This is the version of FLAMES used in the following paper:
The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read tools Xueyi Dong, Luyi Tian, Quentin Gouil, Hasaru Kariyawasam, Shian Su, Ricardo De Paoli-Iseppi, Yair David Joseph Prawer, Michael B. Clark, Kelsey Breslin, Megan Iminitoff, Marnie E. Blewitt, Charity W. Law, Matthew E. Ritchie bioRxiv 2020.06.28.176727; doi: https://doi.org/10.1101/2020.06.28.176727
To see most recent update of FLAMES, please go to this repository.
The easiest way to install the dependencies for this package is using a conda environment. The scripts can then be cloned from git.
conda create -n FLAMES \
python=2.7 samtools pysam minimap2 numpy editdistance \
-c bioconda -c conda-forge
git clone https://github.com/LuyiTian/FLAMES.git
Before using this software, remember to activate the FLAMES conda environment. The main scripts are the pipelines for single cells and bulk samples.
conda activate FLAMES
FLAMES/python/sc_long_pipeline.py --help
FLAMES/python/bulk_long_pipeline.py --help
An example has been included with a small subset of SIRV data in the examples folder.
PATH=$PWD/FLAMES/python:$PATH
cd examples/SIRV
bulk_long_pipeline.py \
--gff3 data/SIRV_isoforms_multi-fasta-annotation_C_170612a.gtf \
--genomefa data/SIRV_isoforms_multi-fasta_170612a.fasta \
--outdir FLAMES_output \
--config_file data/SIRV_config.json \
--fq_dir data/fastq
# output data is in FLAMES_output
ls FLAMES_output
For single cell, the first step is to find cell barcode in each long read. You can use a precompiled linux binary in src/bin/match_cell_barcode. If you encontered error, espically due to the different running environment. you can compile it from source code using the following command. Please use a C++ compiler that support C++11 features.
g++ -std=c++11 -lz -O2 -o match_cell_barcode ssw/ssw_cpp.cpp ssw/ssw.c match_cell_barcode.cpp kseq.h edit_dist.cpp
Then you can run ./match_cell_barcode without argument to print the help message. match_cell_barcode requires a folder that contains all fastq file (can be .gz file), a file name/path for the statistics of barcode matching, a csv file of cell barcode that used as reference and a file name/path to the output fastq.gz file. The cell barcode for 10x will be in filtered_feature_bc_matrix/barcodes.tsv.gz, please unzip the file and use barcodes.tsv as input. It also support scPipe cell barcode annotation file generated from sc_detect_bc function.
Next, after you get the fastq file from match_cell_barcode. you could run sc_long_pipeline.py with the following command.
usage: FLTSA [-h] -a GFF3 [-i INFQ] [-b INBAM] --outdir OUTDIR --genomefa
GENOMEFA --minimap2_dir MINIMAP2_DIR [--config_file CONFIG_FILE]
[--downsample_ratio DOWNSAMPLE_RATIO]
# semi-supervised isoform detection and annotation from long read data.
# output:
# outdir:
# transcript_count.csv.gz // transcript count matrix
# isoform_annotated.filtered.gff3 // isoforms in gff3 format
# transcript_assembly.fa // transcript sequence from the isoforms
# align2genome.bam // sorted bam file with reads aligned to genome
# realign2transcript.bam // sorted realigned bam file using the
# transcript_assembly.fa as reference
# tss_tes.bedgraph // TSS TES enrichment for all reads (for QC)
################################################################
optional arguments:
-h, --help show this help message and exit
-a GFF3, --gff3 GFF3 The gene annotation in gff3 format.
-i INFQ, --infq INFQ input fastq file.
-b INBAM, --inbam INBAM
aligned bam file (should be sorted and indexed). it
will overwrite the `--infq` parameter and skip the
first alignment step
--outdir OUTDIR, -o OUTDIR
directory to deposite all results in rootdir, use
absolute path
--genomefa GENOMEFA, -f GENOMEFA
genome fasta file
--minimap2_dir MINIMAP2_DIR, -m MINIMAP2_DIR
directory contains minimap2, k8 and paftools.js
program. k8 and paftools.js are used to convert gff3
to bed12.
--config_file CONFIG_FILE, -c CONFIG_FILE
json configuration files (default
config_sclr_nanopore_default.json)
--downsample_ratio DOWNSAMPLE_RATIO, -d DOWNSAMPLE_RATIO
downsampling ratio if performing downsampling analysis
FLAMES provides a default set of parameters, but can be changed by the configuration JSON file.
The pipeline_parameters section specifies which step to be excuated in the pipeline, by default you should go though all steps.
The isoform_parameters section determines the results isoform detection, some key parameters include Min_sup_cnt which means transcript with less read aligned than Min_sup_cnt will be discarded, MAX_TS_DIST which will merge transcripts with the same intron chain and TSS/TES distance less than MAX_TS_DIST. strand_specific will specify whether the the read is strand specific, such as the reads are in the same strand as the mRNA (1) or the reverse complement (-1), or the reads are not strand specific (0), which means the method will determine the strand information based on reference annotation.