Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplification and flow cytometry

This dataset contains the raw data that were used for the publication entitled, "Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplificaiton and flow cytometry" published in Biosensors and Bioelectronics on 5 October 2024.

opencc-by-4.0Dec 2020View details →

Pre-amplification of nucleic acids comparison in the context of sewage metaviromics

Raw data from three different pre-amplification nucleic acids pipelines prior to library preparation for metaviromics of sewage samples.

opencc-by-4.0Oct 2022View details →

DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids

This dataset contains the raw data that were used for the publication entitled, "DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids" published in Nanoscale.   Abstract DNA nanotechnology provides a promising approach for the development of biomedical point-of-care diagnostic nanoscale devices that are easy to use and cost-effective, highly sensitive and thus constitute an alternative to expensive, complex diagnostic devices. Moreover, DNA nanotechnology-based devices are particularly advantageous for applications in oncology, owing to being ideally suited for the detection of cancer-associated nucleic acids, including circulating tumor-derived DNA fragments (ctDNAs), circulating microRNAs (miRNAs) and other RNA species. Here, we present a dynamic DNA origami book biosensor that is precisely decorated with arrays of fluorophores acting as donors and acceptors and also fluorescence quenchers that produce a strong optical readout upon exposure to external stimuli for the single or dual detection of target oligonucleotides and miRNAs. This biosensor allowed the detection of target molecules either through the decrease of Förster resonance energy transfer (FRET) or an increase in the fluorescence intensity profile owing to a rotation of the constituent top layer of the structure. Single-DNA origami experiments showed that detection of two targets can be achieved simultaneously within 10 min with a limit of detection in the range of 1–10 pM. Overall, our DNA origami book biosensor design showed sensitive and specific detection of synthetic target oligonucleotides and natural miRNAs extracted from cancer cells. Based on these results, we foresee that our DNA origami biosensor may be developed into a cost-effective point-of-care diagnostic strategy for the specific and sensitive detection of a variety of DNAs and RNAs, such as ctDNAs, miRNAs, mRNAs, and viral DNA/RNAs in human samples.

openapache2.0Dec 2022View details →

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

Figure 1 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 1. Body mass (◆) and total length (■) of Japanese flounder during larval and juvenile development related to days after hatching. Values are given as the mean ± SD. Each value is obtained from 35 individuals.

Figure 2 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 2. Instantaneous growth rate (A) and absolute growth rate (B) of body mass (◆) and total length (v) in Japanese flounder during larval and juvenile development related to days after hatching. Values are given as the mean ± SD. Each value is obtained from 35 individuals.

Figure 4 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 4. Changes in DNA content (■) and DNA concentration (◆) of Japanese flounder larvae and juveniles. Values are given as the mean ± SD. The numbers of samples collected each day range from 5 pools of 560 fish initially to 10 individual fish.

Figure 7 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 7. Diel variation of the RNA/DNA ratio in the fed () and starved (■) Japanese flounder larvae during the 48-h period. Values are given as the mean ± SD. Each value is obtained from 15 individuals. The dark line means the dark time.

Figure 3 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 3. Changes in protein content (■) and protein concentration (◆) of Japanese flounder larvae and juveniles. Values are given as the mean ± SD. The numbers of samples collected each day range from 5 pools of 560 fish initially to 10 individual fish.

Figure 6 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 6. Changes in protein/DNA (■) and RNA/DNA (◆) of Japanese flounder larvae and juveniles. Values are given as the mean ± SD. The numbers of samples collected each day range from 5 pools of 560 fish initially to 10 individual fish.

Figure 5 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 5. Changes in RNA content (■) and RNA concentration (◆) of Japanese flounder larvae and juveniles. Values are given as the mean ± SD. The numbers of samples collected each day range from 5 pools of 560 fish initially to 10 individual fish.

Figure 8 in Ontogenetic changes in nucleic acid, protein contents, and growth of larval and juvenile Japanese flounder

Figure 8. Changes in RNA/DNA ratios of Japanese flounder exposed to different starved-refeeding treatments from 20 to 27 days after hatching: (A) fed (control treatment), (B) 1-day starved, (C) 2-day starved, (D) 3-day starved, (E) 4-day starved. Values are given as the mean ± SD. Each value is obtained from 10 individuals. Dark symbols mean feeding days and empty symbols mean starved days.

opencc-by-4.0Apr 2022View details →

ProtNAff: Protein-bound Nucleic Acid filters and fragment libraries

This archive contains data obtained by running the ProtNAff pipeline (<a href="https://github.com/isaureCdB/ProtNAff">https://github.com/isaureCdB/ProtNAff</a>) and used to perform the analyses described in the original ProtNAff paper. The input list of PDB IDs was obtained in October 2021 by searching all PDB structures that contain protein chains and RNA chains but no DNA, and where the resolution is less than 3 A or where the method is NMR. The ribosomes are removed from this database due to their size. The files provided are: - structures.json : the database containing metadata and parsing data for all the protein-RNA structures from the input list - fragments_clust.json : the list and description of all the trinucleotide fragments extracted from those structures - trinucl_clust1A_allatom: the all-atom coordinates of the trinucleotide fragment library , i.e. the centers of 1A clusters such that each initial fragment has a RMSD of less than 1A from at least one of those centers. - trinucl_clust1A_ATTRACT: the coordinates of the trinucleotide fragment library , but reduced into ATTRACT coarse-grained representation (Setny and Zacharias, NAR 2011).

dryad40/100

Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA

Nucleic acids released by organisms and isolated from environmental substrates are increasingly being used for molecular biomonitoring. While environmental DNA (eDNA) has received attention recently, the potential of environmental RNA as a biomonitoring tool remains less explored. Several recent studies using paired DNA and RNA metabarcoding of bulk samples suggest that RNA might better reflect "metabolically active" parts of the community. However, such studies mainly capture organismal eDNA and eRNA. For larger eukaryotes, isolation of extra-organismal RNA will be important, but viability needs to be examined in a field-based setting. In this study we evaluate (a) whether extra-organismal eRNA release from macroeukaryotes can be detected given its supposedly rapid degradation, and (b) if the same field collection methods for eDNA can be applied to eRNA. We collected eDNA and eRNA from water in lakes where fish community composition is well documented, enabling a comparison between the two nucleic acids in two different seasons with monitoring using conventional methods. We found that eRNA is released from macroeukaryotes and can be filtered from water and metabarcoded in a similar manner as eDNA to reliably provide species composition information. eRNA had a small but significantly greater true positive rate than eDNA, indicating that it correctly detects more species known to exist in the lakes. Given relatively small differences between the two molecules in describing fish community composition, we conclude that if eRNA provides significant advantages in terms of lability, it is a strong candidate to add to the suite of molecular monitoring tools.

opencc-zeroJun 2022View details →

dryad40/100

Leak-resilient enzyme-free nucleic acid dynamical systems through shadow cancellation

DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviors. However, strand displacement circuits are susceptible to background noise that disrupts the circuit behavior, commonly known as leaks. The side effects of leaks are particularly severe in circuits with complex dynamical elements (e.g., feedback loops), as their leaks amplify nonlinearly, disrupting the circuit function. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the proposed method introduce a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method's capabilities, the inner workings of its components, and, most importantly, robustness to practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several leak-affected catalytic and autocatalytic dynamical systems of practical importance. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function through simple adjustments to the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work.

opencc-zeroApr 2024View details →

Figure 4 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases

Figure 4. Identification of PCR inhibitors in 18 biological samples positive for Aspergillus. Graph A: Ct values obtained from pure and diluted DNA samples (dilution rate 1/20). Graph B: Ct values obtained with 20 copies of a plasmid DNA systematically added to the same biological samples (undiluted and diluted) and a negative control sample (NC).

Figure 2 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases

Figure 2. Influence of proteinase K digestion (56 °C overnight) on DNA extraction. Graph A shows the Ct values obtained by quantifying THP1 cell DNA derived from direct extraction with the NucliSENS easyMAG system and extraction performed on the same quantity of cells following overnight (ON) digestion with Proteinase K. Graph B shows Leishmania quantification after extraction with the NucliSENS easyMAG system both with and without PK and quantification after extraction using a QIAamp DNA Mini kit after ON digestion with PK.

Figure 7 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases

Figure 7. Variation of the ratio between kinetoplastic DNA and nuclear DNA extraction with various Leishmania quantities in the presence of 103 THP1 cells.

Figure 5 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases

Figure 5. Yield of DNA extraction from Leishmania and THP1 cells using the NucliSENS easyMAG system.

Figure 3 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases

Figure 3. Results of the extraction experiments performed on yeast (Candida albicans) and filamentous fungi (Aspergillus fumigatus). A presents the kinetics of the extraction process after vortexing and glass-bead treatment. B shows the differences in DNA quantity obtained from fungal cells using the FastPrep system (with) compared to the same process without grinding.