Release v2.0.0

See release note

README.md

@@ -1,7 +1,13 @@
 # tRFtarget
-**tRFtarget** (http://trftarget.net/) is a relational database to explore binding targets of transfer RNA-derived fragments (tRFs).
+![tRFtarget](static/img/tRFtarget.png)
 
-For running our pipeline to get targets of tRFs not indexed in our database or other small non-coding RNAs (such as miRNAs), please submit a request via web service in [http://trftarget.net/online_targets](http://trftarget.net/online_targets) or refer our GitHub repository [tRFtarget-pipeline](https://github.com/ZWang-Lab/tRFtarget-pipeline).
+**tRFtarget** (http://trftarget.net/) integrates **computational predicted** and **experimental validated** binding interactions between transfer RNA-derived fragments (tRFs) and target transcripts for multiple organisms.
+
+Users can:
+
+* Search or browse predicted tRF targets
+* Run our pipeline [tRFtarget-pipeline](https://github.com/ZWang-Lab/tRFtarget-pipeline) to get targets of tRFs or other small non-coding RNAs (such as miRNAs) locally or via our web service in [http://trftarget.net/online_targets](http://trftarget.net/online_targets)
+* Browse experimental evidences of the predicted tRF-RNA interactions based on manually curated publications
 
 ## Citation
 
@@ -10,6 +16,6 @@ If you use tRFtarget or tRFtarget pipeline, please cite:
 Ningshan Li, Nayang Shan, Lingeng Lu, Zuoheng Wang. tRFtarget: a database for transfer RNA-derived fragment targets. *Nucleic Acids Research*, 2020, gkaa831. https://doi.org/10.1093/nar/gkaa831.
 
 ## Contact Us
-Users are welcome to send feedbacks, suggestions or comments on the tRFtarget database, and report tRF target experiments or tRF functional studies that are not included in the database through [Issues](https://github.com/ZWang-Lab/tRFtarget/issues).
+Users are welcome to send feedbacks, suggestions or comments on the tRFtarget database, and report tRF target experiments that are not included in the database through [Issues](https://github.com/ZWang-Lab/tRFtarget/issues).
 
-For issues in using tRFtarget pipeline, please report to GitHub repository [tRFtarget-pipeline](https://github.com/ZWang-Lab/tRFtarget-pipeline)
+For issues in using tRFtarget pipeline, please report to GitHub repository [tRFtarget-pipeline](https://github.com/ZWang-Lab/tRFtarget-pipeline).

datatables.py

@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct 19 02:13:05 2022
+
+@author: hill103
+
+this script defines data tables in MySQL database
+"""
+
+
+
+import peewee as pw
+from operator import attrgetter, and_
+from functools import reduce
+
+
+
+# number of binding site records displayed in result page
+n_result = 5e4
+
+
+MYSQLDB = pw.MySQLDatabase('trftarget', user='root', password='12345678',
+                           host='127.0.0.1', port=3306, autoconnect=False)
+
+
+# 定义MySQL data types (ref http://docs.peewee-orm.com/en/latest/peewee/models.html?highlight=table%20generation#field-types-table)
+class UnTinyIntField(pw.IntegerField):
+    # Range 0-255
+    field_type = 'TINYINT UNSIGNED'
+
+class UnSmallIntField(pw.IntegerField):
+    # Range 0-65535
+    field_type = 'SMALLINT UNSIGNED'
+
+class UnMediumIntField(pw.IntegerField):
+    # Range 0-16777215
+    field_type = 'MEDIUMINT UNSIGNED'
+
+class myVarBinaryField(pw.Field):
+    # Range 0-1024
+    field_type = 'VARBINARY(1024)'
+
+
+# peewee CharField对应MySQL varchar(255)类型，corresponding to max 255 bytes so 255 chars! max value can up to 65535 bytes (the maximum row size which is shared among all columns)
+# Note that if strict SQL mode is not enabled and you assign a value to a CHAR or VARCHAR column that exceeds the column's maximum length, the value is truncated to fit and a warning is generated
+class MySQLBaseModel(pw.Model):
+    # 定义columns
+    trf_id = UnSmallIntField()
+    trf_type = UnTinyIntField()
+    trf_source = UnTinyIntField()
+    rna_id = UnMediumIntField()
+    rna_name = UnMediumIntField(null=True)
+    rna_type = UnTinyIntField()
+    area = UnTinyIntField()
+    gene_id = UnMediumIntField(null=True)
+    gene_name = UnMediumIntField(null=True)
+    mfe = pw.FloatField()
+    mcl = UnTinyIntField()
+    # update: now we compress the string of interaction illustration
+    # demo = pw.CharField(max_length=2048)
+    demo_bin = myVarBinaryField()
+    consensus = UnTinyIntField()
+    gene_evi = pw.CharField(max_length=1024, null=True)
+    site_evi = pw.CharField(max_length=1024, null=True)
+    has_gene_evi = UnTinyIntField()
+    has_site_evi = UnTinyIntField()
+
+    # 所用数据库为MYSQLDB
+    class Meta:
+        database = MYSQLDB # MYSQLDB为所定义的数据库变量
+
+
+# 定义数据库中的表格模型
+class mus_musculus_rnahybrid(MySQLBaseModel):
+    pass
+
+class mus_musculus_intarna(MySQLBaseModel):
+    pass
+
+class drosophila_melanogaster_rnahybrid(MySQLBaseModel):
+    pass
+
+class drosophila_melanogaster_intarna(MySQLBaseModel):
+    pass
+
+class danio_rerio_rnahybrid(MySQLBaseModel):
+    pass
+
+class danio_rerio_intarna(MySQLBaseModel):
+    pass
+
+class caenorhabditis_elegans_rnahybrid(MySQLBaseModel):
+    pass
+
+class caenorhabditis_elegans_intarna(MySQLBaseModel):
+    pass
+
+class xenopus_tropicalis_rnahybrid(MySQLBaseModel):
+    pass
+
+class xenopus_tropicalis_intarna(MySQLBaseModel):
+    pass
+
+class rhodobacter_sphaeroides_rnahybrid(MySQLBaseModel):
+    pass
+
+class rhodobacter_sphaeroides_intarna(MySQLBaseModel):
+    pass
+
+class schizosaccharomyces_pombe_rnahybrid(MySQLBaseModel):
+    pass
+
+class schizosaccharomyces_pombe_intarna(MySQLBaseModel):
+    pass
+
+class rattus_norvegicus_rnahybrid(MySQLBaseModel):
+    pass
+
+class rattus_norvegicus_intarna(MySQLBaseModel):
+    pass
+
+class homo_sapiens_rnahybrid(MySQLBaseModel):
+    pass
+
+class homo_sapiens_intarna(MySQLBaseModel):
+    pass
+
+
+# define the nested dict of data tables for using in main function
+datatable_dict = {'Mus musculus': {'RNAhybrid': mus_musculus_rnahybrid, 'IntaRNA': mus_musculus_intarna},
+                  'Drosophila melanogaster': {'RNAhybrid': drosophila_melanogaster_rnahybrid, 'IntaRNA': drosophila_melanogaster_intarna},
+                  'Danio rerio': {'RNAhybrid': danio_rerio_rnahybrid, 'IntaRNA': danio_rerio_intarna},
+                  'Caenorhabditis elegans': {'RNAhybrid': caenorhabditis_elegans_rnahybrid, 'IntaRNA': caenorhabditis_elegans_intarna},
+                  'Xenopus tropicalis': {'RNAhybrid': xenopus_tropicalis_rnahybrid, 'IntaRNA': xenopus_tropicalis_intarna},
+                  'Rhodobacter sphaeroides': {'RNAhybrid': rhodobacter_sphaeroides_rnahybrid, 'IntaRNA': rhodobacter_sphaeroides_intarna},
+                  'Schizosaccharomyces pombe': {'RNAhybrid': schizosaccharomyces_pombe_rnahybrid, 'IntaRNA': schizosaccharomyces_pombe_intarna},
+                  'Rattus norvegicus': {'RNAhybrid': rattus_norvegicus_rnahybrid, 'IntaRNA': rattus_norvegicus_intarna},
+                  'Homo sapiens': {'RNAhybrid': homo_sapiens_rnahybrid, 'IntaRNA': homo_sapiens_intarna}}
+
+
+# peewee query returns an iterator
+# For simple queries you can see further speed improvements by returning rows as dictionaries, namedtuples or tuples.
+# Don’t forget to append the iterator() method call to also reduce memory consumption
+def doBasicSearch(arg_dict, search_way, tool, num, add_consensus, count_only=False):
+    '''generate peewee commands to perform MysQL database search
+    search_way is one of 'trf_id', 'rna_id', 'rna_name', 'gene_id', 'gene_name'
+    tool is either 'RNAhybrid' or 'IntaRNA'
+    num is decided in outter function, for Consensus predictions, return num/2 RNAhybrid + num/2 IntaRNA predictions
+    note search item for trf_id is a list or int
+    '''
+
+    table = datatable_dict[arg_dict['organism']][tool]
+
+    clauses = []
+
+    # add search conditions based on the order of table index
+    if search_way == 'trf_id':
+        if isinstance(arg_dict['trf_id'], list):
+            if len(arg_dict['trf_id']) > 1:
+                clauses.append(table.trf_id.in_(arg_dict['trf_id']))
+            else:
+                clauses.append(table.trf_id == arg_dict['trf_id'])
+        else:
+            clauses.append(table.trf_id == arg_dict['trf_id'])
+    else:
+        clauses.append(attrgetter(search_way)(table) == arg_dict[search_way])
+
+    clauses.append(table.mfe <= arg_dict['fe_threshold'])
+    clauses.append(table.mcl >= arg_dict['mcl_threshold'])
+
+    if add_consensus:
+        clauses.append(table.consensus == 1)
+    else:
+        clauses.append(table.consensus <= 1)
+
+    # different filtering for different search way
+    if search_way == 'trf_id':
+        clauses.append(table.rna_type.in_(arg_dict['rna_type']))
+    else:
+        clauses.append(table.trf_type.in_(arg_dict['trf_type']))
+        clauses.append(table.trf_source.in_(arg_dict['trf_source']))
+
+    clauses.append(table.area.in_(arg_dict['binding_region']))
+
+    if arg_dict['evidence_only']:
+        clauses.append((table.has_gene_evi == 1) | (table.has_site_evi == 1))
+
+
+    if count_only:
+        # just count the number of retrieved entries
+        return (table.select()
+                     .where(reduce(and_, clauses))
+                     .order_by(table.mfe, table.mcl.desc())
+                     .limit(num)
+                     .count()
+               )
+    else:
+        return (table.select(table.trf_id,
+                             table.rna_id,
+                             table.rna_name,
+                             table.gene_id,
+                             table.gene_name,
+                             table.demo_bin,
+                             table.mfe,
+                             table.mcl,
+                             table.trf_type,
+                             table.trf_source,
+                             table.rna_type,
+                             table.area,
+                             table.has_gene_evi,
+                             table.has_site_evi)
+                     .where(reduce(and_, clauses))
+                     .order_by(table.mfe, table.mcl.desc())
+                     .limit(num)
+                     .tuples()
+               )
+
+
+def doSearch(arg_dict, search_way):
+    '''considering Consensus predictions, call doBasicSearch function to perform search
+    '''
+
+    if arg_dict['tool'] == 'Consensus':
+        return (doBasicSearch(arg_dict, search_way, 'RNAhybrid', int(n_result/2), True),
+                doBasicSearch(arg_dict, search_way, 'IntaRNA', int(n_result/2), True))
+    else:
+        return doBasicSearch(arg_dict, search_way, arg_dict['tool'], int(n_result), False)
+
+
+
+# since count elements in a iterator will consume it, we need another query to get the count
+def countSearch(arg_dict, search_way):
+    '''considering Consensus predictions, call doBasicSearch function to perform search
+    '''
+
+    if arg_dict['tool'] == 'Consensus':
+        return (doBasicSearch(arg_dict, search_way, 'RNAhybrid', int(n_result/2), True, True),
+                doBasicSearch(arg_dict, search_way, 'IntaRNA', int(n_result/2), True, True))
+    else:
+        return doBasicSearch(arg_dict, search_way, arg_dict['tool'], int(n_result), False, True)
+
+
+
+# addtional functions for Advanced Search
+def doBasicSearchAdv(arg_dict, search_ways, tool, num, add_consensus, count_only=False):
+    '''generate peewee commands to perform MysQL database search
+    search_ways is two fields from 'trf_id', 'rna_id', 'rna_name', 'gene_id', 'gene_name'
+    tool is either 'RNAhybrid' or 'IntaRNA'
+    num is decided in outter function, for Consensus predictions, return num/2 RNAhybrid + num/2 IntaRNA predictions
+    '''
+
+    table = datatable_dict[arg_dict['organism']][tool]
+
+    # determine the 2nd search field
+    for s in search_ways:
+        if s != 'trf_id':
+            search_way = s
+            break
+
+    clauses = []
+
+    # add search conditions based on the order of table index
+    # note we use the index for RNA or Gene, and add trf_id to the last one in conditions
+    clauses.append(attrgetter(search_way)(table) == arg_dict[search_way])
+    clauses.append(table.mfe <= arg_dict['fe_threshold'])
+    clauses.append(table.mcl >= arg_dict['mcl_threshold'])
+
+    if add_consensus:
+        clauses.append(table.consensus == 1)
+    else:
+        clauses.append(table.consensus <= 1)
+
+    clauses.append(table.area.in_(arg_dict['binding_region']))
+
+    if arg_dict['evidence_only']:
+        clauses.append((table.has_gene_evi == 1) | (table.has_site_evi == 1))
+
+    if isinstance(arg_dict['trf_id'], list):
+        if len(arg_dict['trf_id']) > 1:
+            clauses.append(table.trf_id.in_(arg_dict['trf_id']))
+        else:
+            clauses.append(table.trf_id == arg_dict['trf_id'])
+    else:
+        clauses.append(table.trf_id == arg_dict['trf_id'])
+
+
+    if count_only:
+        # just count the number of retrieved entries
+        return (table.select()
+                     .where(reduce(and_, clauses))
+                     .order_by(table.mfe, table.mcl.desc())
+                     .limit(num)
+                     .count()
+               )
+    else:
+        return (table.select(table.trf_id,
+                             table.rna_id,
+                             table.rna_name,
+                             table.gene_id,
+                             table.gene_name,
+                             table.demo_bin,
+                             table.mfe,
+                             table.mcl,
+                             table.trf_type,
+                             table.trf_source,
+                             table.rna_type,
+                             table.area,
+                             table.has_gene_evi,
+                             table.has_site_evi)
+                     .where(reduce(and_, clauses))
+                     .order_by(table.mfe, table.mcl.desc())
+                     .limit(num)
+                     .tuples()
+               )
+
+
+def doSearchAdv(arg_dict, search_ways):
+    '''considering Consensus predictions, call doBasicSearchAdv function to perform search
+    '''
+
+    if arg_dict['tool'] == 'Consensus':
+        return (doBasicSearchAdv(arg_dict, search_ways, 'RNAhybrid', int(n_result/2), True),
+                doBasicSearchAdv(arg_dict, search_ways, 'IntaRNA', int(n_result/2), True))
+    else:
+        return doBasicSearchAdv(arg_dict, search_ways, arg_dict['tool'], int(n_result), False)
+
+
+def countSearchAdv(arg_dict, search_ways):
+    '''considering Consensus predictions, call doBasicSearch function to perform search
+    '''
+
+    if arg_dict['tool'] == 'Consensus':
+        return (doBasicSearchAdv(arg_dict, search_ways, 'RNAhybrid', int(n_result/2), True, True),
+                doBasicSearchAdv(arg_dict, search_ways, 'IntaRNA', int(n_result/2), True, True))
+    else:
+        return doBasicSearchAdv(arg_dict, search_ways, arg_dict['tool'], int(n_result), False, True)