Overview of Tennis US Open Women's Single Qualification USA
The US Open Women's Single Qualification is a highly anticipated event in the tennis calendar, showcasing emerging talents from around the globe. This tournament serves as a gateway for players to compete in one of the Grand Slam events, offering a platform for rising stars to make their mark. With matches updated daily, fans and enthusiasts can follow the latest developments and expert betting predictions to enhance their viewing experience.
The qualification rounds are not just a prelude to the main event but a battleground where future champions are forged. The intensity and competitiveness of these matches are unparalleled, making it a must-watch for tennis aficionados.
Daily Match Updates
Keeping up with the fast-paced action of the US Open Women's Single Qualification requires staying updated with daily match results. Our platform provides real-time updates, ensuring you never miss a moment of the excitement. Whether you're following your favorite player or scouting new talent, our comprehensive coverage has you covered.
- Live Scores: Get instant access to live scores and match progress.
- Match Highlights: Watch key moments and thrilling rallies from each match.
- Player Statistics: Dive into detailed stats for each player, including serve accuracy, break points converted, and more.
Expert Betting Predictions
For those looking to add an extra layer of excitement to their viewing experience, expert betting predictions are available. Our team of seasoned analysts provides insights and forecasts based on comprehensive data analysis, player form, and historical performance. Whether you're a seasoned bettor or new to the scene, these predictions can guide your decisions.
- Match Odds: Discover the latest odds for each match and understand the betting landscape.
- Prediction Analysis: Read in-depth analyses explaining why certain players are favored in upcoming matches.
- Betting Tips: Receive strategic tips from experts to enhance your betting strategy.
Featured Players
The US Open Women's Single Qualification features a diverse lineup of players from across the globe. Here are some of the standout athletes to watch:
- American Favorites: Keep an eye on promising American talents who are eager to make their mark on home soil.
- International Contenders: Witness international players bringing their unique styles and strategies to the court.
- Rising Stars: Discover new faces who could become future stars of women's tennis.
Tournament Format
Understanding the tournament format is key to appreciating the challenges faced by players during the qualification rounds. The US Open Women's Single Qualification consists of multiple rounds, with players battling it out for a limited number of spots in the main draw.
- Initial Rounds: Players compete in initial rounds to secure their place in subsequent stages.
- Semifinals: The stakes are higher as players vie for one of the coveted spots in the main draw.
- Finals: The ultimate showdown where only two players will earn their place in the US Open main event.
Historical Insights
Delving into the history of the US Open Women's Single Qualification reveals fascinating stories of triumphs and upsets. Many past qualifiers have gone on to achieve great success in their careers, making this tournament a crucial stepping stone.
- Past Winners: Explore profiles of past winners who have made significant impacts in professional tennis.
- Memorable Matches: Relive iconic matches that have left an indelible mark on tennis history.
- Trends and Patterns: Analyze trends and patterns that have emerged over the years in qualification performances.
Interactive Features
Enhance your experience with interactive features designed to engage fans and provide deeper insights into the tournament.
- Tournament Bracket: Navigate through an interactive bracket to track player progress throughout the qualification rounds.
- Player Profiles: Access detailed profiles for each player, including biographies, career highlights, and head-to-head records.
- User Polls: Participate in polls and share your predictions with fellow fans.
Tips for Fans
Whether you're attending live or watching from home, here are some tips to enhance your viewing experience:
- Schedule Your Viewing: Plan your schedule around key matches and finals to ensure you don't miss any action.
- Fan Engagement: Join online forums and social media groups to connect with other fans and share your thoughts.
- Leverage Expert Analysis: Incorporate expert insights into your viewing strategy to gain a deeper understanding of the game dynamics.
The Future of Women's Tennis
The US Open Women's Single Qualification is more than just a tournament; it's a celebration of women's tennis and its bright future. As new talents emerge and established players continue to inspire, this event plays a pivotal role in shaping the sport's landscape.
- Growing Popularity: The increasing popularity of women's tennis is evident in record-breaking attendance figures and viewership numbers.
- Diversity and Inclusion: The tournament highlights the diversity within women's tennis, showcasing players from various backgrounds and cultures.
- Innovation in Training: New training techniques and technologies are being adopted by players to enhance performance and longevity in their careers.
Frequently Asked Questions (FAQs)
<|repo_name|>erikgunderson/CompBioProject<|file_sep|>/README.md
# CompBioProject
This repository contains files used for our Computational Biology project.
### Members:
* Erik Gunderson
* Kelsey LaRue
### Research Topic:
The effect of reduced glucose availability on protein translation
### Background Information:
In order for cells to survive they need energy which they get from glucose through glycolysis followed by either aerobic or anaerobic respiration depending on oxygen availability. If glucose is not available then cells may use other sources such as lipids or amino acids.
If there is low glucose availability then cells may also go through autophagy which allows them to recycle cellular components such as proteins so that they can be used as an energy source.
The focus of our research was how low glucose availability affects protein translation by affecting ribosome biogenesis.
Ribosomes consist of two subunits (40S &60S) which bind together during translation after mRNA is brought into them by initiation factors. Ribosomes consist primarily of rRNA with some proteins bound along side them.
We looked at two genes (ATP5F1A & ATP5F1B) which encode proteins that are part of ATP synthase complex which is located in mitochondria.
#### ATP5F1A:
This gene encodes subunit alpha of ATP synthase which is found within mitochondria. It plays an important role in oxidative phosphorylation where it helps synthesize ATP using energy derived from electrons being passed down electron transport chain.
#### ATP5F1B:
This gene encodes subunit beta (also called epsilon) which binds tightly with alpha subunit forming core complex required for ATP synthase activity.
### Data Source:
We used [The Cancer Genome Atlas](https://cancergenome.nih.gov/) data set which has been sequenced using RNA-seq.
### Data Analysis:
We used RStudio (R version: R-3.4.3) & Python (Python version: Python-3.6.4) for data analysis.
Our data analysis consisted mainly of looking at differential gene expression between high glucose & low glucose samples by plotting gene expression vs sample group & doing statistical tests such as t-tests & Wilcoxon rank sum tests on them.
For more information please see [Data Analysis](https://github.com/erikgunderson/CompBioProject/tree/master/Data%20Analysis).
### Results:
Our results showed that there was no significant difference between high glucose & low glucose samples when we looked at ATP5F1A & ATP5F1B genes individually or together using t-tests or Wilcoxon rank sum tests.
When we looked at other genes involved in ribosome biogenesis we saw that some were upregulated while others were downregulated under low glucose conditions but these differences were not statistically significant either using t-tests or Wilcoxon rank sum tests.
For more information please see [Results](https://github.com/erikgunderson/CompBioProject/tree/master/Results).
### References:
* [Ribosome Structure](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271983/)
* [ATP Synthase Complex](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109189/)
* [RNA-Seq Analysis](https://cancer.sanger.ac.uk/cancergenome/projects/tcga/)
<|file_sep|># Data Analysis
## Data Set Information:
The TCGA RNA-Seq dataset consists of RNA-seq data from several different types cancer samples (i.e., colon adenocarcinoma (COAD), rectal adenocarcinoma (READ), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), breast invasive carcinoma (BRCA), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), prostate adenocarcinoma (PRAD), bladder urothelial carcinoma (BLCA), head & neck squamous cell carcinoma (HNSC), uterine corpus endometrial carcinoma (UCEC)). Each type contains multiple patients who had tumor samples taken before treatment along with matched normal samples taken from non-tumor tissue.
The data was generated using Illumina HiSeq2000 sequencing platform which produces paired end reads with average length around ~100 bp per read pair.
## Preprocessing Steps:
To prepare our dataset we did following preprocessing steps:
* Quality control checks were performed on raw reads using FastQC tool ([link](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) ) which checks quality scores per base position along sequence reads as well as presence/absence issues like adapter contamination etc.. If any issues were found then appropriate actions were taken like trimming adapters if present etc..
* Reads were aligned against human genome hg19 reference using STAR aligner ([link](https://github.com/alexdobin/STAR)) tool which uses seed-and-extend approach for efficient alignment allowing mismatches between read sequences & reference genome allowing gaps due presence insertion/deletion events etc..
* Post alignment processing was done using Picard tools ([link](https://broadinstitute.github.io/picard/) ) package which includes functions like marking duplicates removing unmapped reads etc.. These steps help improve accuracy downstream analyses such as differential gene expression analysis etc..
## Differential Gene Expression Analysis:
To identify differentially expressed genes between high glucose & low glucose conditions we used DESeq2 package ([link](https://bioconductor.org/packages/release/bioc/html/DESeq.html)) developed by Love et al.,2014). This package uses negative binomial distribution model based approach for modeling counts data allowing us account overdispersion due biological variability among samples etc.. We performed normalization steps prior running DESeq function call so that counts could be compared across different samples without being affected by sequencing depth differences etc.. We then performed differential gene expression analysis between high glucose vs low glucose conditions using DESeq function call provided by this package followed by extracting top ranked genes based on p-value cutoff value set at .05 after correcting multiple testing using Benjamini-Hochberg method ([link](https://en.wikipedia.org/wiki/Benjamini%E2%80%93Hochberg_procedure)).
## Results:
After performing differential gene expression analysis between high glucose vs low glucose conditions we obtained list containing top ranked genes based on p-value cutoff value set at .05 after correcting multiple testing using Benjamini-Hochberg method ([link](https://en.wikipedia.org/wiki/Benjamini%E2%80%93Hochberg_procedure)). We then plotted these results showing log fold change vs -log10(pvalue) scatter plot where each point represents one gene & color coded based on whether it was upregulated downregulated or not significant under given condition i.e., red points represent upregulated genes blue points represent downregulated genes green points represent not significant genes respectively under given condition i.e., high glucose vs low glucose condition respectively). We observed that many genes were differentially expressed between high glucose vs low glucose conditions however none reached statistical significance level after correcting multiple testing using Benjamini-Hochberg method ([link](https://en.wikipedia.org/wiki/Benjamini%E2%80%93Hochberg_procedure)) even though some showed trend towards being upregulated/downregulated under given condition i.e., high glucose vs low glucose condition respectively).
## Conclusion:
Based on our findings we conclude that although many genes were differentially expressed between high glucose vs low glucose conditions none reached statistical significance level after correcting multiple testing using Benjamini-Hochberg method ([link](https://en.wikipedia.org/wiki/Benjamini%E2%80%93Hochberg_procedure)). This suggests that although there may be some biological differences between high glucose vs low glucose conditions they may not be strong enough enough cause significant changes at molecular level i.e., changes seen at transcriptome level may not necessarily translate into changes seen at protein level etc..
<|repo_name|>erikgunderson/CompBioProject<|file_sep|>/Data Analysis/DataAnalysis.Rmd
---
title: "Data Analysis"
author: "Erik Gunderson"
date: "4/17/2018"
output:
html_document:
code_folding: hide
toc: yes
toc_float: yes
---
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
## Data Set Information
The TCGA RNA-Seq dataset consists of RNA-seq data from several different types cancer samples (i.e., colon adenocarcinoma (COAD), rectal adenocarcinoma (READ), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), breast invasive carcinoma (BRCA), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), prostate adenocarcinoma (PRAD), bladder urothelial carcinoma (BLCA), head & neck squamous cell carcinoma (HNSC), uterine corpus endometrial carcinoma (UCEC)). Each type contains multiple patients who had tumor samples taken before treatment along with matched normal samples taken from non-tumor tissue.
The data was generated using Illumina HiSeq2000 sequencing platform which produces paired end reads with average length around ~100 bp per read pair.
## Preprocessing Steps
To prepare our dataset we did following preprocessing steps:
- Quality control checks were performed on raw reads using FastQC tool ([link](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) ) which checks quality scores per base position along sequence reads as well as presence/absence issues like adapter contamination etc.. If any issues were found then appropriate actions were taken like trimming adapters if present etc..
- Reads were aligned against human genome hg19 reference using STAR aligner ([link](https://github.com/alexdobin/STAR)) tool which uses seed-and-extend approach for efficient alignment allowing mismatches between read sequences & reference genome allowing gaps due presence insertion/deletion events etc..
- Post alignment processing was done using Picard tools ([link](https://broadinstitute.github.io/picard/) ) package which includes functions like marking duplicates removing unmapped reads etc.. These steps help improve accuracy downstream analyses such as differential gene expression analysis etc..
## Differential Gene Expression Analysis
To identify differentially expressed genes between high glucose & low glucose conditions we used DESeq2 package ([link](https://bioconductor.org/packages/release/bioc/html/DESeq.html)) developed by Love et al.,2014). This package uses negative binomial distribution model based approach for modeling counts data allowing us account overdispersion due biological variability among samples etc.. We performed normalization steps prior running DESeq function call so that counts could be compared across different samples without being affected by sequencing depth differences etc.. We then performed differential gene expression analysis between high glucose vs low glucose conditions using DESeq function call provided by this package followed by extracting top ranked genes based on p-value cutoff value set at .05 after correcting multiple testing using Benjamini-Hochberg method ([link](https://en.wikipedia.org/wiki/Benjamini%E2%80%93Hochberg_procedure)).
{r message=FALSE}
library(DESeq2)
library(ggplot2)
library(ggrepel)
library(gridExtra)
#Reads count matrix
count_data <- read.csv("data/data_normalized.csv", header = TRUE)
#colnames(count_data)[1] <- "geneid"
#Metadata file
metadata <- read.csv("data/metadata.csv", header = TRUE)
#colnames(metadata)[1] <- "sampleid"
#Creates DESeq object
dds <- DESeqDataSetFromMatrix(countData = count_data,
colData = metadata,
design = ~ group)
#Normalizes count data
dds <- estimateSizeFactors(dds)
#Performs differential expression analysis
dds <- DESeq(dds)
#Extracts results table sorted by p-value
results_table <- results(dds)
results_table <- results
