Davide Cacchiarelli grew up in Rome where he carried out his undergraduate studies in Biotechnology and Biology. He obtained a Master Degree and a Doctorate Degree in Genetics and Molecular Biology from SAPIENZA – University of Rome, working on mechanisms of RNA regulation. In 2011 he moved to The Broad Institute of MIT and Harvard and The Department of Stem Cell and Regenerative Biology at Harvard University to focus his research on the rules governing cell fate transitions and reprogramming using genomic approaches.
He returned to Italy in 2017 thanks to the Armenise-Harvard Foundation Career Development Award and now he leads a young research group focused on understanding the dynamics of cell fate decisions at TIGEM, the Telethon Institute of Genetics and Medicine in Naples.
His work aims to identify the mechanisms controlling cell fate decisions during cellular differentiation, conversion and reprogramming, and how such processed are affected by genetic mutations of key regulatory proteins including transcription factors. To achieve this goal he proposes to integrate descriptive, functional and single cell genomics to dissect how genetic elements and their variants impinge on the temporal and spatial control of gene expression.

Understanding transcription factors through quantitative biology
The original concept of cell differentiation as a unidirectional process of progressively restricted potential and increased specialization has been dramatically revised by the discovery of cellular reprogramming. The appropriate cocktail of transcription factors (TFs) allows the production of Induced Pluripotent Stem Cells (IPSCs) from almost any type of somatic cell, with extended self-renewal capabilities and broad differentiation potential. This concept has infused an unprecedented boost in the use of TFs not only in reprogramming to pluripotency, but, in general, to drive cell fate decisions in vitro.
In our laboratory, we design and apply quantitative methods to dissect the role of transcription factors during reprogramming, conversion, and differentiation of human cells. We also study how rare genetic disorders alter the normal function of TFs (and proteins in general) with the final aim to predict the severity of rare genetic variants even before their onset in the general population.
I will describe two general approaches to these scopes:

1. an approach of quantitative single-cell genomics to identify the subpopulations that arise during the reprogramming process to pluripotency and reconstruct their relationships
2. a quantitative method to test in parallel hundreds of distinct rare variants of TP63, a TF that acts as the master regulator of skin development and whose mutation are associated with AEC syndrome, a monogenic disorder with severe skin defects.

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Martin Eisenacher is the leader of the “Medical Bioinformatics” department at the Medizinisches Proteom-Center, Ruhr-Universität Bochum, since 2015.
Since 2014, he is consortium speaker and project leader of several running projects funded by BMBF, Federal Ministry for Research and Innovation, Deutsche Gesetzliche Unfallversicheung (DGUV) and consortium speaker of the service center “Bioinformatics for Proteomics – BioInfra.Prot” within the BMBF funded “German Network for Bioinformatics Infrastructure – de.NBI”.
In 2014, he got his Habilitation (German qualification for higher education) in the medical faculty, at Ruhr-Universität Bochum, with a cumulative habilitation thesis on “Standard data formats, algorithms and analysis strategies for the bioinformatics of proteomics” and had his Habilitation colloquium at the faculty council on “Big data – chances and risks of personalised medicine“.
Since 2006, is has been project leader and coordinator of several finished projects funded by EU, BMBF, Federal Ministry for Research and Innovation, Deutsche Gesetzliche Unfallversicheung (DGUV), Cluster Industrielle Biotechnologie (CLIB), medical faculty RUB (FoRUM).

Bioinformatics tools and analyses in Proteomics
Proteomics, especially with mass spectrometry has reached many milestones. Several challenges postulated as being show stoppers have been addressed: identification with limited false positives, quantification, finding “all” gene-coded proteins, modifications (plus localization), usable standard formats. In parallel, instruments and algorithms became more sensitive, more exact and data more sustainable.
But there are still some unexplained phenomenons, all-day questions to solve, closed doors to open. For example, the increasing mass accuracy creates new challenges to false-discovery rate estimation. Or, shared peptides could be used for a better quantification.
To open the box of pandora – all our method development in mass spectrometry for Proteomics may become obsolete some day.

Artemis Hatzigeorgiou is Principal Investigator at the B.S.R.C. “Alexander Fleming” and adjunct assistant Professor at the Department of Computer and Information Science at the University of Pennsylvania.
She received an MS in Computer Science from the University of Stuttgart and a PhD in Molecular Biology from the University of Jena in 2001. In the same year, she joined the University of Pennsylvania as assistant professor of bioinformatics with a joined appointment at the Department of Genetics, Medical School and the Department of Computer and Information Science at the Engineering School. In 2007, she joined as Principal Investigator the Institute of Molecular Oncology at the B.S.R.C. “Alexander Fleming” and she was elected adjunct professor at the Computer and Information Science department of the University of Pennsylvania.
Artemis Hatzigeorgiou received in 2003 the “Early Carrier Award” from the National Science Foundation of the USA.
She is co-author of the Stuttgart Neural Network Simulator (SNNS), a world-wide used open-source software for the simulation of Artificial Neural Networks. In 2003 she developed DIANA-microT, one of the first published microRNA target prediction programs. She has published in top tier journals as Nature, Science, PNAS, AJHG and G&D and has served as a panelist for NSF and the National Institute of Health of the USA. In 1996, she has been a co-founder of the computer science company Synaptic, Ltd, located at Herakleion, Crete.

Exploring the coding and non-coding miRNA targetome
microRNAs (miRNAs) are short (~23nts) non-coding RNAs that act as central post-transcriptional gene expression regulators through target cleavage, degradation and/or translational suppression. More recently, miRNA:lncRNA (long non-coding RNA) interactions have been characterized.
DIANA-TarBase is a reference database devoted to the indexing of experimentally-supported miRNA targets. Its 8th version is the first database to index > 1 million entries, supported by more than 33 experimental methodologies, applied to 592 cell types/tissues under ~430 experimental conditions.
DIANA-LncBase is a comprehensive repository of thousands of miRNA:lncRNA interactions supported by low/high-throughput, (in)direct experiments. The upcoming version of LncBase (October 2019) is significantly enhanced providing an unprecedented set of transcriptome-wide experimentally verified MREs on human and mouse lncRNAs on a wide range of tissues and cell types.
More than 60% of TarBase and LncBase content derives from the analysis of Argonaute crosslinking and immunoprecipitation (CLIP) experiments. Photoactivatable Ribonucleoside-Enhanced (PAR) CLIP methodology is considered one of the most powerful high-throughput methodologies for miRNA target identification. microCLIP is an innovative framework that combines deep learning classifiers under a super learning scheme for CLIP-Seq-guided detection of miRNA interactions. Former AGO-CLIP-guided implementations depend strongly on the T-to-C conversions to define miRNA bindings, while the efficacy of neglected interactions remained unknown. By analysing miRNA perturbation experiments and structural sequencing data, we showed that the previously neglected non-T-to-C clusters exhibit functional miRNA binding events and strong accessibility. microCLIP operates on every AGO-enriched cluster providing an average 14% increase in miRNA-target interactions per PAR-CLIP library, uncovering previously elusive regulatory events and miRNA-controlled pathways.
Indexing thousands of (non-)coding miRNA interactions is a valuable aid to the ncRNA community, demonstrated by the access of more than 6,000 users per month to the two databases content.
References
Paraskevopoulou MD et al. microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions, Nature Communications 9, 2018
Karagkouni D et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions, Nucleic Acids Res. 46, 2017
Paraskevopoulou MD et al. DIANA-LncBase v2: Indexing microRNA targets on non-coding transcripts, Nucleic Acids Res. 44, 2015

Oliver Kohlbacher is a professor for applied and translation bioinformatics at University of Tübingen and the University Hospital Tübingen as well as a fellow at the Max Planck Institute for Developmental Biology. His groups are currently focusing on research in computational mass spectrometry, structural bioinformatics, translational bioinformatics, and medical informatics.

Cross-linking for all: XL-MS for protein-protein, protein-DNA, and protein-RNA interactions
A short abstract of the talk will be included here at a later time.

Lennart Martens is Full Professor of Systems Biology at Ghent University, group leader of the Computational Omics and Systems Biology (CompOmics) group at VIB, and Associate Director of the VIB-UGent Center for Medical Biotechnology, all in Ghent, Belgium.
He has been working in proteomics bioinformatics since his Master’s degree, which focused on the computational interpretation of peptide mass spectra. He then worked as a software developer and framework architect for a software company for a few years, before returning to Ghent University to pursue a Ph.D. focused on proteomics and proteomics informatics. During this time, he worked on the development of high-throughput peptide centric proteomics techniques and on bioinformatics tools to support these new approaches.
In 2003 he started the PRIDE proteomics database at EMBL-EBI as a Marie Curie fellow of the European Commission. After obtaining his Ph.D., he rejoined the PRIDE group at EBI, which he coordinated for several years before moving back to Ghent University to take up his current position.
Prof. Martens has been elected to the Human Proteome Organisation (HUPO) Council, served on the HUPO Executive Board, and is the current Vice-President of the European Proteomics Association. He received the 2014 Prometheus Award for Research Excellence from Ghent University, and the 2015 ‘Juan Pablo Albar’ Proteomics Pioneer Award from the European Proteomics Association. An author on more than 230 peer-reviewed papers, he has also co-written two popular Wiley textbooks on computational mass spectrometry proteomics.

To boldly go: uncovering protein post-translational modifications at the proteome scale
We have long-standing knowledge in biochemistry that post-translational modifications (PTMs) affect protein role and function, and that PTMs can function as activation or deactivion switches for proteins. As a result, many individual modifications have been extensively studied using tailored protocols and approaches. The most studied eukaryotic modification is phosphorylation, likely followed by acylations (methylation and acetylation), with ubiquitinylation (and related types of modification such as sumoylation) becoming very popular more recently. Despite all of this interest in, and efforts dedicated to, the study of protein modifications, the focus on single modifications has precluded the assembly of a complete, proteome-wide view of the protein modification landscape. Interestingly, this is not due to an instrumentation or methodological issue, but because of bioinformatics problems.
The issue in the bioinformatics is that we have not been able to detect modified peptides using our existing identification algorithms, and this because of two hurdles: the combinatorial explosion when all possible modifications (and mutations) have to be considered, and the very limited use of the information available in the recorded fragmentation mass spectra.
Over the past ten years, we have been diligently working on solutions to this fundamental problem, which has led to steady progress and has recently culminated in our new ionbot search engine for open modification searches. Ionbot is extremely fast and highly reliable, and sets a new standard for the discovery of unexpected protein modifications. When applied to prominent, large-scale data sets, our results indicate a plethora of unexpected modifications in the human proteome. While quite a few of these are artefactual in origin (and are caused by our sample processing protocols), many biologically relevant modifications are prominently identified as well.

Juan Antonio Vizcaíno is the Proteomics Team Leader at the EMBL-European Bioinformatics Institute (EMBL-EBI, Cambridge, UK). His team is responsible of the development of the PRIDE database, the world-leading public repository for mass spectrometry proteomics data (http://www.ebi.ac.uk/pride). In addition, he is coordinating the ProteomeXchange Consortium, aiming to standardize data submission and dissemination in proteomics resources worldwide. He has also heavily contributed to the development of proteomics data standard formats (mzIdentML, mzQuantML, mzTab, proBed, proBAM) and related software, and has participated in the development of several data deposition (e.g. PX submission tool) and visualization (PRIDE Inspector) stand-alone tools. Furthermore, he coordinated the development of the two iterations of the “PRIDE Cluster” spectral clustering algorithm and have participated in the maintenance and refinement of other widely-used bioinformatics services. He actively promotes open data policies in the proteomics field.
He has been the leading author of high impact publications in Nature Biotechnology, Nature Methods, Nucleic Acids Research, Genome Biology and Molecular and Cellular Proteomics, among other journals. Overall, he has published >110 articles with >9,700 citations (h-index=41, Google Scholar), largely in computational mass spectrometry and bioinformatics. Originally, he earned undergraduate degrees in Pharmacy and in Biochemistry, a Masters’ degree in Microbiology, and a doctoral degree in Molecular Biology from the University of Salamanca, Spain.

“Big data” approaches in proteomics: Re-use of public proteomics datasets
First of all, I will summarize the work we have done in the last years to create an infrastructure to enable data sharing of mass spectrometry (MS) proteomics data in the public domain, including the development of the world-leading PRIDE database (https://www.ebi.ac.uk/pride/), the related tools and software, open data standards and the establishment of the worldwide ProteomeXchange Consortium of proteomics resources (http://www.proteomexchange.org/).
Thanks, among other efforts, to the great success of PRIDE and ProteomeXchange, the proteomics community is now widely embracing open data policies, an opposite scenario to the situation just a few years ago.
To corroborate this, during 2018 approximately 300 datasets per month have been submitted to PRIDE, which is now approaching the PB scale. This plethora of public proteomics data is being increasingly reused by the research community, since there are indeed highly attractive applications for data scientists. Some of them are proteomics centric (e.g. meta-studies to expand the knowledge of the human proteome, generation of spectral libraries, etc…), but others involve the integration between proteomics and other omics data types, especially genomics.
In this context, I will outline a few projects that we are carrying out in-house, e.g. the generation of the functionally-relevant human phospho-proteome or the integrative analysis of protein expression in human cancer. I will explain at least one of them in higher detail.