update README.md

sky1ove · Feb 23, 2024 · d4c738f · d4c738f
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-# Katlas
+# KATLAS
 
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
-<img alt="Katlas logo" width="500" caption="Katlas logo" src="dataset/images/logo.png" id="logo"/>
+<img alt="Katlas logo" width="1600" caption="Katlas logo" src="dataset/images/logo.png" id="logo"/>
+
+KATLAS is a repository containing python tools to predict kinases given
+a substrate sequence. It also contains data: phosphophrylation sites,
+and human phosphoproteomics.
+
+***References***: Please cite the appropriate papers if KATLAS is
+helpful to your research.
+
+- KATLAS was initially described in the paper \[Decoding Human Kinome
+  Specificities through a Computational Data-Driven Approach
+  (manuscript)\]
+
+- The position-specific scoring matrices (PSSMs) of human kinome derived
+  from positional scanning peptide array (PSPA) is based on the paper
+  [An atlas of substrate specificities for the human serine/threonine
+  kinome](https://www.nature.com/articles/s41586-022-05575-3)
+
+- The kinase substrate datasets used for generating PSSMs are derived
+  from
+  [PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/)
+  and paper [Large-scale Discovery of Substrates of the Human
+  Kinome](https://www.nature.com/articles/s41598-019-46385-4)
+
+- Phosphorylation sites are acquired from
+  [PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/),
+  paper [The functional landscape of the human
+  phosphoproteome](https://www.nature.com/articles/s41587-019-0344-3),
+  and [CPTAC](https://pdc.cancer.gov/pdc/cptac-pancancer) /
+  [LinkedOmics](https://academic.oup.com/nar/article/46/D1/D956/4607804)
 
 ## Install
 
@@ -52,21 +81,6 @@ df = Data.get_ochoa_site().head()
 df.iloc[:,-2:]
 ```
 
-<div>
-
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 |     | site_seq        | gene_site      |
 |-----|-----------------|----------------|
 | 0   | VDDEKGDSNDDYDSA | A0A075B6Q4_S24 |
@@ -75,9 +89,7 @@ df.iloc[:,-2:]
 | 3   | KSRFTEYSMTSSVMR | A0A075B6Q4_S68 |
 | 4   | FTEYSMTSSVMRRNE | A0A075B6Q4_S71 |
 
-</div>
 
-</div>
 
 ``` python
 results = predict_kinase_df(df,'site_seq',**param4)
@@ -91,20 +103,7 @@ results
 
     100%|██████████| 289/289 [00:00<00:00, 6727.01it/s]
 
-<div>
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+
 
 | kinase | SRC      | EPHA3    | FES      | NTRK3    | ALK      | EPHA8    | ABL1     | FLT3     | EPHB2    | FYN      | ... | MEK5     | PKN2     | MAP2K7   | MRCKB    | HIPK3    | CDK8     | BUB1     | MEKK3    | MAP2K3   | GRK1     |
 |--------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|-----|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
@@ -114,10 +113,8 @@ results
 | 3      | 0.803826 | 0.836527 | 0.800759 | 0.894570 | 0.839905 | 0.781001 | 0.847847 | 0.807040 | 0.805877 | 0.801402 | ... | 1.110307 | 1.703637 | 1.795092 | 1.469653 | 1.549936 | 1.491344 | 1.446922 | 1.055452 | 1.534895 | 1.741090 |
 | 4      | 0.822793 | 0.796532 | 0.792343 | 0.839882 | 0.810122 | 0.781420 | 0.805251 | 0.795022 | 0.790380 | 0.864538 | ... | 1.062617 | 1.357689 | 1.485945 | 1.249266 | 1.456078 | 1.422782 | 1.376471 | 1.089629 | 1.121309 | 1.697524 |
 
-<p>5 rows × 289 columns</p>
-</div>
+5 rows × 289 columns
 
-</div>
 
 ### Input sequences are in phosphorylated status
 
@@ -157,20 +154,7 @@ results
 
     100%|██████████| 289/289 [00:00<00:00, 6732.43it/s]
 
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+
 
 | kinase | SRC      | EPHA3    | FES      | NTRK3    | ALK      | EPHA8    | ABL1     | FLT3     | EPHB2    | FYN      | ... | MEK5     | PKN2     | MAP2K7   | MRCKB    | HIPK3    | CDK8     | BUB1     | MEKK3    | MAP2K3   | GRK1     |
 |--------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|-----|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
@@ -180,10 +164,8 @@ results
 | 3      | 0.646383 | 0.701740 | 0.676063 | 0.782448 | 0.729290 | 0.648563 | 0.697185 | 0.678297 | 0.687545 | 0.651732 | ... | 1.030239 | 1.558569 | 1.619387 | 1.321234 | 1.394759 | 1.366344 | 1.306156 | 0.971825 | 1.384048 | 1.692299 |
 | 4      | 0.695749 | 0.706868 | 0.697352 | 0.766069 | 0.739252 | 0.682100 | 0.703865 | 0.712811 | 0.703658 | 0.744865 | ... | 1.039890 | 1.324106 | 1.344163 | 1.215167 | 1.363295 | 1.400055 | 1.283447 | 1.042570 | 1.098709 | 1.661380 |
 
-<p>5 rows × 289 columns</p>
-</div>
+5 rows × 289 columns
 
-</div>
 
 ### To replicate the results from PSPA
 
@@ -226,20 +208,7 @@ results
 
     100%|██████████| 303/303 [00:00<00:00, 6683.64it/s]
 
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+
 
 | kinase | AAK1       | ACVR2A    | ACVR2B    | AKT1      | AKT2      | AKT3      | ALK2      | ALK4      | ALPHAK3   | AMPKA1    | ... | VRK1      | VRK2      | WNK1      | WNK3      | WNK4      | YANK2     | YANK3     | YSK1      | YSK4      | ZAK       |
 |--------|------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
@@ -249,10 +218,8 @@ results
 | 3      | -4.849113  | 2.271636  | 2.057240  | -2.886034 | -2.379830 | -3.634907 | 1.547144  | 2.735341  | -2.825794 | -1.696886 | ... | -2.757951 | -1.699232 | -1.725384 | -0.091196 | -0.672545 | 0.313278  | -0.207212 | -2.315848 | -0.053572 | -1.117657 |
 | 4      | -6.596842  | -1.387696 | -0.956218 | -2.834231 | -3.794276 | -4.968521 | -1.862002 | -1.717226 | -2.653170 | -3.514512 | ... | -1.546328 | -1.457323 | -1.277532 | 0.510635  | -1.045845 | -0.314193 | -1.023331 | -2.482345 | -2.227114 | -1.592725 |
 
-<p>5 rows × 303 columns</p>
-</div>
+5 rows × 303 columns
 
-</div>
 
 ## Datasets of phosphorylation sites