Search results for "COMPUTATION"

AlkAniline-Seq: Profiling of m7 G and m3 C RNA Modifications at Single Nucleotide Resolution.

2018

RNA modifications play essential roles in gene expression regulation. Only seven out of >150 known RNA modifications are detectable transcriptome-wide by deep sequencing. Here we describe a new principle of RNAseq library preparation, which relies on a chemistry based positive enrichment of reads in the resulting libraries, and therefore leads to unprecedented signal-to-noise ratios. The proposed approach eschews conventional RNA sequencing chemistry and rather exploits the generation of abasic sites and subsequent aniline cleavage. The newly generated 5'-phosphates are used as unique entry for ligation of an adapter in library preparation. This positive selection, embodied in the AlkAnilin…

Recent discoveries of anticancer flavonoids.

2017

Abstract In this review we report the recent advances in anticancer activity of the family of natural occurring flavonoids, covering the time span of the last five years. The bibliographic data will be grouped, on the basis of biological information, in two great categories: reports in which the extract plants bioactivity is reported and the identification of each flavonoid is present or not, and reports in which the anticancer activity is attributable to purified and identified flavonoids from plants. Wherever possible, the targets and mechanisms of action as well as the structure-activity relationships of the molecules will be reported. Also, in the review it was thoroughly investigated t…

All-atom simulations to studying metallodrugs/target interactions.

2021

Abstract Metallodrugs are extensively used to treat and diagnose distinct disease types. The unique physical–chemical properties of metal ions offer tantalizing opportunities to tailor effective scaffolds for selectively targeting specific biomolecules. Modern experimental techniques have collected a large body of structural data concerning the interactions of metallodrugs with their biomolecular targets, although being unable to exhaustively assess the molecular basis of their mechanism of action. In this scenario, the complementary use of accurate computational methods allows uncovering the minutiae of metallodrugs/targets interactions and their underlying mechanism of action at an atomic…

Differential binding cell-SELEX method to identify cell-specific aptamers using high-throughput sequencing

2018

AbstractAptamers have in recent years emerged as a viable alternative to antibodies. High-throughput sequencing (HTS) has revolutionized aptamer research by increasing the number of reads from a few (using Sanger sequencing) to millions (using an HTS approach). Despite the availability and advantages of HTS compared to Sanger sequencing, there are only 50 aptamer HTS sequencing samples available on public databases. HTS data in aptamer research are primarily used to compare sequence enrichment between subsequent selection cycles. This approach does not take full advantage of HTS because the enrichment of sequences during selection can be due to inefficient negative selection when using live…

Deep learning architectures for prediction of nucleosome positioning from sequences data

2018

Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…

Application of Graph Clustering and Visualisation Methods to Analysis of Biomolecular Data

2018

In this paper we present an approach based on integrated use of graph clustering and visualisation methods for semi-supervised discovery of biologically significant features in biomolecular data sets. We describe several clustering algorithms that have been custom designed for analysis of biomolecular data and feature an iterated two step approach involving initial computation of thresholds and other parameters used in clustering algorithms, which is followed by identification of connected graph components, and, if needed, by adjustment of clustering parameters for processing of individual subgraphs.

Quantitatively characterizing drug-induced arrhythmic contractile motions of human stem cell-derived cardiomyocytes.

2018

Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e. Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms doesn't produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion dat…

Transcriptome Analysis of PA Gain and Loss of Function Mutants

2017

Functional genomics has become a forefront methodology for plant science thanks to the widespread development of microarray technology. While technical difficulties associated with the process of obtaining raw expression data have been diminishing, allowing the appearance of tremendous amounts of transcriptome data in different databases, a common problem using "omic" technologies remains: the interpretation of these data and the inference of its biological meaning. In order to assist to this complex task, a wide variety of software tools have been developed. In this chapter we describe our current workflow of the application of some of these analyses. We have used it to compare the transcr…

Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy

2017

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive b…

2019

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call "snippets". These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learni…