Two Supplementary Tables and a data file to accompany this paper. Sup. Table 1 is the acknowledgment table for GISAID COVID-19 sequence data. Sup. Table 2 is a spreadsheet that details the diversity found among SARS-CoV-2 protein sequences, and indicates the sequence names, locations and sampling dates of common variants. Data S1 show isotonic regression plots of transitions from one variant of the spike protein (D614) to another (G614) in local regions over time, based on the geographic location and date of sampling.
Behavior across development of C. elegans individuals of the following genotypes:
Each genotype population is a Matlab cell array, each cell shows the center of mass XY positions of a single individual (3fps) across development time. 1's in XY positions indicate that the animal was not detected .
Contributors:Kermany Daniel, Zhang Kang, Goldbaum Michael
Be sure to download the most recent version of this dataset to maintain accuracy.
This dataset contains thousands of validated OCT and Chest X-Ray images described and analyzed in "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning". The images are split into a training set and a testing set of independent patients. Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL.
This repository of images is made available for use in research only.
How to cite this data:
Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.
Autoradiography files generated by Typhoon scanner, can be opened by ImageJ or TotalQuant;
western blot scans generated by LiCoR Odyssey, should be opened by Odyssey software.
quantitation of the biochemistry assay were analyzed in attached excel file.
statistical analysis also included in the same excel file
Pathogenic Germline Variants in 10,389 Adult Cancers
We conducted the largest investigation of predispo- sition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large dele- tions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evi- dences involving case-control frequency, loss of het- erozygosity, expression effect, and co-localization with mutations and modified residues. Our integra- tive approach links rare predisposition variants to functional consequences, informing future guide- lines of variant classification and germline genetic testing in cancer.
Raw_data_Northern_blots contains original images of exposed membranes from Northern blot with gene specific probes and 16S loading control probes (sequential hybridizations). The targets for the Northern blots are bacterial operons with complex isoforms (3' extensions from partial termination).
The validation figure contains the various evidence that Rend-seq allows for precise 5' and 3' ends mapping as well as mRNA abundance measurements (comparison to curated databases of transcription start sites, comparison to processing sites and 3' ends precisely mapped in the literature, our own experimental validation of some novel 5' ends by 5' RACE, comparison to other recent high-throughput mapping of 3' ends, and comparison to micro-array data for mRNA abundance measurements).
Table 1 contains mRNA abundances from Rend-seq for wild-type and mutants in B. subtilis, E. coli, V. natriegens, and C. crescentus.
Table 2 contains estimated translation efficiencies (ribosome footprint read density divided by Rend-seq read density) for genes in B. subtilis, V. natriegens, and C. crescentus.
Table 3 contains mRNA 5' ends identified in Rend-seq validated by primer extension assays (within 1 nt), as compiled in curated databases (EcoCyc for E. coli, DBTBS for B. subtilis).