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UZ Gent Centrum voor Medische Genetica

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Abstract Bachelor Project FBT 2018-2019In vitro evaluation of new treatments in ovarian cancer cell lines

Ovarian cancer is the seventh most commonly diagnosed cancer among women in the world and unfortunately associated with bad prognosis. Despite high initial response to currently used therapies, most patients relapse and develop chemoresistance. Alternative molecular based specific therapies for patients with ovarian cancer are urgently needed to improve clinical outcomes and the quality of life.

The aim of this study is to evaluate the cytotoxic effects of both bromodomain and extra- terminal motif inhibitor (BETi) and MAPK/ERK-kinase inhibitor (MEKi) and their relation to an activated mitogen-activated protein kinase pathway (MAPKp) in ovarian cancer cell lines. First, the KRAS mutation c.35G > T was evaluated in several cell lines by Sanger sequencing.

Secondly, both single and combination therapies of a BETi (1 µM) and MEKi (1 µM) were evaluated on the following ovarian cancer cell lines: ES-2, M28/2, SKOV-3 and A2780 by in vitro plate based assays. The cytotoxicity was evaluated in three ways: 1) Crystal violet staining is a cheap and indirect method to quantify possible cytotoxicity by measuring the cell viability, 2) Apoptosis was evaluated by a luminescent assay, called Caspase-Glo® 3/7 that determinates caspase-3 en -7 activity, 3) Proliferation was observed using IncuCyte®, with subjective masking.

The KRAS mutation c.35G > T was detected in the M28/2 cell line which leads to a protein modification resulting in an ATP-independent MAPKp activation. Hypothetically, there is more apoptosis in cells treated with BETi which has cytostatic effects compared to untreated cells. More apoptosis is expected in M28/2 in the test condition MEKi in comparison to BETi because MEKi induces direct apoptosis in the MAPKp activated cells resulting in a higher cytotoxicity compared to BETi. In the non-mutated cell lines (A2780 and SKOV-3), the difference between BETi and MEKi depends on the degree of MAPKp activation. A synergy has to be observed in the wild-type cell lines because of the double blockade of MEK and BET pathway and increased activation of MAPKp induced by JQ1. More apoptosis is expected in the test conditions MEKi and MEKi + BETi in the M28/2 in comparison to a non-mutated cell line, due to the constitutive activation of the MAPKp. Literature reported that ES-2 has a BRAF-mutation which causes increased sensitivity for MEKi. There is more information needed about the effect on the MAPKp to make a hypothesis about the difference between MEKi and BETi, but there is definitely a synergy expected because of the double BET and MEK blockade.

The crystal violet staining reveals that the viability is significant higher in the untreated cells compared to the test conditions containing MEKi, BETi or a combination of both (p – value < 0,05). Between MEKi, BETi or MEKi + BETi there is no significant difference found for the M28/2 and A2780 cell line (p-value > 0,05).

The same trends were obtained by the caspasetest showing more apoptosis in the test condition with the inhibitors compared to untreated cells. In contrast to the crystal violet assay, caspase3/7 showed significant differences between MEKi, BETi and MEKi + BETi for the M28/2 and ES-2 cell line (p-value < 0,05). For the M28/2 cell line there is more apoptosis measured in the test condition MEKi in comparision to BETi confirming the hypothesis. Furthermore cytotoxic synergy is observed in both cell lines when BETi and MEKi are combined.

The SKOV-3 cell line gives the most expected results for the IncuCyte® experiment; the cell proliferation is inhibited in the test condition containing MEKi or BETi with a noticeable synergy seen when both inhibitors are combined.

In future studies more reliable results can be generated by improving the standard deviations and repeatibility. Therefore, critical steps such as seeding of cells and application/removal of fluids from wells need standardization. Eventually the wash steps during crystal violet staining can be optimized. In addition to that, more technical and real repeats are needed to assure the reproducibility of the assays.

 

Abstract advanced bachelor of bioinformatics (1) 2018-2019: Benchmarking of protein coding potential prediction algorithms on small ORF datasets

Protein coding prediction algorithms are tools that predict the coding potential of protein sequences. Most of these prediction algorithms work on and are benchmarked on long open reading frames (ORF; ≥ 300 nucleotides). The aim of this research is to compare a selection of such algorithms and benchmark them on small open reading frames (sORF; <300 nucleotides). The selected algorithms were CPAT, PLEK and PORTRAIT. First, four different sORF datasets were obtained from the sORF.org website, an online collection of known sORFs based primarily on ribosome profiling studies. These four datasets comprise either all sORFs or subsets based on conservation and whether or not the sORFs were reported in the landmark Bazzini et al,2012. After the analysis with the three different prediction tools, CPAT and PORTRAIT had similar results. With CPAT a specificity was obtained ranging from 23.80% (highly conserved sORFs reported by Bazzini et al) to 44% (full set) and for PORTRAIT slightly higher specificities were found (between 31,74% and 48%). PLEK could not predict any of the protein coding small ORFs and as such had a specificity of 0% for all four datasets. To study the effect of the size of the ORF on the predictions, a second benchmarking approach was made by creating positive and negative datasets in silico. The positive sets are made by truncating sequences of known protein coding genes. Three positive sets were created, with a length of 150, 300 and 450 nucleotides (nt), followed by a stopcodon. For the negative sets, known non-coding sequences were used, adding a start and stopcodon at the beginning and end of the 150, 300 and 450 nt sequences. After the analysis, CPAT had a specificity ranging from 32.29% (150 nt) to 94.33% (450nt) and a sensitivity ranging from 95.52% (450nt) to 99.25% (50nt). This implying the specificity increases, and sensitivity decreases over the nucleotide length. PORTRAIT only obtained results for sequences longer 160 nucleotides, with a specificity ranging from 76.58% (300 nt) to 94.33% (450 nt) and a sensitivity of 100% (150 nt) to 83.80% (450 nt). Indicating the same trend as CPAT, with a increasing specificity and decreasing sensitivity over sequence length. PLEK had a specificity of 0% for the 150 and 300 nt sets. This moved up slowly with a 2.20% specificity for the 450 nt sequences. It did record the highest sensitivity ranging from 99.98% (450 nt) to 100% (150 nt and 300 nt). This research shows that none of the selected algorithms recorded a high specificity for detecting the coding potential for small ORFs lower than 160 sequences. For small ORFs longer than 160 nucleotides CPAT and PORTRAIT are both reliable algorithms, keeping in mind that PORTRAIT has a lower sensitivity. PLEK, however, is not advised to predict the coding potential of small ORF, not being able to predict any protein coding small ORF. Further studies are necessary to analyze the existing algorithms to what cause this bias for long ORF, and how to create accurate, and therefore reliable algorithms for the prediction of small ORF.

Abstract advanced bachelor of bioinformatics (2) 2018-2019: Decoding plasma RNA profiles

Is it possible to use circular RNA (circRNA) as a cancer biomarker? To answer this question, a pilot project was started where circRNA data from plasma pools (1 pool per cancer type) was collected in the Center for Medical Genetics Ghent. To proceed with the research and discovery of these potential biomarkers for cancers, the database will be extended with more samples in the near future. However, in this project the comparison was made between circRNA profiles in these plasma pools and in the tissue of origin of the cancer. The publicly available data from MiOncoCirc will be used for the comparison with the inhouse data, this dataset contains 2000+ cancer tissue samples across 40 cancer types (Josh N. Vo, 2019) (The University of Michigan, sd). To compare the MiOncoCirc with the inhouse data, it needs to be adapted to a useable format by making use of the R programming language. The data will also be visualized to get an overview of how the data is formatted and to select the data that will be useable for the project. As a start to the project the MiOncoCirc data was adapted to a more useable format. This was accomplished by firstly cleaning up the data to make sure the data is in the same format as the inhouse plasma data. In order to do so, all cancer type annotations were matched, data was capitalized and spaces were replaced by underscores. In the original data no identifier was given for the different circRNAs so these were created by merging the chromosome name with the start and end position on the chromosome. Because of the size of the dataset there where some memory issues when a count matrix was constructed. As a solution, a more compact matrix was created by making use of the min, max, mean or median of the counts. The data exploration part of the project consisted out of  General statistics: Number of samples for each cancer type, distribution of gender for the cancer types, are some more common in female/male?  Adaptation of existing scripts for the calculation of fold change and specificity.  Shiny app for visualization of circRNA counts and showing the data from fold change/specificity calculations. To visualize the relationship between the two datasets, Venn diagrams were used. These Venn-diagrams visualize the common circRNAs for each cancer type in relation to each other. From the visualized relation between the two datasets a conclusion can be made that the data from MiOncoCirc is not viable to include in the inhouse dataset at this moment for this specific project, because of the minimal overlap between the 2 data sets. To exclude the possibility that this is a side-effect of different preprocessing, the raw data from MiOncoCirc will be rerun with the inhouse pipeline to make sure the preprocessing of the data is done in the same way.

References

Josh N. Vo, M. C. (2019). The Landscape of Circular RNA in Cancer. The University of Michigan. (sd). mioncocirc. Found at https://mioncocirc.github.io/.

Abstract advanced bachelor of bioinformatics (3) 2018-2019: Genomics data management for analysis and visualization tools

The project is part of the genomics data management platform, a platform used in a flexible analysis environment for pipelines and visualizations among others. An important aspect to keep in mind for this data management are the FAIR principles. FAIR stands for Findable, Accessible, Interoperable and Reusable and is embraced by the Global Alliance for Genomics and Health (GA4GH). It represents the idea to have a general groundwork for data-sharing infrastructures. If these principles are followed, all researchers, clinicians, pipelines,… know that the data can be obtained in a clear, standardized way. Before starting on the main code for the platform, a basic understanding of the MinIO servers was needed. MinIO is an implementation that offers object storage conform with the Amazon Web Service (AWS) S3 REST API. With S3 standing for Simple Storage Service and REST API meaning Representational State Transfer Application Programming Interface. The server is made to store unstructured data, called objects, that can be organized in clearly labelled buckets. The primary work of the project was done on an API codebase for the Data Repository Service (DRS). The DRS API provides a generic interface so a user or workflow can access the data in a standardized way. This is done by using a logical identifier to retrieve the data it represents. The ID itself however has some guidelines. It needs to be URL-safe, always link to the same object and there may be more than one ID per object. The DRS API was split up into multiple scripts to keep a clear overview of everything and to make sure no code repetition was done. The API is started with the main.py script. All routes from the get statements that fetch the needed data, are grouped in the api.py script before being imported in the main file. There are four get statements used in the DRS API. The first one is the GET /bundles/{bundle_id} that returns the bundle metadata and a list of ids that are used to obtain the bundle contents. GET /objects/{object_id} returns in its turn the object metadata and a list of the access methods for the retrieval of the object bytes. The GET /objects/{object_id}/access/ {access_id} goes further on the previous one. It will return a URL that links to the MINIO server in order to collect the object. this will only be called if an access_id is given. An example of when this is the case, is when a server uses a signed URL to collect the object bytes. The last get statement is GET /service-info. It is designed to return the service version and other information. Models were made for the objects, bundles and service-info so that the metadata always gets shown in the same standard way. In order to store the data that link to these models, an SQLite database was made. In this database all the important groups of data from the API models get put in corresponding tables. It is set up in such a way so the right data can be found for each object and/or bundle. The previously mentioned get statements use these models for their output. The end result is to get a link from the DRS to the previously mentioned object storage. This link is a URL that can be optionally secured with an access layer that works with tokens from Oauth2. By making sure that the FAIR principles are kept in mind, users have an easy way to retrieve the desired objects or bundles while the output always remains in the same format. This level of consistency is crucial in a professional work environment in order to maintain a good flowing data sharing system.

Abstract traineeship advanced bachelor of bioinformatics 1 2017-2018: Exploratory data analysis on Total RNA Sequencing from COPD patients

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening pulmonary disease characterized by a persistent airflow limitation and destruction of alveolar walls (=emphysema), whose pathobiology is not completely understood. COPD results from a complex interplay between genetic susceptibility and environmental exposure, most importantly tobacco smoke. Nevertheless, it is estimated that only 15-20% of smokers develop COPD, suggesting that underlying (epi)genetic mechanisms could be involved. The goal of this study is to identify a set of non-coding and protein-coding RNAs which are dysregulated in lung tissue of patients with COPD using total RNA sequencing.

Methods: RNA was extracted from lung tissue of 32 patients, encompassing 10 never smokers, 9 smokers without airflow limitation and 13 smokers with COPD. Total RNA sequencing was performed with an Illumina HiSeq 4000 Sequencing System on paired-end TruSeq RNASeq libraries. The sequencing reads were aligned to the reference human  transcriptome using 3 different tools, namely STAR, BWA-MEM and Bowtie2, after which HTSeq was used to quantify transcript expression. A filtering step was performed to remove genes with zero or low counts. Next, statistical analyses were performed using the statistical programming language R (v 3.5.0) and the R packages Limma, edgeR or DESeq2. Differentially expressed (DE) genes between 2 studied groups were based on an adjusted p-value < 0.05 and a log2-fold change > 1. Finally, Gene Set Enrichment Analysis (GSEA) was carried out with the javaGSEA desktop application using Gene Ontology – Biological Process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets (downloaded from the Molecular Signatures Database v4.0, Broad Institute). A comprehensive overview of the RNA seq analysis workflow is demonstrated in Figure 1.

Results: Exploratory analysis showed that the read count distribution was highly variable between the samples, after which we removed the samples where the reads were below 75 million, ending up with a total of 21 samples (6 never smokers, 7 smokers and 8 patients with COPD). 22109, 22344 and 20845 genes were detected as expressed with respectively STAR, BWA-MEM and Bowtie2 after filtering. DE analysis found only 2 genes or less (depending on which R package was used) to be significantly upregulated in smokers versus never smokers and 4 (or less) to be upregulated in patients with COPD compared to never smokers. No genes were DE between smokers with and without COPD. Importantly, the 3 software packages (Limma, DESeq2 and edgeR) identified the same DE genes. GSEA pointed towards positive enrichment of pathways such as inflammatory response, innate immune response, regulation of cytokine production, in smokers and patients with COPD compared to never smokers.  

Conclusion: Total RNA sequencing analysis on lung tissue of never smokers,  smokers and patients with COPD detected only a minority of genes to be differentially expressed between diseased and healthy subjects, whereas GSEA pointed towards an upregulation of pathways associated with inflammation/immunity in smokers with or without COPD.

 

Abstract traineeship advanced bachelor of bioinformatics 2 2017-2018: DNA copy number analysis using RNAseq data

Cancer genomes are characterized by DNA copy number changes, which are typically measured at the DNA level. Here, we evaluated the possibility to infer DNA copy number changes from RNA-seq data, based on measuring the ratio of expression between the two alleles.

The main advantage is that RNA can be used for different research purposes. It would be useful to add detecting aberrations to the list.

To confirm the self-made pipeline is correct, we compared results with copy number profiles derived from matching DNA samples.

The package ‘Changepoint’ is introduced to calculate the changepoints of the Allelic ratio. The penalty method of the function was optimized. If the changepoints are calculated separately for each chromosome, the outcome is more accurate. To avoid false exclusion of important parts, a variable baseline is calculated. A baseline of 15% above the lowest changepoint in combination with ‘Hannan-Quinn’ for penalty and the method ’PELT’ shows the best results.

Subsequently, we compared expression of genes in aberrant regions between the tumor sample and the matching normal sample (if available). The tumor sample and the mean of all the normal samples comparison is below and the last new comparison is the matching tumor versus the mean of all the tumor samples.

To make the third graphic more clear, a new script is written. It compiles a CSV-file which contains the genes that occur and doesn’t occur in the wanted regions for all samples of the same cancer type. The main script will only take the tumor samples where there isn’t an aberration (those who don’t occur in the wanted region) in the region to calculate the mean. Thus the tumor sample of interest is compared with the mean expression of the tumors where there is no aberration in that region.

The figure shows the output of the script. All graphs have the same layout: the full vertical lines indicate the end/beginning of the chromosomes, striped lines locate the centromeres.

The graph on the top shows the allelic ratio in red. The lines in blue are the regions determined by the changepoint package. The calculated baseline is the horizontal line. All regions above the line are the regions that are going to be looked at more closely in the next graphs.

The purpose of the CNV graph is to compare the self-made algorithm with the results derived from the matching DNA. When the line is clearly above the zero a gain is called, while below is a loss.

The third graph shows the comparison between the matching normal sample of the same person and the tumor sample. Below is the comparison of all gene points of the matching tumor versus the mean of the normal samples. The last graph is the tumor sample of interest compared to mean of the tumor samples without aberrations in the specific regions.

To conclude, the pipeline needs to be tested on multiple samples of multiple cancer types to determine its accuracy and points that need to be optimized. As yet today, the pipeline is correct in most of the cases.

 

Abstract traineeship advanced bachelor of bioinformatics 3 2017-2018: Creating a responsive interface for long non-coding RNA database LNCipedia

LNCipedia, https://lncipedia.org, is a database for human long non-coding RNA (lncRNA) transcripts and genes. LncRNAs constitute a large and diverse class of non-coding RNA genes.

The database is publicly available and allows users to query and download lncRNA sequences and metadata based on different search criteria. The database may serve as a source of information on individual lncRNAs or as a starting point for large-scale studies.

LNCipedia is built using Mojolicious, a web framework for the Perl programming language based on the MVC (Model View Control) pattern.

The current interface of LNCipedia is however not responsive or mobile friendly. Therefore, the main goal of this project was to redesign the website as a mobile first web application.

Bootstrap's CSS and JavaScript libraries were used to create the design. Docker was used to build and run the app locally. GitHub was used for version control.

Additionally, a python tool was created to add meta-information to the LNCipedia database. More specifically, the tool determines if the human lncRNA transcripts are conserved in other species.

Abstract traineeship advanced bachelor of bioinformatics 4 2017-2018Development of a new variant classification tool based on Sherloc classification criteria for variants in Mendelian diseases

The American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines are used as a common framework for variant classification. However these guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. Implementation of the current guidelines has been shown insufficient for a good variant classification.

The “Centrum Medische Genetica Gent” (CMGG) uses these ACMG-AMP guidelines to classify variants from hereditary diseases. However a refinement of the variant classification criteria is needed. This is done by implementing a new classification model based on Sherloc classification criteria (Nykamp et al, 2017).

Sherloc builds on the framework of the established ACMG-AMP guidelines and makes 108 refinements to it, which makes it a more consistent and transparent variant classification tool. It is based on a weighting system. It uses a semiquantitative system in which each criterion is awarded a preset number of points on benign or pathogenic scales (1B-5B or 1P-5P), which reflect the value of the data type toward the overall classification argument (Figure 1a). Accumulated benign and pathogenic evidence types are summed separately and compared against preset thresholds. Eventually classifying the variant in one of the five groups; benign, likely benign, variants of uncertain significance, likely pathogenic and pathogenic. There are five evidence categories (Figure 1b) divided in two groups which contribute to the final score.

Clinical criteria:

  • Population data
  • Clinical observations

Functional criteria:

  • Variant affect
  • Experimental studies
  • Computational & Predictive

The goal of this project is to design and implement a new web tool, which uses the refined ACMG-AMP guidelines and is more detailed/precise than the previous one.

The 108 Sherloc classification criteria and underlying results were successfully implemented in a web based tool written in HTML/PHP. The tool consists of a series of interactive and dynamic decision trees guiding the user to the selection of benign or pathogenic evidence types. Depending on which evidence types are checked, the variant gets classified in a certain class. The submitted evidence types and final variant class are reported afterwards.

 

Abstract bachelorproef 2017-2018Molecular characterization of MGMT promotor methylation and IDH1, IDH2 and BRAF mutations in patients diagnosed with glioblastoma

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Abstract traineeship (advanced bachelor of bioinformatics) 1 2016-2017: Targeted mutation detection in patients with Hereditary Hemochromatosis and MTHFR deficiency

The main aim of the research was to detect specific mutations that cause the diseases hereditary hemochromatosis and homocystinuria due to MTHFR deficiency. Hereditary hemochromatosis is a clinical disorder with bad regulation of iron in the body, which can cause (severe) damage to the organs. This disease can be caused by three specific substitutions in the HFE gene: c.845G>A (p.Cys282Tyr), c.197G>C (p.His63Asp) and c.193A>T (p.Ser65Cys). The most important mutation of these three is c.845G>A. Up to 90% (40-90%) of the clinical cases of hemochromatosis have this mutation. Homocystinuria is a disease where the amino acid homocysteine is increased in the body. This disease can be caused by a rare mutation in the MTHFR gene which leads to MTHFR deficiency: c.665C>T (p.Ala222Val). The enzyme made by MTHFR play a role in converting homocysteine to methionine.

Before the research, these mutations were detected using a Lightscanner® system. Because support was no longer available for this instrument, a new way to detect these mutations was necessary. Polymerase Chain Reaction (PCR) combined with Next Generation Sequencing (NGS) could be the solution. However, preliminary results with this new way of mutation detection showed a detection of too many false positive variants. To prevent unwanted mutations being reported, the goal was to write a Python script which examines and reports only these four mutations of interest.

The process starts with a PCR reaction on, from blood extracted, DNA from selected patients. For the amplification of the region of interest in the HFE gene, two PCR reactions are needed, for the amplification of the MTHFR region of interest only one PCR reaction is sufficient. The PCR products are then handed over to a specific ‘MiSeq team’. NGS is performed by this team on a MiSeq instrument (Illumina sequencing technology). The result of the sequencing process are .bcl files. These are converted to multiple fastq files. The next step is a quality control (quality trimming) on these files; bad quality ends are removed from the sequences. The final step is the mapping against the human reference genome (Hg19). After the mapping, five files are obtained: a coverage file, a variant track file (the most important one), a mapping file, a structural variants (SV) file and an indel file (insertions and deletions).

For each MiSeq run, a runinfo file is created. In this file, patients from the run are listed with the tested gene or gene panel. Only the patients with HFE and/or MTHFR as tested gene will be evaluated by the script.

The script reads the coverage and variant track files for each HFE/MTHFR patient in the runinfo file. All variants in the variant track file are compared with the three mutations from hemochromatosis and the mutation from homocystinuria. When a mutation is found in a patients file, following information about the mutation in the patient is captured from the variant track file and coverage file: c-notation, p-notation, status (heterozygote, homozygote, wild type, Sanger), the used PCR assay and the exon number. As output file, an Excel file is generated per patient per gene. In this file, all of the captured information is written. The script also creates an overview file which summarize all this information for all patients.

The written script was successfully tested and can now be used to analyze new MiSeq runs with HFE and/or MTHFR patients.

 

Abstract traineeship (advanced bachelor of bioinformatics) 2 2016-2017: Development of an RNA-seq pipeline to determine DNA copynumber status

RNA sequencing (RNA-seq) is considered a powerful tool for gene expression analysis and gene discovery. Because of its nucleotide resolution, RNA-seq can also be applied to identify variants and quantify allelic expression levels. As allelic expression levels are, in part, driven by the copy number of the individual alleles, imbalances in allelic expression ratio’s may point to changes in DNA copy numbers.

Most cancer cells are characterized by DNA copy number changes that result in the gain or loss of alleles. The chromosomal regions that are subject to these changes often harbour important oncogenes or tumour suppressor genes and can be highly characteristic for individual cancer types. Typically, these changes are quantified at the DNA-level, using shallow whole genome sequencing or array CGH (Comparative genomic hybridization). With this project, we aimed to evaluate the use of allelic expression ratio’s, derived from RNA-seq data, to map genome-wide copy number variations in cancer cells. The performance of our approach was assessed by direct comparison with matching DNA copy number data (determined using aCGH).

Regions with an allelic ratio of more than 1,75 in the RNA-seq data were marked as regions with ‘allelic imbalance’. These regions nicely coincided with gains or losses that were called based on the available DNA data. To be able to determine if the allelic imbalance was caused by a gain or a loss, we investigate the genes within the region of allelic imbalance and compare their expression in the tumour sample to a matching normal sample or to the mean of all the normal samples when there is no match. The overall tumour to normal ratio of genes within the region are applied to distinguish gains from losses.

The allelic imbalances are on the same position as the gains and losses shown by DNA copy number data. When there is more expression in tumour, there is a gain and a ratio of more than 0 on the bottom line. If there is less expression in tumour, there is a loss and a ratio of less than 0.

To be able to get DNA copy number information directly from RNA-seq data is a huge advantage as this would allow us to investigate both gene expression, variants and DNA copy number status from a single dataset without the need for DNA analysis.

 

Abstract bachelorproef 2016-2017Optimalisatie en validatie van een NGS approach voor mutatiedetectie in HFE en MTHFR en bepalen van de prevalentie van varianten in geselecteerde extragenische regio’s

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Abstract bachelorproef 2015-2016Molecular analysis of RECQL and cancer susceptibility genes in families with a strong predisposition to breast and ovarian cancer

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Address

De Pintelaan 185 gebouw MRB
9000 Gent
09/3323603 (BIT) / 09/3322478 (FBT)
Belgium
De Pintelaan 185
9000 Gent
09/3323603 (BIT) / 09/3322478 (FBT)
Belgium

Contacts

Traineeship supervisor
Kathleen Claes (FBT)
09/3322478
Kathleen.Claes@UGent.be
Traineeship supervisor
Jo Vandesompele (BIT)
09/3325187
Joke.Vandesompele@UGent.be
Traineeship supervisor
Jan Hellemans (BIT)
09/3320158
Jan.Hellemans@UGent.be
Traineeship supervisor
Bram De Wilde (BIT)
09/3324812
Bram.Dewilde@ugent.be
Traineeship supervisor
Kim De Leeneer (FBT)
09/3323972
kim.deleeneer@ugent.be
Traineeship supervisor
Tom Sante (BIT)
09/3323946
tom.sante@gmail.com
Traineeship supervisor
Jasper Anckaert (BIT)
jasper.anckaert@ugent.be
Traineeship supervisor
Pieter-Jan Volders (BIT)
pieterjan.volders@ugent.be
Traineeship supervisor
Björn Menten (BIT)
Traineeship supervisor
Toon Rosseel (BIT)
toon.rosseel@ugent.be
Traineeship supervisor
Joni Van der Meulen (FBT)
Joni.VanderMeulen@UGent.be
Traineeship supervisor
Francisco Avila Cobos (BIT)
Francisco.AvilaCobos@UGent.be
Traineeship supervisor
Annelien Morlion (BIT)
093326976
Annelien.Morlion@UGent.be
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