From RNA-Seq data to bioinformatics analysis using Nanopore - PowerPoint PPT Presentation

Mar 13, 2023 •385 likes •425 views

From RNA-Seq data to bioinformatics analysis using Nanopore sequencers ASTE TER - Al Algorithm hms an and softw twar are e for th thir ird ge generation RN RNA A se sequencing Bonsai, CRIStAL, Lille Erable, LBBE, Lyon

From RNA-Seq data to bioinformatics analysis using Nanopore sequencers
ASTE TER - Al Algorithm hms an and softw twar are e for th thir ird ge generation RN RNA A se sequencing • Bonsai, CRIStAL, Lille • Erable, LBBE, Lyon • Institut Pasteur de Lille • R&D Bioseq, Genoscope
11:30 James Platt (Oxford Nanopore), Full length transcript sequencing of RNA and cDNA with Oxford 10:45 10:45 - 11:30 Nanopore 12:00 Jean-Marc Aury (Genoscope), RNA sequencing with the MinION at Genoscope 11:30 11:30 - 12:00 12:45 Laurent Jourdren and Sophie Lemoine (IBENS Paris), One year of developments and 12:00 12:00 - 12:45 collaborations around the MinION on the Genomic facility of the IBENS 14:00 Lunch and open discussions 12:45 12:45 - 14:00 14:30 Quentin Carradec (Genoscope), MinION on board of the Tara expedition 14:00 14:00 - 14:30 15:00 Rayan Chikhi (CNRS, CRIStAL, Lille) and Leandro Lima (ERABLE, Lyon), A first look at error- 14:30 14:30 - 15:00 correction tools on Nanopore RNA-seq data 15:45 Hagen Tilgner (BRMI, Weill Cornell), What genes are saying, not how much they talk 15:00 15:00 - 15:45 16:30 Coffee break 15:45 15:45-16:30 17:00 Vincent Lacroix (ERABLE, Lyon), Identification and quantification of isoforms in RNAseq data: 16:30 16:30- 17:00 deep short reads Vs shallow long reads: initial answers and open questions 17:30 Camille Marchet (IRISA Rennes), De novo Clustering of Gene Expressed Variants in 17:00 17:00- 17:30 Transcriptomic Long Reads Data Sets

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

The goal of bioinformatics is the extension of experimental data by predictions. A fundamental

Bioinformatics 1 B ioinformatics is a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine. Bioinformatics is fed by

597 views • 22 slides

Data Mining in Bioinformatics Day 6: Classification in Next Generation Sequencing Data Analysis

Data Mining in Bioinformatics Day 6: Classification in Next Generation Sequencing Data Analysis Dominik Grimm February 18 to March 1, 2013 Machine Learning & Computational Biology Research Group Max Planck Institute Tbingen and Eberhard

673 views • 49 slides

Compressive Structural Bioinformatics: Large-scale analysis and visualization of the Protein Data

Compressive Structural Bioinformatics: Large-scale analysis and visualization of the Protein Data Bank archive Peter W. Rose, Anthony R. Bradley, Alexander S. Rose, Yana Valasatava, Jose M. Duarte, Andreas Prli Structural Bioinformatics

195 views • 16 slides

Experiments Design and Analysis Fotis E. Psomopoulos CODATA-RDA Advanced Bioinformatics

Experiments Design and Analysis Fotis E. Psomopoulos CODATA-RDA Advanced Bioinformatics Workshop, 19-23 August 2019, Trieste, Italy A short intro to me 2 Bioinformatics and Data Mining tools and pipelines to address

884 views • 43 slides

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Clustering Gene Expression Data

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Clustering Gene Expression Data Chlo-Agathe Azencott & Karsten Borgwardt February 18 to March 1, 2013 Machine Learning & Computational Biology Research Group Max

538 views • 51 slides

1 The data analysis process An Example: An Example: Microarray scans The Transcriptional

Implications of microarray data Predicting Gene Ontology Biological Process Issues: The Linnaeus Centre for Bioinformatics The Linnaeus Centre for Bioinformatics Analysis, especially statistical aspects of developing From Temporal Gene

246 views • 13 slides

Bioinformatics Bioinformatics Tools for RNA Tools for RNA Data Analysis Data Analysis Joseph

Bioinformatics Bioinformatics Tools for RNA Tools for RNA Data Analysis Data Analysis Joseph Santos Joseph Santos Bloomfield Tech High School Bloomfield Tech High School Bloomfield, New Jersey Bloomfield, New Jersey 1 1 Contents

280 views • 23 slides

Data Mining in Bioinformatics Days 6 and 7: The Need for Data Mining in Bioinformatics Karsten

Data Mining in Bioinformatics Days 6 and 7: The Need for Data Mining in Bioinformatics Karsten Borgwardt February 10 to February 21, 2014 Machine Learning & Computational Biology Research Group Max Planck Institute Tbingen and Eberhard

954 views • 83 slides

SANBio BIOINFORMATICS TRAINING COURSE THE MICROBIOME: ANALYSIS OF NGS DATA CBIO-PIPELINE SAMSON,

SANBio BIOINFORMATICS TRAINING COURSE THE MICROBIOME: ANALYSIS OF NGS DATA CBIO-PIPELINE SAMSON, KM 10/23/2017 Microbiome : Analysis of NGS Data 1 Outline Background Wet Lab! Raw reads Quality Assessment Quality Control Merging and

775 views • 38 slides

Bioinformatics Outline What is bioinformatics? Who are bioinformaticians? Hardware

Bioinformatics Outline What is bioinformatics? Who are bioinformaticians? Hardware Software What is bioinformatics? What is bioinformatics? Someone to analyze my data The boring stuff I do Someone to help me between experiments

204 views • 16 slides

National Bioinformatics Infrastructure Sweden (NBIS ) and Introduction to NGS data analysis

National Bioinformatics Infrastructure Sweden (NBIS ) and Introduction to NGS data analysis Jeanette Tngrot CLiC Computational Life Science Cluster NBIS National Bioinformatics Infrastructure Sweden jeanette.tangrot@umu.se /

555 views • 41 slides

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Karsten Borgwardt February 21

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Karsten Borgwardt February 21 to March 4, 2011 Machine Learning & Computational Biology Research Group MPIs Tbingen Karsten Borgwardt: Data Mining in Bioinformatics, Page

618 views • 24 slides

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Karsten Borgwardt February 25

Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Karsten Borgwardt February 25 to March 10 Bioinformatics Group MPIs Tbingen Karsten Borgwardt: Data Mining in Bioinformatics, Page 1 Clustering in bioinformatics Microarrays

290 views • 24 slides

Data Mining in Bioinformatics Day 8: Feature Selection in Bioinformatics Karsten Borgwardt

Data Mining in Bioinformatics Day 8: Feature Selection in Bioinformatics Karsten Borgwardt February 25 to March 10 Bioinformatics Group MPIs Tbingen Karsten Borgwardt: Data Mining in Bioinformatics, Page 1 Gene Selection via the BAHSIC

686 views • 23 slides

Data Mining in Bioinformatics Day 8: Clustering in Bioinformatics Clustering Gene Expression Data

Data Mining in Bioinformatics Day 8: Clustering in Bioinformatics Clustering Gene Expression Data Chlo-Agathe Azencott & Karsten Borgwardt February 10 to February 21, 2014 Machine Learning & Computational Biology Research Group Max

522 views • 50 slides

Data Mining in Bioinformatics Day 9: String & Text Mining in Bioinformatics Karsten Borgwardt

Data Mining in Bioinformatics Day 9: String & Text Mining in Bioinformatics Karsten Borgwardt February 25 to March 10 Bioinformatics Group MPIs Tbingen Karsten Borgwardt: Data Mining in Bioinformatics, Page 1 Why compare sequences?

786 views • 25 slides

Brief Announcement Hyaline : Fast and Transparent Memory Reclamation Ruslan Nikolaev and Binoy

Brief Announcement Hyaline : Fast and Transparent Memory Reclamation Ruslan Nikolaev and Binoy Ravindran rnikola@vt.edu, binoy@vt.edu Systems Software Research Group Virginia Tech, USA Memory Reclamation Problem Concurrent programming is

236 views • 6 slides

AIT-WATCHMAN A Remote Reactor Monitor and Advanced Instrumentation Testbed Christopher Grant On

AIT-WATCHMAN A Remote Reactor Monitor and Advanced Instrumentation Testbed Christopher Grant On behalf of the WATCHMAN Collaboration The link between nuclear reactors and nuclear weapons 235 U 85 Kr A source of fissionable material, ! such as

321 views • 31 slides

SAH guided spatial split partitioning for fast BVH construction Per Ganestam and Michael Doggett

SAH guided spatial split partitioning for fast BVH construction Per Ganestam and Michael Doggett Lund University Opportunistic triangle splitting for higher quality BVHs Bounding Volume Hierarchies (BVH) are a simple, compact 3D data

462 views • 30 slides

Lattice-Based Signature Scheme with Verifier Local Revocation Adeline Langlois 1 San Ling 2 Khoa

Lattice-Based Signature Scheme with Verifier Local Revocation Adeline Langlois 1 San Ling 2 Khoa Nguyen 2 Huaxiong Wang 2 1 LIP, ENS de Lyon, France 2 Nanyang Technological University, Singapore March 27, 2014 PKC 2014 Group Signature with VLR

257 views • 23 slides

Post-Newtonian Dynamics Post-Newtonian Dynamics And the future: And the future: Billion bodies

Post-Newtonian Dynamics Post-Newtonian Dynamics And the future: And the future: Billion bodies and Exascale? Billion bodies and Exascale? SFB881 Rainer Spurzem*, Peter Berczik, Silk Road T eam Rainer Spurzem*, Peter Berczik, Silk Road T

532 views • 39 slides

Spacetime programming A Synchronous Language for Composable Search Strategies Pierre Talbot

Spacetime programming A Synchronous Language for Composable Search Strategies Pierre Talbot (pierre.talbot@univ-nantes.fr) University of Nantes LS2N Laboratory 8th October 2019 Constraint programming Holy grail of computing

488 views • 37 slides

Indexing de Bruijn graph with minimizers Antoine Limasset Bonsai Team, CRIStAL, Universit de

Indexing de Bruijn graph with minimizers Antoine Limasset Bonsai Team, CRIStAL, Universit de Lille, CNRS November, 2018 1 / 19 Introduction Problem SOTA Blight Conclusion Data Deluge NovaSeq: 1TB/day 2 / 19 Introduction Problem SOTA

203 views • 19 slides

Survival Guide Philipp Krenn @xeraa

Survival Guide Philipp Krenn @xeraa Electronic Data Interchange (EDI) ViennaDB Papers We Love Vienna Who uses AWS, Azure,...? Does the cloud solve all your security issues? "We can

934 views • 78 slides