which the significance of intensity ratios between varying biological conditions may be compromised following amplification as well as identifying the underlying cause of this effect. On the basis of these findings, recommendations regarding the usability of RNA amplification protocols with microarray screening were drawn in the context of varying microarray experimental conditions. On the data analysis side, this work has had the important outcome of developing an automatic framework for the validation of functional analysis methods for microarray. This is based on using a GO semantic similarity scoring metric to assess the similarity between functional terms found enriched by functional analysis of a model dataset and those anticipated from prior knowledge of the biological phenomenon under study. Using such validation system, this work has shown, for the first time, that ‘Catmap’, an early functional analysis method performs better than the more recent and most popular methods of its kind. Crucially, the effectiveness of this validation system implies that such system may be reliably adopted for validation of newly developed functional analysis methods for microarray