nf-core/scnanoseq

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Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

• Preprocessing
  • Nanofilt - Read Quality Filtering and Trimming
• Barcode Calling
  • BLAZE - Barcode caller
• Alignment
  • Minimap2 - Long read alignment
• Alignment Post-processing
  • Samtools - Sort and index alignments and make alignment qc
  • Barcode Tagging - Barcode tagging with quality metrics and barcode information
  • UMI Deduplication - UMI-based deduplication
• Feature-Barcode Quantification
  • IsoQuant - Feature-barcode quantification (gene and transcript level)
  • oarfish - Feature-barcode quantification (transcript-level only)
  • Seurat - Feature-barcode matrix QC
• Other steps
  • UCSC - Annotation BED file
• Quality Control
  • FastQC - FASTQ QC
  • NanoComp - Long Read FASTQ QC
  • NanoPlot - Long Read FASTQ QC
  • ToulligQC - Long Read FASTQ QC
  • RSeQC - Various RNA-seq QC metrics
  • Read Counts - Read Counts QC
  • MultiQC - Aggregate report describing results and QC from the whole pipeline
• Pipeline information - Report metrics generated during the workflow execution

Preprocessing

Nanofilt

Nanofilt is a tool used for filtering and trimming of long read sequencing data.

Barcode Calling

BLAZE

BLAZE enables the accurate identification of barcodes and UMIs from Nanopore reads. The files produced by BLAZE can be used to assess the quality of the barcode calling and the data.

The knee plot (an example is listed above) that is provided by BLAZE shows all barcodes detected in a sample, ranked from highest to lowest read count. The “cliff-and-knee” shape (similar to the image above) is indicative of good quality. Deviations from this shape can be indicative of concerns with the data, such as low barcode counts. The *.bc_count.txt file can be used to accompany this figure to show every barcode and its abundance in the dataset.

Alignment

Minimap2

Minimap2 is a versatile sequence alignment program that aligns DNA or mRNA sequences against a large reference database. Minimap2 is optimized for large, noisy reads making it a staple for alignment of nanopore reads.

Alignment Post-processing

Samtools

Samtools is a suite of programs for reading, writing, editing, indexing, and viewing files that are in SAM, BAM, or CRAM format

Barcode Tagging

Barcode tagging is a custom script which adds metadata to the BAM files with commonly used single-cell tags which can be useful for custom down stream analysis (e.g.: subsetting BAMs based on cell barcodes). Specifically the following tags are added:

barcode tag = "CR"
barcode quality tag = "CY"
UMI tag = "UR"
UMI quality tag = "UY"

Please see Barcode Correction below for metadata added post-correction.

UMI Deduplication

UMI-Tools deduplicate reads based on the mapping co-ordinate and the UMI attached to the read. The identification of duplicate reads is performed in an error-aware manner by building networks of related UMIs.

Users should note that oarfish requires input reads to be deduplicated. As a result, the skip_dedup option is only applicable to IsoQuant. By default, scnanoseq will perform deduplication for IsoQuant unless the skip_dedup option is explicitly enabled, while deduplication will always be executed for oarfish quantification.

Feature-Barcode Quantification

IsoQuant

IsoQuant is a tool for the genome-based analysis of long RNA reads, such as PacBio or Oxford Nanopores. IsoQuant allows to reconstruct and quantify transcript models with high precision and decent recall. If the reference annotation is given, IsoQuant also assigns reads to the annotated isoforms based on their intron and exon structure. IsoQuant further performs annotated gene, isoform, exon and intron quantification. The outputs of IsoQuant can be important for downstream analysis with tools specialized in single-cell/nuclei analysis (e.g.: Seurat).

In order to assist with the performance of IsoQuant, the inputs are split by chromosome to add a further degree of parallelization.

It should also be noted that IsoQuant can only accurately perform quantification on a genome aligned bam, and will produce both gene and transcript level matrices

oarfish

oarfish is a program, written in Rust (https://www.rust-lang.org/), for quantifying transcript-level expression from long-read (i.e. Oxford nanopore cDNA and direct RNA and PacBio) sequencing technologies. oarfish requires a sample of sequencing reads aligned to the transcriptome (currntly not to the genome). It handles multi-mapping reads through the use of probabilistic allocation via an expectation-maximization (EM) algorithm.

It should also be noted that oarfish can only accurately perform quantification on a transcript aligned bam, and will only produce transcript level matrices. It’s also recommended to ensure that the --save_transcript_secondary_alignment is enabled to produce the most accurate oarfish results (true by default for oarfish quantification). Notably, this can lead to much higher number of reads reported as aligned, however, this is expected behavior when secondary aligments are included in the analysis.

Seurat

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data.

Other steps

UCSC

ucsc-gtftogenepred and ucsc-genepredtobed are stand-alone applications developed by UCSC which, together, converts a GTF file the BED file format.

Quality Control

FastQC

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

NanoComp

NanoComp compares multiple runs of long read sequencing data and alignments. It creates violin plots or box plots of length, quality and percent identity and creates dynamic, overlaying read length histograms and a cumulative yield plot

NanoPlot

NanoPlot is a plotting tool for long read sequencing data and alignments.

ToulligQC

ToulligQC is a post sequencing QC tool for Oxford Nanopore sequencers.

RSeQC

RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data.

Read Counts

This is a custom script written using BASH scripting. Its purpose is to report the amount of reads that are filtered out at steps in the pipeline that will result in filtered reads, such as barcode detection, barcode correction, alignment, etc. Elevated levels of filtering can be indicative of quality concerns.

For performance, this step parses the read counts from the output of either FastQC or NanoPlot rather than computing it. If the options --skip_fastqc and --skip_nanoplot or --skip_qc is used, this file will not be produced.

MultiQC

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Pipeline information

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.