Upcoming Non-League Division One Southern Central England Matches
The Non-League football scene in Southern Central England is buzzing with anticipation as Division One teams gear up for tomorrow's fixtures. This weekend promises thrilling encounters, with teams battling for supremacy and points crucial for their league standings. Fans and bettors alike are eagerly awaiting the action, with expert predictions offering insights into potential outcomes. Let's delve into the key matches, standout teams, and betting tips for tomorrow's fixtures.
Key Matches to Watch
Tomorrow's schedule features several high-stakes matches that could significantly impact the league table. Here are the top encounters:
- Team A vs. Team B: This clash is one of the most anticipated matches of the weekend. Both teams are in excellent form, and a victory for either side could propel them up the standings.
- Team C vs. Team D: Known for their defensive solidity, Team C faces a stern test against the attacking prowess of Team D. This match is expected to be a tactical battle.
- Team E vs. Team F: With both teams struggling to find consistency this season, this match offers a chance for redemption and a much-needed boost in confidence.
Standout Teams and Players
Several teams have been making waves in Division One, thanks to their impressive performances and key players:
- Team A: Led by their prolific striker, who has netted over 20 goals this season, Team A is a formidable force. Their attacking play is a spectacle to watch.
- Team B: With a solid defense and a creative midfield, Team B has been consistent in their performances. Their ability to control the game makes them tough opponents.
- Player Spotlight: John Doe: The midfield maestro of Team C, John Doe has been instrumental in their recent successes. His vision and passing accuracy set him apart from his peers.
Betting Predictions and Tips
Betting on Non-League football can be both exciting and rewarding. Here are some expert predictions and tips for tomorrow's matches:
- Team A vs. Team B: Bet on Team A to win by a narrow margin. Their home advantage and current form make them slight favorites.
- Team C vs. Team D: A draw seems likely given both teams' defensive strengths. Consider betting on under 2.5 goals for this match.
- Team E vs. Team F: With both teams desperate for points, expect an open game. Betting on both teams to score could be a smart move.
Tactical Insights
Analyzing team tactics provides deeper insights into how these matches might unfold:
- Team A's Strategy: Known for their high-pressing game, Team A aims to dominate possession and create scoring opportunities through quick transitions.
- Team B's Defense: With a focus on maintaining a compact shape, Team B relies on disciplined defending and counter-attacks to outwit their opponents.
- Team C's Midfield Control: Central to their strategy is controlling the midfield battle. By dictating the tempo, they aim to disrupt their opponents' rhythm.
Potential Upsets
In football, surprises are always around the corner. Here are some potential upsets to watch out for:
- Underdog Triumphs: Keep an eye on lower-ranked teams like Team G and Team H, who have been showing signs of improvement and could spring an upset.
- Injuries and Suspensions: Key absences due to injuries or suspensions can tilt the balance in favor of the underdog, making certain matches more unpredictable.
Fan Expectations and Atmosphere
The passion of Non-League football fans is unmatched, with vibrant atmospheres at grounds across Southern Central England:
- Spirit of the Game: Fans bring an infectious energy that often inspires players to perform at their best.
- Venue Highlights: Stadiums like Ground X and Ground Y are known for their lively crowds and unique local traditions that add to the matchday experience.
Injury Updates and Squad News
Injuries can significantly impact team dynamics, so here are the latest updates on squad availability:
- Team A's Concerns: Key defender injured but expected to return soon; backup options being tested in training.
- Team B's Midfield Boost: Midfielder returns from suspension, adding depth to an already strong lineup.
- Youth Integration: Several teams are integrating young talents into their squads, providing fresh energy and potential surprise elements.
Historical Context
The history of these clubs adds depth to tomorrow's matches:
- Rivalries Rekindled: Historical rivalries bring an extra layer of intensity, with past encounters often remembered by fans for dramatic finishes.
- Past Performances: Reviewing previous seasons can offer insights into team trajectories and potential outcomes based on historical data.
Betting Markets Overview
Betting markets offer various ways to engage with tomorrow's matches beyond simple win/lose bets:
- Total Goals Market: Predicting the total number of goals scored can be lucrative, especially in matches expected to be high-scoring affairs.
- To Score First Market: Betting on which team will score first can be influenced by early-game strategies and player matchups.
- Doubles/Masters Market: For those looking for higher stakes, predicting multiple correct outcomes across different matches offers big rewards.
<|repo_name|>dungnguyen/nanopore<|file_sep|>/README.md
# nanopore
This repository contains code developed while working at [Genomic Health](https://genomichealth.com/), including:
* Code used during my PhD thesis (2017-2019), including:
* Python notebooks used during research:
* [Heterozygous SNPs](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/heterozygous.ipynb)
* [Indels](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/indels.ipynb)
* [Deletions](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/deletions.ipynb)
* [Paper](https://github.com/dungnguyen/nanopore/blob/master/Dungh_NGS_2019.pdf) (Nature Communications)
* Python notebooks used during my internship (2016):
* [Mutation Calling](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/internship/mutation_calling.ipynb)
* [Mutation Filtering](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/internship/mutation_filtering.ipynb)
* [Data Processing](https://nbviewer.jupyter.org/github/dungnguyen/nanopore/blob/master/internship/data_processing.ipynb)
## License
This project is licensed under MIT license.
<|repo_name|>dungnguyen/nanopore<|file_sep|>/internship/mutation_filtering.py
import os
import pandas as pd
import numpy as np
from itertools import product
from Bio import SeqIO
from Bio.Seq import Seq
def get_all_strains():
return ['KIR6', 'KIR7', 'KIR8', 'KIR9', 'KIR10', 'KIR11',
'KIR12', 'KIR13', 'KIR14', 'KIR15', 'KIR16',
'KIR17', 'KIR18', 'KIR19']
def get_ref_seq(ref):
ref_seq = list(SeqIO.parse(ref,'fasta'))
return ref_seq[0].seq
def get_strain_seq(strain_fasta):
strain_seq = list(SeqIO.parse(strain_fasta,'fasta'))
return strain_seq[0].seq
def load_mutation_file(file_name):
mutations = pd.read_csv(file_name)
mutations['RefSeq'] = mutations['RefSeq'].apply(lambda x: x.replace(' ',''))
mutations['StrainSeq'] = mutations['StrainSeq'].apply(lambda x: x.replace(' ',''))
return mutations
def convert_to_one_based_index(mutations):
mutations['ref_start'] = mutations['ref_start'].apply(lambda x: int(x)+1)
mutations['ref_end'] = mutations['ref_end'].apply(lambda x: int(x)+1)
return mutations
def filter_indels_by_length(mutations,max_indel_length=100):
mutations = mutations[(mutations['mutation_type']!='SNP') |
(mutations['mutation_size'].abs()<=max_indel_length)]
return mutations
def filter_by_quality_score(mutations,min_quality=20):
mutations = mutations[mutations['quality']>=min_quality]
return mutations
def filter_by_read_count(mutations,min_read_count=5):
mutations = mutations[mutations['read_count']>=min_read_count]
return mutations
def filter_by_mapping_quality(mutations,min_mapping_quality=30):
mutations = mutations[mutations['mapping_quality']>=min_mapping_quality]
return mutations
def filter_by_reference_genotype(mutations,is_homozygous=True):
if is_homozygous:
mutations = mutations[(mutations['reference_genotype']=='HOM_REF') |
(mutations['reference_genotype']=='HOM_ALT')]
else:
mutations = mutations[(mutations['reference_genotype']=='HET')]
return mutations
def filter_by_strain_genotype(mutations,is_homozygous=True):
if is_homozygous:
mutations = mutations[(mutations['strain_genotype']=='HOM_REF') |
(mutations['strain_genotype']=='HOM_ALT')]
else:
mutations = mutations[(mutations['strain_genotype']=='HET')]
return mutations
def filter_by_alignment_position(mutations,min_alignment_position=0,
max_alignment_position=None):
if max_alignment_position==None:
max_alignment_position=len(get_ref_seq('/home/dung/genomic_health/interim/ref/KIR.fasta'))
mutations = mutations[(mutations['ref_start']>=min_alignment_position) &
(mutations['ref_start']<=max_alignment_position)]
return mutations
def filter_mutations(mutations,criteria={}):
if criteria=={}:
criteria={
'max_indel_length':100,
'min_quality':20,
'min_read_count':5,
'min_mapping_quality':30,
'is_homozygous_reference':True,
'is_homozygous_strain':True,
'min_alignment_position':0,
'max_alignment_position':None
}
for c in criteria.keys():
if c=='max_indel_length':
mutations = filter_indels_by_length(mutations,criteria[c])
continue
if c=='min_quality':
mutations = filter_by_quality_score(mutations,criteria[c])
continue
if c=='min_read_count':
mutations = filter_by_read_count(mutations,criteria[c])
continue
if c=='min_mapping_quality':
mutations = filter_by_mapping_quality(mutations,criteria[c])
continue
if c=='is_homozygous_reference':
mutations = filter_by_reference_genotype(mutations,criteria[c])
continue
if c=='is_homozygous_strain':
mutations = filter_by_strain_genotype(mutations,criteria[c])
continue
if c=='min_alignment_position':
max_alignment_position=criteria.get('max_alignment_position',None)
mutations = filter_by_alignment_position(mutations,criteria[c],max_alignment_position)
return mutations
def get_intra_strain_variants(strain_fasta,mutation_file,max_indel_length=100,
min_quality=20,min_read_count=5,min_mapping_quality=30,
min_alignment_position=0,max_alignment_position=None):
strain_name=strain_fasta.split('/')[-1].split('.')[0]
strain_seq=get_strain_seq(strain_fasta)
ref_seq=get_ref_seq('/home/dung/genomic_health/interim/ref/KIR.fasta')
ref_name='/home/dung/genomic_health/interim/ref/KIR.fasta'
ref_start=1
ref_end=len(ref_seq)+1
ref_alt=''
strains=[strain_name]
strains.sort()
strains.insert(0,'reference')
strains=['%s_vs_%s'%(x,y) for x,y in product(strains,strains) if x!=y]
print('nIntra-strain variants: %s'%strain_name)
print('-----------------------n')
print('Strains: %s'%(strains))
print('-----------------------n')
print('Loading mutation file...')
print('%sn'%mutation_file)
mutation_file_base=os.path.basename(mutation_file).replace('.csv','')
print('Mutation file base: %sn'%mutation_file_base)
print('Converting mutation coordinates...')
mutation_file_base=os.path.basename(mutation_file).replace('.csv','')
print('Loading mutation file...')
mutation_file_base=os.path.basename(mutation_file).replace('.csv','')
mutations=pd.read_csv(mutation_file)
print('Converting mutation coordinates...')
print('%sn'%mutation_file)
print('%sn'%mutation_file_base)
print('%sn'%mutation_file.split('/')[-1])
print('nMutation file base: %sn'%mutation_file_base)
print('nLoading mutation file...')
print('%sn'%mutation_file.split('/')[-1])
mutation_file_base=os.path.basename(mutation_file).replace('.csv','')
print('Mutation file base: %sn'%mutation_file_base)
try:
ref_start_column=mutation_file_base+'_ref_start'
ref_end_column=mutation_file_base+'_ref_end'
ref_alt_column=mutation_file_base+'_ref_alt'
strain_start_column=mutation_file_base+'_strain_start'
strain_end_column=mutation_file_base+'_strain_end'
strain_alt_column=mutation_file_base+'_strain_alt'
except:
ref_start_column='%s_ref_start'%ref_name.split('/')[-1].split('.')[0]
ref_end_column='%s_ref_end'%ref_name.split('/')[-1].split('.')[0]
ref_alt_column='%s_ref_alt'%ref_name.split('/')[-1].split('.')[0]
strain_start_column='%s_strain_start'%strain_name
strain_end_column='%s_strain_end'%strain_name
strain_alt_column='%s_strain_alt'%strain_name
try:
mutation_type_column='%s_mutation_type'%ref_name.split('/')[-1].split('.')[0]
except:
mutation_type_column='mutation_type'
try:
mapping_quality_column='%s_mapping_quality'%ref_name.split('/')[-1].split('.')[0]
except:
mapping_quality_column='mapping_quality'
try:
quality_column='%s_quality'%ref_name.split('/')[-1].split('.')[0]
except:
quality_column='quality'
try:
read_count_column='%s_read_count'%ref_name.split('/')[-1].split('.')[0]
except:
read_count_column='read_count'
try:
reference_genotype_column='%s_reference_genotype'%ref_name.split('/')[-1].split('.')[0]
except:
reference_genotype_column='reference_genotype'
try:
strain_genotype_column='%s_strain_genotype'%strain_name
except:
strain_genotype_column='strain_genotype'
for c in ['%s_ref'%(x) for x in ['start','end','alt']] +
['%s_strain'%(x) for x in ['start','end','alt']] +
['reference_genotype','strain_genotype']:
try:
column=c.replace('_','-')
mutation_file_base=os.path.basename(mutation_file).replace('.csv','')
if column not in mutations.columns.tolist():
mutation_type='SNP'
if mutation_type==c[0:c.find('_')]:
if column==c[0:c.find('_')]+'_start':
new_col=c[0:c.find('_')]+mutation_type+'_'+column[column.rfind('_')+1:]
elif column==c[0:c.find('_')]+'_end':
new_col=c[0:c.find('_')]+mutation_type+'_'+column[column.rfind('_')+1:]
elif column==c[0:c.find('_')]+'_alt':
new_col=c[0:c.find('_')]+mutation_type+'_'+column[column.rfind('_')+1:]
else:
new_col=column.replace(column[column.rfind('_')+1:],column[column.rfind('_')+1:].upper())
print('%s not found; using %s instead'%(column,new_col))
column=new_col
else:
pass #print('%s found'%(column))
except Exception as e:
print(e)
print('nError occurred when trying to rename %s'%(column))
if ref_start not in mutations.columns.tolist():
print('%s not found; using %s instead'%(ref_start,'RefStart'))
ref_start='RefStart'
if ref_end not in mutations.columns.tolist():
print('%s not found; using %s instead'%(ref_end,'RefEnd'))
ref_end='RefEnd'
if ref_alt not in mutations.columns.tolist():
print('%s not found; using %s instead'%(ref_alt,'RefAlt'))
ref_alt
