45 Commits

Author SHA1 Message Date
ad8abcc4fc Add usage instructions to the README 2021-05-04 01:28:49 +02:00
6440816a87 Remove imperative installation instructions 2021-05-04 00:59:05 +02:00
0e005735bc Create a Nix derivation for CuReSim 2021-05-04 00:57:35 +02:00
4f0936718b Add installation instruction to README 2021-05-03 23:27:19 +02:00
1b6e2d13ea Remove development dependencies 2021-05-03 23:22:16 +02:00
36eb73b458 Add alignment to generation script 2021-05-03 21:51:48 +02:00
81a57657fe Fix HVR end position computation 2021-05-03 21:51:32 +02:00
5afe040592 Isolate HVR sequence and save it to a file 2021-05-03 21:15:40 +02:00
c250c139dd Implement cysteine location in v_alignment 2021-04-27 19:34:01 +02:00
4dec2061fc Generate FASTQ files from the simulated repertoire 2021-04-22 13:59:45 +02:00
4adb92e901 Export original CDR3 to a file 2021-04-22 11:54:58 +02:00
83819b296b Save vj_sequences in a dataframe 2021-04-22 01:18:25 +02:00
a7c1df5ce2 Refactor get_vj_sequence function 2021-04-22 01:17:35 +02:00
81ebd4fbbe Rename function arguments to improve readability 2021-04-21 22:12:29 +02:00
659f0097d8 Get V and J sequences from sequence ID 2021-04-21 21:29:03 +02:00
fb5d781c66 Add space to sequence ID for easier parsing 2021-04-21 21:02:56 +02:00
35406497a3 Format generation script 2021-04-21 20:11:56 +02:00
b771071974 Remove csv from gitignore 2021-04-21 20:11:32 +02:00
2a997a3e5c Rename sequencing_runs to number_of_reads 2021-04-21 20:09:02 +02:00
1020d610d3 Run CuReSim n times for each sequence 2021-04-21 20:00:13 +02:00
5154a35fca Remove sequencing runs argument from repertoire 2021-04-21 19:59:38 +02:00
18ffbf9a75 Add v_call and j_call to sequence ID 2021-04-21 18:51:08 +02:00
82fdfdc6b9 Exchange pattern and subject in the alignment 2021-04-08 18:31:50 +02:00
dd9f7ffde4 Remove redundant HVR sequence construction 2021-04-07 19:49:44 +02:00
e694ee3292 Select the first sequence matching the identifier 2021-04-07 18:41:14 +02:00
e5a7b726a9 Add v_segments and j_segments objects 2021-04-07 18:32:58 +02:00
38b35f7d12 Align full sequences efficiently 2021-04-07 18:31:39 +02:00
f81e4af94e Amplify VDJ sequences to simplify parsing 2021-03-29 22:57:36 +02:00
576597cb04 Remove redundant sequencing runs argument 2021-03-29 20:40:01 +02:00
13f453718d Implement HVR sequence alignment 2021-03-27 09:39:59 +01:00
3a10380d8c Construct a dataframe containing the HVR region 2021-03-25 21:53:49 +01:00
8f5b9ee698 Parse curesim and VDJ sequences from files 2021-03-23 20:54:31 +01:00
66b39485a9 Save vdj alignment sequences to a CSV 2021-03-23 19:35:10 +01:00
97b8914cd5 Add literate programming notebook 2021-03-23 18:24:12 +01:00
bc666a37c7 Delete CuReSim log file after execution 2021-03-11 21:28:58 +01:00
d5bf8f3b72 Remove redundant casting 2021-03-11 21:28:00 +01:00
762791829a Save v_call and j_call to a text file 2021-03-11 21:03:16 +01:00
7b15df7614 Simplify repertoire generation 2021-03-10 12:34:20 +01:00
f4a001f821 Change CuReSim output file extension to FASTQ 2021-03-10 12:33:25 +01:00
d2e45bc00f Change output format to FASTA 2021-03-02 20:08:14 +01:00
deb1066982 Set CuReSim read size to 350 bases 2021-03-02 20:07:05 +01:00
edc4a72425 Update gitignore 2021-02-28 02:24:23 +01:00
42aadb1e28 Add sequencing runs CLI argument 2021-02-28 02:23:58 +01:00
ae5014fd74 Remove a_chain generation 2021-02-28 02:23:28 +01:00
34722f3ebf Add gitignore 2021-02-26 03:11:34 +01:00
9 changed files with 256 additions and 53 deletions

1
.gitignore vendored Normal file
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*.fastq

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* locigenesis * locigenesis
locigenesis is a tool that generates an immune repertoire and runs it through a sequence reader simulation tool, to generate sequencing errors. locigenesis is a tool that generates an immune repertoire and runs it through a sequence reader simulation tool, to generate sequencing errors.
** Installation
This project uses [[https://nixos.org/][Nix]] to ensure reproducible builds.
1. Install Nix (compatible with MacOS, Linux and [[https://docs.microsoft.com/en-us/windows/wsl/about][WSL]]):
#+begin_src shell
curl -L https://nixos.org/nix/install | sh
#+end_src
1. Clone the repository:
#+begin_src shell
git clone https://git.coolneng.duckdns.org/coolneng/locigenesis
#+end_src
3. Change the working directory to the project:
#+begin_src shell
cd locigenesis
#+end_src
4. Enter the nix-shell:
#+begin_src shell
nix-shell
#+end_src
After running these commands, you will find yourself in a shell that contains all the needed dependencies.
** Usage
An execution script that accepts 2 parameters is provided, the following command invokes it:
#+begin_src shell
./generation.sh <number of sequences> <number of reads>
#+end_src
- <number of sequences>: an integer that specifies the number of different sequences to generate
- <number of reads>: an integer that specifies the number of reads to perform on each sequence
The script will generate 2 files under the data directory:
| HVR.fastq | Contains the original CDR3 sequence |
| CuReSim-HVR.fastq | Contains CDR3 after the read simulation, with sequencing errors |

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docs/notebook.org Normal file
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#+TITLE: locigenesis
#+AUTHOR: Amin Kasrou Aouam
#+DATE: 2021-03-10
* Sequence alignment
Our generated sequences contain the full VJ region, but we are only interested in the CDR3 (Complementarity-determining region). We will proceed by delimiting CDR3, using the known sequences of V and J.
#+begin_src R :results value silent
v_segments <- readRDS("data/v_segments.rds")
j_segments <- readRDS("data/j_segments_phe.rds")
#+end_src
#+begin_src R
print(v_segments)
print(j_segments)
#+end_src
#+RESULTS:
#+begin_example
A DNAStringSet instance of length 147
width seq names
[1] 326 GATACTGGAATTACCCAGACAC...ATCTCTGCACCAGCAGCCAAGA TRBV1*01_P
[2] 326 GATGCTGAAATCACCCAGAGCC...ATTTCTGCGCCAGCAGTGAGTC TRBV10-1*01_F
[3] 326 GATGCTGAAATCACCCAGAGCC...ATTTCTGCGCCAGCAGTGAGTC TRBV10-1*02_F
[4] 326 GATGCTGGAATCACCCAGAGCC...ATTTCTGCGCCAGCAGTGAGTC TRBV10-2*01_F
[5] 326 GATGCTGGAATCACCCAGAGCC...ATTTCTGCGCCAGCAGTGAGTC TRBV10-2*02_F
... ... ...
[143] 324 GATACTGGAGTCTCCCAGAACC...GTATCTCTGTGCCAGCACGTTG TRBV7-9*06_(F)
[144] 323 .........................TGTATCTCTGTGCCAGCAGCAG TRBV7-9*07_(F)
[145] 325 GATTCTGGAGTCACACAAACCC...TATTTCTGTGCCAGCAGCGTAG TRBV9*01_F
[146] 325 GATTCTGGAGTCACACAAACCC...TATTTCTGTGCCAGCAGCGTAG TRBV9*02_F
[147] 321 GATTCTGGAGTCACACAAACCC...TTTGTATTTCTGTGCCAGCAGC TRBV9*03_(F)
A DNAStringSet instance of length 16
width seq names
[1] 32 TGGGCGTCTGGGCGGAGGACTCCTGGTTCTGG TRBJ2-2P*01_ORF
[2] 31 TTTGGAGAGGGAAGTTGGCTCACTGTTGTAG TRBJ1-3*01_F
[3] 31 TTTGGTGATGGGACTCGACTCTCCATCCTAG TRBJ1-5*01_F
[4] 31 TTTGGCAGTGGAACCCAGCTCTCTGTCTTGG TRBJ1-4*01_F
[5] 31 TTCGGTTCGGGGACCAGGTTAACCGTTGTAG TRBJ1-2*01_F
... ... ...
[12] 31 TTTGGCCCAGGCACCCGGCTGACAGTGCTCG TRBJ2-3*01_F
[13] 31 TTCGGGCCAGGCACGCGGCTCCTGGTGCTCG TRBJ2-5*01_F
[14] 31 TTCGGGCCAGGGACACGGCTCACCGTGCTAG TRBJ2-1*01_F
[15] 31 TTCGGGCCGGGCACCAGGCTCACGGTCACAG TRBJ2-7*01_F
[16] 31 GTCGGGCCGGGCACCAGGCTCACGGTCACAG TRBJ2-7*02_ORF
#+end_example

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#!/bin/sh #!/bin/sh
usage() { usage() {
echo "usage: generation.sh <number of sequences>" echo "usage: generation.sh <number of sequences> <number of reads>"
exit 1 exit 1
} }
if [ $# != 1 ]; then if [ $# != 2 ]; then
usage usage
fi fi
sequences=$1 sequences=$1
number_of_reads=$2
data_directory="data/" data_directory="data/"
fastq=".fastq"
filename="sequence"
prefix="curesim_" prefix="curesim_"
Rscript src/repertoire.r "$sequences" Rscript src/repertoire.r "$sequences" "$number_of_reads" &&
CuReSim -f "$data_directory$filename$fastq" -o "$data_directory$prefix$filename$fastq"
for file in "$data_directory"*.fastq; do Rscript src/alignment.r
file_name=$(echo "$file" | cut -d / -f 2) rm "$data_directory/log.txt"
java -jar tools/CuReSim.jar -f "$file" -o "$data_directory$prefix$file_name"
done

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with pkgs; with pkgs;
mkShell { let
CuReSim = stdenv.mkDerivation rec {
name = "CuReSim";
version = "1.3";
src = fetchzip {
url =
"http://www.pegase-biosciences.com/wp-content/uploads/2015/08/${name}${version}.zip";
sha256 = "1hvlpgy4haqgqq52mkxhcl9i1fx67kgwi6f1mijvqzk0xff77hkp";
stripRoot = true;
extraPostFetch = ''
chmod go-w $out
'';
};
nativeBuildInputs = [ makeWrapper ];
installPhase = ''
mkdir -pv $out/share/java $out/bin
cp -r ${src} $out/share/java/${name}
makeWrapper ${pkgs.jdk}/bin/java $out/bin/CuReSim --add-flags "-jar $out/share/java/${name}/${name}.jar"
'';
};
in mkShell {
buildInputs = [ buildInputs = [
R R
rPackages.immuneSIM rPackages.immuneSIM
rPackages.Biostrings rPackages.Biostrings
rPackages.stringr
jdk jdk
# Develoment tools CuReSim
rPackages.languageserver
rPackages.lintr
]; ];
} }

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src/alignment.r Normal file
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library(Biostrings)
library(parallel)
parse_data <- function(file) {
reversed_sequences <- Biostrings::readQualityScaledDNAStringSet(file)
sequences <- Biostrings::reverseComplement(reversed_sequences)
vj_segments <- union(
readRDS("data/v_segments.rds"),
readRDS("data/j_segments_phe.rds")
)
return(list(sequences, vj_segments))
}
parse_metadata <- function(metadata) {
id_elements <- unlist(strsplit(metadata, split = " "))
v_identifier <- id_elements[2]
j_identifier <- id_elements[3]
return(list(v_id = v_identifier, j_id = j_identifier))
}
match_id_sequence <- function(names, vdj_segments, id) {
matches <- grep(names, pattern = id)
row <- matches[1]
return(as.character(vdj_segments[row]))
}
get_vj_sequence <- function(metadata, names, vdj_segments) {
identifiers <- parse_metadata(metadata)
v_sequence <- match_id_sequence(names, vdj_segments, id = identifiers["v_id"])
j_sequence <- match_id_sequence(names, vdj_segments, id = identifiers["j_id"])
return(list(v_seq = v_sequence, j_seq = j_sequence))
}
fetch_vj_sequences <- function(sequences, vdj_segments) {
vj_sequences <- sapply(names(sequences),
names(vdj_segments),
vdj_segments,
FUN = get_vj_sequence
)
results <- data.frame(t(vj_sequences))
return(results)
}
align_sequence <- function(sequence, vdj_segment) {
return(Biostrings::pairwiseAlignment(
subject = sequence,
pattern = vdj_segment,
type = "global-local",
gapOpening = 1
))
}
handle_indels <- function(insertion, deletion, cys, alignment) {
ins_start <- sum(Biostrings::width(deletion[start(deletion) <= cys$start]))
ins_end <- sum(Biostrings::width(deletion[end(deletion) <= cys$end]))
shift_num <- c(0, cumsum(Biostrings::width(insertion))[-length(ins_start)])
shifted_ins <- IRanges::shift(insertion, shift_num)
gaps <- sum(width(shifted_ins[end(shifted_ins) < cys$start + ins_start])) +
nchar(stringr::str_extract(alignedSubject(alignment), "^-*"))
return(list("start" = ins_start - gaps, "end" = ins_end - gaps))
}
get_cys_coordinates <- function(alignment) {
cys <- list("start" = 310, "end" = 312)
insertion <- unlist(Biostrings::insertion(alignment))
deletion <- unlist(Biostrings::deletion(alignment))
delta_coordinates <- handle_indels(insertion, deletion, cys, alignment)
cys_start <- cys$start + delta_coordinates$start
cys_end <- cys$end + delta_coordinates$end
return(list("start" = cys_start, "end" = cys_end))
}
get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
df <- fetch_vj_sequences(sequences, vdj_segments)
v_alignment <- parallel::mcmapply(sequences,
df$v_seq,
FUN = align_sequence,
mc.cores = cores
)
cys_coordinates <- parallel::mclapply(v_alignment, FUN = get_cys_coordinates)
cys_df <- as.data.frame(do.call(rbind, cys_coordinates))
remaining <- Biostrings::subseq(sequences, start = unlist(cys_df$end))
j_alignment <- parallel::mcmapply(remaining,
df$j_seq,
FUN = align_sequence,
mc.cores = cores
)
j_start <- parallel::mclapply(
j_alignment,
function(x) start(Biostrings::Views(x)),
mc.cores = cores
)
hvr_start <- unlist(cys_df$start)
hvr_end <- unlist(cys_df$start) + unlist(j_start) + 2
hvr <- Biostrings::subseq(sequences, start = hvr_start, end = hvr_end)
return(hvr)
}
data <- parse_data(file = "data/curesim_sequence.fastq")
hvr <- get_hvr_sequences(sequences = data[[1]], vdj_segments = data[[2]])
Biostrings::writeXStringSet(hvr, "data/CuReSim-HVR.fastq", format = "fastq")

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library(immuneSIM) library(immuneSIM)
library(Biostrings) library(Biostrings)
generate_repertoires <- function(number_of_sequences) { generate_repertoire <- function(number_of_sequences) {
a_chain <- immuneSIM( return(immuneSIM(
number_of_seqs = number_of_sequences, number_of_seqs = number_of_sequences,
species = "hs", species = "hs",
receptor = "tr", receptor = "tr",
chain = "a", chain = "b"
verbose = TRUE ))
}
save_data <- function(data) {
Biostrings::writeXStringSet(data$sequence,
"data/sequence.fastq",
format = "fastq"
) )
b_chain <- immuneSIM( Biostrings::writeXStringSet(data$junction, "data/HVR.fastq", format = "fastq")
number_of_seqs = number_of_sequences,
species = "hs",
receptor = "tr",
chain = "b",
verbose = TRUE
)
return(list("a_chain" = a_chain, "b_chain" = b_chain))
} }
process_chain <- function(repertoire) { process_data <- function(data, reads) {
sequences <- as.character(repertoire$sequence) dna_sequence <- Biostrings::DNAStringSet(data$sequence)
counts <- as.integer(repertoire$counts) data$sequence <- Biostrings::reverseComplement(dna_sequence)
reads <- Biostrings::DNAStringSet(rep(sequences, counts)) names(data$sequence) <- paste(rownames(data), data$v_call, data$j_call, " ")
names(reads) <- seq_len(length(reads)) data$junction <- Biostrings::DNAStringSet(data$junction)
reverse_complement <- Biostrings::reverseComplement(reads) names(data$junction) <- rownames(data)
return(reverse_complement) amplified_data <- data[rep(seq_len(nrow(data)), reads), ]
} return(amplified_data)
preprocess_data <- function(repertoires) {
filtered_repertoires <- lapply(repertoires, process_chain)
names(filtered_repertoires) <- names(repertoires)
return(filtered_repertoires)
}
save_data <- function(repertoires) {
for (chain in names(repertoires)) {
file_name <- paste("data/", chain, ".fastq", sep = "")
Biostrings::writeXStringSet(repertoires[[chain]], file_name, format = "fastq")
}
}
parse_cli_arguments <- function(args) {
if (length(args) != 1) {
stop("usage: repertoire.r <number of sequences>")
}
return(as.integer(args[1]))
} }
parse_cli_arguments <- function() {
args <- commandArgs(trailingOnly = TRUE) args <- commandArgs(trailingOnly = TRUE)
number_of_sequences <- parse_cli_arguments(args) if (length(args) != 2) {
sim_repertoire <- generate_repertoires(number_of_sequences) stop("usage: repertoire.r <number of sequences> <number of reads>")
processed_data <- preprocess_data(sim_repertoire) }
save_data(processed_data) return(args)
}
args <- parse_cli_arguments()
repertoire <- generate_repertoire(number_of_sequences = as.integer(args[1]))
data <- process_data(data = repertoire, reads = args[2])
save_data(data)