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| 1 | High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay显示文摘Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors(HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference(HT-RNAi) screens has led to the identification of several key factors involved in the viral lifecycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested(such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase Ⅲα in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HTRNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies. | Sandeep Amberkar Narsis A Kiani Ralf Bartenschlager Gualtiero Alvisi Lars Kaderali | 2013 | World Journal of Virology2013，2，2： | 9 |
| 2 | Biosorption of chromium(Ⅵ)from aqueous and elcctroplating wasterwater using fungal biomass显示文摘 | Rajender Kumar Narsi R Bishnoi Garima Kiran Bishnoi | | 0，，03： | 1 |
| 3 | Adsorption of Cr^4+ on activated rice husk carbon and activated alumina显示文摘 | NARSI R B MINI B NIVEDITA S ASHA G | 2004 | Bioresourcc Technology2004，91，： | 1 |
| 4 | Adsorption of Cr(Ⅵ)on Activated Rice Husk Carbon and Activated Alumina 显示文摘 | Narsi R Bujaj M | 2004 | Bioresource Technology2004，91，3： | 1 |
| 5 | Optimization of enzymatic hydrolysis of pretreated rice straw显示文摘 | Anita S Narsi RB | 2012 | Appl Microbiol Biotechnol and Ethanol Production2012，93，： | 1 |
| 6 | Removal of chromium and nickel from aqueous solution in constructed wetland: Mass balance, adsorption–desorption and FTIR study显示文摘 | Asheesh Kumar Yadav Naresh Kumar T.R. Sreekrishnan Santosh Satya Narsi R. Bishnoi | 2010 | Chemical Engineering Journal2010，，1： | 1 |
| 7 | Biosorption of chromium (VI) from aqueous solution and electroplating wastewater using fungal biomass显示文摘 | Kumar Rajender Bishnoi Narsi R Bishnoi Kiran | 2008 | Chemical Engineering Journal2008，135，3： | 1 |
| 8 | Biosorption of chromium (Ⅵ) from aqueous solution and electroplating wastewater using fungal biomass 显示文摘 | Rajender K Narsi R B Garima | 2008 | Chemical Engineering Journal2008，135，： | 1 |
| 9 | Biosorption of chromium(VI) from aqueous solution and electroplating wastewater using fungal biomass显示文摘 | Rajender Kumar Narsi R. Bishnoi Garima Kiran Bishnoi | 2007 | Chemical Engineering Journal2007，，3： | 1 |
| 10 | Enzymatic hydrolysis of microwave alkali pretreated rice husk for ethanol production by saccharo- myces cerevisiae, scheffersomyces stipitis and their co-culture 显示文摘 | Anita S Somvir B Narsi R | 2014 | Fuel2014，116，15： | 1 |
| 11 | Biosorption of chromium (Ⅵ) from aqueous solution and electroplating wasterwater using fungal biomass 显示文摘 | Rajender Kumar Narsi R Bishnoi Garima Kiran Bishnoi | 2008 | Chemical Engineering Journal2008，135，3： | 1 |
| 12 | Optimization of enzymatic hydrolysis of pretreated rice straw and ethanol production显示文摘 | Anita Singh Narsi R. Bishnoi | 2012 | Applied Microbiology and Biotechnology2012，，4： | 1 |
| 13 | High‐performance thin layer chromatography to assess pharmaceutical product quality显示文摘 | Eliangiringa Kaale Vicky Manyanga Narsis Makori David Jenkins Samuel Michael Hope Thomas Layloff | 2014 | Trop Med Int Health2014，，6： | 1 |
| 14 | LIBRA:an adaptative integrative tool for paired single-cell multi-omics data显示文摘Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is challenging.In response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell analysis.It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine-tuning.Methods:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is constructed.Additionally,we implemented a variation,aLIBRA,that allows automatic fine-tuning by identifying parameter combinations that optimize both the integrative and predictive tasks.All model parameters and evaluation metrics are made available to users with minimal user iteration.Furthermore,aLIBRA allows experienced users to implement custom configurations.The LIBRA toolbox is freely available as R and Python libraries at GitHub(TranslationalBioinformaticsUnit/LIBRA).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of omics.We observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(clustering)resolution in the integrated latent space.Furthermore,when assessing the predictive power across data modalities,such as predictive chromatin accessibility from gene expression,LIBRA outperforms existing tools.As expected,adaptive parameter optimization(aLIBRA)significantly boosted the performance of learning predictive models from paired data-sets.Conclusion:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics data.LIBRA is a data-driven robust platform that includes an adaptive learning scheme. | Xabier Martinez-de-Morentin Sumeer AKhan Robert Lehmann Sisi Qu Alberto Maillo Narsis AKiani Felipe Prosper Jesper Tegner David Gomez-Cabrero | 2023 | Quantitative Biology2023，11，3： | 0 |
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