Biology subjects

Sebastian, P.

Publications and source records attributed to Sebastian, P..

3 recordsLinked to original sources

bioRxiv2022

Deploying genomics workflows on high performance computing (HPC) platforms: storage, memory, and compute considerations

Next Generation Sequencing (NGS) workloads largely consist of pipelines of tasks with heterogeneous compute, memory, and storage requirements. Identifying the optimal system configuration has historically required expertise in both system architecture and bioinformatics. This paper outlines infrastructure recommendations for one commonly used genomics workload based on extensive benchmarking and profiling, along with recommendations on how to tune genomics workflows for high performance computing (HPC) infrastructure. The demonstrated methodology and learnings can be extended for other genomics workloads and for other infrastructures such as the cloud.

bioinformatics↗

bioRxiv2020

Modeling the Impact of Lock-down on COVID-19 Spread in Malaysia

After a breakdown notified in Wuhan, China in December 2019, COVID-19 is declared as pandemic diseases. To the date more than 13 million confirmed cases and more than half a million are dead around the world. This virus also attached Malaysia in its immature stage where 8718 cases were confirmed and 122 were declared as death. Malaysia responsibly controlled the spread by enforcing MCO. Hence, it is required to visualize the pattern of Covid-19 spread. Also, it is necessary to estimate the impact of the enforced prevention measures. In this paper, an infectious disease dynamic modeling (SEIR) is used to estimate the epidemic spread in Malaysia. The main assumption is to update the reproduction number Rt with respect to the implemented prevention measures. For a time-frame of five month, the Rt was assumed to vary between 2.9 and 0.3. Moreover, the manuscript includes two possible scenarios: the first will be the extension of the stricter measures all over the country, and the second will be the gradual lift of the lock-down. After implementing several stages of lock-down we have found that the estimated values of the Rt with respect to the strictness degree varies between 0.2 to 1.1. A continuous strict lock-down may reduce the Rt to 0.2 and accordingly the estimated active cases will be reduced to 20 by the beginning of September 2020. In contrast, the second scenario considers a gradual lift of the enforced prevention measures by the end of June 2020, here we have considered three possible outcomes according to the MCO relaxation. Thus, the estimated values of Rt = 0.7, 0.9, 1.1, which shows a rapid increase in the number of active cases. The implemented SEIR model shows a close resemblance with the actual data recorded from 10, March till 7, July 2020. Author summaryConceptualization, A.A.A; methodology, A.A.A, N.M; validation, A.A.A, N.M; formal analysis, A.A.A; investigation, N.M, A.A.A; resources, G.E.M.A, L.T; data collection, L.T, N.M; writing--original draft preparation, A.A.A, L.T, G.E.M.A, N.M; writing--review and editing, V.S.A, S.C.D, B.S.G, P.S, S.A.B.M.Z, N.M; visualization, N.M; supervision, V.S.A; project administration, V.S.A. All authors have read and agreed to the published version of the manuscript

bioinformatics↗

bioRxiv2018

Parameterizing neural power spectra

Electrophysiological signals across species and recording scales exhibit both periodic and aperiodic features. Periodic oscillations have been widely studied and linked to numerous physiological, cognitive, behavioral, and disease states, while the aperiodic \"background\" 1/f component of neural power spectra has received far less attention. Most analyses of oscillations are conducted on a priori, canonically-defined frequency bands without consideration of the underlying aperiodic structure, or verification that a periodic signal even exists in addition to the aperiodic signal. This is problematic, as recent evidence shows that the aperiodic signal is dynamic, changing with age, task demands, and cognitive state. It has also been linked to the relative excitation/inhibition of the underlying neuronal population. This means that standard analytic approaches easily conflate changes in the periodic and aperiodic signals with one another because the aperiodic parameters--along with oscillation center frequency, power, and bandwidth--are all dynamic in physiologically meaningful, but likely different, ways. In order to overcome the limitations of traditional narrowband analyses and to reduce the potentially deleterious effects of conflating these features, we introduce a novel algorithm for automatic parameterization of neural power spectral densities (PSDs) as a combination of the aperiodic signal and putative periodic oscillations. Notably, this algorithm requires no a priori specification of band limits and accounts for potentially-overlapping oscillations while minimizing the degree to which they are confounded with one another. This algorithm is amenable to large-scale data exploration and analysis, providing researchers with a tool to quickly and accurately parameterize neural power spectra.

neuroscience↗