Course project for the Bioinformatics course.
The novel coronavirus responsible for COVID-19, SARS-CoV-2, expanded to reportedly over 30.6 million confirmed cases worldwide by September 20, 2020. The global SARS-CoV-2 pandemic highlights the importance of tracking viral transmission dynamics in real-time. Through September 2020, researchers have obtained genetic sequences of SARS-CoV-2 from over 100 thousand samples from infected individuals worldwide. Since the virus readily mutates, each sequence of an infected individual contains useful mutational signature information linked to the virus subtypes. But, there are over 30,000 bases in the full SARS-CoV-2 genome, so tracking genetic variants on a whole-sequence basis becomes unwieldy. EESI laboratory led by Dr. Gail Rosen has developed an efficient tool to quickly idenitfy and label genetic variants, a.k.a., "subtypes" of SARS-CoV-2 sequences. This method defines a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals, called an Informative Subtype Marker or ISM. This tool defines viral subtypes for each ISM, and analyze the regional distribution of subtypes to track the progress of the pandemic. ncov_ism (Novel Coronavirus Informative Subtype Marker) is an efficient framework for genetic subtyping of a pandemic virus and implement it for SARS-CoV-2, the novel coronavirus that causes COVID-19. For detailed information about this method, please refer to the manuscript pubilished in PLOS Computational Biology, Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization. A command line tool, ncov-ism, based on the method described in the paper is made available at here. Scientists have detected SARS-CoV-2 virus in the wastewater in California (see the preprint here). And in their preprint, the results indicates that their wastewater sequencing can provide evidence for recent introductions of viral lineages before they are detected by local clinical sequencing. Their method mainly considered single nucleotide variant (SNV). The goal of this project is to use ncov-ism method developed by EESI laboratory to label SARS-CoV-2 subtypes and compare them with subtypes identified worldwide.
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(Due by September 30th!!) Register for an account at GISAID website to get access to the latest sequence data. Also, register for a CIPRES account. Please check the privacy policy of GISAID website and make sure you don't share the GISAID data with anyone else who doesn't have a GISAID account for hCoV-19 data.
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In lieu of running on Proteus (and installing and running the default ncov_ism), you will be given the supporting files generated from ncov_ism. ncov_ism generates ISM positions in ISM_annotation.txt file and ISM subtypes for all sequences in ISM_df_with_correction.csv file. You will be given these files for the next step.
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Combine the SARS-CoV-2 sequences from wastewater project and the reference sequence NC_045512.2 (the combined fasta file can be found here), run multiple sequence alignment (MAFFT) on CIPRES and then extract the nucleotides at ISM hotspots according to ISM_annotation.txt file from step 2 and form ISMs (subtypes) for those sequences.
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Perform exploratory analysis to answer the following questions:
- What subtype is each sample assigned to?
- How is the quality of the ISMs, do you see any ambiguous bases? If so, why is that?
- Can you find the consensus ISM of these 7 ISMs?
- Can you replace ambiguous bases with nonambiguous ones based on other ISMs from the wastewater project?
- Which region/country in the world does the cleaned ISMs found most abundant?
- These samples are collected in California in different dates. What are the most abundant subtype in California at those dates according to the ISM_df_with_correction.csv file?
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The ISM_df_with_correction.csv file can be used to make ordination to reveal the ISM distribution similarity across different regions in the world (Fig). Please make such PCA plots for data collected by each months from 2020-1 to 2020-9 to show how the similarity of different regions change over time.