two discussion paragraphs; need one more

Author: lh3

tex/minimap2.tex

@@ -558,16 +558,33 @@ \subsection{Aligning short genomic reads}
 \subsection{Other applications}
 
 Minimap2 retains minimap's functionality to find overlaps between long reads
-and to search against huge multi-species databases such as \emph{nt} from NCBI.
-Minimap2 can also align similar genomes or different assemblies of the same
-species. It took 7 wall-clock minutes over 8 CPU cores to align a human SMRT
-assembly (AC:GCA\_001297185.1) to GRCh38, over 20 times as fast as
+and to search against large multi-species databases such as \emph{nt} from
+NCBI. Minimap2 can also align similar genomes or different assemblies of the
+same species. It took 7 wall-clock minutes over 8 CPU cores to align a human
+SMRT assembly (AC:GCA\_001297185.1) to GRCh38, over 20 times as fast as
 MUMmer4~\citep{Kurtz:2004zr}.
 
-\section{Conclusion}
-
-Minimap2 is a fast, accurate and versatile aligner for long nucleotide
-sequences.
+\section{Discussions}
+
+Minimap2 is a versatile mapper and pairwise aligner for nucleotide sequences.
+It works with short reads, assembly contigs and long noisy genomic and RNA-seq
+reads. It can be used as a read mapper, long-read overlapper or a full-genome
+aligner. Minimap2 is also accurate and efficient, often outperforming other
+domain-specific alignment tools in terms of both speed and accuracy.
+
+The capability of minimap2 comes from a fast base-level alignment algorithm and
+an accurate chaining algorithm. When aligning long query sequences, base-level
+alignment is often the performance bottleneck. The Suzuki-Kasahara algorithm
+greatly alleviates the bottleneck and enables DP-based splice alignment
+involving $>$100kb introns, which was impractically slow ten years ago.  The
+minimap2 chaining algorithm is fast and highly accurate by itself.  In fact,
+chaining alone is more accurate than all the other long-read mappers in
+Fig.~\ref{fig:eval}a (data not shown). This accuracy helps to reduce downstream
+base-level alignment of candidate chains, which is still times slower than
+chaining even with the Suzuki-Kasahara improvement. In addition, taking a
+general form, minimap2 chaining can be adapted to non-typical data types such
+spliced reads and multiple reads per fragment. This gives us the opportunity to
+extend the same base algorithm to a variety of use cases.
 
 \section*{Acknowledgements}
 We owe a debt of gratitude to H. Suzuki and M. Kasahara for releasing their

tex/mm2.approx.eval

@@ -1,30 +1,12 @@
-Q	60	32066	0	0.000000000
-Q	40	32	1	0.000031155
-Q	38	19	1	0.000062272
-Q	36	11	1	0.000093376
-Q	35	32	1	0.000124378
-Q	33	15	1	0.000155400
-Q	32	58	1	0.000186145
-Q	27	11	1	0.000217095
-Q	26	80	1	0.000247494
-Q	21	19	2	0.000309186
-Q	20	16	1	0.000339936
-Q	19	19	1	0.000370622
-Q	18	22	2	0.000432099
-Q	17	37	5	0.000585751
-Q	15	24	2	0.000646930
-Q	14	18	3	0.000738939
-Q	13	30	6	0.000922821
-Q	12	18	1	0.000953054
-Q	11	29	2	0.001013638
-Q	10	30	1	0.001043393
-Q	9	20	5	0.001196099
-Q	8	25	8	0.001440348
-Q	7	28	6	0.001622830
-Q	6	35	12	0.001988132
-Q	5	34	12	0.002352725
-Q	4	29	8	0.002594865
-Q	3	36	14	0.003018937
-Q	2	46	15	0.003471482
-Q	1	69	36	0.004558162
-Q	0	167	94	0.007377173
+Q	60	32084	0	0.000000000	32084
+Q	24	318	2	0.000061725	32402
+Q	11	98	2	0.000123077	32500
+Q	8	37	2	0.000184405	32537
+Q	7	37	3	0.000276294	32574
+Q	6	40	3	0.000367940	32614
+Q	5	34	2	0.000428816	32648
+Q	4	37	5	0.000581306	32685
+Q	3	28	6	0.000764222	32713
+Q	2	38	6	0.000946536	32751
+Q	1	50	21	0.001585318	32801
+Q	0	286	150	0.006105117	33087