diff --git a/_quarto.yml b/_quarto.yml index 15a0bca..a7fabe5 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -40,8 +40,6 @@ website: href: publications/index.qmd - text: "Tutorials" href: tutorials/index.qmd - - text: "News" - href: news/index.qmd - icon: github href: https://github.com/compomics aria-label: GitHub diff --git a/assets/compomics.scss b/assets/compomics.scss index 4cf5f5f..4f80f37 100644 --- a/assets/compomics.scss +++ b/assets/compomics.scss @@ -6,7 +6,7 @@ $font-family-heading: "Bricolage Grotesque", system-ui, sans-serif; $font-family-monospace: "JetBrains Mono", monospace; $headings-font-family: $font-family-heading; $headings-font-weight: 700; -$font-size-root: 13px; +$font-size-root: 14px; // Colour tokens ($ink, $cream, $line, $primary, $surface-hover, $icon-bg …) // are defined per-theme in compomics-light.scss / compomics-dark.scss diff --git a/assets/images/tools/ideeplc.png b/assets/images/tools/ideeplc.png new file mode 100644 index 0000000..138ede6 Binary files /dev/null and b/assets/images/tools/ideeplc.png differ diff --git a/assets/images/tools/ptmvision.png b/assets/images/tools/ptmvision.png new file mode 100644 index 0000000..30c734e Binary files /dev/null and b/assets/images/tools/ptmvision.png differ diff --git a/news/index.qmd b/news/index.qmd deleted file mode 100644 index 73fd119..0000000 --- a/news/index.qmd +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: "News" ---- - -```{=html} - -
- -``` diff --git a/news/posts/2024-06-01-welcome.qmd b/news/posts/2024-06-01-welcome.qmd deleted file mode 100644 index 4708003..0000000 --- a/news/posts/2024-06-01-welcome.qmd +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: "Welcome to the new CompOmics website" -date: 2026-06-09 -description: "Our new website is live." -categories: [announcement] ---- - -Welcome to the new CompOmics website, built with [Quarto](https://quarto.org). - -More news and updates will follow here. diff --git a/research/index.qmd b/research/index.qmd index 26e63ab..70db70c 100644 --- a/research/index.qmd +++ b/research/index.qmd @@ -16,31 +16,49 @@ toc: false align exactly under them via a five-column grid. -->
@@ -55,45 +73,40 @@ toc: false
- 01 +

Analyte prediction

-

Analyte prediction

Proteomics becomes more powerful when we can predict what a peptide should look like before it is measured. We develop machine learning models that translate peptide sequences into expected mass spectrometry behaviour, including fragment ion intensities, retention time, and ion mobility. Tools such as MS²PIP, DeepLC, and IM2Deep make these predicted signals available for practical analysis workflows. This work supports spectral library generation, targeted method design, and identification rescoring, but its broader aim is more fundamental: to make peptide behaviour computationally explicit, testable, and reusable across instruments, datasets, and experimental designs.

- 02 +

Identification

-

Identification

Peptide and protein identification is not only a database search problem; it is an ambiguity problem. A first-pass search engine proposes candidate matches, but much of the information in the data remains underused. We develop post-processing and data-driven rescoring approaches that combine search engine scores with predicted peptide behaviour, spectrum evidence, retention time, and ion mobility information. Through tools such as MS²Rescore/TIMS²Rescore combined with SearchGUI & PeptideShaker, we aim to increase confident identifications in DDA and DIA workflows while keeping false discovery control central. Our goal is to recover reliable signal without compromising statistical confidence.

- 03 +

Metaproteomics

-

Metaproteomics

Metaproteomics asks which organisms are present in a microbial community, what they are doing, and how they are doing it and importantly how confidently this can be inferred from peptide evidence. This is difficult because many peptides are shared across related taxa, while many functions are distributed across complex and unevenly characterized microbial proteomes. We develop methods for taxonomic and functional interpretation that explicitly account for this ambiguity rather than hiding it. Tools such as Unipept and peptonizer 2000 help translate peptide-level evidence into interpretable community profiles. We want to make metaproteomics scalable, transparent, and statistically grounded, from small microbiome studies to analyses spanning thousands of species.

- 04 +

FAIR data principles

-

FAIR data principles

Public proteomics data only becomes scientifically reusable when it is described in a way that both people and machines can understand. We therefore work on metadata annotation, data standards, and community-facing tools that make proteomics data findable, accessible, interoperable, and reusable. Resources such as lesSDRF and annotation workflows around public repositories help turn deposited datasets into structured research objects that can be searched, compared, reprocessed, and used for machine learning. We want to move proteomics from data availability to data usability, where public datasets can support new questions beyond the study for which they were originally generated.

- 05 +

Reprocessing

-

Reprocessing

Reprocessing brings our research lines together: peptide-property prediction improves how we model analyte behaviour, identification and rescoring software recover confident evidence from complex spectra, and FAIR annotation makes public datasets comparable at scale. We combine these developments to revisit existing proteomics data with new computational questions, extracting biological information that was not accessible in the original analysis. This includes large-scale tissue profiling, biomarker discovery, and the systematic recovery of previously missed post-translational modifications, as illustrated by Scop3PTM. Our aim is to make public proteomics data scientifically active beyond deposition, turning the vast amount of data that already exists into a continuing source of new biological insight.

diff --git a/research/research.scss b/research/research.scss index 8036f80..dcfb4e2 100644 --- a/research/research.scss +++ b/research/research.scss @@ -32,8 +32,7 @@ // padding so intro + spectrum + the EXPLORE cue still fit above the fold @media (max-height: 860px) { padding-bottom: 3rem; - .research-spectrum { margin-top: 2.8rem; } - .research-spectrum svg { max-height: 340px; } + .research-spectrum { margin-top: 1.5rem; } } } @@ -106,21 +105,53 @@ // ── Signature: five research lines drawn as a five-channel mass spectrum ─────── .research-spectrum { - max-width: 68rem; // wider = larger (viewBox is short, so width governs size) - margin: 4.5rem auto 0; // always centered, nudged lower in the header + --spectrum-w: min(80vw, 700px); + width: var(--spectrum-w); + max-width: none; + margin: 3.0rem auto 0; // auto margins centre it (even when wider than column) padding: 0; svg { display: block; width: 100%; height: auto; - max-height: 440px; + aspect-ratio: 960 / 224; // fixes height to the width; never collapses overflow: visible; } .spec-base { stroke: $line; stroke-width: 1; } .spec-minor rect { fill: $line; } + // axes: intensity (y) vs m/z (x), so the motif reads as a mass spectrum + .spec-axis { stroke: $line; stroke-width: 1; } + .spec-axis-arrow { fill: $line; } + .spec-axis-label { + fill: $muted; + font-family: $font-family-monospace; + font-size: 12px; + font-weight: 600; + letter-spacing: .08em; + } + + // residue ladder annotation (difference arrows between successive b-ions) + .spec-annot { + line { stroke: $ink-soft; stroke-width: 1; } + polygon { fill: $ink-soft; } + } + .spec-annot-res { + fill: $ink; + font-family: $font-family-monospace; + font-size: 12px; + font-weight: 700; + text-anchor: middle; + } + .spec-annot-mz { + fill: $muted; + font-family: $font-family-monospace; + font-size: 9px; + text-anchor: middle; + } + // each accent peak grows up from the baseline on load, staggered by --i .spec-peak { rect { @@ -145,14 +176,19 @@ } } -// clickable line titles under each peak, aligned to the peaks via a 5-col grid +// clickable line titles, each pinned under its peak (peaks sit at b-ion m/z, so +// they are unevenly spaced — positioned via the inline `left` per label). On +// narrow screens this would crowd, so below 640px it falls back to an even grid. .research-spectrum-nav { - display: grid; - grid-template-columns: repeat(5, 1fr); - gap: .4rem; - margin-top: .55rem; + position: relative; + height: 3rem; + margin-top: .6rem; } .rs-line { + position: absolute; + top: 0; + transform: translateX(-50%); + white-space: nowrap; // keep each label on one line (e.g. "FAIR data principles") color: var(--accent); font-family: $font-family-sans-serif; font-size: clamp(.68rem, 1.5vw, .84rem); @@ -160,7 +196,6 @@ line-height: 1.2; text-align: center; text-decoration: none; - padding: 0 .2rem; opacity: 0; animation: research-peak-label .5s ease forwards; animation-delay: calc(.12s * var(--i) + .5s); @@ -170,6 +205,17 @@ &:focus-visible { text-decoration: underline; text-underline-offset: 3px; } } +@media (max-width: 640px) { + .research-spectrum-nav { + position: static; + display: grid; + grid-template-columns: repeat(5, 1fr); + gap: .4rem; + height: auto; + } + .rs-line { position: static; transform: none; width: auto; } +} + @keyframes research-peak-rise { to { transform: scaleY(1); } } @keyframes research-peak-label { to { opacity: 1; } } @@ -193,12 +239,13 @@ @media (max-width: 620px) { padding: 2.5rem 0; } } -// accent peak + number: this section's mark on the spectrum, left-aligned +// accent peak + title on one line: the coloured bar marks the section, sitting +// just before the heading (no number) .research-section-top { display: flex; align-items: flex-end; gap: .7rem; - margin-bottom: 1.2rem; + margin-bottom: .9rem; color: var(--accent); } @@ -225,16 +272,6 @@ // peak draws up once the section scrolls into view (class added by inline JS) .research-section.is-revealed .peak-bar { transform: scaleY(1); } -.research-num { - font-family: $font-family-monospace; - font-size: 1rem; - font-weight: 700; - letter-spacing: .14em; - color: var(--accent); - line-height: 1; - padding-bottom: .1rem; -} - // respect reduced-motion: render peaks at full height, no animation @media (prefers-reduced-motion: reduce) { .research-spectrum .spec-peak rect, @@ -256,7 +293,7 @@ line-height: 1.12; letter-spacing: -0.02em; color: $ink; - margin: 0 0 .8rem; + margin: 0; padding: 0; border: none; } diff --git a/team/index.qmd b/team/index.qmd index a0bac3d..41d3580 100644 --- a/team/index.qmd +++ b/team/index.qmd @@ -232,7 +232,7 @@ toc: false email: 'lennart.martens@ugent.be', about: 'Lennart is Senior Full Professor of Systems Biology at Ghent University and Group Leader of the CompOmics group. He is also a Director of Research at CNRS-Université de Strasbourg in France. He has been working in mass spectrometry-related bioinformatics since his Master\'s degree. After a few years as a software developer and framework architect in industry, he returned to Ghent University to pursue a PhD in proteomics informatics, creating PRIDE at EMBL-EBI in 2003. After his PhD he rejoined EMBL-EBI as head of the PRIDE group.', currentWork: 'He moved back to Ghent University and VIB in 2009, where his research focuses on novel machine learning algorithms for mass spectrometry data analysis and their application to the large-scale orthogonal reprocessing of public proteomics data.', - recognition: 'Lennart has received numerous honours, including the Prometheus Award for Research Excellence at Ghent University (2014), the Juan Pablo Albar Proteomics Pioneer Award of the European Proteomics Association (2014), and the Prix Gutenberg in France (2023). He was elected to the Young Academy of the Belgian Royal Academy of Sciences (2013), inducted into the High Council of Medicine in Belgium (2016), inducted as Fellow of the Royal Society for Chemistry, UK (2017), and inducted as Full Member of the Sigma Xi Scientific Honor Society, USA (2022).', + recognition: 'Lennart has received numerous honours, including the Prometheus Award for Research Excellence at Ghent University (2014), the Juan Pablo Albar Proteomics Pioneer Award of the European Proteomics Association (2014), and the Prix Gutenberg in France (2023). He was elected to the Young Academy of the Belgian Royal Academy of Sciences (2013), inducted as Fellow of the Royal Society for Chemistry, UK (2017), and inducted as Full Member of the Sigma Xi Scientific Honor Society, USA (2022).', publications: [ { title: 'PRIDE: The proteomics identifications database', url: 'https://doi.org/10.1002/pmic.200401303', venue: 'Proteomics · 2005' }, { title: 'Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups', url: 'https://doi.org/10.1038/s41467-026-68981-5', venue: 'Nature Communications · 2026' }, @@ -245,7 +245,7 @@ toc: false title: 'Sakina Bombaywala', role: 'Postdoctoral Researcher', email: 'sakina.bombaywala@ugent.be', - about: 'Sakina completed her Ph.D. in Bioinformatics from India in 2024. Her doctoral research focused on applying multi-omics approaches to understand microbiome interactions, with applications in wastewater management. Soon after, she joined the CompOmics lab, where her current work focuses on developing omics workflows for disease tracking and environmental surveillance. Outside work, she enjoys trying out different athletic sports.', + about: 'Sakina obtained her PhD at the National Environmental Engineering Research Institute in India (Academy of Scientific and Innovative Research). Her doctoral research focused on applying multi-omics approaches to understand microbiome interactions, with applications in wastewater management. Soon after, she joined the CompOmics lab, where her current work focuses on developing omics workflows for disease tracking and environmental surveillance. Outside work, she enjoys trying out different athletic sports.', currentWork: 'Sakina works across whole-genome sequencing, metagenomics, RNA-seq, variant calling, and phylogenetic analysis. She develops scalable, reproducible bioinformatics solutions that support disease tracking and environmental surveillance. In addition to data analysis, she contributes to bioinformatics training for researchers, laboratory technicians, and clinicians within international collaborations.', publications: [ { title: 'Meta-analysis of wastewater microbiome for antibiotic resistance profiling', url: 'https://doi.org/10.1016/j.mimet.2024.106953', venue: 'Journal of Microbiological Methods · 2024' }, @@ -257,11 +257,11 @@ toc: false role: 'Postdoctoral Researcher', email: 'robbin.bouwmeester@ugent.be', about: 'Robbin obtained his Master\'s degree in Bioinformatics and Systems Biology at VU University Amsterdam. He joined the CompOmics group after applying for a PhD position within an Innovative Training Network (ITN). During his PhD he worked on the machine learning of lipid oxidation products and how they interact with proteins. Outside work he enjoys working on creative projects like 3D printing and crochet.', - currentWork: 'Robbin is currently a postdoctoral researcher at CompOmics. He is an expert in applying machine learning and deep learning for peptide property prediction, specifically LC retention time prediction. His research further involves DIA data analysis and benchmarking. Lastly, he fosters several industrial collaborations with Johnson & Johnson, Bruker, and Waters. Robbin is the main developer of DeepLC, and has contributed to iDeepLC, ProteoBench and CALLC (for small molecules).', + currentWork: 'Robbin is currently a postdoctoral researcher at CompOmics. He is an expert in applying machine learning and deep learning for peptide property prediction, specifically LC retention time prediction. His research further involves DIA data analysis and benchmarking. Lastly, he fosters several industrial collaborations with Johnson & Johnson, Bruker, and Waters. Robbin is one of the main persons responsible for DeepLC, iDeepLC, ProteoBench, and CALLC.', publications: [ { title: 'DeepLC can predict retention times for peptides with as-yet unseen modifications', url: 'https://doi.org/10.1038/s41592-021-01301-5', venue: 'Nature Methods · 2021' }, { title: 'Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups', url: 'https://doi.org/10.1038/s41467-026-68981-5', venue: 'Nature Communications · 2026' }, - { title: 'The E. coli PeptideAtlas Build: Characterizing the Observed Pan-Proteome and Its Post-Translational Modifications', url: 'https://doi.org/10.1021/acs.jproteome.5c00902', venue: 'Journal of Proteome Research · 2026' } + { title: 'Generalized Calibration Across Liquid Chromatography Setups for Generic Prediction of Small-Molecule Retention Times', url: 'https://doi.org/10.1021/acs.analchem.0c00233', venue: 'Analytical Chemistry · 2020' } ] }, tine: { @@ -280,9 +280,9 @@ toc: false title: 'Arthur Declercq', role: 'Postdoctoral Researcher', email: 'arthur.declercq@ugent.be', - about: 'Arthur obtained his Master\'s degree in Biomedical Sciences, with a major in Systems Biology, at Ghent University in 2021. He carried out his master thesis at CompOmics after developing a keen interest in machine learning during his studies. He then continued pursuing a PhD in our group, working on optimization of immunopeptide identification with mass spectrometry, for which he developed MS²Rescore. Outside research, Arthur enjoys photography, running, and science fiction books. He also created his own website, which can be found at arthurdeclercq.bio.', - currentWork: 'Arthur is now a postdoctoral researcher, focusing on binding affinity prediction and immunoinformatics. He is the main developer of MS²Rescore, and has contributed to MS²PIP, Mumble, psm_utils, and BAXERNA.', - recognition: 'He has received a number of honours, including the Faculty of Medicine thesis prize (2026), the PhD Innovation Café award (VOKA UGent, 2023), a second place presentation award from the French Proteomics Society, and a finalist place for the VIB Aureus Nova Scientia prize.', + about: 'Arthur\'s interest in immunology began in 2018 during an Honours Programme in life sciences, where he studied varicella zoster virus in an immunology lab. Alongside this, he developed a keen interest in machine learning over the course of his studies, and he brought the two together in his master thesis at CompOmics. He obtained his Master\'s degree in Biomedical Sciences, with a major in Systems Biology, at Ghent University in 2021, and continued in the group with a PhD on optimizing immunopeptide identification with mass spectrometry. For this work he developed MS²Rescore, reflecting his enthusiasm for building tools that the wider community can use. Outside research, Arthur enjoys photography, running, and science fiction books. More about Arthur can be found at arthurdeclercq.bio.', + currentWork: 'As a postdoctoral researcher, Arthur now focuses on binding affinity prediction and immunoinformatics, continuing the interplay between immunology and machine learning that has shaped his work. He is the main developer of MS²Rescore and has contributed to MS²PIP, Mumble, psm_utils, and BAXERNA.', + recognition: 'Arthur\'s work has been recognized with the Faculty of Medicine thesis prize (2026), the PhD Innovation Café award (VOKA UGent, 2023), a second place presentation award from the French Proteomics Society, and recently, a finalist place for the VIB Aureus Nova Scientia prize.', publications: [ { title: 'MS²Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates', url: 'https://doi.org/10.1016/j.mcpro.2022.100266', venue: 'Molecular & Cellular Proteomics · 2022' }, { title: 'TIMS²Rescore: A DDA-PASEF-Optimized Data-Driven Rescoring Pipeline Based on MS²Rescore', url: 'https://doi.org/10.1021/acs.jproteome.4c00609', venue: 'Journal of Proteome Research · 2025' } @@ -292,9 +292,9 @@ toc: false title: 'Ralf Gabriels', role: 'Postdoctoral Researcher', email: 'ralf.gabriels@ugent.be', - about: 'Ralf obtained his Master\'s degree in Biomedical Sciences at Ghent University in 2017. In January 2018 he started his PhD under the supervision of Prof. Sven Degroeve and Prof. Lennart Martens, graduating as Doctor in Life Sciences in 2022.', - currentWork: 'He is now a postdoctoral researcher, focusing on machine learning methods in mass spectrometry-based proteomics, with an emphasis on peptide fragmentation prediction, identification rescoring, and post-translational modification analysis. He is the main developer of MS²PIP and contributed to MS²Rescore and psm_utils. Ralf is also very active in community initiatives.', - recognition: 'He is an elected representative of the European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) and contributes to the HUPO Proteomics Standards Initiative (HUPO-PSI), where he recently became co-chair of the AI Readiness working group.', + about: 'Ralf is an FWO Senior Postdoctoral Research Fellow working at the intersection of proteomics, bioinformatics, and machine learning. He holds a PhD in Life Sciences from Ghent University (2022), where he developed machine learning methods for peptide identification and fragmentation prediction in mass spectrometry-based proteomics. He is the developer of several widely used open-source tools, including MS²PIP, MS²Rescore, and psm_utils, and is an active contributor to open science and community data standards in the field.', + currentWork: 'His research focuses on mapping post-translational modifications (PTMs) across the proteome using deep learning, with applications to molecular aging. He is developing graph neural network-based approaches for PTM-resolved fragmentation prediction, building on his continued development of MS²PIP and MS²Rescore. Beyond his own research, Ralf contributes to the broader computational proteomics community as co-founder and co-chair of the HUPO-PSI AI-Readiness Working Group, ontology curator for PSI-MOD, an active member of EuBIC-MS, and co-founder of ProteomicsML, an open educational platform for machine learning in proteomics.', + recognition: 'Ralf received the Bioinformatics for Mass Spectrometry Award from the European Proteomics Association (2021) and the Best Doctoral Thesis Award in Basic Sciences from the Ghent University Faculty of Medicine and Health Sciences (2023). His open-source tools have been integrated into commercial platforms by Bruker and Mascot, and his work is supported by consecutive personal FWO fellowships at the PhD, junior postdoctoral, and senior postdoctoral levels.', publications: [ { title: 'Updated MS²PIP web server supports cutting-edge proteomics applications', url: 'https://doi.org/10.1093/nar/gkad335', venue: 'Nucleic Acids Research · 2023' }, { title: 'MS²Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0', url: 'https://doi.org/10.1021/acs.jproteome.3c00785', venue: 'Journal of Proteome Research · 2024' }, @@ -345,7 +345,7 @@ toc: false title: 'Tim Van Den Bossche', role: 'Postdoctoral Researcher', email: 'tim.vandenbossche@ugent.be', - about: 'Tim obtained a graduate degree in Chemistry and Biochemistry (2013), followed by a Bachelor\'s (2014) and Master\'s (2016) in Biomedical Sciences, and later a Master of Business Administration (MBA, 2022). His Master\'s thesis was carried out in the wet lab under supervision of Prof. Dr. Sven Eyckerman, but his preference for dry-lab data analysis led him to contact Prof. Lennart Martens, who offered a PhD in the group. He completed his PhD in 2022. During his PhD he worked in the field of metaproteomics, on the development of tools for metaproteomics data analysis, and on tool benchmarking in collaboration with several labs, which led him to start the CAMPI study. He also founded the Metaproteomics Initiative. Outside research, Tim likes to run, has a keen interest in policy and organisation, and enjoys going to the theatre.', + about: 'Tim obtained a graduate degree in Chemistry and Biochemistry (2013), followed by a Bachelor\'s (2014) and Master\'s (2016) in Biomedical Sciences, and later a Master of Business Administration (MBA, 2022). After his graduate\'s thesis and Master\'s thesis in the wet lab, he wanted to specialize in dry-lab data analysis which led him to contact Prof. Lennart Martens, who offered him a PhD in the group. He completed his PhD in 2022. During his PhD he worked in the field of metaproteomics, on the development of tools for metaproteomics data analysis, and on tool benchmarking in collaboration with several labs, which led him to start the CAMPI study. He also founded the Metaproteomics Initiative. Outside research, Tim likes to run, has a keen interest in policy and organisation, and enjoys going to the theatre.', currentWork: 'He is currently a postdoctoral researcher in CompOmics, continuing his research lines from his PhD. Tim has contributed to Unipept, Peptonizer2000, and several tools in the Ghent University group of Prof. Dr. Bart Mesuere.', recognition: 'He has received numerous honours, including the EuPA Vision and Commitment Award (2025), the EuPA Best Doctoral Thesis Award (2023), a finalist place in the HUPO 2023 PostDoc Competition, runner-up for the Best PhD Thesis Award at Ghent University (2023), and inclusion in the Journal of Proteome Research "Rising Stars in Proteomics and Metabolomics: 40 under 40" list (2021).', publications: [ @@ -394,7 +394,7 @@ toc: false ] }, sude: { - title: 'Sude Güneş', + title: 'Sude Gunes', role: 'Predoctoral Researcher', email: 'sude.gunes@ugent.be', about: 'Sude obtained her Bachelor\'s degree in Bioengineering at the Izmir Institute of Technology, Türkiye, in 2022, and her Master\'s degree in Polymer Science and Engineering at the same institute in 2024. During her Master\'s she focused on investigating the effect of allosteric ligand binding using molecular dynamics simulations and on the prediction of allosteric and cryptic sites. Wanting to explore protein dynamics further, she applied for the joint PhD position on PTMs and protein dynamics. In her free time, Sude loves playing the piano and exploring new places.', diff --git a/team/network.qmd b/team/network.qmd index 83379f7..31601e5 100644 --- a/team/network.qmd +++ b/team/network.qmd @@ -84,6 +84,10 @@ toc: false Dr. Wim Vranken VUB, Belgium +
+ Ben Binke + UGent, Belgium +

Alumni & Former Colleagues

diff --git a/tools/index.qmd b/tools/index.qmd index fc0464d..1b33655 100644 --- a/tools/index.qmd +++ b/tools/index.qmd @@ -93,6 +93,15 @@ toc: false }, devs: ['robbin', 'ralf'] }, + { + name: 'iDeepLC', cat: 'prediction', icon: 'bi-clock-history', logo: 'ideeplc.png', + desc: 'Retention-time prediction from peptide chemical structure (SMILES), generalising to modifications unseen during training.', + links: { + github: 'https://github.com/compomics/iDeepLC', + paper: 'https://doi.org/10.1021/acs.analchem.5c08017', + }, + devs: ['alireza', 'robbin'] + }, { name: 'IM2Deep', cat: 'prediction', icon: 'bi-wind', logo: 'im2deep.svg', desc: 'Predicts collisional cross-section (CCS) values for peptides, including modified, and multiple conformations.', @@ -219,9 +228,18 @@ toc: false links: { github: 'https://github.com/compomics' }, devs: ['enrico'] }, + { + name: 'PTMVision', cat: 'biological', icon: 'bi-eye', logo: 'ptmvision.png', + desc: 'Interactive web platform for exploratory visual analysis of post-translational modifications from mass spectrometry data.', + links: { + web: 'https://ptmvision-tuevis.cs.uni-tuebingen.de/', + paper: 'https://doi.org/10.1021/acs.jproteome.4c00679', + }, + devs: ['caro'] + }, { name: 'E. coli PeptideAtlas Build', cat: 'biological', icon: 'bi-virus', logo: 'bacterium.png', - desc:'Characterizing the observed Eschericia coli pan-proteome and its post-translational modifications.', + desc:'Characterizing the observed Escherichia coli pan-proteome and its post-translational modifications.', links: { web: 'https://peptideatlas.org/builds/ecoli/', paper: 'https://doi.org/10.1021/acs.jproteome.5c00902'