Framework for module5 pset (not ready for delivery yet)

modules/module5/problemset/20231006_module5_problemset.py

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+#%% [markdown]
+# # Module 5 Problem Set
+# Name: **_Your Name_**
+
+#%% [markdown]
+# For this week's problemset, we will be looking at the following study:
+#
+# * Dumitriu, A., Golji, J., Labadorf, A.T. et al. Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease. BMC Med Genomics 9, 5 (2015). [https://doi.org/10.1186/s12920-016-0164-y](https://doi.org/10.1186/s12920-016-0164-y)
+#
+# Summary:
+#
+# Parkinson disease (PD) is marked by the accumulation of specific proteins in neuron aggregates, with previous studies focusing mainly on DNA sequence variants and RNA levels.
+# In this research, both state-of-the-art proteomics and RNA-sequencing technologies were employed to analyze prefrontal cortex samples from PD patients and healthy controls. 
+# The study identified significant differences in proteins and genes between PD and controls, revealing insights into mitochondrial-related pathways and protein folding pathways, and highlighted the benefit of combining multiple genome-wide platforms to gain a deeper understanding of PD pathology.
+#
+# You will be locating the data and metadata associated with this study, and performing some basic quality control analyses on a subset of the RNA-Seq data.
+#
+#
+# ## 1. Data Access:
+#
+# 
+
+#%% [markdown]
+# 1.1 What is the GEO accession number for this study?  Describe the different types of data that the authors have made available under this study accession number?
+
+#%% [markdown]
+#
+#
+
+#%% [markdown]
+# Visit the GEO(Gene Expression Omnibus) database and locate the GEO series with the above accession number.  What is the url of the GEO series for this dataset?
+
+#%% [markdown]
+# How many samples (in total, all samples) are present in this GEO series?
+
+#%% [markdown]
+#
+
+#%% [markdown]
+# Open the 'SRA Run Selector' for this series and download the metadata (sample information) for this study. 
+#
+# Load the metadata into a pandas dataframe, show your work below and print the first 5 rows of the dataframe.
+
+#%%
+import pandas as pd
+
+#%% [markdown]
+#
+#
+
+
+#%% [markdown]
+# Locate and download the .fastq file associated with the sample named `C_0003`. We have provided a function below that will download the file for you, given the SRA `RUN` accession number (SRR...) for a given sample. 
+#
+# You will need to provide the SRA `RUN` accession number for the sample you selected above to the function below.
+# 
+# **Note:** This file is ~2.2Gb so it may take some time to download and will take up space on your computer.
+
+#%%
+from urllib.request import urlretrieve
+
+def download_fastq(SRRAccession):
+    baseURL = "https://www.be-md.ncbi.nlm.nih.gov/Traces/sra-reads-be/fastq?acc="
+    fname = SRRAccession + ".fastq.gz"
+    path, headers = urlretrieve(baseURL + SRRAccession, fname)
+    return path
+
+# Use the function above to download the fastq.gz file for the sample you selected
+
+
+#%% [markdown]
+
+
+
+#%% [markdown]
+# 2. FastQC Analysis
+# After downloading the FASTQ file, perform a quality control check using FastQC.
+# 
+# a. Provide a summary of the basic statistics tab.
+# b. Which regions of the reads, if any, are flagged as having poor quality?
+# c. Interpret the "Per base sequence content" graph. Are there any irregularities? What could they indicate?
+# d. Comment on the "Overrepresented sequences" section. Are there any overrepresented sequences? If so, what might be the cause?