UMI Barcoded Illumina MiSeq 325+275 paired-end 5’RACE BCR mRNA

Overview of Experimental Data

This example uses publicly available data from:

Dysregulation of B Cell Repertoire Formation in Myasthenia Gravis Patients Revealed through Deep Sequencing.

Vander Heiden JA, et al.

J Immunol. 2017 198(4):1460-1473. doi:10.4049/jimmunol.1601415.

which may be downloaded from the NCBI Sequence Read Archive under BioProject accession ID: PRJNA248475. For this example, we will use the first 25,000 sequences of sample HD09_N_AB8KB (accession: SRR4026043), which may downloaded using fastq-dump from the SRA Toolkit:

fastq-dump --split-files -X 25000 SRR4026043

Primer sequences are available online from the protocols/AbSeq directory in the Immcantation repository.

Read Configuration

Schematic of the Illumina MiSeq 325+275 paired-end reads with UMI barcodes. Each read was sequenced from one end of the target cDNA so that the two reads together cover the entire variable region of the Ig heavy chain. Sequencing was performed using an uneven number of cycles for read 1 and read 2 using a 2x300 kit. The V(D)J reading frame proceeds from the start of read 2 to the start of read 1. Read 1 is in the opposite orientation (reverse complement), contains a partial C-region, and is 325 nucleotides in length. Read 2 contains the 5’RACE template switch site with a 17 nucleotide UMI barcode preceding it, and is 275 nucleotides in length.

Example Data

We have hosted a small subset of the data (Accession: SRR1383456) on the pRESTO website in FASTQ format with accompanying primer files. The sample data set and workflow script may be downloaded from here:

Vander Heiden et al, 2017 Example Files

Overview of the Workflow

In the following sections, we demonstrate each step of the workflow to move from raw sequence reads to a fully annotated repertoire of complete V(D)J sequences. The workflow is divided into four high-level tasks:

1. Quality control, UMI annotation and primer masking

2. Generation of UMI consensus sequences

3. Paired-end assembly of UMI consensus sequences

4. Deduplication and filtering

A graphical representation of the workflow along with the corresponding sequence of pRESTO commands is shown below.

Flowchart

Flowchart of processing steps. Each pRESTO tool is shown as a colored box. The workflow is divided into four primary tasks: (red) quality control, UMI annotation and primer masking; (orange) generation of UMI consensus sequences; (green) paired-end assembly of UMI consensus sequences; and (blue) deduplication and filtering to obtain the high-fidelity repertoire. Grey boxes indicate the initial and final data files. The intermediate files output by each tool are not shown for the sake of brevity.

Commands

#!/usr/bin/env bash
FilterSeq.py quality -s SRR4026043_1.fastq -q 20 --outname HD09N-R1 --log FS1.log
FilterSeq.py quality -s SRR4026043_2.fastq -q 20 --outname HD09N-R2 --log FS2.log
MaskPrimers.py score -s HD09N-R1_quality-pass.fastq -p AbSeq_R1_Human_IG_Primers.fasta \
    --start 0 --mode cut --outname HD09N-R1 --log MP1.log
MaskPrimers.py score -s HD09N-R2_quality-pass.fastq -p AbSeq_R2_TS.fasta \
    --start 17 --barcode --mode cut --maxerror 0.5 --outname HD09N-R2 --log MP2.log
PairSeq.py -1 HD09N-R1_primers-pass.fastq -2 HD09N-R2_primers-pass.fastq \
    --2f BARCODE --coord sra
BuildConsensus.py -s HD09N-R1_primers-pass_pair-pass.fastq --bf BARCODE --pf PRIMER \
    --prcons 0.6 --maxerror 0.1 --maxgap 0.5 --outname HD09N-R1 --log BC1.log
BuildConsensus.py -s HD09N-R2_primers-pass_pair-pass.fastq --bf BARCODE \
    --maxerror 0.1 --maxgap 0.5 --outname HD09N-R2 --log BC2.log
PairSeq.py -1 HD09N-R1_consensus-pass.fastq -2 HD09N-R2_consensus-pass.fastq \
    --coord presto
AssemblePairs.py sequential -1 HD09N-R2_consensus-pass_pair-pass.fastq \
    -2 HD09N-R1_consensus-pass_pair-pass.fastq -r IMGT_Human_IG_V.fasta \
    --coord presto --rc tail --scanrev --1f CONSCOUNT --2f CONSCOUNT PRCONS \
    --aligner blastn --outname HD09N-C --log AP.log
MaskPrimers.py align -s HD09N-C_assemble-pass.fastq \
    -p AbSeq_Human_IG_InternalCRegion.fasta --maxlen 100 --maxerror 0.3 \
    --mode tag --revpr --skiprc --pf CREGION --outname HD09N-C --log MP3.log 
ParseHeaders.py collapse -s HD09N-C_primers-pass.fastq -f CONSCOUNT --act min
CollapseSeq.py -s HD09N-C_primers-pass_reheader.fastq -n 20 --inner \
    --uf CREGION --cf CONSCOUNT --act sum --outname HD09N-C
SplitSeq.py group -s HD09N-C_collapse-unique.fastq \
    -f CONSCOUNT --num 2 --outname HD09N-C
ParseHeaders.py table -s HD09N-C_atleast-2.fastq -f ID CREGION CONSCOUNT DUPCOUNT
ParseLog.py -l FS1.log FS2.log -f ID QUALITY
ParseLog.py -l MP1.log MP2.log MP3.log -f ID PRIMER BARCODE ERROR
ParseLog.py -l BC1.log BC2.log -f BARCODE SEQCOUNT CONSCOUNT PRCONS PRFREQ ERROR
ParseLog.py -l AP.log -f ID REFID LENGTH OVERLAP GAP ERROR IDENTITY

Download Commands

Quality control, UMI annotation and primer masking

Removal of low quality reads

Quality control begins with the identification and removal of low-quality reads using the quality subcommand of the FilterSeq.py tool. In this example, reads with mean Phred quality scores less than 20 (-q 20) are removed:

FilterSeq.py quality -s SRR4026043_1.fastq -q 20 --outname HD09N-R1 --log FS1.log
FilterSeq.py quality -s SRR4026043_2.fastq -q 20 --outname HD09N-R2 --log FS2.log

The ParseLog.py tool is then used to extract results from the FilterSeq.py logs into tab-delimited files:

ParseLog.py -l FS1.log FS2.log -f ID QUALITY

Extracting the following information from the log:

Field	Description
ID	Sequence name
QUALITY	Quality score

UMI annotation and removal of primer regions

Next, the score subcommand of MaskPrimers.py is used to identify and remove the PCR primers for both reads. When dealing with Ig sequences, it is important to cut or mask the primers, as B cell receptors are subject to somatic hypermutation (the accumulation of point mutations in the DNA) and degenerate primer matches can look like mutations in downstream applications. The MaskPrimers tool is also used to annotate each read 2 sequence with the 17 nucleotide UMI that precedes the template switch site. (MaskPrimers score --barcode):

MaskPrimers.py score -s HD09N-R1_quality-pass.fastq -p AbSeq_R1_Human_IG_Primers.fasta \
    --start 0 --mode cut --outname HD09N-R1 --log MP1.log
MaskPrimers.py score -s HD09N-R2_quality-pass.fastq -p AbSeq_R2_TS.fasta \
    --start 17 --barcode --mode cut --maxerror 0.5 --outname HD09N-R2 --log MP2.log

To summarize these steps, the ParseLog.py tool is used to build a tab-delimited file from the MaskPrimers.py log:

ParseLog.py -l MP1.log MP2.log MP3.log -f ID PRIMER BARCODE ERROR

Containing the following information:

Field	Description
ID	Sequence name
PRIMER	Primer name
BARCODE	UMI sequence
ERROR	Primer match error rate

Note

For this data, we are using the 5’RACE template switch sequences as proxy primers. We’ve set the match error rate extremely high (--maxerror 0.5) because an accurate match isn’t important. Mostly, we are just concerned with extracting the UMI barcode that precedes the template switch site.

Generation of UMI consensus sequences

Copying the UMI annotation across paired-end files

In this task, a single consensus sequence is constructed for each set of reads annotated with the same UMI barcode. As the UMI barcode is part of read 2, the BARCODE annotation identified by MaskPrimers.py must first be copied to the read 1 mate-pair of each read 2 sequence. Propogation of annotations between mate pairs is performed using PairSeq.py which also removes unpaired reads and ensures that paired reads are sorted in the same order across files:

PairSeq.py -1 HD09N-R1_primers-pass.fastq -2 HD09N-R2_primers-pass.fastq \
    --2f BARCODE --coord sra

Note

For both the PairSeq.py and AssemblePairs.py commands using the correct --coord argument is critical for matching mate-pairs. If this was raw data from Illumina, rather than data downloaded from SRA, then the appropriate argument would be --coord illumina.

Multiple alignment of UMI read groups

Before generating a consensus for a set of reads sharing a UMI barcode, the sequences must be properly aligned. Sequences may not be aligned if more than one PCR primer is identified in a UMI read group - leading to variations in the the start positions of the reads. Ideally, each set of reads originating from a single mRNA molecule should be amplified with the same primer. However, different primers in the multiplex pool may be incorporated into the same UMI read group during amplification if the primers are sufficiently similar. This type of primer misalignment can be corrected using the AlignSets.py tool.

This step is not required for this data, but if necessary for your data see the section on fixing UMI problems.

Generating UMI consensus reads

After alignment, a single consensus sequence is generated for each UMI barcode using BuildConsensus.py:

BuildConsensus.py -s HD09N-R1_primers-pass_pair-pass.fastq --bf BARCODE --pf PRIMER \
    --prcons 0.6 --maxerror 0.1 --maxgap 0.5 --outname HD09N-R1 --log BC1.log
BuildConsensus.py -s HD09N-R2_primers-pass_pair-pass.fastq --bf BARCODE \
    --maxerror 0.1 --maxgap 0.5 --outname HD09N-R2 --log BC2.log

To correct for UMI chemistry and sequencing errors, UMI read groups having high error statistics (mismatch rate from consensus) are removed by specifiying the --maxerror 0.1 threshold. Additional filtering of read 1 is carried out during this step by specifying the --prcons 0.6 threshold which: (a) removes individual sequences that do not share a common primer annotation with the majority of the set, (b) removes entire read groups which have ambiguous primer assignments, and (c) constructs a consensus primer assignment for each UMI.

Note

The --maxgap 0.5 argument tells BuildConsensus.py to use a majority rule to delete any gap positions which occur in more than 50% of the reads. The --maxgap argument is not really necessary for this example data set as we did not perform a multiple alignment of the UMI read groups. However, if you have performed an alignment, then use of --maxgap during consensus generation is highly recommended.

The ParseLog.py tool is then used to build a tab-delimited file contain the consensus results:

ParseLog.py -l BC1.log BC2.log -f BARCODE SEQCOUNT CONSCOUNT PRCONS PRFREQ ERROR

With the following annotations:

Field	Description
BARCODE	UMI sequence
SEQCOUNT	Number of total reads in the UMI group
CONSCOUNT	Number of reads used for the UMI consensus
PRCONS	Consensus primer name
PRFREQ	Frequency of primers in the UMI group
ERROR	Average mismatch rate from consensus

Paired-end assembly of UMI consensus sequences

Syncronizing paired-end files

Following UMI consensus generation, the read 1 and read 2 files may again be out of sync due to differences in UMI read group filtering by BuildConsensus.py. To synchronize the reads another instance of PairSeq.py must be run, but without any annotation manipulation:

PairSeq.py -1 HD09N-R1_consensus-pass.fastq -2 HD09N-R2_consensus-pass.fastq \
    --coord presto

Assembling UMI consensus mate-pairs

Once the files have been synchronized, each paired-end UMI consensus sequence is assembled into a full length Ig sequence using the sequential subcommand of AssemblePairs.py. 5’RACE creates long amplicons which are not guaranteed to overlap when sequenced using a 2x300 kit. The sequential subcommand will first attempt de novo paired-end assembly. If that approach fails, it will attempt assembly guided by the V segment reference sequences specified by the -r argument. Mate-pairs that fail to overlap can thus be assembled, with the full length sequence containing an appropriate number of intervening between the two reads.

AssemblePairs.py sequential -1 HD09N-R2_consensus-pass_pair-pass.fastq \
    -2 HD09N-R1_consensus-pass_pair-pass.fastq -r IMGT_Human_IG_V.fasta \
    --coord presto --rc tail --scanrev --1f CONSCOUNT --2f CONSCOUNT PRCONS \
    --aligner blastn --outname HD09N-C --log AP.log

During assembly, the consensus primer annotation (PRCONS) from read 1 and the number of reads used to define the consensus sequence (CONSCOUNT) for both reads are propagated into the annotations of the full length Ig sequence (--1f CONSCOUNT --2f CONSCOUNT PRCONS.

ParseLog.py is then uses to extract the results from the AssemblePairs.py log into a tab-delimited file:

ParseLog.py -l AP.log -f ID REFID LENGTH OVERLAP GAP ERROR IDENTITY

Containing the following information:

ID REFID LENGTH OVERLAP GAP ERROR IDENTITY

Field	Description
ID	Sequence name (UMI)
REFID	The reference sequence name for reference guided assembly
LENGTH	Length of the assembled sequence
OVERLAP	Length of the overlap between mate-pairs
GAP	Length of the gap between mate-pairs
ERROR	Mismatch rate of the overlapping region from de novo assembly
IDENTITY	Identity score for the aligned region in reference guided assembly

Deduplication and filtering

Annotating sequences with the constant region

In the final stage of the workflow, the high-fidelity Ig repertoire is obtained by a series of filtering steps. First, an additional annotation defining the constant region is added to each sequence by aligning against the constant region sequence internal to the primer. This step is not strictly necessary, as the constant region primer is usually sufficient for isotype calls. However, primer calls may be inaccurate with some primer sets, so this additional confirmatory step is beneficial.

Internal constant regions are assigned using the align subcommand of MaskPrimers.py with a set of manually constructed sequences as the “primers” (-p AbSeq_Human_IG_InternalCRegion.fasta):

MaskPrimers.py align -s HD09N-C_assemble-pass.fastq \
    -p AbSeq_Human_IG_InternalCRegion.fasta --maxlen 100 --maxerror 0.3 \
    --mode tag --revpr --skiprc --pf CREGION --outname HD09N-C --log MP3.log 

Here we use a high error rate for the match (--maxerror 0.3), restrict the match to 100 nucleotides at the end of the sequence (--maxlen 100 --revpr --skiprc), and use mode the argument --mode tag to specify that the input sequence should not be modified. The aligned C-region name will be added to each sequence headers in the CREGION field (--pf CREGION):

Combining UMI read group size annotations

The annotation specifying the number of raw reads used to build each sequence (-f CONSCOUNT) is updated to be the minimum (--act min) of the forward and reverse reads using the collapse subcommand of ParseHeaders.py:

ParseHeaders.py collapse -s HD09N-C_primers-pass.fastq -f CONSCOUNT --act min

Removal of duplicate sequences

Second, duplicate nucleotide sequences are removed using the CollapseSeq.py tool with the requirement that duplicate sequences share the same constant region annotation (--uf CREGION). The duplicate removal step also removes sequences with a high number of interior N-valued nucleotides (-n 20 and --inner) and combines the read counts for each UMI read group (--cf CONSCOUNT and --act sum).

CollapseSeq.py -s HD09N-C_primers-pass_reheader.fastq -n 20 --inner \
    --uf CREGION --cf CONSCOUNT --act sum --outname HD09N-C

Filtering to sequences with at least two representative reads

Finally, unique sequences are filtered to those with at least 2 contributing sequences using the group subcommand of SplitSeq.py, by splitting the file on the CONSCOUNT annotation with a numeric threshold (-f CONSCOUNT and --num 2):

SplitSeq.py group -s HD09N-C_collapse-unique.fastq \
    -f CONSCOUNT --num 2 --outname HD09N-C

Creating an annotation table

For further analysis, the annotations of the final repertoire are then converted to into a table using the table subcommand of ParseHeaders.py:

ParseHeaders.py table -s HD09N-C_atleast-2.fastq -f ID CREGION CONSCOUNT DUPCOUNT

Output files

The final set of sequences, which serve as input to a V(D)J reference aligner (Eg, IMGT/HighV-QUEST or IgBLAST), and tables that can be plotted for quality control are:

File	Description
HD09N-C_collapse-unique.fastq	Total unique sequences
HD09N-C_atleast-2.fastq	Unique sequences represented by at least 2 reads
HD09N-C_atleast-2_headers.tab	Annotation table of the atleast-2 file
FS1_table.tab	Table of the read 1 FilterSeq log
FS2_table.tab	Table of the read 2 FilterSeq log
MP1_table.tab	Table of the C-region primer MaskPrimers log
MP2_table.tab	Table of the TS site MaskPrimers log
MP3_table.tab	Table of the internal C-region MaskPrimers log
BC1_table.tab	Table of the read 1 BuildConsensus log
BC2_table.tab	Table of the read 2 BuildConsensus log
AP_table.tab	Table of the AssemblePairs log

A number of other intermediate and log files are generated during the workflow, which allows easy tracking/reversion of processing steps. These files are not listed in the table above.