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48 results for “random sequences”
Sequence data for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers"
<p>Sequence data (Illumina MiSeq runs) for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers". File names indicate unique run identifiers. In the manuscript, shorter names are used:</p> <ul> <li>NC12: 140602_M00528_0019_000000000-A88YD</li> <li>NC17: 140918_M00528_0047_000000000-A8GHU</li> <li>NC22b: 141105_M00528_0062_000000000-AAPC2</li> <li>NCki: 140207_M00528_0069_000000000-A5TY9</li> </ul> <p> </p>
Figure 7 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 7. Summary of Random Forests classifications for each empirical comparison. Each row shows results from the stratum with the smallest fraction of individuals correctly classified, with comparisons labeled by their taxonomic codes as listed in Table 2. Colors identify comparison type as species (blue), subspecies (green), and populations (red). Points show the fraction of individuals correctly classified with probabilities> 50% (PD50, circles), and> 95% (PD95, triangles). Thin colored lines show 95% confidence intervals (CI) around PD50 estimates. Gray bars show range of a priori random classification rates based on individual size (left) to maximum possible classification rates based on shared haplotypes (right).
Figure 6 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 6. Frequency distributions of the change in observed diagnosability (x-axis) in the simulated data for increasing levels of the probability of misstratification (vertical panels). Figures on the left and right columns are censored by data sets for original diagnosability ≤50% and>50%, respectively.
Figure 5 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 5. Two-dimensional GAM fits of theta (Ɵ), number of migrants (Nem), and divergence time in generations (T) from Model 2 simulated data. From left to right, columns show results from models without migration (m = 0), with migration and Nem <1, and Nem ≥ 1. Colors indicate model prediction of percent correctly classified.
Figure 4 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 4. GAM fit of number of migrants (Nem) from Model 2 parameters. Solid line shows median value of predicted percent correctly classified, and shaded area shows 95% CI. The switch from bimodal distribution to a normal distribution occurs at Nem = 1 (log10Nem = 0).
Figure 3 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 3. Two-dimensional GAM fits of effective population size (Ne), divergence time in generations (T), and mutation rate (µ) from Model 1 simulated data. Results from models without migration to the left and those with migration to the right. Colors indicate model prediction of percent correctly classified.
Figure 1 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 1. (A) Distribution of a hypothetical character for two putative subspecies (red and blue) demonstrating minimum overlap necessary to satisfy 75% rule of Amadon (1949). Character is continuous on the x-axis. Dashed lines indicate the point at which 75% of each distribution is outside of 99%+ of the other. Solid line indicates point of overlap where 97% of both distributions are outside one another. (B) Probability of membership to subspecies for specimens having values along the character axis. Probability is based on the ratio of the distribution frequencies at each point along the x-axis, with a 50:50 probability occurring at the threshold point.
AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes
<p>Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines<span> randomized</span> <span><span> </span>alanine<span> </span>sequence masking<span> </span>with shallow multiple sequence alignment<span> </span>subsampling to expand the conformational diversity of the predicted structural<span> </span>ensembles and<span> </span>capture conformational changes between apo and holo protein forms. Using several well-established datasets of<span> </span>structurally diverse apo-holo protein pairs, the proposed approach </span><span>enables<span> </span>robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with<span> </span>the view </span><span> </span>that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes among orthologs. We also found<span> </span>a significant <span>correlation </span>between conformational flexibility and <span> </span>AlphaFold2 metric of statistical significance pLDDT for the apo-holo pairs in which ligand binding induced local moderate conformational changes. For apo-holo pairs exhibiting larger structural changes, this relationship<span> </span>becomes nonlinear, reflecting inability of AlphaFold2 confidence metrics to identify high energy functional conformations. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with <span> </span>moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AF2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that modeling of multiple functional states of proteins may require more accurate detection of flexible region conformations and cannot solely rely on the pLDDT metric as the major determinant of the prediction accuracy in reproducing functional conformational ensembles.<span> </span></p>
Figure 2 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 2. Illustration of steps in constructing a Random Forests ensemble of classification trees.
Random samples of SARS-CoV-2 sequences for Germany
<p>Mutation profiles and meta data for SARS-CoV-2 lineages in Germany submitted via DESH to the Robert Koch Institute (<a href="https://github.com/robert-koch-institut/SARS-CoV-2-Sequenzdaten_aus_Deutschland">https://github.com/robert-koch-institut/SARS-CoV-2-Sequenzdaten_aus_Deutschland</a>), containing a subset from https://doi.org/10.5281/zenodo.12806236.<br>For this CSV file, the raw data (fasta) was processed with covSonar (https://github.com/rki-mf1/covsonar) and the resulting database queried for random samples (RKI_STICHPROBE, DESH_STICHPROBE) and with dates ranging from 2021-01-01 until 2024-07-24.</p>
Dual randomly barcoded transposon sequencing (Dual Tn-seq) data for <em>Streptococcus pneumoniae</em> D39
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Neuromodulation of premotor and posterior parietal cortices for enhancing explicit motor sequence learning in healthy individuals: a randomized, sham-controlled crossover trial
<p>Data collected and analysed in the manuscript '<strong>Neuromodulation of premotor and posterior parietal cortices for enhancing explicit motor sequence learning in healthy individuals: a randomized, sham-controlled crossover trial', </strong>by Russo et al. PPCR. The Principles and Practice of Clinical Research, 2021</p>
Random peptide sequences binding amyloid monoclonal antibodies
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Data from: Random sequences are an abundant source of bioactive RNAs or peptides
It is generally assumed that new genes arise through duplication and/or recombination of existing genes. The probability that a new functional gene could arise out of random non-coding DNA is so far considered to be negligible, as it seems unlikely that such an RNA or protein sequence could have an initial function that influences the fitness of an organism. Here, we have tested this question systematically, by expressing clones with random sequences in Escherichia coli and subjecting them to competitive growth. Contrary to expectations, we find that random sequences with bioactivity are not rare. In our experiments we find that up to 25% of the evaluated clones enhance the growth rate of their cells and up to 52% inhibit growth. Testing of individual clones in competition assays confirms their activity and provides an indication that their activity could be exerted by either the transcribed RNA or the translated peptide. This suggests that transcribed and translated random parts of the genome could indeed have a high potential to become functional. The results also suggest that random sequences may become an effective new source of molecules for studying cellular functions, as well as for pharmacological activity screening.
Figure 8 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 8. Relationship of diagnosability to Nei's net nucleotide divergence (dA, Nei and Kumar 2000). Colors indicate taxonomic level of comparison: species (blue), subspecies (green), and populations (red). Vertical lines indicate the binomial 95% CI around diagnosability. Note that x-axis for dA is log10-scaled.
Figure 1 from: Hughes KW, Morris SD, Reboredo-Segovia A (2015) Cloning of ribosomal ITS PCR products creates frequent, non-random chimeric sequences – a test involving heterozygotes between Gymnopus dichrous taxa I and II. MycoKeys 10: 45-56. https://doi.org/10.3897/mycokeys.10.5126
Figure 1 - The ITS2 region of two haplotypes of Gymnopus dichrous: Haplotypes DI (represented by TENN68084) and DII (represented by TENN68078). The TC pair at position 15 and the indel at position 25 were used to determine which the haplotype was represented by the 5' end of a cloned sequence. Bases in red are points where DI and DII haplotypes differ in sequence and were used to determine if template switching had occurred in a cloned PCR product. Eight base pairs at which template switching can be detected are indicated by numbers 1-8. The possible area in which template switching (ts) could have occurred is indicated by vertical arrows and the number of observed template switching events is given above the vertical arrow. Bases that may be involved in intra-strand base pairing as determined by MFOLD are outlined with black boxes. Ambiguity codes indicate intraspecific variation.
Data from: Random sequences are an abundant source of bioactive RNAs or peptides
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mRNA Sequencing of Human PromoCells Using Random Primed mRNA-Sequencing Technique
GEO Series GSE98428. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Deep sequencing of the murine Igh repertoire reveals complex regulation of non-random V gene rearrangement frequencies
GEO Series GSE47766. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Learning the regulatory grammar of yeast 5' untranslated regions from a large library of random sequences
GEO Series GSE104252. Saccharomyces cerevisiae. 6 samples. Type: Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.