{"id":6191,"date":"2024-08-23T11:31:33","date_gmt":"2024-08-23T09:31:33","guid":{"rendered":"https:\/\/ubc.uu.nl\/?page_id=6191"},"modified":"2024-10-04T09:44:54","modified_gmt":"2024-10-04T07:44:54","slug":"the-10th-ubc-annual-symposium","status":"publish","type":"page","link":"https:\/\/ubc.uu.nl\/the-10th-ubc-annual-symposium\/","title":{"rendered":"The 10th UBC Annual Symposium"},"content":{"rendered":"\t\t
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The 10th UBC Annual Symposium

\n\"Bioinformatics in Utrecht:
Past 10 years and the future\"<\/span><\/i><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Monday, October 7, from 8:30h at the Princess M\u00e1xima Center for Pediatric Oncology<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The 10th UBC Annual Symposium - Bioinformatics in Utrecht: Past 10 years and the future<\/i><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We are happy to announce that the 10th UBC annual symposium will take place at the Princess M\u00e1xima Center (Utrecht Science Park) on Monday, October 7, 2024. The theme of this year’s edition is \u201cBioinformatics in Utrecht: Past 10 years and the future\u201d. We will reflect on the compliments of the past decade and look forward to the innovations that lie ahead.<\/p>\n

At this yearly event, we aim to bring the bioinformatics community working at the Utrecht Science Park together to exchange knowledge and expertise and discuss the latest progress in the field of bioinformatics.<\/p>\n

We strongly encourage you to register (for free) and submit an abstract for a poster presentation at the symposium, to promote your bioinformatics research, gain more exposure, and win a cash prize of 150 euros!<\/p>\n

Location:<\/strong>
6th Floor of the Princess M\u00e1xima Center for Pediatric Oncology \u2028
Heidelberglaan 25
3584 CS Utrecht<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Preliminary Programme<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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08:30 \u2013 09:00 Registration
09:00 \u2013 09:15 Welcome by the UBC Executive Committee and Prof. dr. Isabel Arends (Chair of the Life Sciences Deans)
09:15 \u2013 10:00 Keynote talk by Prof. dr. Berend Snel
10:00 \u2013 10:30 Coffee break and poster presentations
10:30 \u2013 12:00 Pitch talks Sequencing and Bioinformatics<\/i>
12:00 \u2013 13:30 Walking lunch and poster presentations
13:30 \u2013 14:15 Keynote talk by Prof. dr. Ana Conesa 
14:15 \u2013 15:45 Pitch talks Modelling and Machine Learning<\/i>
15:45 \u2013 16:15 Break and poster presentations
16:15 \u2013 17:00 Keynote talk by Prof dr. Erik van Nimwegen
17:00 \u2013 18:00 Closing ceremony followed by drinks<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Keynote Talks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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In this lecture we will discuss two things. First I will shortly reflect on the past ten years in bioinformatics with a focus on the Utrecht Science Park and the UBC. Subsequently we will discuss our recent investigations in utilizing spliceosomal introns to unlock one of the biggest questions in comparative genomics. Spliceosomal introns are a unique feature of eukaryotic genes. Previous studies have established that many introns were present in the protein-coding genes of the last eukaryotic common ancestor (LECA). Intron positions shared between genes that duplicated before LECA could in principle provide insight into the emergence of the first introns. In this study we use ancestral intron position reconstructions in two large sets of duplicated families to systematically identify these ancient paralogous intron positions. We found that 20-35% of introns inferred to have been present in LECA were shared between paralogs. These shared introns, which likely preceded ancient duplications, were wide spread across different functions, with the notable exception of nuclear transport. Since we observed a clear signal of pervasive intron loss prior to LECA, it is likely that substantially more introns were shared at the time of duplication than we can detect in LECA. The large extent of shared introns indicates an early origin of introns during eukaryogenesis and suggests an early origin of a nuclear structure, before most of the other complex eukaryotic features were established.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Prof. dr. Berend Snel<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Berend Snel studied biology at the university of Utrecht specialising in Theoretical Biology and Bioinformatics. He did his PhD project on comperative genome analysis and genome evolution in the group of Peer Bork at EMBL (Heidelberg, Germany). Subsequently he first worked as a normal post-doc and then as a VENI post-doc in Martijn Huynen\u2019s Comparative Genomics group at the CMBI \/ NCMLS in Nijmegen, Holland.\u00a0<\/span>In 2006 he moved to Utrecht University as an Associate Professor. In Utrecht he leads the Evolutionary Genomics and Integrative Bioinformatics group as part of Theoretical Biology and Bioinformatics in the Department of Biology.\u00a0<\/span>As of June 2014 he was appointed Professor in Bioinformatics and chair of the Executive Committee of the Utrecht Bioinformatics Center.\u00a0<\/span>In 2016 Berend Snel was awarded a VICI grant.\u00a0<\/span>Berend has co-authored more than 110 peer reviewed papers which have been cited more than 15.000 times resulting in an H-index of 52.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Long-read sequencing (LRS) technologies have firmly established themselves as viable alternatives to short-read methods in various applications. Over the past five years, LRS platforms such as Oxford Nanopore and Pacific Biosciences have significantly improved in accuracy and throughput, becoming realistic alternatives to Illumina RNA-seq for transcriptomics experiments. Our lab’s pioneering work led to the creation of SQANTI, a widely used tool for the quality control and quantification of long-read transcript models. However, as technologies advance, the need for additional algorithms to support transcriptomics applications of LRS has grown. This includes not only accurate transcript identification and quantification but also analysis and correction of technological biases, experimental design considerations for large experiments, benchmarking resources, data visualization, and functional annotation of full-length transcript variants.<\/p>

I will present the current suite of SQANTI tools, which provide a comprehensive solution to these data processing needs. For benchmarking, SQANTI-SIM is unique for simulating long-read and orthogonal data with precise control over transcript novelty. This enables the assessment of the identification of both annotated and novel transcript models. BUGSI is a set of universal single-isoform genes that can serve as an internal standard to troubleshoot RNA degradation and library preparation errors. The raw data quality in multi-sample experiments is assessed with SQANTI-reads, which identifies outliers, technology biases, and evaluates whether the data meets quality standards for discovery. SQANTI3 evaluates the performance of transcript reconstruction algorithms to accurately identify transcript models from long reads. The Filter, Rescue, and Requant modules curate transcript models to enhance transcriptome quality and quantification. Our methodologies reveal that lrRNA-seq data exhibit quantification biases distinct from those seen in short-read RNA-seq, necessitating specialized normalization methods. I will also discuss alternative approaches to defining joint transcriptomes in multi-sample experiments and their impact on transcript identification. Finally, I will introduce IsoAnnot, now integrated into the SQANTI framework, which distinguishes productive from unproductive transcripts and annotates them with functional labels to explore the biological impact of alternative splicing.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Prof. dr. Ana Conesa <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Ana Conesa is a senior scientist in Bioinformatics and Computational Biology and a research professor at the Institute for Integrative Systems Biology (I2SysBio) at the Spanish National Research Council, leading the Genomics of Gene Expression Lab. She is a Professor in Bioinformatics at the University of Florida and co-founded the Biobam Bioinformatics, a spin-off company specialised in user-friendly software of biologists. She holds an Engineering degree from the Polytechnical University of Valencia (1993) and a PhD in Molecular Microbiology from Leiden University in The Netherlands (2001). Ana has built a scientific program in the development of algorithms and bioinformatics solutions for the analysis of Big Genomics Data, particularly gene expression and their impact on the phenotype. She has conceived and lead the creation of over a dozen of bioinformatics tools that apply to all kind of species, model and non-model organisms, plants, microbiomes, and humans. These include Blast2GO, maSigPro, NOISeq, Paintomics, SQANTI and TAPPAS. Her tools have tens of users world-wide and have received over 25,000 scientific citations. Ana has published over 135 scientific papers and lead international EU (STATegra, DEANN) and US (UF-TEDDY) projects.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Single-cell omics methods promise to revolutionize our understanding of the behavior of biological systems, measurement technologies have undergone spectacular improvements over the last decades, and the number of available multi-omics datasets is increasing rapidly.

However, to realize the promise of these multi-omics data requires rigorous computational methods that can unambiguously quantify what the raw measurements tell us about the underlying biophysical states of individual cells. I will argue that, in this regard, the current state of the single-cell omics analysis field unfortunately provides a rather depressing picture. Most tools subject the data to multiple layers of ad hoc and complex transformations and filters, with countless arbitrarily tunable parameters, and return results as abstract quantities that lack physical interpretation or error bars. Moreover, the fact that the number of proposed analysis methods increases almost as fast as the number of published datasets shows that any consensus on how to analyze such data is yet to emerge in this field.<\/p>

In this talk I will discuss some of the reasons why rigorous analysis of single-cell omics data is so challenging, taking single-cell RNA-seq as an example, and make some proposals for a way forward that is arguably simpler, more transparent, and directly rooted in our understanding of the underlying biophysics and measurement processes.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Prof dr. Erik van Nimwegen<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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After studying theoretical physics in Amsterdam, Erik van Nimwegen moved to the United States in 1995, performing his PhD studies at the Santa Fe Institute (SFI) in Santa Fe, New Mexico, and receiving his PhD from the Faculty of Biology at Utrecht University in 1999. After a year of post-doc studies at the SFI, he spent three years as a fellow at the Center of Studies in Physics and Biology at the Rockefeller University, in New York. Since 2003 he has been a Professor of Computational Biology at the Biozentrum of the University of Basel, and group leader at the Swiss Institute of Bioinformatics since 2004. His main research topics concern genome evolution and the function and evolution of the regulatory networks by which cells control gene expression. He develops mathematical models for analyzing how regulatory networks evolve and function, and computational methods for the reconstruction of such networks from large biological data-sets. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Pitch Talks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Multiomic measurements of tRNAs and ribosome extension dynamics in single cells<\/td>\nMees van der Ent (Hubrecht Institute)<\/td>\nProf. dr. ir. Alexander van Oudenaarden<\/td>\n<\/tr>\n
Searching for robust protein biomarkers in human plasma<\/td>\nIsabel Houtkamp (UU)<\/td>\nProf. dr. Sanne Abeln<\/td>\n<\/tr>\n
NanoRCS: Multimodal tumor cell-free DNA profiling using nanopore-based consensus sequencing<\/td>\nLi-Ting Chen (UMCU)<\/td>\nDr. ir. Jeroen de Ridder<\/td>\n<\/tr>\n
Comparative transcriptomics reveals a conserved immune response in six mushroom-forming fungi during interaction with their competitors<\/td>\nMarieke van Maanen (UU)<\/td>\nDr. Robin Ohm<\/td>\n<\/tr>\n
Fungal biocontrol agents for Striga weed eradication<\/td>\nDr. Khyati Mheta Bhatt (Westerdijk Institute)<\/td>\nDr. J\u00e9r\u00f4me Collemare<\/td>\n<\/tr>\n
Bioinformatic approaches to decipher lettuce innate immunity<\/td>\nI\u00f1igo Ba\u00f1ales Belaunde (UU)<\/td>\nProf. dr. Guido Van den Ackerveken<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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Quantitative modelling of fate specification in the C. elegans<\/em> postembryonic M lineage reveals a missing spatiotemporal signal<\/p>\n<\/td>\n

Dr. Benjamin Planterose Jimenez (UU)<\/td>\nProf. dr. Kirsten ten Tusscher and Dr. Erika Tsingos<\/td>\n<\/tr>\n
Decomposing plasmid evolution using a simple pangenomic language model<\/td>\nDr. Weizhen Xu (UMCU)<\/td>\nDr. Anita Sch\u00fcrch
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Methodology for Biomarker Discovery with Reproducibility in Microbiome Data using Machine Learning<\/td>\nDavid Rojas-Velazquez (UU)<\/td>\nDr. Alejandro Lopez-Rincon<\/td>\n<\/tr>\n
M&M: An RNA-seq based Pan-Cancer Classifier for Pediatric Tumors
<\/td>\n		Fleur Wallis (PMC)<\/td>\nDr. Patrick Kemmeren<\/td>\n<\/tr>\n
Exploring the diversity of fungal plant biomass conversion<\/td>\nDr. Jiajia Li (Westerdijk Institute)<\/td>\nProf. dr. ir. Ronald de vries  and  Dr. Mao Peng<\/td>\n<\/tr>\n
RNA splicing as novel immune regulator<\/td>\nDr. Alejandra Bodel\u00f3n de Frutos (UMCU)<\/td>\nDr. Julia Drylewicz<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Registration<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t