Ewan Birney
Compressing Oxford Nanopore signal into CRAM
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Ewan Birney
EMBL-EBI
Compressing Oxford Nanopore signal into CRAM
Bio
Ewan Birney is Director of EMBL-EBI with Dr Rolf Apweiler and runs a small research group. He is also EMBL-EBI's Joint Head of Research, alongside Dr Nick Goldman. Ewan completed his PhD at the Wellcome Sanger Institute with Richard Durbin. In 2000, he became Head of Nucleotide data at EMBL-EBI and in 2012 he took on the role of Associate Director at the institute. He became Director of EMBL-EBI in 2015. Ewan led the analysis of the Human Genome gene set, mouse and chicken genomes and the ENCODE project, focusing on non-coding elements of the human genome. Ewan’s main areas of research include functional genomics, DNA algorithms, statistical methods to analyse genomic information (in particular information associated with individual differences in humans and Medaka fish) and use of images for chromatin structure. Ewan is a non-executive Director of Genomics England, and a consultant and advisor to a number of companies, including Oxford Nanopore Technologies. Ewan was elected an EMBO member in 2012, a Fellow of the Royal Society in 2014 and a Fellow of the Academy of Medical Sciences in 2015. He has received a number of awards including the 2003 Francis Crick Award from the Royal Society, the 2005 Overton Prize from the International Society for Computational Biology and the 2005 Benjamin Franklin Award for contributions in Open Source Bioinformatics.
James Ferguson
SquiggleKit: a toolkit for manipulating nanopore signal data
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James Ferguson
Garvan Institute of Medical Research
SquiggleKit: a toolkit for manipulating nanopore signal data
Abstract
The management of raw nanopore sequencing data poses a challenge that must be overcome to accelerate the development of new bioinformatics algorithms predicated on signal analysis. SquiggleKit is a toolkit for manipulating and interrogating nanopore data that simplifies file handling, data extraction, visualisation, and signal processing. Its modular tools can be used to reduce file numbers and memory footprint, identify poly-A tails, target barcodes, adapters, and find nucleotide sequence motifs in raw nanopore signal, amongst other applications. SquiggleKit serves as a bioinformatics portal into signal space, for novice and experienced users alike. It is comprehensively documented, simple to use, cross-platform compatible and freely available (https://github.com/Psy-Fer/SquiggleKit).
Bio
James Ferguson is a Genomic Systems Analyst in the Genomic Technologies Group at the Kinghorn Centre for Clinical Genomics, located at the Garvan institute of Medical Research in Sydney, Australia. With a background in clinical pathology testing, algorithm development, and computer hacking, James applies his unique skill set to develop new bioinformatic tools, as well as design and support nanopore sequencing infrastructure.
Kuan-Ting Lin
PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis
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Kuan-Ting Lin
Cold Spring Harbor Laboratory
PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis
Abstract
Percent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e., precision, recall, false positive rate, and correlation) in comparison with three leading tools (rMATS, SUPPA2, and Whippet). PSI-Sigma outperformed these tools, especially in the case of AS events with multiple alternative exons and intron-retention events. We also briefly evaluated its performance in long-read RNA-seq analysis, by sequencing a mixture of human RNAs and RNA Sequins with nanopore long-read sequencers. Based on the long-read RNA-seq data of RNA sequins, we found that nanopore long-read RNA-seq is qualitatively reliable. Also, in human U87 cells, we found that ~1 million long reads can already detect major AS changes in ~3,500 protein-coding genes with at least 10 supporting long reads. PSI-Sigma is implemented in Perl and is available at https://github.com/wososa/PSI-Sigma
Bio
Kuan-Ting Lin is a Computational post-doc at Cold Spring Harbour Laboratory, where he focusses on quantitative biology and transcriptomics technologies. His long-term research interests involve the use of mathematical, statistical or computational techniques to develop understanding of how alterations in RNA transcription contribute to human health and his academic training and research experience has provided an excellent background in drug discovery, data mining and quantitative biology.
Mike Vella
Accelerated de novo assembly on GPUs
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Mike Vella
NVIDIA
Accelerated de novo assembly on GPUs
Abstract
Recent years has seen an uptake in the use of GPUs for Genomics, from basecalling (e.g. Guppy) to variant calling (e.g. Deep Variant). Long-read sequencing technology such as Oxford Nanopore sequencing holds the promise of simple and cost-effective de novo assembly. This is important for generating reference sequences (even for complex, polyploid organisms) and identifying structural variants such as deletions and translocations. One of the difficulties however of high-quality de novo assembly is its substantial computational cost. Post-sequencing assembly can take longer than the sequencing experiment itself. The Nvidia genomics team is harnessing the power of GPUs to develop a pipeline for massive acceleration of de novo assembly. Our end goal is real-time long-read de novo assembly.
Bio
Mike Vella is a Senior Deep Learning and Genomics Engineer at NVIDIA corporation. Mike works on using GPUs to help researchers with the analysis of high-throughput sequencing data. Mike has a PhD in Computational Neuroscience from the University of Cambridge and an undergraduate degree in Physics from the University of Bristol.
Sam Kovaka
Rapidly mapping raw nanopore signal with UNCALLED to enable real-time targeted sequencing
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Sam Kovaka
Johns Hopkins University
Rapidly mapping raw nanopore signal with UNCALLED to enable real-time targeted sequencing
Abstract
UNCALLED is a tool that maps raw nanopore reads to large DNA references as they are being sequenced. It is a streaming algorithm, meaning the mapping begins as soon as the first bit of signal comes from the sequencer. UNCALLED can currently map reads from all active MinION pores to a 31Mbp reference containing eight bacterial genomes after less than three seconds of sequencing and analysis per read. The main application for UNCALLED is ReadUntil sequencing, where reads can be ejected from the pore depending on whether or not they map to the reference.
Bio
Sam Kovaka is a third year PhD student in computer science at Johns Hopkins University, co-advised by Michael Schatz and Mihaela Pertea. He attended Clark University for his undergraduate degree, majoring in biology and computer science. Sam started working with nanopore sequencing in the first year of his PhD with a class project that turned into the work that he will be presenting at London Calling 2019.
Stephen Pascoe
Don’t let data management be your bottleneck
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Stephen Pascoe
Oxford Nanopore Technologies
Don’t let data management be your bottleneck
Abstract
Once you can generate terabytes of data from a single flowcell, simply moving and storing that data can become the bottleneck for your workflow. We present some recent and up-coming MinKNOW features designed to help with your data management challenges. Find out how to trigger analyses automatically and hear some rules of thumb to help you plan what kit you need. We will also give you some insight into how Oxford Nanopore has scaled out data management to hundreds of devices.
Bio
Stephen is Associate Director of Data Engineering at Oxford Nanopore Technologies. He is the principal architect of Oxford Nanopore’s automated mirroring, analysis and archiving system as well as coordinating the development of various applications supporting the Research and Development groups. Previously, Stephen worked in climate science and cheminformatics where he has developed many systems supporting UK, European and International research, including a major role in the ESGF architecture for sharing climate model outputs across the globe.