Journal: Computational toxicology (Amsterdam, Netherlands)

Article Title: Navigating through the minefield of read-across tools: A review of in silico tools for grouping

doi: 10.1016/j.comtox.2017.05.003

Figure Lengend Snippet: Summary of key features of selected publicly available read-across tools

Article Snippet: IUCLID 8 5-supported endpoints (43 total) Any as per the regulatory endpoints Any based on user input Mutagenicity and Bioconcentration Factor (BCF) Any based on user input Analogue Identification Approach fragment matching Distance and correlation based similarity indices based on descriptors or fingerprints Substructure or similarity searching using structure, name, SMILES, InChI Category definition followed by subcategorisations Tanimoto distance using chemical and biological descriptors VEGA similarity algorithm Weighted Estimated Biological Similarity Neighbour Selection Automatic Automatic Manual Automatic + Manual Filter Automatic Automatic Automatic + Manual Filter Data Source Tool provides inventory index User provided or tool provided User and tool provided User provided or tool provided User provided Tool provided as a result of the EU ANTARES project User provided but tool provides PubChem in vitro data Quantitative vs Qualitative N/A Both User determined - Qualitative Both Qualitative Qualitative for mutagenicity, quantitative for BCF Qualitative Visualisation None Standard 2D plots, histograms and similarity matrix None Standard 2D Plots Radial plot of neighbours Interactive Neighbour plot Activity Plot Output/Export Output reports in the form of HTML, pdf or Excel sdf or txt files of data, image files of plots assessment report as docx or xlsx, data matrix as xlsx IUCLID format, pdf and rtf files of prediction report, text files of data, image files of plots etc NA image file of plot ND Open in a separate window Summary of key features of selected publicly available read-across tools Analog Identification Methodology (AIM) Software development The Analog Identification Methodology (AIM) was developed by SRC Inc for the EPA’s OCSPP (Office of Chemical Safety and Pollution Prevention).

Techniques: Selection, In Vitro, Activity Assay

Journal: Cell

Article Title: Virome characterization of game animals in China reveals a spectrum of emerging pathogens

doi: 10.1016/j.cell.2022.02.014

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER TRIzol Thermofisher Cat. #15596026 TruSeqTM Stranded Total RNA Sample Preparation Kit Illumina Cat. #20020597 DNase I TAKAR Cat. #2270B Ribo-ZeroTM rRNA Removal Kits Illumina Cat. #20037135 Biological samples Samples are described in Table S1 This paper N/A Deposited data Raw sequencing reads after QC This paper NCBI-SRA BioProject: PRJNA793740 and PRJNA795267 Data for all viral genomes This paper NCBI-Genbank: OM132156-OM132190 Data for all phylogenetic tree This paper https://doi.org/10.6084/m9.figshare.18120236 Software and algorithms bbmap v38.62 https://sourceforge.net/projects/bbmap/ https://sourceforge.net/projects/bbmap/ MEGAHIT v1.2.8 Li et al., 2015 https://github.com/voutcn/megahit Diamond v0.9.25.26 Buchfink et al., 2021 https://github.com/bbuchfink/diamond SeqMan v7.0 Clewley, 1995 https://www.dnastar.com/software/ Geneious v2019.2.1 The Biomatters development team https://www.geneious.com/ Bowtie2 v2.3.5.1 Langmead and Salzberg, 2012 http://bowtiebio.sourceforge.net/bowtie2 TransDecoder v5.5.0 http://transdecoder.github.io http://transdecoder.github.io MAFFT v7.475 Katoh and Standley, 2013 https://mafft.cbrc.jp/alignment/software/ TrimAL v1.2rev59 Capella-Gutiérrez et al., 2009 https://github.com/scapella/trimal PhyML v3.1 Guindon and Gascuel, 2003 http://www.atgc-montpellier.fr/phyml/ Simplot v3.5.1 Lole et al., 1999 https://www.mybiosoftware.com/simplot-3-5-1-sequence-similarityplotting.html SplitsTree v4.15.1 Huson, 2006 https://software-ab.informatik.uni-tuebingen.de/download/splitstree4/welcome.html RDP4 v4.97 Martin et al., 2015 http://web.cbio.uct.ac.za/~darren/rdp.html IQ-Tree v2.0.3 Minh et al., 2020 http://www.iqtree.org/ Other Sequencing systems Illumina Novaseq 6000 NCBI GenBank database Sayers et al., 2021 https://www.ncbi.nlm.nih.gov/genbank/ RefSeq (viral genomes) Brister et al., 2015 https://www.ncbi.nlm.nih.gov/refseq/ SILVA database Quast et al., 2013 www.arb-silva.de Open in a separate window KEY RESOURCES TABLE METHOD DETAILS .

Techniques: Sample Prep, Sequencing, Software

Journal: Acta Crystallographica. Section D, Structural Biology

Article Title: Structural features of NS3 of Dengue virus serotypes 2 and 4 in solution and insight into RNA binding and the inhibitory role of quercetin

doi: 10.1107/S2059798317003849

Figure Lengend Snippet: Data-collection and scattering-derived parameters for DENV-2 NS2B 18 NS3 in the absence or presence of substrates

Article Snippet: The normalized Kratky plots of DENV-2 NS2B 18 NS3 in the absence or presence of nucleotides exhibit similar profiles, indicating no flexibility change upon nucleotide binding (Supplementary Fig. S8 c ). table ft1 table-wrap mode="anchored" t5 Table 3 caption a7 Protein sample Empty form + MgATP + MgADP + MgAMPPNP + RNA (AGA CUA ACA ACU) Data-collection parameters Instrument (source and detector) Bio-SAXS beamline BL4-2 at SSRL with Rayonix MX225-HE CCD detector Beam geometry 200 μm slit Wavelength (nm) 0.1127 q range (nm −1 ) 0.068–4.7 Exposure time (s) 15 (15 frames × 1 s) Concentration range (mg ml −1 ) 1.13 and 4.5 Temperature (K) 288.15 Structural parameters † I (0) (from Guinier) (arbitrary units) 1173.7 ± 2.2 1163.5 ± 1.8 1153.3 ± 1.2 1195.5 ± 2.1 1301.3 ± 1.7 R g (from Guinier) (nm) 3.22 ± 0.01 3.23 ± 0.01 3.20 ± 0.01 3.22 ± 0.01 3.25 ± 0.01 D max (nm) 10.3 ± 1 10.3 ± 1 10.2 ± 1 10.5 ± 1 10.5 ± 1 Porod volume estimate ( V p ) (nm 3 ) ∼95.1 ∼94.6 ∼95.1 ∼95.5 ∼108.3 Dry volume from sequence ‡ (nm 3 ) ∼87.2 ∼87.2 ∼87.2 ∼87.2 ∼87.2 Software employed Primary data reduction SasTool Data processing PRIMUS Open in a separate window † Reported for merged data; the uncertainties for the R g and I (0) values were calculated from the variance of the least-squares fit, and that for the D max value was estimated by testing P ( r ) near the best-fitting value.

Techniques: Concentration Assay, Sequencing, Software