minimap2/tex/minimap2.tex

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\title[Aligning long nucleotide sequences with minimap2]{Minimap2: fast pairwise alignment for long nucleotide sequences}
\author[Li]{Heng Li}
\address{Broad Institute, 415 Main Street, Cambridge, MA 02142, USA}

\maketitle

\begin{abstract}
\section{Summary:} Minimap2 is a general-purpose mapper to align long noisy DNA
or mRNA sequences against a large reference database. It targets query
sequences of 1kb--100Mb in length with per-base divergence typically below
25\%. For DNA sequence reads, minimap2 is $\sim$30 times faster than many
mainstream long-read aligners and achieves higher accuracy on simulated data.
It also employs concave gap cost and rescues inversions for improved alignment
around potential structural variations. For real long RNA-seq reads, minimap2
is $\sim$40 times faster than peers and produces alignment more consistent with
existing gene annotations.

\section{Availability and implementation:}
\href{https://github.com/lh3/minimap2}{https://github.com/lh3/minimap2}

\section{Contact:} hengli@broadinstitute.org
\end{abstract}

\section{Introduction}

Single Molecule Real-Time (SMRT) sequencing technology and Oxford Nanopore
technologies (ONT) produce reads over 10kbp in length at an error rate
$\sim$15\%. Several aligners have been developed for such
data~\citep{Chaisson:2012aa,Li:2013aa,Liu:2016ab,Sovic:2016aa,Liu:2017aa,Lin:2017aa,Sedlazeck169557}.
Most of them were five times as slow as mainstream short-read
aligners~\citep{Langmead:2012fk,Li:2013aa} in terms of the number of bases
mapped per second. We speculated there could 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 regions, which are often the
bottleneck of short-read alignment. We confirmed our speculation by achieving
approximate mapping 50 times faster than BWA-MEM~\citep{Li:2016aa}.
\citet{Suzuki:2016} extended our work with a fast and novel algorithm on
generating base-level alignment, which in turn inspired us to develop minimap2
towards higher accuracy and more practical functionality.

Both SMRT and ONT have been applied to sequence spliced mRNAs (RNA-seq). While
traditional mRNA aligners work~\citep{Wu:2005vn,Iwata:2012aa}, they are not
optimized for long noisy sequence reads and are tens of times slower than
dedicated long-read aligners. When developing minimap2 initially for aligning
genomic DNA only, we realized minor modifications could make it competitive for
aligning mRNAs as well. Minimap2 is a first RNA-seq aligner specifically
designed for long noisy reads.

\begin{methods}
\section{Methods}

Minimap2 follows a typical seed-chain-align procedure as is used by most
full-genome aligners. It collects minimizers~\citep{Roberts:2004fv} of the
reference sequences and indexes them in a hash table. Then for each query
sequence, minimap2 takes query minimizers as \emph{seeds}, finds matches to the
reference, and identifies sets of colinear seeds, which are called
\emph{chains}. If base-level alignment is requested, minimap2 applies dynamic
programming (DP) to extend from the ends of chains and to close unseeded
regions between adjacent seeds in chains.

Minimap2 uses indexing and seeding algorithms similar to
minimap~\citep{Li:2016aa}, and furthers the predecessor with more accurate
chaining, the ability to produce base-level alignment and the support of
spliced alignment.

\subsection{Chaining}

\subsubsection{Chaining}
An \emph{anchor} is a 3-tuple $(x,y,w)$, indicating interval $[x-w+1,x]$ on the
reference matching interval $[y-w+1,y]$ on the query. Given a list of anchors
sorted by ending reference position $x$, let $f(i)$ be the maximal chaining
score up to the $i$-th anchor in the list. $f(i)$ can be calculated with
dynamic programming:
\begin{equation}\label{eq:chain}
f(i)=\max\big\{\max_{i>j\ge 1} \{ f(j)+\alpha(j,i)-\beta(j,i) \},w_i\big\}
\end{equation}
where $\alpha(j,i)=\min\big\{\min\{y_i-y_j,x_i-x_j\},w_i\big\}$ is the number of
matching bases between the two anchors. $\beta(j,i)>0$ is the gap cost. It
equals $\infty$ if $y_j\ge y_i$ or $\max\{y_i-y_j,x_i-x_j\}>G$ (i.e. the
distance between two anchors is too large); otherwise
\begin{equation}\label{eq:chain-gap}
\beta(j,i)=\gamma_c\big((y_i-y_j)-(x_i-x_j)\big)
\end{equation}
In implementation, a gap of length $l$ costs $\gamma_c(l)=0.01\cdot \bar{w}\cdot
|l|+0.5\log_2|l|$, where $\bar{w}$ is the average seed length. For $m$ anchors, directly computing all $f(\cdot)$ with
Eq.~(\ref{eq:chain}) takes $O(m^2)$ time. Although theoretically faster
chaining algorithms exist~\citep{Abouelhoda:2005aa}, they
are inapplicable to generic gap cost, complex to implement and usually
associated with a large constant. We introduced a simple heuristic to
accelerate chaining.

We note that if anchor $i$ is chained to $j$, chaining $i$ to a predecessor
of $j$ is likely to yield a lower score. When evaluating Eq.~(\ref{eq:chain}),
we start from anchor $i-1$ and stop the process if we cannot find a better
score after up to $h$ iterations. This approach reduces the average time to
$O(h\cdot m)$. In practice, we can almost always find the optimal chain with
$h=50$; even if the heuristic fails, the optimal chain is often close.

\subsubsection{Backtracking}
Let $P(i)$ be the index of the best predecessor of anchor $i$. It equals 0 if
$f(i)=w_i$ or $\argmax_j\{f(j)+\eta(j,i)-\gamma(j,i)\}$ otherwise. For each
anchor $i$ in the descending order of $f(i)$, we apply $P(\cdot)$ repeatedly to
find its predecessor and mark each visited $i$ as `used', until $P(i)=0$ or we
reach an already `used' $i$. This way we find all chains with no anchors used
in more than one chains.

\subsubsection{Identifying primary chains}
In the absence of copy number changes, each query segment should not be mapped
to two places in the reference. However, chains found at the previous step may
have significant or complete overlaps due to repeats in the reference.
Minimap2 used the following procedure to identify \emph{primary chains} that do
not greatly overlap on the query. Let $Q$ be 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\% or higher percentage of
the shorter chain, mark the chain as secondary to the chain in $Q$; otherwise,
add the chain to $Q$. In the end, $Q$ contains all the primary chains. We did
not choose a more sophisticated data structure (e.g. range tree or k-d tree)
because this step is not the performance bottleneck.

\subsection{Aligning genomic DNA}

\subsubsection{Alignment with 2-piece affine gap cost}

Minimap2 performs DP-based global alignment between adjacent anchors in a
chain. It uses a 2-piece affine gap cost~\citep{Gotoh:1990aa}:
\begin{equation}\label{eq:2-piece}
\gamma_a(l)=\min\{q+|l|\cdot e,\tilde{q}+|l|\cdot\tilde{e}\}
\end{equation}
Without losing generality, we always assume $q+e<\tilde{q}+\tilde{e}$.
On the condition that $e>\tilde{e}$, it applies cost $q+|l|\cdot e$ to gaps
shorter than $\lceil(\tilde{q}-q)/(e-\tilde{e})\rceil$ and applies
$\tilde{q}+|l|\cdot\tilde{e}$ to longer gaps.  This scheme helps to recover
longer insertions and deletions~(INDELs).

The equation to compute the optimal alignment under $\gamma_a(\cdot)$ is
\begin{equation}\label{eq:ae86}
\left\{\begin{array}{l}
H_{ij} = \max\{H_{i-1,j-1}+s(i,j),E_{ij},F_{ij},\tilde{E}_{ij},\tilde{F}_{ij}\}\\
E_{i+1,j}= \max\{H_{ij}-q,E_{ij}\}-e\\
F_{i,j+1}= \max\{H_{ij}-q,F_{ij}\}-e\\
\tilde{E}_{i+1,j}= \max\{H_{ij}-\tilde{q},\tilde{E}_{ij}\}-\tilde{e}\\
\tilde{F}_{i,j+1}= \max\{H_{ij}-\tilde{q},\tilde{F}_{ij}\}-\tilde{e}
\end{array}\right.
\end{equation}
where $s(i,j)$ is the score between the $i$-th reference base and $j$-th query
base. Eq.~(\ref{eq:ae86}) is a natural extension to the equation under affine
gap cost~\citep{Gotoh:1982aa,Altschul:1986aa}.

\subsubsection{Suzuki's formulation}

When we allow gaps longer than several hundred base pairs, nucleotide-level
alignment is much slower than chaining. SSE acceleration is critical to the
performance of minimap2. Traditional SSE implementations~\citep{Farrar:2007hs}
based on Eq.~(\ref{eq:ae86}) can achieve 16-way parallelization for short
sequences, but only 4-way parallelization when the peak alignment score reaches
32767. Long sequence alignment may exceed this threshold. Inspired by
\citet{Wu:1996aa} and the following work, \citet{Suzuki:2016} proposed a
difference-based formulation that lifted this limitation.
In case of 2-piece gap cost, define
\[
\left\{\begin{array}{ll}
u_{ij}\triangleq H_{ij}-H_{i-1,j} & v_{ij}\triangleq H_{ij}-H_{i,j-1} \\
x_{ij}\triangleq E_{i+1,j}-H_{ij} & \tilde{x}_{ij}\triangleq \tilde{E}_{i+1,j}-\tilde{H}_{ij} \\
y_{ij}\triangleq F_{i,j+1}-H_{ij} & \tilde{y}_{ij}\triangleq \tilde{F}_{i,j+1}-\tilde{H}_{ij}
\end{array}\right.
\]
We can transform Eq.~(\ref{eq:ae86}) to
\begin{equation}\label{eq:suzuki}
\left\{\begin{array}{lll}
z_{ij}&=&\max\{s(i,j),x_{i-1,j}+v_{i-1,j},y_{i,j-1}+u_{i,j-1},\\
&&\tilde{x}_{i-1,j}+v_{i-1,j},\tilde{y}_{i,j-1}+u_{i,j-1}\}\\
u_{ij}&=&z_{ij}-v_{i-1,j}\\
v_{ij}&=&z_{ij}-u_{i,j-1}\\
x_{ij}&=&\max\{0,x_{i-1,j}+v_{i-1,j}-z_{ij}+q\}-q-e\\
y_{ij}&=&\max\{0,y_{i,j-1}+u_{i,j-1}-z_{ij}+q\}-q-e\\
\tilde{x}_{ij}&=&\max\{0,\tilde{x}_{i-1,j}+v_{i-1,j}-z_{ij}+\tilde{q}\}-\tilde{q}-\tilde{e}\\
\tilde{y}_{ij}&=&\max\{0,\tilde{y}_{i,j-1}+u_{i,j-1}-z_{ij}+\tilde{q}\}-\tilde{q}-\tilde{e}
\end{array}\right.
\end{equation}
where $z_{ij}$ is a temporary variable that does not need to be stored.

An important property of Eq.~(\ref{eq:suzuki}) is that all values are bounded
by scoring parameters. To see that,
\[
x_{ij}=E_{i+1,j}-H_{ij}=\max\{-q,E_{ij}-H_{ij}\}-e
\]
With $E_{ij}\le H_{ij}$, we have
\[
-q-e\le x_{ij}\le\max\{-q,0\}-e=-e
\]
and similar inequations for $y_{ij}$, $\tilde{x}_{ij}$ and $\tilde{y}_{ij}$.
In addition,
\[
u_{ij}=z_{ij}-v_{i-1,j}\ge\max\{x_{i-1,j},\tilde{x}_{i-1,j}\}\ge-q-e
\]
As the maximum value of $z_{ij}=H_{ij}-H_{i-1,j-1}$ is $M$, the maximal
matching score, we can derive
\[
u_{ij}\le M-v_{i-1,j}\le M+q+e
\]
In conclusion, in Eq.~(\ref{eq:suzuki}), $x$ and $y$ are bounded by $[-q-e,-e]$,
$\tilde{x}$ and $\tilde{y}$ by $[-\tilde{q}-\tilde{e},-\tilde{e}]$, and $u$ and
$v$ by $[-q-e,M+q+e]$. When $-128\le-q-e<M+q+e\le127$, each of them can be stored as
a 8-bit integer. This enables 16-way SSE vectorization regardless of the peak
score of the alignment.

For a more efficient SSE implementation, we transform the row-column coordinate
to the diagonal-antidiagonal coordinate by letting $r\gets i+j$ and $t\gets i$.
Eq.~(\ref{eq:suzuki}) becomes:
\begin{equation*}
\left\{\begin{array}{lll}
z_{rt}&=&\max\{s(t,r-t),x_{r-1,t-1}+v_{r-1,t-1},y_{r-1,t}\\
&&+u_{r-1,t},\tilde{x}_{r-1,t-1}+v_{r-1,t-1},\tilde{y}_{r-1,t}+u_{r-1,t}\}\\
u_{rt}&=&z_{rt}-v_{r-1,t-1}\\
v_{rt}&=&z_{rt}-u_{r-1,t}\\
x_{rt}&=&\max\{0,x_{r-1,t-1}+v_{r-1,t-1}-z_{rt}+q\}-q-e\\
y_{rt}&=&\max\{0,y_{r-1,t}+u_{r-1,t}-z_{rt}+q\}-q-e\\
\tilde{x}_{rt}&=&\max\{0,\tilde{x}_{r-1,t-1}+v_{r-1,t-1}-z_{rt}+\tilde{q}\}-\tilde{q}-\tilde{e}\\
\tilde{y}_{rt}&=&\max\{0,\tilde{y}_{r-1,t}+u_{r-1,t}-z_{rt}+\tilde{q}\}-\tilde{q}-\tilde{e}
\end{array}\right.
\end{equation*}
In this formulation, cells with the same diagonal index $r$ are independent of
each other. This allows us to fully vectorize the computation of all cells on
the same anti-diagonal in one inner loop. It also simplifies banded alignment,
which would be difficult with striped vectorization~\citep{Farrar:2007hs}.

On the condition that $q+e<\tilde{q}+\tilde{e}$ and $e>\tilde{e}$, the initial
values in the diagonal-antidiagonal formuation is
\[
\left\{\begin{array}{l}
x_{r-1,-1}=y_{r-1,r}=-q-e\\
\tilde{x}_{r-1,-1}=\tilde{y}_{r-1,r}=-\tilde{q}-\tilde{e}\\
u_{r-1,r}=v_{r-1,-1}=\eta(r)\\
\end{array}\right.
\]
where
\[
\eta(r)=\left\{\begin{array}{ll}
-q-e & (r=0) \\
-e & (r<\lceil\frac{\tilde{q}-q}{e-\tilde{e}}-1\rceil) \\
r\cdot(e-\tilde{e})-(\tilde{q}-q)-\tilde{e} & (r=\lceil\frac{\tilde{q}-q}{e-\tilde{e}}-1\rceil) \\
-\tilde{e} & (r>\lceil\frac{\tilde{q}-q}{e-\tilde{e}}-1\rceil)
\end{array}\right.
\]
These can be derived from the initial values for Eq.~(\ref{eq:ae86}).

In practice, our 16-way vectorized implementation of global alignment is three
times as fast as Parasail's 4-way vectorization~\citep{Daily:2016aa}.  Without
banding, our implementation is slower than Edlib~\citep{Sosic:2017aa}, but with
a 1000bp band, it is considerably faster. When performing global alignment
between anchors, we expect the alignment to stay close to the diagonal of the
DP matrix. Banding is applicable most of time.

\subsubsection{The Z-drop heuristic}

With global alignment, minimap2 may force to align unrelated sequences between
two adjacent anchors. To avoid such an artifact, we compute accumulative
alignment score along the alignment path and break the alignment where the
score drops too fast in the diagonal direction. More precisely, let $S(i,j)$ be
the alignment score along the alignment path ending at cell $(i,j)$ in the DP
matrix. We break the alignment if there exist $(i',j')$ and $(i,j)$, $i'<i$ and
$j'<j$, such that
\[
S(i',j')-S(i,j)>Z+e\cdot|(i-i')-(j-j')|
\]
where $e$ is the gap extension cost and $Z$ is an arbitrary threshold.
This strategy is first used in BWA-MEM. It is similar to X-drop employed in
BLAST~\citep{Altschul:1997vn}, but unlike X-drop, it would not break the
alignment in the presence of a single long gap.

When minimap2 breaks a global alignment between two anchors, it performs local
alignment between the two subsequences involved in the global alignment, but
this time with the one subsequence reverse complemented. This additional
alignment step may identify short inversions that are missed during chaining.

\subsection{Aligning spliced sequences}

The algorithm described above can be adapted to spliced alignment. In this
mode, the chaining gap cost distinguishes insertions to and deletions from the
reference: $\gamma_c(l)$ in Eq.~(\ref{eq:chain-gap}) takes the form of
\[
\gamma_c(l)=\left\{\begin{array}{ll}
0.01\cdot\bar{w}\cdot l+0.5\log_2 l & (l>0) \\
\min\{0.01\cdot\bar{w}\cdot|l|,\log_2|l|\} & (l<0)
\end{array}\right.
\]
Similarly, the gap cost function used for DP-based alignment is changed to
\[
\gamma_a(l)=\left\{\begin{array}{ll}
q+l\cdot e & (l>0) \\
\min\{q+|l|\cdot e,\tilde{q}\} & (l<0)
\end{array}\right.
\]
In alignment, a deletion no shorter than $\lceil(\tilde{q}-q)/e\rceil$ is
regarded as an intron, which pays no cost to gap extensions.

To pinpoint precise splicing junctions, minimap2 introduces reference-dependent
cost to penalize non-canonical splicing:
\begin{equation}\label{eq:splice}
\left\{\begin{array}{l}
H_{ij} = \max\{H_{i-1,j-1}+s(i,j),E_{ij},F_{ij},\tilde{E}_{ij}-a(i)\}\\
E_{i+1,j}= \max\{H_{ij}-q,E_{ij}\}-e\\
F_{i,j+1}= \max\{H_{ij}-q,F_{ij}\}-e\\
\tilde{E}_{i+1,j}= \max\{H_{ij}-d(i)-\tilde{q},\tilde{E}_{ij}\}\\
\end{array}\right.
\end{equation}
Let $T$ be the reference sequence. $d(i)$ is the cost of a non-canonical donor
site, which takes 0 if $T[i+1,i+2]={\tt GT}$, or a positive number $p$
otherwise. Similarly, $a(i)$ is the cost of a non-canonical acceptor site, which
takes 0 if $T[i-1,i]={\tt AG}$, or $p$ otherwise. Eq.~(\ref{eq:splice}) is
almost equivalent to the equation used by EXALIN~\citep{Zhang:2006aa} except
that we allow insertions immediately followed by deletions and vice versa; in
addition, we use Suzuki's diagonal formulation in actual implementation.

%Given that $d_i$ and $a_i$
%are a function of the reference sequence, it is possible to incorporate
%splicing signals with more sophisticated models, such as positional weight
%matrices. We have not tried this approach.

If RNA-seq reads are not sequenced from stranded libraries, the read strand
relative to the underlying transcript is unknown. By default, minimap2 aligns
each chain twice, first assuming ${\tt GT}$--${\tt AG}$ as the splicing signal
and then assuming ${\tt CT}$--${\tt AC}$, the reverse complement of ${\tt
GT}$--${\tt AG}$, as the splicing signal. The alignment with a higher score is
taken as the final alignment. This procedure also infers the relative strand of
reads that span canonical splicing sites.

In the spliced alignment mode, minimap2 further increases the density of
minimizers and disables banded alignment. Together with the two-round DP-based
alignment, spliced alignment is several times slower than DNA sequence
alignment.

\end{methods}

\section{Results}

\subsection{Aligning genomic reads}

\begin{figure}[!tb]
\centering
\includegraphics[width=.5\textwidth]{roc-color.pdf}
\caption{Evaluation on simulated SMRT reads aligned against human genome
GRCh38.  33,088 $\ge$1000bp reads were simulated using pbsim~\citep{Ono:2013aa}
with error profile sampled from file `m131017\_060208\_42213\_*.1.*' downloaded
at \href{http://bit.ly/chm1p5c3}{http://bit.ly/chm1p5c3}. The N50 read length
is 11,628. A read is considered correctly mapped if the true position overlaps
with the best mapping position by 10\% of the read length. All aligners were
run under the default setting for SMRT reads.  (a) ROC-like curve. Alignments
are sorted by mapping quality in the descending order. For each mapping quality
threshold, the fraction of alignments with mapping quality above the threshold
and their error rate are plotted.  Kart outputted all alignments at mapping
quality 60, so is not shown in the figure. It mapped nearly all reads with
4.1\% of alignments being wrong, less accurate than others.  (b) Accumulative
mapping error rate as a function of mapping quality.}\label{fig:eval}
\end{figure}

As a sanity check, we evaluated minimap2 on simulated human reads along with
BLASR~(v1.MC.rc64; \citealp{Chaisson:2012aa}),
BWA-MEM~(v0.7.15; \citealp{Li:2013aa}),
GraphMap~(v0.5.2; \citealp{Sovic:2016aa}),
Kart~(v2.2.5; \citealp{Lin:2017aa}),
minialign~(v0.5.3; \citealp{Suzuki:2016}) and
NGMLR~(v0.2.5; \citealp{Sedlazeck169557}). We excluded rHAT~\citep{Liu:2016ab}
and LAMSA~\citep{Liu:2017aa} because they either
crashed or produced malformatted output. In this evaluation, minimap2 has
higher power to distinguish unique and repetitive hits, and achieves overall
higher mapping accuracy (Fig.~\ref{fig:eval}a). It is still the most accurate
even if we skip DP-based alignment (data not shown), confirming chaining alone
is sufficient to achieve high accuracy for approximate mapping. Minimap2 and
NGMLR provide better mapping quality estimate: they rarely give repetitive hits
high mapping quality (Fig.~\ref{fig:eval}b).  Apparently, other aligners may
occasionally miss close suboptimal hits and be overconfident in wrong mappings.
On run time, minialign is slightly faster than minimap2 and Kart. They are over
30 times faster than the rest.  Minimap2 consumed 6.1GB memory at the peak,
more than BWA-MEM but less than others.

On real human SMRT reads, the relative performance and sensitivity of
these aligners are broadly similar to the metrics on simulated data. We are
unable to provide a good estimate of mapping error rate due to the lack of the
truth.  On ONT $\sim$100kb human reads~\citep{Jain128835}, BWA-MEM failed.
Kart, minialign and minimap2 are over 70 times faster than others. We have also
examined tens of $\ge$100bp INDELs in IGV~\citep{Robinson:2011aa} and can
confirm the observation by~\citet{Sedlazeck169557} that BWA-MEM often breaks
them into shorter gaps. The issue is much alleviated with minimap2, thanks
to the 2-piece affine gap cost.

\subsection{Aligning spliced reads}

We evaluated minimap2 on SIRV control data~(AC:SRR5286959;
\citealp{Byrne:2017aa}) where the truth is known. Minimap2 predicted 59\,916
introns from 11\,017 reads. 93.0\% of splice juctions are precise. We examined
wrongly predicted junctions and found the majority were caused by clustered
splicing signals (e.g. two adjacent ${\tt GT}$ sites). When INDEL sequencing
errors are frequent, it is difficult to find precise splicing sites in this
case. If we allow up to 10bp distance from true splicing sites, 98.4\% of
aligned introns are approximately correct.  Given this observation, we might be
able to improve boundary detection by initializing $d(\cdot)$ and $a(\cdot)$ in
Eq.~(\ref{eq:splice}) with position-specific scoring matrices or more
sophisticated models. We have not tried this approach.

\begin{table}[!tb]
\processtable{Evaluation of junction accuracy on 2D ONT reads}
{\footnotesize\label{tab:intron}
\begin{tabular}{p{3.1cm}rrrr}
\toprule
& GMAP & minimap2 & SpAln & STAR\\
\midrule
Run time (CPU min) & 631 & 15.5 & 2\,076 & 33.9 \\
Peak RAM (GByte) & 8.9 & 14.5 & 3.2 & 29.2\vspace{1em}\\
\# aligned reads & 103\,669 & 103\,917 & 103\,711 & 26\,479\\
\# chimeric alignments & 1\,904 & 1\,671 & 0 & 0\\
\# non-spliced alignments & 15\,854 & 14\,483 & 17\,033 & 10\,545\vspace{1em}\\
\# aligned introns & 692\,275 & 694\,237 & 692\,945 & 78\,603 \\
\# novel introns & 11\,239 & 3\,217 & 8\,550 & 1\,214 \\
\% exact introns & 83.8\% & 91.8\% & 87.9\% & 55.2\% \\
\% approx. introns & 91.8\% & 96.5\% & 92.5\% & 82.4\% \\
\botrule
\end{tabular}
}{Mouse reads (AC:SRR5286960) were mapped to the primary assembly of mouse
genome GRCm38 with the following tools and command options: minimap2 (`-ax
splice'); GMAP (`-n 0 --min-intronlength 30 --cross-species'); SpAln (`-Q7 -LS
-S3'); STARlong (according to
\href{http://bit.ly/star-pb}{http://bit.ly/star-pb}). The alignments were
compared to the EnsEMBL gene annotation, release 89. A predicted intron
is \emph{novel} if it has no overlaps with any annotated introns. An intron
is \emph{exact} if it is identical to an annotated intron. An intron is
\emph{approximate} if both its 5'- and 3'-end are within 10bp around the ends
of an annotated intron.}
\end{table}

We next aligned real mouse reads~\citep{Byrne:2017aa} with GMAP~(v2017-06-20;
\citealp{Wu:2005vn}), minimap2, SpAln~(v2.3.1; \citealp{Iwata:2012aa}) and
STAR~(v2.5.3a; \citealp{Dobin:2013kx}). In general, minimap2 is more
consistent with existing annotations (Table~\ref{tab:intron}): it finds
more junctions with a higher percentage being exactly or approximately correct.
Minimap2 is over 40 times faster than GMAP and SpAln. While STAR is close to
minimap2 in speed, it does not work well with noisy reads.  We have also
evaluated spliced aligners on public Iso-Seq data (human Alzheimer brain
from \href{http://bit.ly/isoseqpub}{http://bit.ly/isoseqpub}). The observation
is similar: minimap2 is faster at higher junction accuracy.

We noted that GMAP and SpAln have not been optimized for noisy reads. We are
showing the best setting we have experimented, but their developers should be
able to improve their accuracy further.

%\begin{table}[!tb]
%\processtable{Evaluation of junction accuracy on SMRT Iso-Seq reads}
%{\footnotesize
%\begin{tabular}{lrrrr}
%\toprule
%& GMAP & minimap2 & SpAln & STAR\\
%\midrule
%Run time (CPU min) & & 243 & 2\,352 & 1\,647 \\
%\# aligned reads & & 1\,123\,025 & 1\,094\,092 & 682\,452\\
%\# chimeric alignments & & 33\,091 & 0 & 0\\
%\# non-spliced alignments & & 339\,081 & 291\,447 & 272\,536\vspace{1em}\\
%\# aligned introns & & 9\,071\,755 & 9\,208\,564 & 3\,029\,121 \\
%\# novel introns & & 42\,773 & 82\,230 & 17\,791 \\
%\% exact introns & & 94.9\% & 91.7\% & 84.7\% \\
%\% approx. introns&& 96.9\% & 93.4\% & 93.8\% \\
%\botrule
%\end{tabular}
%}{}
%\end{table}


\section{Conclusion}

Minimap2 is a fast, accurate and versatile aligner for long nucleotide
sequences.  In addition to reference-based read mapping, minimap2 inherits
minimap's functionality to search against huge multi-species databases and to
find read overlaps. On a few test data sets, minimap2 appears to yield slightly
better miniasm assembly~\citep{Li:2016aa}. Minimap2 can also align similar
genomes or different assemblies of the same species. However, full-genome
alignment is an intricate research topic. More thorough evaluations would be
necessary to justify the use of minimap2 for such applications.

\section*{Acknowledgements}
We owe a debt of gratitude to Hajime Suzuki for releasing his masterpiece and
insightful notes before formal publication. We thank M. Schatz, P. Rescheneder
and F.  Sedlazeck for pointing out the limitation of BWA-MEM. We are also
grateful to early minimap2 testers who have greatly helped to suggest features
and to fix various issues.

\bibliography{minimap2}

\end{document}