###### tags: `groupthink` # Center for Viral Evolution (CVE) Viruses are as old as life itself, infecting everything from bacteria to humans. Since the very beginning, viruses have left an indelible imprint on the human genome, human history, and medical research. SARS-CoV-2, the viral pathogen responsible for the COVID-19 pandemic, is just the latest in a long line of highly impactful human pathogens, including smallpox, Influenza A virus (IAV) and HIV-1. By some estimates, close to 30% of human proteins are involved in combating viral infections. Viral pathogens, especially RNA viruses such (HIV-1, IAV, SARS-CoV-2), mutate very quickly, with some viruses generating every possible single-nuclotide mutation in each new viral generation. Immune reponses, antiviral drugs, competition between viral strains, transmission between species and hosts create some of strongest evolutionary forces that have been reported in evolutionary biology. Viruses are also some of the most sequenced organisms in existence (e.g. ~15M SARS-CoV-2 genomes have been sequenced in 2020-2022). **The mission of the Center for Viral Evolution at Temple University is to** 1. Create computational and statistical learning approaches for the analysis of genomic data from rapidly evolving viral pathogens. 2. Develop scalable software tools for processing large volumes of viral sequence data and deliver actionable and interpretable results. 3. Apply these tools and techniques to learn how past viral evolution informs their present ability to adapt to our responses, and predict likely future paths viruses may take. ### Examples of CVE success 1. Software tools (e.g. [HyPhy](http://www.hyphy.org) and [Datamonkey](http://www.datamonkey.org)) and statistical methods for studying natural selection, recombination, and other evolutionary features of viral genomes are "gold-standard" tools (>10000 citations), and have been relied upon by the research community to study every significant human and animal pathogen. For example, in 2020-2022 the Datamonkey server ran well over 20,000 analyses submitted by researchers worldwide on SARS-CoV-2 data for free, enabling "real-time" research to help combat the pandemic. 2. Our team has developed methods and tools powering national and international public health efforts to reduce HIV-1 tranmsssion and advance towards its eventual eradication by tracking clusters of transmission in the US ([Secure HIV-TRACE](https://secure.hivtrace.cdc.gov), used by the US CDC) and other countries (e.g., Mexico). 3. Since early 2020, members of CVE have been very active in SARS-CoV-2 research, leading or contributing to studies on - Rapid (within two weeks) analysis of the potential impact of many mutations in the Omicron genome on its fitness and transmissibility. - The origin and selection on key variants of conern (beta, gamma, omicron) - Analytical tools (RASCL, Stanford CoV-RDB) for studying the evolution of viral variants and tracking the importance of individual mutations. - Approaches to predict near-term evolution of viral variants - Investigations into viral origins and ancestral sequences. 4. Members of CVE have lead or contributed to research on related Coronaviruses that infect animals or have jumped between animals and humans - Detection of selection in a Canine Coronavirus associated with the jump from dogs to humans. - Characterization of selection in Feline Coronaviruses associated with increased pathogenicity within host. 5. Computational approaches to improve vaccine design, and understand the development of drug resistance.
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SELEXTIAL: A SARS-Cov-2 selection analysis framework

BW Indexed in: https://hackmd.io/@hannahkimincompbio/Sk9T_TIBY Writeup leader: Steven WeaverGoogle doc draft 1: https://docs.google.com/document/d/1ERRQVBIyBt_98uRQ7f4EvkgT1L2xJ9pNT-pmHBd1MzA/edit?usp=sharing Google doc draft 2: https://docs.google.com/document/d/1rnGZZZrcIzI6YtZlFgXri3j-mknFHXHonn_kK8h8U_g/edit?usp=sharing Project board: https://github.com/users/stevenweaver/projects/2/views/1 Authors: Sergei Pond, Steven Weaver, Jordan D Zehr, Alexander Lucaci, Hannah Kim, Avery Selberg Institutions: iGEM Potential delivery date: November 19th, 2021 (earliest) Abstract Write last

1/7/2023
Topics in bioinformatics HW assignment

Your task is to write a Python script which implements a simple Neighbor Joining algorithm for phylogeny inference based on the Jukes Cantor distance, that works reasonably fast for ~20 or so sequences. Do not use "prepackaged" routines from BioPython, other than to read sequence files. Input : a FASTA multiple sequence alignment Output: inferred phylogenetic tree with branch lengths (you can choose the format, but Newick format is standard) For example $python3 NJ.py --msa test.fas ... (((human:0.01, chimp: 0.02): 0.03, gorilla : 0.03, orangutan : 0.03) : 0.01, gibbon : 0.05)

10/24/2022
Notes on selection analyses of the nAChR epitope

From Drabeck et el "Tall" dataset. This dataset has unusual dimensions: short (30) codons and relatively many (199) sequences. This creates some statistical issues that could potentially be impactful. Many branch lengths are =0 (for example, HyPhy collapses 130 internal tree branches because they are 0). This is not biologically realistic. If possible, it would be better to estimate branch lengths from a longer gene alignment, even if not all species are present. The precision with which non-0 branch lengths are estimated is degraded (likely biased). This could create downstream issues with all method. Methods which draw power from sequence length (e.g. aBSREL, BUSTED, and also PAML), are going to suffer power loss. Basic data exploration.

9/19/2022
BUSTED-PH simulation

What you need: A labeled tree. Something like this Parameter settings. We are going to basically ignore everything except ω distributions here Foreground distribution (3 bins) Background distribution (3 bins) Suggested distributions are below. Simulated data. For this, we can use https://github.com/veg/hyphy-analyses/tree/master/SimulateMG94 An example call (notice how the distribution is specified; the one here is ω = 0.1 (weight 0.5), ω = 0.5 (weight 0.3), ω = 1 (weight 0.2)) would be hyphy ~/Development/hyphy-analyses/SimulateMG94/SimulateMG94.bf --model BS-REL --tree marine.txt --output sims/BSREL --replicates 100 --branch-variation bs-rel --omegas 0.1,0.5,0.5,0.3,1,0.2 --omegas-Foreground 0.1,0.5,0.5,0.3,1,0.2

9/16/2022
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