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opencc-by-4.0Sep 2019View details →

Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

This upload contains the expression prediction dataset discussed in the manuscript "Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development" and used by the webserver https://find.princeton.edu. Abstract: Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation.  While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation.  Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts.  Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes.  We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion.  We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues.  The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy.  We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling.  This resource, together with the exploratory tools are freely available at our webserver <a href="http://find.princeton.edu/">http://find.princeton.edu</a>,  which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.

opencc-by-4.0Sep 2019View details →

Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations

Supplementary data of manuscript 'Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations  Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations'

Fig. 2 in Major range loss predicted from lack of heat adaptability in an alpine Drosophila species

Fig. 2. Heat knock-down results for the three regimes (C, L, A) of two populations (Kaserstattalm, Pfitscherjoch). Colours indicate the temperature at which the flies were reared in the generation used for testing knock-down performance. Significances based on analyses of variance and Bonferroni post hoc tests (n = 875, alpha = 0.05) are indicated by lower-case letters. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 in Major range loss predicted from lack of heat adaptability in an alpine Drosophila species

Fig. 4. Ecological niche modelling of habitable area of Drosophila nigrosparsa for pre-current, immanent, proximate, and distant future. (a) Multi-model consensus predictions of precurrent and future high emission scenarios (SRES A2 and RCP8.5). (b) PNO profiles for different time periods (pre-current, 2020s, 2030s, 2080s) scaled according to the suitable area based on the MTP (whole distribution area) and MTSS (core area) as well as histograms of frequencies of available altitude in the study region. For details, see Section 2.6 and Supporting Information S1.

Fig. 1 in Major range loss predicted from lack of heat adaptability in an alpine Drosophila species

Fig. 1. Overview of experiments. Years 2012–2016 with months abbreviated; G1-G20…generation number for selection regimes, L and A; controls C developed more slowly resulting in a difference of one generation at Generation 19 of the selection regimes; coll…collecting of flies in two populations, Kaserstattalm and Pfitscherjoch; breeding…flies were reared at a fluctuating ambient temperature mimicking natural daily conditions; red upward pointing arrow…increasing the ambient temperature by 0.5 ̊C for L and A; KD…artificial selection of A in heat knock-down tube; N14…founding of lines using fresh flies collected from the field in 2014 to test for possible laboratory adaptation; s… replicate lines from each regime were combined to create new synthetic lines to control for possible inbreeding effects; larv…assays of egg-to-adult viability and wing size in non-competitive and competitive situations; w…upscaling of lines and switches to the opposed rearing temperature (L and A switched to C rearing temperature; C to L = A rearing temperature) in addition to keeping the lines at the rearing temperatures corresponding to their regime; *…for evaluating progress in selection response, heat resistance was measured as knock-down temperature under gradual heating, and for safeguarding against unexpected loss of genetic diversity during the selection experiments, three polymorphic microsatellite loci were analysed; sim… simulating technical defect of climate chamber that occurred in previous generation for evaluating validity of data for that generation; horizontal bars denote replicate fly lines of the three regimes, i.e., C…control; L…laboratory natural selection (in Generation 14, one replicate each of L from Kaserstattalm and Pfitscherjoch were lost); A…artificial selection combined with laboratory natural selection; blue…fluctuating ambient temperature mimicking natural daily conditions (control temperature); red…increased ambient temperature (selection temperature). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 in Major range loss predicted from lack of heat adaptability in an alpine Drosophila species

Fig. 3. Egg-to-adult viability and wing size under non-competitive and competitive situations. (a) Egg-to-adult viability of the three regimes (C, L, A) under regular larval density. Significances based on analyses of variance (n = 30). (b) Wing size of the three regimes separately for females and males under regular larval density. Significances based on analyses of variance (n = 374). (c) Inter-regime competition (increased larval density). Output proportion of C relative to L and A at end of experiment (adults) plotted against input proportion of C relative to L and A at beginning of experiment (eggs). Dashed line: theoretical 45̊ equilibrium line on which output proportion equals input proportion. Significance values based on Wilcoxon Signed-rank tests calculated separately for each output proportion by comparison with corresponding value of theoretical equilibrium line (n = 10 for all tests except for C against A at input ratio 0.25, for which n = 9). (d) Wing size at inter-regime competition of different ratios. Females are shown as solid lines, males as dashed lines. Significance values at alpha = 0.05 are indicated by lower-case letters (a, b) and * (c).

opencc-by-4.0Oct 2019View details →

Observed and predicted variables of in vitro methanogenesis

This data sets shows the dynamics of in vitro methanogenesis by three gut archaea: Methanobrevibacter smithii, Methanobrevibacter ruminantium and Methanobacterium formicium. Experimental data are compared against variables predicted by a kinetic model.

opencc-by-4.0Oct 2019View details →

Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives

This file contains the data we collected and analyzed in the paper <a href="https://www.frontiersin.org/articles/10.3389/fgene.2019.00914/abstract">https://www.frontiersin.org/articles/10.3389/fgene.2019.00914/abstract</a>.

opencc-by-4.0Oct 2018View details →

Three principles of data science: predictability, computability, and stability (PCS)

This repository contains the data, codes, and PCS documentation described in: Three principles of data science: predictability, computability, and stability (PCS).

opencc-byNov 2019View details →

Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-g Mie

Calculated and experimental data for all the figures in the publication

opennotspecifiedJul 2010View details →

Actual and predicted distributions of Adoxotoma and Helpis, triangles: actual distribution from field data, and predicted distribution based upon bioclimatic profile for each genus. Growing shade intensity illustrates likelihood of occurrence. in A new genus and five new species of Astieae (Araneae: Salticidae) from Australia, with remarks on distribution

Actual and predicted distributions of Adoxotoma and Helpis, triangles: actual distribution from field data, and predicted distribution based upon bioclimatic profile for each genus. Growing shade intensity illustrates likelihood of occurrence.

opencc-by-4.0Nov 2019View details →

Predicting the global potential distribution of vine mealybug, Planococcus ficus under climate change

Figure S1: Performances of niche model of P.ficus in current model. L=Linear; Q=Quadratic; H=Hinge; P=Product; T=Threshold, Figure S2: Partial AUC Values and Graphics, null model (red distribution), distribution of expectations created via bootstrapping replacement of 50% of the total available points and 1000 resampling replicates (blue distribution), Table S1: References used to compile the dataset, Table S2: Occurrence sites for P.ficus, Table S3: Correlation analysis of environmental variables for pest, Table S4: ENMeval results for P.ficus from SDMs.

restrictedcc-by-4.0Aug 2024View details →

Dataset for Machine Learning-Based Prediction and Optimization of As-Extruded Viability in Extrusion-Based 3D Bioprinting

The dataset supports the findings presented in the paper "Machine learning-based prediction and optimization framework for as-extruded cell viability in extrusion-based 3D bioprinting." <ul> <li>Sodium alginate viscosity data: "alg_i1g_viscosity_data.zip"</li> <li>Cross Power Law parameter fitting results: "alg_i1g_viscosity_fittings.zip"</li> <li>Rheological stability measurement: "alg_i1g_contact_angle_data.zip"</li> <li>OpenFOAM simulation results: "alg_i1g_simulation_data.zip"</li> <li>Post-extrusion cell viability results: "cell_viability_data.zip"</li> </ul> Keywords: 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate Code availability statement The scripts used for data analysis, machine learning models, and numerical simulations in this study are available on GitHub at: <a href="https://github.com/KORINZ/in-silico-bioink-viability-prediction">https://github.com/KORINZ/in-silico-bioink-viability-prediction</a>

opencc-by-4.0Aug 2024View details →

The AF2 predicted structures and MD simulation results for the paper "In-situ structural insights into activity regulation of mammalian pyruvate dehydrogenase complex"

Thank you for your interest in our work. You can discover content that interests you within the respective compressed packages, accompanied by “readme” files.

FIG. 6 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 6. Demography and adaptation are predicted to interact to shape the abundance, body size distribution, and trophic role of Atlantic Cod.

FIG. 5 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 5. (A) Change in mean trophic level and (B) the abundance of fish with trophic level greater than 5.5, relative to the unfished population, with size-selective fishing mortality in both scenarios. In both panels, black lines are demography only, gray lines are demography with adaptation.

FIG. 2 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 2. (A) Age distribution of mature females in the unfished population. (B) Age distribution of mature females after size-selective fishing mortality. Note that growth, maturation, and fecundity functions are fixed functions of age and do not change with additional mortality (solid lines, Fig. 1B, C). Inset: Fishery selectivity (risk of fishing mortality) increases steeply with length. (C) Age distribution of mature females when maturation age decreases in response to size-selective fishing (dashed lines, Fig. 1B, C). Selectivity is identical to the inset in panel B.

FIG. 1 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 1. Functions describing (A) somatic growth, (B) maturation, and (C) fecundity with age of our representative species, the Georges Bank stock of Atlantic Cod (GADUS MORHUA). A maturation ogive is the cumulative probability that an individual is mature. Black lines represent fixed relationships; dashed lines are plastic or evolutionary changes that occur with the advent of size-selective mortality. The decrease in size and age at maturation (dashed line, panel B) could be due to selection for earlier reproduction or due to lower relative size or density.

FIG. 3 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 3. Size structure of the three populations in Figure 2. (A) The unfished population, (B) size-structure after fishing if maturation probability does not change, (C) size-structure after fishing when maturation decreases as a response to size-selective mortality. In this example, fishing mortality FMAX ¼ 0.4.

FIG. 4 in Predicting Eco-evolutionary Impacts of Fishing on Body Size and Trophic Role of Atlantic Cod

FIG. 4. Distributions of trophic level in the three scenarios: unfished baseline (white), demography-only with fixed age at maturation (black) and demography-plus-trait-change, with plastic or evolutionary change in age at maturation (gray). Note that gray bars are always equal to or greater than black bars in height.