Journal: Journal of molecular biology

Article Title: A Generalizable Optogenetic Strategy to Regulate Receptor Tyrosine Kinases during Vertebrate Embryonic Development

doi: 10.1016/j.jmb.2020.03.032

Figure Lengend Snippet: Design and evaluation of the CMT-OptoTrk system. (a) Schematic of the CMT-OptoTrk constructs representing the two-step recruitment and dimerization model of activation explained in Figure 1 (a). Trk can be TrkA, TrkB, or TrkC. (b) Confocal microscopic images of live HEK293T cells grown on glass-bottomed petri dishes and transiently transfected with CMT-OptoTrkC. Ten pulses of 1-s blue laser light were delivered via the microscope objective, and the before and after images were recorded. Plasma membrane translocation of cytosolic TrkC after blue light is apparent in the red channel. The green channel visualizes the membrane-anchored CIBN-EGFP module, which marks the plasma membrane and seems unresponsive to blue light. (c) cFos luciferase assay for HEK293T cells in 24-well plates transiently co-transfected with plasmids encoding CMT-OptoTrks, cFOS promoter-controlled luciferase, and Renilla luciferase (control), and after illumination and processing as described in Figure 2(d). Values represent mean ± s.d. of three biological replicates. (d) CMT-OptoTrks induce robust PC12 cell differentiation with light. Representative images of CMT-OptoTrk- and CIBN-EGFP-CaaX(control)-transfected PC12 cells following incubation in the light or dark as explained in Figure 2(e). (e) Quantification of PC12 cell differentiation with CMT-OptoTrkA, -OptoTrkB, and -OptoTrkC as in Figure 2(e). The total number of counted cells is indicated on top of the bars.

Article Snippet: CMT-OptoTrkA, CMT-OptoTrkB, and CMT-OptoTrkC were made by inserting the rat TrkA intracellular domain (NCBI {"type":"entrez-protein","attrs":{"text":"NP_067600.1","term_id":"11024666","term_text":"NP_067600.1"}} NP_067600.1 ; residues 443–799), human TrkB intracellular domain (NCBI {"type":"entrez-protein","attrs":{"text":"NP_001018074.1","term_id":"65506745","term_text":"NP_001018074.1"}} NP_001018074.1 ; residues 455–822), and rat TrkC intracellular domain (NCBI {"type":"entrez-protein","attrs":{"text":"NP_001257585.1","term_id":"397174822","term_text":"NP_001257585.1"}} NP_001257585.1 ; residues 454–864), respectively, into the OptoRaf1 KpnI and Kpn2I sites by Infusion-cloning. pFOS WT-GL3 was a gift from Ron Prywes (Addgene plasmid no. 11983).

Techniques: Construct, Activation Assay, Transfection, Microscopy, Clinical Proteomics, Membrane, Translocation Assay, Luciferase, Control, Cell Differentiation, Incubation

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: Modification

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: