Running the pipeline - exon-based approach¶
As you may have read in General concepts, the pipeline as a whole is comprised of five parts, three of which are used for exon-based analyses, and remaining two for splice-site-based method.
Snakemake config file¶
The first step of the pipeline is editing the Snakemake JSON config file config_TAQLoRe.json
. All the parameters and paths to input files are stored there. There are five main keys (‘sections’) in the configuration file.
workdir¶
In this section there is only one key - `workdir
, denoting the path to the working directory.
samples¶
This section of config file contains two keys:
fasta_file_dir
: Directory where FASTA files are stored.
Warning
All the FASTA files must have consistent naming in the format of {run_prefix}.{barcode}.fa, where {run_prefix} is a prefix of the run (i.e. date of sequencing), and {barcode} is a barcode number. This must also be consistent with barcode-to-sample mapping file, as described in How to create a barcode-to-sample mapping file.
downsampled_reads_num
: Number of reads to downsample to in third part of the pipeline - Read counts bias - downsampling.
input_files¶
This section contains paths to all necessary input files:
transcriptome_fasta
: Path to the FASTA file with all transcript sequences.
Warning
All transcript names (i.e. headers of each FASTA sequence) MUST NOT contain any spaces, as they cannot be parsed correctly.
Moreover, FASTA file should be single-line FASTA file (i.e. each sequence must contain two lines - first being FASTA header, and the second one being a sequence).
last_transcriptome_index
: Prefix of LAST index for a transcriptome.last_transcriptome_index_dummy_file
: Path to the dummy file to be created after generating LAST index for a transcriptome (required to maintain the order of steps in the pipeline).genome_fasta
: Path to the file with genome FASTA sequence.
Warning
All chromosome names must be consistent between this file and both GTF file and sections of Snakemake config file. The file MUST NOT contain any spaces and be single-line FASTA file.
chrom_sizes
: Path to the file containing chromosome sizes for a genome.last_genome_index
: Prefix of the LAST index for a genome.last_genome_index_dummy_file
: Path to the dummy file being created after generating LAST index for a genome (required to maintain the order of steps in the pipeline).gtf_file
: Path to the GTF file with gene annotations.
Warning
All chromosome names must be consistent between this file and both genome FASTA file and sections of Snakemake config file.
all_exons_positions_ENSEMBL
: Path to the file containing gene annotations from ENSEMBL BioMart. You can find how to create this file in this section: How to create an annotation file with ENSEMBL bioMart.barcode_to_sample_file
: Path to the file with barcode-to-sample mappings. The structure of the file can be found in this section: How to create a barcode-to-sample mapping file.barcode_to_sample_file_downsampled
: Path to the file with barcode-to-sample mappings for downsampling. The structure of the file can be found in this section: How to create a barcode-to-sample mapping file. More about downsampling can be found in Read counts bias - downsampling.
How to create an annotation file with ENSEMBL bioMart¶
To create a file needed in the pipeline, you need to use ENSEMBL BioMart. The reason behind it is that some information required to run the pipeline may not be included in the GTF file (e.g. UTRs).
The file can be created with following steps:
- Go to http://www.ensembl.org/biomart/martview
- From ENSEMBL Genes database choose the dataset of interest (e.g. Human Genes).
- From Filters section on the left-hand side of the website choose the gene of interest (e.g. select Input external references ID list [Max 500 advised], and put ENSEMBL gene ID into the field).
- From Attributes section on the left-hand side of the website select Structures, deselect all attributes (from GENE subsection), and select following attributes (IN THIS PARTICULAR ORDER) (subsections of attributes are written in square brackets):
- Gene stable ID [GENE]
- Gene start (bp) [GENE]
- Gene end (bp) [GENE]
- Transcript stable ID [GENE]
- Transcript start (bp) [GENE]
- Transcript end (bp) [GENE]
- Transcription start site (TSS) [GENE]
- Exon stable ID [EXON]
- Exon region start (bp) [EXON]
- Exon region end (bp) [EXON]
- Exon rank in transcript [EXON]
- cDNA coding start [EXON]
- cDNA coding end [EXON]
- Genomic coding start [EXON]
- Genomic coding end [EXON]
- Click Results button on top-left side of the website.
- From Export all results to ** section, choose **File and TSV format, then click Go and save the file in the desired location.
How to create a barcode-to-sample mapping file¶
The file with metadata is a tab-delimited file (without header) containing three columns:
Run prefix | Barcode | Sample name |
---|---|---|
2017_01_13 | barcode01 | Jan_5238_cingulate |
2017_06_15 | barcode12 | Jun_5346_striatum |
where:
Run prefix
- is the run prefix for each file. This can be e.g. a date of the sequencing or any string that denotes different sequencing batches.Barcode
- is the barcode of each file.Sample name
- is the underscore-separated string with a following structure:{run_name}_{sample_id}.{sample_sub_id}`
. In the example above{run_name}
denotes two sequencing runs (one in January, second one in June),{sample_id}
denotes different individuals, and{sample_sub_id}
denotes different brain regions.
gene_info¶
This section of config file contains the information about analysed gene.
gene_name
: Gene name for the gene of interest.chromosome_name
: Chromosome name for the gene of interest.
Warning
All chromosome names must be consistent between this section and both genome FASTA file and GTF file.
gene_start
: Start position of the gene (0-based coordinates).gene_end
: End position of the gene (0-based coordinates).strand
: Strand of the gene (‘+’ or ‘-‘).
parameters¶
This section of config file contains all the parameters being used by scripts.
min_prop
: Minimum proportion of read covering a transcript.min_prop_align
: Minimum proportion of transcript being covered by aligned read.min_insert
: Minimum length of potential novel exon (insertion in the alignment).min_exon_distance
: Minimum distance from annotated exon.distance_between_exons
: Minimum distance between exons (both known and novel).exon_coverage_threshold
: Minimum coverage of exon for exon to be included in transcripts.min_exon_length
: Minimum length of the exon.min_reads_threshold
: Minimum number of reads covering the exon for the exon to be included in transcripts.min_num_individuals_threshold
: Minimum number of different individuals ({sample_id}
) having at leastmin_reads_threshold
reads per exon.min_num_libraries_threshold
: Minimum number of different tissues ({sample_sub_id}
) having at leastmin_reads_threshold
reads per exon.sum_threshold
: Minimum sum of reads for a transcript to be included in the annotation.reads_in_sample_threshold
: Minimum number of reads per sample for a transcript to be included in the annotation.sample_threshold
: Minimum number of samples having at leastreads_in_sample_threshold
reads for a transcript to be included in the annotation.
Running the pipeline¶
Local computer/server¶
To run each part of the pipeline on a computer/server, you can run it by typing:
cd /path/to/TAQLoRe
conda activate taqlore
snakemake -j {number_of_cores} -s TAQLore_part_1
snakemake -j {number_of_cores} -s TAQLore_part_2
etc.
where {number_of_cores}
is the number of cores to use for a Snakemake run.
Note
LAST alignements are very memory-intensive - for 100k reads LAST uses ~48G of memory (human genome/transcriptome). Therefore, the best way to run the pipeline is to use HPC (see below).
Note
The way how snakemake is run in the example above requires a user to pre-install the whole environment located in envs/taqlore.yaml. To pre-install this environment please refer to Installation. If a user wants to create conda environment from scratch, they can use snakemake -j {number_of_cores} -s TAQLore_part_1 –use-conda command which will create a local copy of the whole environment in the working directory.
High Performance Computing¶
In order to run Snakemake pipeline on a computational cluster (preferred way), two additional files must be created
Cluster configuration file¶
For additional information, refer to Snakemake documentation.
This file contains all parameters for each jobs to be used by the scheduler, such as time, memory, number of CPUs, partition name, etc.
The example JSON file to run using SLURM scheduler (Earlham Institute’s infrastructure) looks like this:
{
"__default__" :
{
"nodes" : 1,
"time" : "7-00:00:00",
"n" : 1,
"ntasks-per-node": 1,
"cpu" : 1,
"partition" : "medium",
"memory" : "64G",
"job_name" : "{rule}.{wildcards}",
"out" : "slurm.%N.%j.{rule}.{wildcards}.out",
"err" : "slurm.%N.%j.{rule}.{wildcards}.err"
},
"last_index_transcriptome" :
{
"time" : "1-00:00:00",
"cpu" : 8,
"memory" : "64G"
},
"last_train_gap_mismatch_transcriptome" :
{
"time" : "1-00:00:00",
"cpu" : 8,
"memory" : "64G"
},
"last_align_transcriptome" :
{
"time" : "7-00:00:00",
"cpu" : 8,
"memory" : "64G"
},
"last_index_genome" :
{
"time" : "1-00:00:00",
"cpu" : 8,
"memory" : "64G"
},
"novel_exons_alignment_to_genome_parsing_last_maf" :
{
"time" : "00:45:00",
"cpu" : 8,
"partition" : "short",
"memory" : "32G"
},
"novel_exons_genomic_coordinates":
{
"time" : "00:45:00",
"partition" : "short",
"memory" : "16G"
},
"filtering_genomic_positions_gene_boundaries" :
{
"time" : "00:05:00",
"partition" : "short",
"memory" : "4G"
},
"novel_exons_per_library" :
{
"time" : "00:45:00",
"partition" : "short",
"memory" : "4G"
},
"novel_exons_summary" :
{
"time" : "00:45:00",
"partition" : "short",
"memory" : "4G"
},
"generate_BedGraph_sum" :
{
"time" : "00:45:00",
"partition" : "short",
"memory" : "4G"
},
"novel_exons_file_1nt_coordinates" :
{
"time" : "00:01:00",
"partition" : "short",
"memory" : "2G"
},
"coordinates_genomic_meta_gene_exons" :
{
"time" : "00:05:00",
"partition" : "short",
"memory" : "16G"
}
}
Note
__default__
section specifies parameters of default job, while parameters under rule names (copied from Snakemake files, e.g. last_index_transcriptome
) denote deviation(s) from default rule (e.g. different time, memory, number of CPUs, etc.).
Batch script to submit¶
In order to run each part of the pipeline using HPC, a batch script to submit must be created. An example of the batch script (SLURM scheduler, Earlham Institute’s infrastructure) can be seen below:
#!/bin/bash
#SBATCH -p medium
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -c 1
#SBATCH --mem 4G
#SBATCH -t 7-00:00:00
#SBATCH -o slurm.%N.%j.out
#SBATCH -e slurm.%N.%j.err
#SBATCH --mail-type=ALL
#SBATCH --mail-user=some.user@some.insitute.ac.uk
module load conda
conda activate taqlore
srun snakemake -s TAQLoRe_part1 --latency-wait 60 -j {number_of_jobs} --cluster-config /path/to/cluster/config.json --cluster "sbatch -p {cluster.partition} -N {cluster.nodes} -n {cluster.n} --ntasks-per-node={cluster.ntasks-per-node} -c {cluster.cpu} -t {cluster.time} --mem {cluster.memory} -J {cluster.job_name} -o slurm.%N.%j.out -e slurm.%N.%j.err --mail-type=FAIL --mail-user=some.user@some.insitute.ac.uk"
where:
--cluster-config
denotes path to the Cluster configuration file.{number_of_jobs}
denotes number of jobs submitted to the cluster at once.
The script can be submitted with command (SLURM scheduler):
sbatch batch_script_to_submit.sh
where `batch_script_to_submit.sh
is the name of the file which contents are shown above (Batch script to submit).
Things to do after running part 1 of the pipeline¶
The first part of the pipeline ends with creating a meta-gene annotation file in `{workdir}/results/meta_gene_construction/meta_gene_genomic_exon_coordinates.txt
.
In order to run the second part of the pipeline, the user must annotate UTRs in the meta_gene_genomic_exon_coordinates.txt
file and/or add/remove additional/unnecessary exons. The reason behind it is that there are different sources of UTRs (e.g. ENSEMBL, GENCODE, RefSeq, UCSC, etc.) and the differences between annotations can be significant.
UTRs must be added as the last column to the meta_gene_genomic_exon_coordinates.txt
file, for each exon position in the meta-gene. The last column should have ‘UTR’ string denoting that a position is an UTR, and any other string (e.g. ‘NA’) denoting the non-UTR/unknown status of a genomic region in a gene. Example:
meta_gene_genomic_exon_coordinates.txt
after part1 of the pipeline:1 277 1970786 1971062 ENST00000543114 ENSE00001774617 No 278 416 1971063 1971201 ENST00000543114 ENSE00001774617 Yes 417 656 2053298 2053537 ENST00000335762;ENST00000399655 ENSE00001539923;ENSE00001539923 No;No 657 681 2053538 2053562 ENST00000335762;ENST00000399655;ENST00000480911 ENSE00001539923;ENSE00001539923;ENSE00001839973 No;No;No 682 730 2053563 2053611 ENST00000335762;ENST00000399655;ENST00000480911;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00001539923;ENSE00001539923;ENSE00001839973;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466 Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes ... 23196 23495 2690900 2691199 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST0000039962 ;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454;ENST00000616390 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00003738703 Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes 23496 23563 2691200 2691267 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454;ENST00000616390 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00003738703 No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No 23564 23589 2691268 2691293 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No 23590 23706 2691294 2691410 ENST00000335762;ENST00000399655;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00002228600;ENSE00001539473;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No;No 23707 24455 2691411 2692159 ENST00000399655;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00001539473;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No 24456 30246 2692160 2697950 ENST00000399655 ENSE00001539473 No
meta_gene_genomic_exon_coordinates.txt
after adding UTR annotations (before running part2 of the pipeline):1 277 1970786 1971062 ENST00000543114 ENSE00001774617 No UTR 278 416 1971063 1971201 ENST00000543114 ENSE00001774617 Yes NA 417 656 2053298 2053537 ENST00000335762;ENST00000399655 ENSE00001539923;ENSE00001539923 No;No UTR 657 681 2053538 2053562 ENST00000335762;ENST00000399655;ENST00000480911 ENSE00001539923;ENSE00001539923;ENSE00001839973 No;No;No UTR 682 730 2053563 2053611 ENST00000335762;ENST00000399655;ENST00000480911;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00001539923;ENSE00001539923;ENSE00001839973;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466;ENSE00001539466 Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes NA ... 23196 23495 2690900 2691199 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454;ENST00000616390 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00003738703 Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes;Yes NA 23496 23563 2691200 2691267 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454;ENST00000616390 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00003738703 No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No UTR 23564 23589 2691268 2691293 ENST00000335762;ENST00000399655;ENST00000399595;ENST00000399644;ENST00000399638;ENST00000399597;ENST00000399621;ENST00000399637;ENST00000399591;ENST00000399641;ENST00000347598;ENST00000399606;ENST00000399601;ENST00000344100;ENST00000399629;ENST00000327702;ENST00000399649;ENST00000402845;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00002228600;ENSE00001539473;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001724521;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No;No UTR 23590 23706 2691294 2691410 ENST00000335762;ENST00000399655;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00002228600;ENSE00001539473;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No;No UTR 23707 24455 2691411 2692159 ENST00000399655;ENST00000399603;ENST00000399634;ENST00000399617;ENST00000406454 ENSE00001539473;ENSE00001539391;ENSE00001539391;ENSE00001539391;ENSE00001539391 No;No;No;No;No UTR 24456 30246 2692160 2697950 ENST00000399655 ENSE00001539473 No UTR
After adding UTR annotation to the meta_gene_genomic_exon_coordinates.txt
file, the pipeline can be run as usual (file: TAQLoRe_part2
).
Read counts bias - downsampling¶
Third part of the pipeline () can be run in order to remove read counts bias (i.e. different number of reads in analysed samples). This step is necessary if you have huge differences in read numbers (> 5000 reads difference between samples with highest and lowest number of reads).
Before running part3 of the pipeline¶
Before running part3 of the pipeline two additional steps are necessary:
- Before running the pipeline
config_TAQLoRe.json
needs to be edited (in the section"samples"
, sub-sectiondownsampled_reads_num
), to reflect the number of reads to choose for all the files. The downsampling will be done on/path/to/workdir/results/meta_gene_exon_counts_splicing_patterns/{run_prefix}.{barcode}_splicing_patterns_cds.tmp
files, where{run_prefix}
and{barcode}
first and second column from How to create a barcode-to-sample mapping file file, respectively. Thus, to see the number of reads in each file (sorted by the number of reads) the following command may be invoked to see the number of reads in each sample:
cd /path/to/workdir
for i in `ls results/meta_gene_exon_counts_splicing_patterns/*_splicing_patterns_cds.tmp`; do j=`cat $i | wc -l`; printf "${i}\t${j}\n"; j=''; done | sort -k2,2n
- A meta-data file needs to be edited, as removing some outliers with the lowest numbers of reads might be necessary to accurately compare the expression between samples. The file has the same structure as the one in How to create a barcode-to-sample mapping file. The path to this file should be put in
config_TAQLoRe.json
, sectioninput_files
, sub-sectionbarcode_to_sample_file_downsampled
.
After these steps the pipeline can be run as usual (file: TAQLore_part3
).