GraphMap for Fast and Sensitive Mapping of Long Noisy Reads

GraphMap for Fast and Sensitive Mapping of Long Noisy Reads

Readers may find this new paper posted at arxiv interesting (h/t: Lex Nederbragt). Although the discussions in the paper are tuned to Oxford Nanopore reads, I think the users of other long, noisy read technologies (e.g. Pacbio) will see value in their aligner. Therefore, I am surprised that the authors did not make any comparison with Gene Myers’ DALIGNER.

Given the performance gain over BLASR and BWA-MEM claimed in the paper, the algorithm is worth taking a look. The aligner runs through five stages.

1. Region selection

GraphMap starts by roughly determining regions on the reference genome where a read could potentially be aligned….. As a first step, region selection relies on finding seeds between the query sequence and the reference, before clustering them into candidate regions. For seed finding, …. we employed a form of gapped spaced seeds similar to gapped q-gram filters for Levenshtein distance….Specifically, we extended the approach proposed in Burkhardt and Krkkinen11 to use both one- and two-gapped q-grams (Figure 1b) as detailed below. This allows us to accommodate an arbitrary number of gaps in the q-gram.

2. Graph-based vertex-centric construction of anchors

In this stage, we attempt to refine candidate regions from stage I by constructing alignment chains or anchors from short seeds matches. To do this, we introduce the notion of a kmer mapping graph.

3. Extending anchors into alignments using LCS

Each anchor reported by GraphMap in stage II represents a shared segment (or subsequence) between the target and the query sequence with known start and end positions in both sequences. Due to the presence of repeats, the set of anchors obtained is not necessarily monotonically increasing in both the target and query coordinates. For this reason, a subset of anchors that satisfy the monotonicity condition needs to be selected. The problem of identifying such a subset can be expressed as finding the Longest Common Subsequence in k Length Substrings (LCSk).

4. Refinining alignments using L1 linear regression

This step takes care of hits to repetitive regions.

5. Construction of final alignment

After all selected regions have been processed, they are sorted by the 4 parameter. The region with the highest value 4 is selected for the final alignment.

The code is available from github under MIT license.


The abstract is posted below.

Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast graph traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWAMEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at

Written by M. //