Browsing by Subject "DNA sequencing"

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Open Access
Bias correction in finding copy number variation with using read depth-based methods in exome sequencing data
(2014) Balcı, Fatma
Medical research has striven for identifying the causes of disorders with the ultimate goal of establishing therapeutic treatments and finding cures since its early years. This aim is now becoming a reality thanks to recent developments in whole genome (WGS) and whole exome sequencing (WES). Despite the decrease in the cost of sequencing, WGS is still a very costly approach because of the need to evaluate large number of populations for more concise results. Therefore, sequencing only the protein coding regions (WES) is a more cost effective alternative. With the help of WES approach, most of the functionally important variants can be detected. Additionally, single nucleotide polymorphisms (SNPs) that are located within coding regions are the most common causes for Mendelian diseases (i.e. diseases caused by a single mutation). Moreover, WES approaches require less analysis effort compared to whole genome sequencing approaches since only 1% of whole genome is sequenced. Besides the advantages, there are also some shortcomings that need to be addressed such as biases in GC−content and probe efficiency. Although there are some previous studies on correcting GC−content related issues, there are no studies on correcting probe efficiency effect. In this thesis, we provide a formal study on the effects of both GC−content and probe efficiency on the distribution of read depth in exome sequencing data. The correction of probe efficiency will make it possible to develop new CNV discovery methods using exome sequencing data.
Open Access
Electrostatics of polymer translocation through membrane nanopores in electrolyte solutions
(2021-02) Mohamed, Ghada Mahmoud Abdullah
The transport of polymers across membranes in electrolyte solutions happens in most biological systems and is necessary for cells to function. Moreover, the poly-mer translocation process has proven to be very important in experiments and applications as well, providing a rich source of information about the polymer’s size and composition [1], [2], making the polymer translocation procedure a po-tential sequencing method that is eﬃcient, cheap, and quick [3], [4]. However, no consensus on the theoretical understanding of the translocation mechanism has been reached yet [3], leaving it a major challenge for theoretical modelling due to its steric, hydrodynamic, and electrostatic interactions [2], [5]. Here, we calculate the electrostatic energy cost of the translocating polymer in both the approach and translocation phases and investigate the dependence of the poly-mer’s grand potential on diﬀerent model tunable parameters. In the case of neu-tral membranes, low permittivity carbon-based membranes repel the approaching polymer with energy magnitude between ∼ 11 kBT and ∼ 27 kBT , while high permittivity engineered membranes attract the approaching polymer with almost the same energy magnitude. This behavior can be attributed to polymer image-charge interactions, which become ampliﬁed with low permittivity membranes. In strong salt solutions, the membrane exhibits a repulsive barrier that turns to a metastable well in dilute solutions. In pure solvents, the metastable well becomes a deep, stable well that traps the polymer in the pore for some time, where the translocation phase is mainly governed by the attractive trans-cis side interac-tion. For weakly charged membranes, the membrane charge attraction wins over the image-charge repulsion, leading to an attractive minimum at zt ≈ −1 nm followed by a repulsive barrier at lt = L/2 while for stronger membrane charges, the attractive well turns to a metastable point followed by an attractive, stable well. These results suggest that, in translocation experiments, DNA motion can be controlled by tuning the system parameters, such as the solution concentration or the membrane charge.
Open Access
Privacy and security in the genomic era
(ACM, 2016-10) Ayday, Erman; Hubaux, Jean-Pierre
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., Open-SNP or 23 and Me). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer's or Parkinson's), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person's genome is correlated to his family members' genomes, thus leading to interdependent privacy risks. This short tutorial will help computer scientists better understand the privacy and security challenges in today's genomic era. We will first highlight the significance of genomic data and the threats for genomic privacy. Then, we will present the high level descriptions of the proposed solutions to protect the privacy of genomic data and we will discuss future research directions. No prerequisite knowledge on biology or genomics is required for the attendees of this proposal. We only require the attendees to have a slight background on cryptography and statistics.