Merge pull request #6 from asntech/j2026

Addeded JASPAR 2026 release

Author: asntech

CITATION.cff

@@ -0,0 +1,13 @@
+cff-version: 1.2.0
+
+message: "If you use this software, please cite it using the following reference."
+title: "pyJASPAR: a Pythonic interface to JASPAR transcription factor motifs"
+date-released: "2021-02-01"
+authors:
+  - family-names: "Khan"
+    given-names: "Aziz"
+    orcid: "https://orcid.org/0000-0002-6459-6224"
+version: 4.0.0
+identifiers:
+  - type: doi
+    value: 10.5281/zenodo.4509415"

LICENSE

@@ -5,7 +5,6 @@ pyJASPAR
 
 **pyJASPAR** uses *Biopython* and *SQLite3* to provide a serverless interface to `JASPAR database <http://jaspar.genereg.net>`_ to query and access TF motif profiles across various releases of JASPAR.
 
-
 .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4509415.svg
    :target: https://doi.org/10.5281/zenodo.4509415
 
@@ -28,11 +27,12 @@ pyJASPAR
     :target: https://github.com/asntech/pyjaspar/issues
 
 
-pyJASPAR provides access to the following releases of JASPAR database: *JASPAR2024*, *JASPAR2022*, *JASPAR2020*, *JASPAR2018*, *JASPAR2016*, *JASPAR2014*.
+pyJASPAR provides access to the following releases of JASPAR database: *JASPAR2026*, *JASPAR2024*, *JASPAR2022*, *JASPAR2020*, *JASPAR2018*, *JASPAR2016*, *JASPAR2014*.
 
 **Note**: This is a serverless SQLite wrapper around the Biopython JASPAR module `Bio.motifs.jaspar.db` which requires JASPAR MySQL database sever connection details.
 
 
+
 Documentation
 -------------
 
@@ -42,6 +42,9 @@ Documentation
 Installation
 ------------
 
+**Note**: The *JASPAR2026* release is available for peer-review purpose via GitHub only. It will be made public after the official release of JASPAR2026 database.
+
+
 Quick installation using conda
 ================================
 pyJASPAR is available on `Bioconda <https://anaconda.org/bioconda/pyjaspar>`_ for installation via ``conda``.
@@ -86,8 +89,8 @@ Once you have installed pyjaspar, you can create jaspardb class object:
 
     >>> from pyjaspar import jaspardb
     
-    #Create the JASPAR2022 release object    
-    >>> jdb_obj = jaspardb(release='JASPAR2024')
+    #Create the JASPAR2026 release object    
+    >>> jdb_obj = jaspardb(release='JASPAR2026')
 
     #Fetch motif by ID
     >>> motif = jdb_obj.fetch_motif_by_id('MA0095.2')
@@ -112,7 +115,7 @@ Once you have installed pyjaspar, you can create jaspardb class object:
             tax_group = ['Vertebrates'],
             all_versions = False)
     >>> print(len(motifs))
-    879
+    1019
     ## loop through the motifs list and perform analysis
     >>> for motif in motifs:
             pass
@@ -125,12 +128,12 @@ Find available releases
 .. code-block:: pycon
     
     >>> print(jdb_obj.get_releases())
-    ['JASPAR2024','JASPAR2022','JASPAR2020', 'JASPAR2018', 'JASPAR2016', 'JASPAR2014']
+    ['JASPAR2026', 'JASPAR2024','JASPAR2022','JASPAR2020', 'JASPAR2018', 'JASPAR2016', 'JASPAR2014']
 
 
 Cite
 =====
-    - Aziz Khan. pyJASPAR: a Pythonic interface to JASPAR transcription factor motifs. (2021). doi:10.5281/zenodo.4509415
+    - Aziz Khan. pyJASPAR: a Pythonic interface to JASPAR transcription factor motifs. (2021). Zenodo, doi:10.5281/zenodo.4485856
 
 .. code-block:: bash
 
@@ -140,7 +143,7 @@ Cite
       month        = feb,
       year         = 2021,
       publisher    = {Zenodo},
-      version      = {v2.0.0},
-      doi          = {10.5281/zenodo.4509415},
-      url          = {https://doi.org/10.5281/zenodo.4509415}
+      version      = {v4.0.0},
+      doi          = {10.5281/zenodo.4485856},
+      url          = {https://doi.org/10.5281/zenodo.4485856}
     }

README.rst

@@ -43,7 +43,7 @@
             pass # do something with the motif
 """
 
-__version__ = '3.0.0'
+__version__ = '4.0.0'
 
 import warnings
 from Bio import BiopythonWarning
@@ -55,6 +55,7 @@
 from .utils import *
 
 jaspar_releases = {
+    'JASPAR2026': 'JASPAR2026.sqlite',
     'JASPAR2024': 'JASPAR2024.sqlite',
     'JASPAR2022': 'JASPAR2022.sqlite',
     'JASPAR2020': 'JASPAR2020.sqlite',
@@ -63,7 +64,7 @@
     'JASPAR2014': 'JASPAR2014.sqlite',
 }
 
-JASPAR_LATEST_RELEASE = "JASPAR2024"
+JASPAR_LATEST_RELEASE = "JASPAR2026"
 
 JASPAR_DFLT_COLLECTION = "CORE"
 

pyjaspar/__init__.py

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,11 +33,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "jdb_obj = jaspardb(release='JASPAR2024')"
+    "jdb_obj = jaspardb(release='JASPAR2026')"
    ]
   },
   {
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -58,7 +58,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "JASPAR2024\n"
+      "JASPAR2026\n"
      ]
     }
    ],
@@ -75,14 +75,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "JASPAR2024\n"
+      "JASPAR2026\n"
      ]
     }
    ],
@@ -101,14 +101,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['JASPAR2024', 'JASPAR2022', 'JASPAR2020', 'JASPAR2018', 'JASPAR2016', 'JASPAR2014']\n"
+      "['JASPAR2026', 'JASPAR2024', 'JASPAR2022', 'JASPAR2020', 'JASPAR2018', 'JASPAR2016', 'JASPAR2014']\n"
      ]
     }
    ],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -152,8 +152,8 @@
       "TF name\tAhr::Arnt\n",
       "Matrix ID\tMA0006.1\n",
       "Collection\tCORE\n",
-      "TF class\t['Basic helix-loop-helix factors (bHLH)', 'Basic helix-loop-helix factors (bHLH)']\n",
-      "TF family\t['PAS domain factors', 'PAS domain factors']\n",
+      "TF class\t['Basic helix-loop-helix factors (bHLH)']\n",
+      "TF family\t['PAS domain factors']\n",
       "Species\t10090\n",
       "Taxonomic group\tvertebrates\n",
       "Accession\t['P30561', 'P53762']\n",
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
@@ -364,7 +364,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "879\n"
+      "1019\n"
      ]
     }
    ],
@@ -374,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,13 +392,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "JASPAR2026\n",
+      "2633\n",
       "JASPAR2024\n",
       "2346\n",
       "JASPAR2022\n",
@@ -426,6 +428,13 @@
     "    print(len(motifs))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -436,7 +445,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -450,9 +459,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.8.20"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }