DOI: 10.5061/dryad.x3ffbg7qx
title: Data from: Deep mutational scanning of HBV reveals a mechanism for cis preferential reverse transcription

description: Hepatitis B virus (HBV) is a small double-stranded DNA virus that
 chronically infects 296 million people. Over half of its compact genome
 encodes protein in two overlapping reading frames, and during evolution,
 multiple selective pressures can act on shared nucleotides. This study
 combines an RNA-based HBV cell culture system with deep mutational
 scanning to uncouple cis- and trans-acting sequence requirements
 in the HBV genome. The results support a leaky ribosome scanning model for
 polymerase translation, provide a fitness map of the HBV polymerase at
 single nucleotide resolution, and identify conserved prolines adjacent to
 the HBV polymerase termination codon that stall ribosomes. Further
 experiments indicated that stalled ribosomes tether the nascent polymerase
 to its template RNA, ensuring cis-preferential RNA packaging and reverse
 transcription of the HBV genome.
description: # Data from: Deep mutational scanning of HBV reveals a mechanism for cis
 preferential reverse transcription [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.x3ffbg7qx) The purpose of this
 dataset is to provide the analysis software, the raw pre-processed
 experimental data files, and the processed result files used in the paper
 “Deep mutational scanning of HBV reveals a mechanism for cis preferential
 reverse transcription”. For detailed information on the experimental
 design, please refer to the paper. Briefly, mutants of the hepatitis B
 virus (HBV) were generated (input population) and transfected into cell
 cultures. In cell culture, HBV mutants were either depleted or enriched
 based on the effects of their mutations (output population). Afterwards,
 both input and output populations were sequenced to quantify the
 enrichment or depletion of each HBV mutant. From these sequencing results,
 so called codoncounts files were generated using barcoded-subamplicon
 sequencing software from Jesse Bloom’s dms_tools2 software package. These
 codoncounts files, simply put, state how often a certain mutant was
 present in the input or output population. The codoncounts files represent
 the main raw pre-processed files in this Dryad dataset
 (/data/codoncounts/). Mutants of HBV are split into groups corresponding
 to certain regions of the HBV genome that were mutated separately. For
 example, one group is called “TP” (in e.g. “TP_plasmid1_codoncounts.csv”),
 meaning that mutations were generated in the terminal protein domain (TP)
 of HBV in that experiment. The term “plasmid” in
 “TP_plasmid1_codoncounts.csv” denotes an input population data file, while
 “cell” in “TP_cell1_codoncounts.csv” indicates an output population data
 file. The number 1 refers to replicate one. There are usually three
 replicates, though the number can vary. A Jupyter Notebook, JNote.ipynb,
 is the primary analysis software file. This notebook takes the codoncounts
 files and calculates the enrichment score of each mutant. Using those
 results, JNote.ipynb generates the majority of figures in the paper,
 including the heatmaps in Figures 2B and 3A. JNote.ipynb also generates
 corresponding data tables with the plotted numeric values for each figure
 in CSV file format (/Fig/CSV/). Additionally, a second Jupyter Notebook is
 for a proline sliding window analysis related to Fig S5C in the paper.
 Please refer to the methods section of the paper for additional
 documentation. Furthermore, the file hbv_pol_model_parameter_details.txt
 contains configurations for natural sequence analysis related to figure
 S3B in the paper. ## Description of the data and file structure After
 unzipping, the dataset R1 contains: * **JNote.ipynb** - Main analysis
 software file. Takes codoncounts files (/data/codoncounts) as input to
 generate most figures and data tables (/Fig/CSV) in the paper. *
 /data/codoncounts - directory containing all codoncounts files. The first
 term denotes the region of the HBV genome that was mutated. The terms
 “cell” or “plasmid” denote the input or output populations, respectively.
 The number refers to the replicate numbering. For example,
 “Core_cell1_codoncounts.csv” is one of the three replicate mutant
 libraries of the Core region in the HBV genome after cell culture
 selection. Column “site” corresponds to the amino acid site position;
 column “wildtype” to the wildtype codon; columns "AAA” to “TTT” to
 the mutation from wildtype, with the sequencing read count given as the
 row value. * /data/other - directory containing additional support files
 necessary for JNote.ipynb * /Fig/CSV – directory containing the
 corresponding data tables of the figures in the paper with the plotted
 numeric values in CSV format. The figure numbering follows the numbering
 in the paper. Please refer to the figure legends in the paper for
 additional information. * **Fig_2B.csv** – Deep mutational scanning of the
 Core region: heatmap X-axis -&gt; column “site” –&gt; amino acid site
 position Y-axis -&gt; column “variable” -&gt; variant mutation codon Color
 scale -&gt; column "logmfactor” -&gt; log2 enrichment factor *
 **Fig_2C.csv** – Deep mutational scanning of the Core region: group
 analysis I X-axis -&gt; column “group” –&gt; certain variants grouped
 Y-axis -&gt; column " logmfactor” -&gt; log2 enrichment factor *
 **Fig_2D.csv** – Deep mutational scanning of the Core region: group
 analysis II X-axis -&gt; column “group” –&gt; certain variants grouped
 Y-axis -&gt; column " logmfactor” -&gt; log2 enrichment factor *
 **Fig_3A.csv** – Deep mutational scanning of Polymerase: heatmap X-axis
 -&gt; column “site” –&gt; amino acid site position Y-axis -&gt; column
 “variable” -&gt; variant mutation codon Color scale -&gt; column
 "logmfactor” -&gt; log2 enrichment factor * **Fig_3D.csv** – Deep
 mutational scanning of Polymerase: group analysis X-axis -&gt; column
 “group” –&gt; certain variants grouped Y-axis -&gt; column
 "norm_abs_logmfactor” -&gt; selective pressure * **Fig_S1B.csv** –
 Nucleotide mutation type bias analysis X-axis -&gt; column “type” -&gt;
 single nucleotide mutation type Y-axis -&gt; column “frac” -&gt;
 percentage of mutants * **Fig_S1C.csv** – Deep mutational scanning of the
 Core region: input plasmid counts heatmap X-axis -&gt; column “site” –&gt;
 amino acid site position Y-axis -&gt; column “variable” -&gt; variant
 mutation codon Color scale -&gt; column "pre_frac” * 1E6 -&gt; log10
 plasmid counts per 1E6 reads * **Fig_S2B.csv** – Deep mutational scanning
 of the Core region: Kozak analysis I X-axis -&gt; column “kozakscore”
 -&gt; Kozak score of C1 Y-axis -&gt; column " logmfactor” -&gt; log2
 enrichment factor of variants affecting C1 Kozak * **Fig_S2C.csv** – Deep
 mutational scanning of the Core region: Kozak analysis II X-axis -&gt;
 column “kozakscore” -&gt; Kozak score of J ORF start Y-axis -&gt; column
 " logmfactor” -&gt; log2 enrichment factor of variants affecting J
 ORF start Kozak * **Fig_S2D.csv** –&gt; Deep mutational scanning of the
 Core region: Kozak analysis III X-axis -&gt; column “group” -&gt; Group
 C/T or A/G Y-axis -&gt; column " logmfactor” -&gt; log2 enrichment
 factor * **Fig_3A.csv** – Deep mutational scanning of Polymerase: input
 plasmid counts heatmap X-axis -&gt; column “site” –&gt; amino acid site
 position Y-axis -&gt; column “variable” -&gt; variant mutation codon Color
 scale -&gt; column "pre_frac” * 1E6 -&gt; log10 plasmid counts per
 1E6 reads * **Fig_3A.csv** – Fitness of polymerase variants obtained from
 natural sequences: heatmap X-axis -&gt; column “pos” –&gt; amino acid site
 position Y-axis -&gt; column “mutant” -&gt; variant mutation codon Color
 scale -&gt; column " prediction_independent” -&gt; log2 factor
 natural enrichment * **PDB_files_Pol.zip** - contains files related to
 three-dimensional models of HBV polymerase (Pol) generated using
 AlphaFold2 related to Figure 3BC in the paper. *
 **hbv_pol_model_parameter_details.txt** - are parameters for the analysis
 of natural sequences in Figure S3B. * **proline_sliding_window.ipynb** -
 second Jupyter Notebook for a proline sliding window analysis for Figure
 S5C in the paper. ## Sharing/Access information Link to other publicly
 accessible locations of the data: *
 [https://github.com/HBV-DMS/R1](https://github.com/HBV-DMS/R1) ##
 Code/Software The main Jupyter Notebook, JNote.ipynb, can be run to
 re-analyze the experimental data starting from codoncounts files and to
 generate the majority of figures in the paper. The simplest way to run
 JNote.ipynb is to open JNote.ipynb through Google Colab by opening
 [https://colab.research.google.com/github/HBV-DMS/R1/blob/main/JNote.ipynb](https://colab.research.google.com/github/HBV-DMS/R1/blob/main/JNote.ipynb) and following the instructions in the notebook.