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\begin{document} \begin{document}
\firstpage{1} \firstpage{1}
\title[Long sequence alignment with minimap2]{Minimap2: fast pairwise alignment for long noisy sequences} \title[Long DNA sequence alignment with minimap2]{Minimap2: fast pairwise alignment for long DNA sequences}
\author[Li]{Heng Li} \author[Li]{Heng Li}
\address{Broad Institute, 415 Main Street, Cambridge, MA 02142, USA} \address{Broad Institute, 415 Main Street, Cambridge, MA 02142, USA}
\maketitle \maketitle
\begin{abstract} \begin{abstract}
\section{Summary:} Minimap2 is a program to align long noisy sequences against \section{Summary:} Minimap2 is a general-purpose mapper to align long noisy DNA
a large reference database. It targets query sequences of 1kb--100Mb in length sequences against a large reference database. It targets query sequences of
with sequence divergence typically below 25\%. Minimap2 is $\sim$30 times 1kb--100Mb in length with per-base divergence typically below 25\%. Minimap2 is
faster than many mainstream long-read aligners and achieves higher accuracy on $\sim$30 times faster than many mainstream long-read aligners and achieves
simulated data. It also employs concave gap cost and rescues inversions for higher accuracy on simulated data. It also employs concave gap cost and rescues
improved alignment around potential structural variations. inversions for improved alignment around potential structural variations.
\section{Availability and implementation:} \section{Availability and implementation:}
\href{https://github.com/lh3/minimap2}{https://github.com/lh3/minimap2} \href{https://github.com/lh3/minimap2}{https://github.com/lh3/minimap2}
@ -50,21 +50,18 @@ They are usually five times as slow as mainstream short-read
aligners~\citep{Langmead:2012fk,Li:2013aa}. We speculated there could be aligners~\citep{Langmead:2012fk,Li:2013aa}. We speculated there could be
substantial room for speedup on the thought that 10kb long sequences should be substantial room for speedup on the thought that 10kb long sequences should be
easier to map than 100bp reads because we can more effectively skip repetitive easier to map than 100bp reads because we can more effectively skip repetitive
regions and dramatically reduce computation. We confirmed our speculation by regions, which are often the bottleneck of short-read alignment. We confirmed
achieving approximate mapping 50 times faster than BWA-MEM~\citep{Li:2016aa}. our speculation by achieving approximate mapping 50 times faster than
\citet{Suzuki:2016} extended our work with with a fast and novel algorithm on BWA-MEM~\citep{Li:2016aa}. \citet{Suzuki:2016} extended our work with a fast
generating detailed alignment, which in turn inspired us to develop minimap2 and novel algorithm on generating detailed alignment, which in turn inspired us
towards higher accuracy. to develop minimap2 towards higher accuracy and more practical functionality.
\begin{methods} \begin{methods}
\section{Methods} \section{Methods}
Minimap2 is the successor of minimap~\citep{Li:2016aa}. It uses similar Minimap2 is the successor of minimap~\citep{Li:2016aa}. It uses similar
indexing and seeding algorithms except that minimap2 optionally uses indexing and seeding algorithms, and further a more accurate chaining algorithm
homopolymer-compressed (HPC; \citealp{Ruan:2016,Lau:2016aa}) $k$-mers in and adds the ability to produce detailed alignment.
addition to normal $k$-mers. Indexing with HPC $k$-mers leads to higher
mapping sensitivity for SMRT reads. Minimap2 further implements a more
accurate chaining algorithm and adds the ability to produce detailed alignment.
\subsection{Chaining} \subsection{Chaining}
@ -107,6 +104,15 @@ find its predecessor and mark each visited $i$ as `used'. This process stops at
$P(j)=0$ or at a `used' $j$. This way we find all chains with no anchors used $P(j)=0$ or at a `used' $j$. This way we find all chains with no anchors used
in more than one chains. in more than one chains.
\subsubsection{Identifying primary chains}
Primary chains are chains that do not greatly overlap on the query sequence.
Minimap2 uses a greedy algorithm to identify them. Let $Q$ be the set of
primary chains, which is an empty set initially. For each chain from the best
to the worst according to their chaining scores: if on the query, the chain
overlaps with a chain in $Q$ by 50\% (by default) or higher fraction of the
shorter chain, mark the chain as secondary to the chain in $Q$; otherwise, add
the chain to $Q$.
\subsection{Alignment} \subsection{Alignment}
Minimap2 performs global alignment between adjacent anchors in a chain. It Minimap2 performs global alignment between adjacent anchors in a chain. It