🚀 TL;DR: pip install kinase-library 🚀 We are excited to introduce #TheKinaseLibrary Python package! 🧵👇 @KinaseLibrary Jared_Johnson @Tomer_M_Yaron @CellSignal https://t.co/k0mp7Vwwfa

Congratulations Tomer! Very well deserved!

Have suggestions/requests for the Kinase Library? Open an issue on GitHub and the team will look at it! https://t.co/wr5fELABMP

Spearheaded by Harin Lee & Sean Landry (@CellSignal) with @Tomer_M_Yaron, and based on the amazing work of Jared Johnson and legendary @mbyaffe @LabYaffe @benturklab @ElementoLab

🚀 We just launched the brand-new #KinaseLibrary website, directly connected to the recently released Python package! Explore kinase-substrate relationships, run enrichment analyses, and visualize phosphoproteomics data—no coding required! https://t.co/uPmrvbq5fs @KinaseLibrary

📔 Full vignette with multiple notebooks can be found here:

✨ In Summary ✨ #TheKinaseLibrary offers a comprehensive, user-friendly solution for unraveling kinase-substrate relationships. From prediction and enrichment to visualization, it streamlines phosphoproteomics analysis. GitHub:

📢 NEW FEATURE: MEA (Motif Enrichment Analysis) 📢 Leverage GSEA-based algorithms to detect kinases likely regulated in your sample for continuous data. Zero in on sequence motifs that drive phosphorylation events and uncover novel signaling pathways. https://t.co/4T15Of4NqO

🌋 Fisher-based Enrichment Tool 🌋 Explore kinase enrichment by comparing two lists of phosphosites or differential phosphorylation data. Pinpoint which kinases are overrepresented in your dataset and generate volcano plots and bubblemaps. https://t.co/GwY3R8cqvr

🔎 Scoring Substrates 🔎 Quickly assess which kinases may act on your phosphorylation site(s) using the KL scoring algorithm. Feed in your sequences and get score-based and percentile-based predictions for each potential kinase. https://t.co/02fuT9nQLY https://t.co/9ulGAgzS5l

🩳 In Short 🩳 The kinase_library package is a toolkit for analyzing phosphoproteomics data with a focus on kinase-substrate relationships. Predict kinase activity, identify downstream substrates, and visualize signaling pathways - now all in one place! https://t.co/j8LHKWEAyC

Coming very soon -> the @KinaseLibrary #Python package, as well as new features for substrate predictions and enrichment analysis, including continuous enrichment methods, phosphopriming, lysine acetylation and more! 9/9

Not a lot has changed over the past 700 million years! Finally, we characterized multiple C. elegans orthologues and found that the motif specificity of worm tyrosine kinases is surprisingly similar to that of humans: 8/9

We then integrated our motif library with SH2 domain sequence specificities, and fascinatingly the topology of known tyrosine signaling networks emerges: 7/9

We also annotated the human tyrosine phosphoproteome and reclassify phosphorylation sites into (1) promiscuous (liked by many kinases), (2) exclusive (liked by a few), and (3) suboptimal (poorly matching the motifs of every conventional tyrosine kinase): 6/9

Based solely on the sequence context of the detected phosphosites, our enrichment algorithm identifies the regulated kinases in high-throughput tyrosine phosphoproteomics experiments: 5/9

Utilizing our scoring algorithm, we can identify the upstream kinases for well-studied kinase-substrate relationships in the insulin, cytokine, and SRC signaling pathways: 4/9

Using synthetic peptide libraries, we characterized the biochemical substrate specificity of every human tyrosine kinase - 78 canonical and 15 non-canonical kinases, creating the tyrosine kinome wheel: 3/9

Work led by Jared Johnson and @Tomer_M_Yaron, together with @LabYaffe and @benturklab! @WeillCornell @harvardmed @DanaFarber @ColumbiaMed @MIT @Yale @CellSignal 2/9

We are delighted to share the #tyrosine @KinaseLibrary, now in @Nature: https://t.co/ZSwnymrBtb Tyrosine predictions and enrichment analysis are now available: https://t.co/uPmrvbqD50 #TheKinaseLibrary #KL #part2 🧵 1/9