Additional programs

When you conda install flair, the following helper programs will be in your $PATH:

collapse_bed_files

usage: flair_combine [-h] -m MANIFEST [-o OUTPUT_PREFIX] [-w ENDWINDOW]

[-p MINPERCENTUSAGE] [-c] [-s] [-f FILTER]

options:
-h, --help

show this help message and exit

-m MANIFEST, --manifest MANIFEST

path to manifest files that points to transcriptomes to combine. Each line of file should be tab separated with sample name, sample type (isoform or fusionisoform), path/to/isoforms.bed, path/to/isoforms.fa, path/to/combined.isoform.read.map.txt. fa and read.map.txt files are not required, although if .fa files are not provided for each sample a .fa output will not be generated

-o OUTPUT_PREFIX, --output_prefix OUTPUT_PREFIX

path to collapsed_output.bed file. default: ‘collapsed_flairomes’

-w ENDWINDOW, --endwindow ENDWINDOW

window for comparing ends of isoforms with the same intron chain. Default:200bp

-p MINPERCENTUSAGE, --minpercentusage MINPERCENTUSAGE

minimum percent usage required in one sample to keep isoform in combined transcriptome. Default:10

-c, --convert_gtf

[optional] whether to convert the combined transcriptome bed file to gtf

-s, --include_se

whether to include single exon isoforms. Default: dont include

-f FILTER, --filter FILTER

type of filtering. Options: usageandlongest(default), usageonly, none, or a number for the total count of reads required to call an isoform

Combines FLAIR transcriptomes or with other FLAIR transcriptomes or annotation transcriptomes to generate accurate combined transcriptome. Only the manifest file is required. Manifest file is in the following format:

sample1     isoform sample1.FLAIR.isoforms.bed      sample1.FLAIR.isoforms.fa       sample1.FLAIR.isoforms.fa sample1.read.map.txt
sample2     isoform sample2.FLAIR.isoforms.bed      sample2.FLAIR.isoforms.fa       sample2.FLAIR.isoforms.fa sample2.read.map.txt

For each line, the sample name and bed path is required. The fasta and read.map.txt file is optional. Without these files there is less ability to filter and more isoforms will be included. If a sample is a FLAIR run, we highly recommend including the read.map.txt file. If you want to combine FLAIR transcriptomes with annotated transcripts, you can convert an annotation gtf file to a bed file using

diff_iso_usage

usage: diff_iso_usage counts_matrix colname1 colname2 diff_isos.txt

Calculates the usage of each isoform as a fraction of the total expression of the gene and compares this between samples.

Requires four positional arguments to identify and calculate significance of alternative isoform usage between two samples using Fisher’s exact tests: (1) counts_matrix.tsv from flair-quantify, (2) the name of the column of the first sample, (3) the name of the column of the second sample, (4) txt output filename containing the p-value associated with differential isoform usage for each isoform. The more differentially used the isoforms are between the first and second condition, the lower the p-value.

Output file format columns are as follows:

  • gene name

  • isoform name

  • p-value

  • sample1 isoform count

  • sample2 isoform count

  • sample1 alternative isoforms for gene count

  • sample2 alternative isoforms for gene count

diffsplice_fishers_exact

usage: diffsplice_fishers_exact events.quant.tsv colname1 colname2 out.fishers.tsv

Identifies and calculates the significance of alternative splicing events between two samples without replicates using Fisher’s exact tests. Requires four positional arguments: (1) flair-diffSplice tsv of alternative splicing calls for a splicing event type, (2) the name of the column of the first sample, (3) the name of the column of the second sample, and (4) tsv output filename containing the p-values from Fisher’s exact tests of each event.

Output

The output file contains the original columns with an additional column containing the p-values appended.

fasta_seq_lengths

usage: fasta_seq_lengths fasta outfilename [outfilename2]

junctions_from_sam

Usage: junctions_from_sam [options]

Options:
  -h, --help           show this help message and exit
  -s SAM_FILE          SAM/BAM file of read alignments to junctions and
                       the genome. More than one file can be listed,
                       but comma-delimited, e.g file_1.bam,file_2.bam
  --unique             Only keeps uniquely aligned reads. Looks at NH
                       tag to be 1 for this information.
  -n NAME              Name prefixed used for output BED file.
                       Default=junctions_from_sam
  -l READ_LENGTH       Expected read length if all reads should be of
                       the same length
  -c CONFIDENCE_SCORE  The mininmum entropy score a junction
                       has to have in order to be considered
                       confident. The entropy score =
                       -Shannon Entropy. Default=1.0
  -j FORCED_JUNCTIONS  File containing intron coordinates
                       that correspond to junctions that will be
                       kept regardless of the confidence score.
  -v                   Will run the program with junction strand ambiguity
                       messages

mark_intron_retention

usage: mark_intron_retention in.bed out_isoforms.bed out_introns.txt

Assumes the bed has the correct strand information

Requires three positional arguments to identify intron retentions in isoforms:

  • in.bed BED of isoforms

  • out_isoforms.bed output filename

  • out_introns.txt output filename for coordinates of introns found.

Outputs

  • an extended BED with an additional column containing either values 0 or 1 classifying the isoform as either spliced or intron-retaining, respectively

  • txt file of intron retentions with format isoform name chromosome intron 5' coordinate intron 3' coordinate.

Note: A bed file with more additional columns will not be displayed in the UCSC genome browser, but can be displayed in IGV.

mark_productivity

usage: mark_productivity reads.psl annotation.gtf genome.fa > reads.productivity.psl

normalize_counts_matrix

usage: normalize_counts_matrix matrix outmatrix [cpm/uq/median] [gtf]

Gtf if normalization by protein coding gene counts only

plot_isoform_usage

plot_isoform_usage <isoforms.bed> counts_matrix.tsv gene_name

Visualization script for FLAIR isoform structures and the percent usage of each isoform in each sample for a given gene. If you supply the isoforms.bed file from running predictProductivity, then isoforms will be filled according to the predicted productivity (solid for PRO, hatched for PTC, faded for NGO or NST). The gene name supplied should correspond to a gene name in your isoform file and counts file.

The script will produce two images, one of the isoform models and another of the usage proportions.

The most highly expressed isoforms across all the samples will be plotted.

The minor isoforms are aggregated into a gray bar. You can toggle min_reads or color_palette to plot more isoforms. Run with –help for options

Outputs

  • gene_name_isoforms.png of isoform structures

  • gene_name_usage.png of isoform usage by sample

For example:

_images/toy_diu_isoforms.png
_images/toy_diu_usage.png 
positional arguments:
  isoforms              isoforms in bed format
  counts_matrix         genomic sequence
  gene_name             Name of gene, must correspond with the gene names in
                        the isoform and counts matrix files

options:
  -h, --help            show this help message and exit
  -o O                  prefix used for output files (default=gene_name)
  --min_reads MIN_READS
                        minimum number of total supporting reads for an
                        isoform to be visualized (default=6)
  -v VCF, --vcf VCF     VCF containing the isoform names that include each
                        variant in the last sample column
  --palette PALETTE     provide a palette file if you would like to visualize
                        more than 7 isoforms at once or change the palette
                        used. each line contains a hex color for each isoform

predictProductivity

usage: predictProductivity -i isoforms.bed -f genome.fa -g annotations.gtf

Annotated start codons from the annotation are used to identify the longest ORF for each isoform for predicting isoform productivity. Requires three arguments to classify isoforms according to productivity: (1) isoforms in bed format, (2) gtf genome annotation, (3) fasta genome sequences. Bedtools must be in your $PATH for predictProductivity to run properly.

Output

Outputs a bed file with either the values PRO (productive), PTC (premature termination codon, i.e. unproductive), NGO (no start codon), or NST (has start codon but no stop codon) appended to the end of the isoform name. When isoforms are visualized in the UCSC genome browser or IGV, the isoforms will be colored accordingly and have thicker exons to denote the coding region.

options:
  -h, --help            show this help message and exit
  -i INPUT_ISOFORMS, --input_isoforms INPUT_ISOFORMS
                        Input collapsed isoforms in bed12 format.
  -g GTF, --gtf GTF     Gencode annotation file.
  -f GENOME_FASTA, --genome_fasta GENOME_FASTA
                        Fasta file containing transcript sequences.
  --quiet               Do not display progress
  --append_column       Append prediction as an additional column in file
  --firstTIS            Defined ORFs by the first annotated TIS.
  --longestORF          Defined ORFs by the longest open reading frame.

File conversion scripts

bam2Bed12

usage: bam2Bed12 -i sorted.aligned.bam
options:
  -h, --help            show this help message and exit
  -i INPUT_BAM, --input_bam Input bam file.
  --keep_supplementary  Keep supplementary alignments

A tool to convert minimap2 BAM to Bed12.

sam_to_map

usage: sam_to_map sam outfile