FLAM-Seq Data Analysis Pipeline

Welcome to FLAM-Seq, nice to see you again.

Software Dependencies

The analysis pipeline is implemented in Python3 and requires STARlong and featureCounts as addtional software. Packages and Software Versions used are listed below:

python 3.6.7 Anaconda, Inc.
STARlong 2.5.4b
FeatureCounts 1.6.0
regex 2018.2.21
pysam 0.14
pandas 0.23.4
yaml 0.1.7

Usage

-h:                 Show help
-p, --parameters:   Parameter YAML file specifying configurations for analysis

all                 Run complete FLAM-Seq Analysis Pipeline. This includes all steps listed below:
                    preprocess, quantTail, mapQuant, cleanGenomic, result

preprocess          Preprocess input fastq reads. Check if reads contain adapters and place reads in correct orientation.
                    This step creates /preprocessDir and creates *_preprocessed_filtered.fq files
                    containing preprocessed reads and *_preprocessed_filtered_error.fq, containing reads without
                    adapters/poly(A) tails.

quantTail           Quantify poly(A) tail length and sequence from preprocessed reads. This step uses 2 Algorithms
                    with different parameter combinations in order to extract putative poly(A) tail sequences from
                    reads. This step creates /quantTailDir with *_trimmed_tail.fq, containing reads with
                    poly(A) tail trimmed for later mapping, *_umis.txt, containing read names and read UMIs,
                    *_tail_length.txt containing poly(A) tail length, sequence and read name and
                    *_no_tail.fq containing reads without detectable poly(A) sequence.

mapQuant            Map trimmed reads and assign reads to genes. This step maps reads with trimmed poly(A) tail
                    sequences using STARLong to genome index specified in parameters.yaml. Next aligned reads
                    are assigned to gene models using FeatureCounts and GTF file specified in parameters.yaml.
                    This step creates /mapQuantGeneDir with *__Aligned.sortedByCoord.out.bam containing
                    mapped read BAM file (sorted), *_Aligned.sortedByCoord.out.bam.featureCounts containing read names
                    tagged with gene-of-origin.

cleanGenomic        cleanGenomic compares the sequences derived from putative poly(A) tail with the genomic sequence
                    of the mapping location of the respective read. This step 'cleans' nucleotides from 5'ends of putative
                    poly(A) sequences from nucleotides which are encoded in the genome.
                    This step requires genome fasta file specified in parameters.yaml.
                    It creates /cleanGenomicDir with *_cleaned.bam conataining aligned reads filtered by
                    reads with valid poly(A) tail, *_clean_genomic_tail_length.txt, containing cleaned poly(A)
                    length/sequence and *_genomic_non_temp_tails.txt containing parts of each read that are not
                    templated by genome.

result              Aggregate data from above steps into *_gene_polyA_length.csv. This file contains for each
                    read poly(A) tail length and sequence, UMI and gene the read maps to. This step creates
                    /resultDir.

Perform FLAM-Seq Analysis

Before running this analysis pipeline, PacBio or Nanopore sequencing data need to ne converted to .fastq format. This can be done using default software of respective sequencing systems. The obtained .fastq file is the starting point for the analysis.

Running the pipeline requires only configuration of the .parameters.yaml file. This file specifies all relevant parameters for the analysis, such as path to input files, output directory, mapping index, etc.

It is recommended to generate a new parameters.yaml file for each sample to be analyzed and store it in the respective output folder.

The pipeline can the simply be run using the FLAMSeqAnalysis.py script:

python3 /path/to/FLAMSeqAnalysis.py command -p /path/to/parameter.yaml

e.g. for running the complete pipeline

python3 /path/to/FLAMSeqAnalysis.py all -p /path/to/parameter.yaml

or for mapping and quantifying the reads

python3 /path/to/FLAMSeqAnalysis.py mapQuant -p /path/to/parameter.yaml

Parameters.yaml

A template parameters.yaml file is provided with the pipeline. The file can be renamed but structure of keywords withing the file (everything before the ':') must not be changed. All paths need to be adapted to user requirements. An example is shown below:

experimentName: "FLAMSeq_1"               # Name of Experiment. This will be prefix for generated analysis files.

experiment:
  rawFastq: "/path/to/pacbioreads.fq"     # Define Path to Input PacBio / Nanopore Reads in fastq format
  outputDir: "/path/to/outputDir"         # Define Path to Output dir for writing analysis results. Pipeline will generate of dir is not present.
  genomeIndexDir: "/path/to/STARIndex"    # Define Dir for STARLong Index for Mapping
  annotationGTF: "/path/to/GTFFile"       # GTF File containing gene annotations for assigning reads to genes
  genomeFasta: "/path/to/genomeFasta"     # Genome Fasta File containing genome sequence (that the index was generated from)

software:
  STARlong: "/path/to/STARlong"           # Path to STARlong executable
  featureCounts: "/path/to/featureCounts" # Path to FeatureCounts executable
  nThreads: 8                             # Number of threads for mapping reads

Results

We recommend continuing your analysis with the *_gene_polyA_length.csv file generated in the /resultDir after running the complete pipeline. This file can easily be loaded into R or Pandas for downstream analysis on poly(A) tails.

Further Info

FLAM-Seq has beed developed by Ivano Legnini, Salah Ayoub, Nikos Karaiskos and Jonathan Alles as members of the Nikolaus Rajewsky Lab at the Max Delbruck Center Berlin. FLAM-Seq allows for sequencing of entire RNA molecules. A preprint describing the FLAM-Seq method can be found here.