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README.md
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README.md
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# locigenesis
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# locigenesis
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locigenesis is a tool that generates a human T-cell receptor (TCR), runs
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locigenesis is a tool that generates a human T-cell receptor (TCR), runs it through a sequence reader simulation tool and extracts CDR3.
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it through a sequence reader simulation tool and extracts CDR3.
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The goal of this project is to generate both HVR sequences with and
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The goal of this project is to generate both HVR sequences with and without sequencing errors, in order to create datasets for a Machine Learning algorithm.
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without sequencing errors, in order to create datasets for a Machine
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Learning algorithm.
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<a id="orgb4db211"></a>
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## Technologies
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## Technologies
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- [immuneSIM](https://github.com/GreiffLab/immuneSIM/): in silico
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- [immuneSIM](https://github.com/GreiffLab/immuneSIM/): in silico generation of human and mouse BCR and TCR repertoires
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generation of human and mouse BCR and TCR repertoires
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- [CuReSim](http://www.pegase-biosciences.com/curesim-a-customized-read-simulator/): read simulator that mimics Ion Torrent sequencing
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- [CuReSim](http://www.pegase-biosciences.com/curesim-a-customized-read-simulator/):
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read simulator that mimics Ion Torrent sequencing
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<a id="orgace1e30"></a>
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## Installation
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## Installation
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This project uses [Nix](https://nixos.org/) to ensure reproducible
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This project uses [Nix](https://nixos.org/) to ensure reproducible builds.
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builds.
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1. Install Nix (compatible with MacOS, Linux and
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1. Install Nix (compatible with MacOS, Linux and [WSL](https://docs.microsoft.com/en-us/windows/wsl/about)):
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[WSL](https://docs.microsoft.com/en-us/windows/wsl/about)):
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```bash
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curl -L https://nixos.org/nix/install | sh
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curl -L https://nixos.org/nix/install | sh
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```
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2. Clone the repository:
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1. Clone the repository:
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```bash
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git clone https://git.coolneng.duckdns.org/coolneng/locigenesis
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git clone https://git.coolneng.duckdns.org/coolneng/locigenesis
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```
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3. Change the working directory to the project:
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1. Change the working directory to the project:
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```bash
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cd locigenesis
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cd locigenesis
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```
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4. Enter the nix-shell:
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1. Enter the nix-shell:
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```bash
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nix-shell
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nix-shell
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```
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After running these commands, you will find yourself in a shell that
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After running these commands, you will find yourself in a shell that contains all the needed dependencies.
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contains all the needed dependencies.
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<a id="org531bad5"></a>
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## Usage
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## Usage
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An execution script that accepts 2 parameters is provided, the following
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An execution script that accepts 2 parameters is provided, the following command invokes it:
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command invokes it:
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```bash
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./generation.sh <number of sequences> <number of reads>
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./generation.sh <number of sequences> <number of reads>
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```
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- \<number of sequences\>: an integer that specifies the number of
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- <number of sequences>: an integer that specifies the number of different sequences to generate
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different sequences to generate
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- <number of reads>: an integer that specifies the number of reads to perform on each sequence
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- \<number of reads\>: an integer that specifies the number of reads
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to perform on each sequence
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The script will generate 2 files under the data directory:
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The script will generate 2 files under the data directory:
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|HVR.fastq | curesim-HVR.fastq |
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<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
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|:----:|:-----:|
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|Contains the original CDR3 sequence|Contains CDR3 after the read simulation, with sequencing errors |
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<colgroup>
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<col class="org-left" />
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<col class="org-left" />
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</colgroup>
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<tbody>
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<tr>
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<td class="org-left">HVR.fastq</td>
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<td class="org-left">Contains the original CDR3 sequence</td>
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</tr>
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<tr>
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<td class="org-left">CuReSim-HVR.fastq</td>
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<td class="org-left">Contains CDR3 after the read simulation, with sequencing errors</td>
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</tr>
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</tbody>
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</table>
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@@ -34,11 +34,7 @@ parse_metadata <- function(metadata) {
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#' @return A \code{character} containing the gene sequence
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#' @return A \code{character} containing the gene sequence
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match_id_sequence <- function(names, vdj_segments, id) {
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match_id_sequence <- function(names, vdj_segments, id) {
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matches <- grep(names, pattern = id)
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matches <- grep(names, pattern = id)
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if(id == "TRBJ2-2"){
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row <- matches[1]
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row <- matches[2]
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} else {
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row <- matches[1]
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}
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return(as.character(vdj_segments[row]))
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return(as.character(vdj_segments[row]))
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}
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}
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@@ -110,9 +106,8 @@ get_cys_coordinates <- function(alignment) {
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insertion <- unlist(Biostrings::insertion(alignment))
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insertion <- unlist(Biostrings::insertion(alignment))
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deletion <- unlist(Biostrings::deletion(alignment))
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deletion <- unlist(Biostrings::deletion(alignment))
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delta_coordinates <- handle_indels(insertion, deletion, cys, alignment)
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delta_coordinates <- handle_indels(insertion, deletion, cys, alignment)
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read_start <- unlist(start(Biostrings::Views(alignment)))
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cys_start <- cys$start + delta_coordinates$start
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cys_start <- cys$start + delta_coordinates$start + read_start - 1
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cys_end <- cys$end + delta_coordinates$end
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cys_end <- cys$end + delta_coordinates$end + read_start
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return(list("start" = cys_start, "end" = cys_end))
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return(list("start" = cys_start, "end" = cys_end))
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}
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}
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@@ -131,7 +126,7 @@ get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
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)
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)
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cys_coordinates <- parallel::mclapply(v_alignment, FUN = get_cys_coordinates)
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cys_coordinates <- parallel::mclapply(v_alignment, FUN = get_cys_coordinates)
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cys_df <- as.data.frame(do.call(rbind, cys_coordinates))
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cys_df <- as.data.frame(do.call(rbind, cys_coordinates))
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remaining <- Biostrings::subseq(sequences, start = unlist(cys_df$end) + 1)
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remaining <- Biostrings::subseq(sequences, start = unlist(cys_df$end))
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j_alignment <- parallel::mcmapply(remaining,
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j_alignment <- parallel::mcmapply(remaining,
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df$j_seq,
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df$j_seq,
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FUN = align_sequence,
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FUN = align_sequence,
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@@ -150,4 +145,4 @@ get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
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data <- parse_data(file = "data/curesim_sequence.fastq")
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data <- parse_data(file = "data/curesim_sequence.fastq")
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hvr <- get_hvr_sequences(sequences = data[[1]], vdj_segments = data[[2]])
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hvr <- get_hvr_sequences(sequences = data[[1]], vdj_segments = data[[2]])
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Biostrings::writeXStringSet(hvr, "data/curesim-HVR.fastq", format = "fastq")
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Biostrings::writeXStringSet(hvr, "data/CuReSim-HVR.fastq", format = "fastq")
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