Project description:MAGE-seq amplicon data from the paper RNA structural determinants of optimal codons revealed by MAGE-seq in Cell Systems 2016 by Kelsic, Chung, Cohen, Park, Wang & Kishony. Data contains read counts for PCR amplicons of the Escherichia coli gene infA: 1) Single codon mutants tiling along the entire gene, with timepoints from growth doublings in rich and minimal medias. 2) Codon pair mutants for positions at the beginning of the gene with timepoints for growth doublings in rich media. 3) Mutations in a hairpin of the 5' UTR for growth in rich media.
Project description:Table of contents A1 Proceedings of 2016 China Cancer Immunotherapy Workshop, Beijing, China Bin Xue, Jiaqi Xu, Wenru Song, Zhimin Yang, Ke Liu, Zihai Li A2 Set the stage: fundamental immunology in forty minutes Zihai Li A3 What have we learnt from the anti-PD-1/PD-L1 therapy of advanced human cancer? Lieping Chen A4 Immune checkpoint inhibitors in lung cancer Edward B. Garon A5 Mechanisms of response and resistance to checkpoint inhibitors in melanoma Siwen Hu-Lieskovan A6 Checkpoint inhibitor immunotherapy in lymphoid malignancies Wei Ding A7 Translational research to improve the efficacy of immunotherapy in genitourinary malignancies Chong-Xian Pan A8 Immune checkpoint inhibitors in gastrointestinal malignancies Weijing Sun A9 What’s next beyond PD-1/PDL1? Yong-Jun Liu A10 Cancer vaccines: new insights into the oldest immunotherapy strategy Lei Zheng A11 Bispecific antibodies for cancer immunotherapy Delong Liu A12 Updates on CAR-T immunotherapy Michel Sadelain A13 Adoptive T cell therapy: personalizing cancer treatment Cassian Yee A14 Immune targets and neoantigens for cancer immunotherapy Rongfu Wang A15 Phase I/IIa trial of chimeric antigen receptor modified T cells against CD133 in patients with advanced and metastatic solid tumors Meixia Chen, Yao Wang, Zhiqiang Wu, Hanren Dai, Can Luo, Yang Liu, Chuan Tong, Yelei Guo, Qingming Yang, Weidong Han A16 Cancer immunotherapy biomarkers: progress and issues Lisa H. Butterfield A17 Shaping of immunotherapy response by cancer genomes Timothy A. Chan A18 Unique development consideration for cancer immunotherapy Wenru Song A19 Immunotherapy combination Ruirong Yuan A20 Immunotherapy combination with radiotherapy Bo Lu A21 Cancer immunotherapy: past, present and future Ke Liu A22 Breakthrough therapy designation drug development and approval Max Ning A23 Current European regulation of innovative oncology medicines: opportunities for immunotherapy Harald Enzmann, Heinz Zwierzina
Project description:As part of the EcoToxChip project, 49 distinct exposure studies were conducted on three lab model species (Japanese quail, fathead minnow, African clawed frog) and three ecologically relevant species (double crested cormorant, rainbow trout, northern leopard frog), at multiple life stages (embryo, adult), exposed to eight chemicals of environmental concern (ethinyl estradiol-EE2, hexabromocyclododecane-HBCD, lead-Pb, selenomethionine-SeMe, 17β trenbolone-TB, chlorpyrifos-CPF, fluoxetine-FLX, and benzo [a] pyrene-BaP. Whole transcriptome analyses were conducted on these samples resulting in a rich RNA seq dataset covering various species, life stages and chemicals, which is one of the largest purposeful complications of RNA seq data within ecotoxicology. Recently, a unified bioinformatics platform of relevance to ecotoxicology, EcoOmicsAnalyst and ExpressAnalyst, was developed to facilitate RNA Seq analysis of non-model species lacking a reference transcriptome. The platform uses the Seq2Fun algorithm to map RNA-seq reads from eukaryotic species to an ortholog database comprised of protein sequences from >600 eukaryotic species (EcoOmicsDB) with a translated search. The availability of these tools presents a unique opportunity to examine the EcoToxChip RNA Seq dataset for cross species comparisons. This work shows the potential of the EcoOmicsAnalyst and Seq2Fun platform to facilitate fast and simple analysis of RNA Seq datasets from non-model organisms with unannotated genomes and conduct comparative transcriptomic analysis across various species and life stages for cross-species extrapolation.