Immune Repertoire Analysis

Using RNA-Seq data as input, the Immune Repertoire Analysis tool can be used to characterize either the T or B cell receptor repertoire.

The tool requires a reference data sequence list (Image seq_list_nucleotide) containing reference sequences for the V, D, J and C segments.

Whether the tool identifies T or B cell receptors depends on the types of reference segments present in the provided sequence list. The tool does not accept sequence lists containing reference sequences for both TCR and BCR.

The Reference Data Manager (see Reference Data Management) offers two QIAGEN sets for this tool. Each set contains a sequences list for Immune Repertoire Analysis:

If reference data is needed for BCR or for a different species than those above, Import Immune Reference Segments can be used to import reference data, see Import Immune Reference Segments.

The tool assumes that one read spans all segment types (V, D, J and C) in order to successfully report the clonotype. It is therefore recommended to collapse overlapping paired-end reads using Merge Overlapping Pairs, see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Merge_Overlapping_Pairs.html.

Identification of clonotypes

Clonotyping a read consists of identifying which V, D, J and C segments from the reference data are used and extracting the CDR3 region found between the conserved amino acids.

V and C segments are rather long ($ >200$ bp), whereas J segments are relatively short ( $ \approx 50-70$ bp) and D segments are even shorter ( $ \approx 10-30$ bp). The segments identification is therefore performed using different strategies.

First, the tool identifies the V and J segments.

For V segments, the Map Reads to Reference tool is used internally.

For the J segment, a strategy similar to IMSEQ [Kuchenbecker et al., 2015] is used. First, a pairwise alignment with a 15 bp subsequence of the full segment called a Segment Core Fragment (SCF) is performed to find candidates for full pairwise alignments. If the pairwise alignment of an SCF to the read has a sufficiently small number of errors, it is nominated as a candidate. A full pairwise alignment is then made for all the segments corresponding to the candidate SCFs. If there is a sufficiently good match among the full alignments it will be assigned to the read.

Once both V and J segments are identified, only valid matches are kept:

The D and C segments are then identified for the reads with assigned V and J segments.

For D segments, a local alignment is performed between the region of the read found between V and J, and the reference D segments for the same chain.

For C segments, the Map Reads to Reference tool is used internally. As the C segment is long and not variable, matches for the C segment for chains other than that identified for V and J indicate a false positive and the read is hence discarded.

The V and J segments are required for successfully clonotyping a read, because otherwise the CDR3 cannot be determined.

The D and C segments are optional. Note that the (lack of) identification of these two segment types can lead to the tool reporting clonotypes as the same or different clonotypes:

  • If two reads have the same assigned V and J segments and share the CDR3 sequence, they would typically be considered to have the same clonotype. However, if for one read the C segment is successfully identified, but the other read is shorter and the C segment cannot be matched, their two clonotypes will be reported separately.
  • If two reads have the same assigned V and J segments and a CDR3 sequence that is almost the same, they would typically be merged and be considered to have the same clonotype (see Merging of clonotypes below). However, due to the non-identical CDR3 sequence, one read might have a D segment assigned, while the other might not, hence their two clonotypes will be reported separately.

A read with multiple segment matches will provisionally have all these segments assigned and in a subsequent merging step, it may be assigned a specific segment.

Merging of clonotypes

After the initial identification of clonotypes, some clonotypes are merged to reduce false positives due to sequencing errors and resolve ambiguities, i.e. multiple assigned segments. Clonotype merging is performed in two steps.

The first step tries to resolve ambiguous segment assignments. Some of the reference segments have a large degree of sequence identity, e.g. in mouse a recent duplication event has resulted in multiple paralogue TCR V segments with a sequence identity of more than 97%. If a sequencing read does not cover the regions where paralogue segments differ, the segment cannot be unambiguously identified. In these cases all possible segments will be listed using a comma for separation of the different options. However, there might be reads with the same CDR3 nucleotide sequence where the segment can be uniquely determined. It is unlikely that two different clonotypes would share the same CDR3 and have almost identical segments. We thus merge a clonotype with ambiguous segments into another clonotype if it has the same CDR3 sequence and segments that are a subset of the former clonotype's segments.

The second merging step tries to correct sequencing errors in the CDR3 region, where a highly expressed clonotype would result in multiple clonotypes being reported if not corrected for. In this step, clonotypes are merged if their segments are identical and the CDR3 region is sufficiently similar. For two CDR3 regions to be deemed sufficiently similar, two types of errors are considered: errors occurring in positions of low quality and errors occurring anywhere within the CDR3 region.

Running the tool

To run Immune Repertoire Analysis, go to:

        Toolbox | Biomedical Genomics Analysis (Image biomedical_folder_closed_16_n_p) | Immune Repertoire Analysis (Image immune_rept_folderclosed_16_n_p) | Immune Repertoire Analysis (Image immune_rept_tool_16_n_p)

This opens a dialog where the reads can be selected. The following options for mapping, merging and frequencies can then be configured (see figure 7.6 and fig 7.7):

Image immune_mapping
Figure 7.6: Mapping options for Immune Repertoire Analysis.

Image immune_clustering_and_frequency
Figure 7.7: Clustering and frequency options for Immune Repertoire Analysis.

The optimal values for the Similarity fraction and Length fraction are different for the different segment types.

As the V and C segments are at the ends of the read, they might not be covered entirely and the length fraction is expected to be considerably smaller than one. On the other hand, the length fraction would typically be close to one for J. For V, J and C, the similarity fraction is usually close to one as not a lot of mutations are expected in these segments.

As the D segment is located in a region of high variability, both the similarity and length fractions would typically be lower to account for the high mutation rate.


Subsections