More minor corrections

diff --git a/tags/assembly.mdwn b/tags/assembly.mdwn
index f244ca890fd3fc4b8018f62f1a81b7dc9ef9b3f0..2d3c63a754489c1125c53f3178d47882dacf350c 100644 (file)
--- a/tags/assembly.mdwn
+++ b/tags/assembly.mdwn
@@ -8,11 +8,11 @@ Prior assembly, MinIONQC ([[Lanfear and coll., 2018|biblio/30052755]]) allows
 for the comparison of multiple Nanopore runs on the same plot, to assess if
 read length is satisfactory.
 
-The Flye assembler ([[Kolmogorov and coll. (2018)|biblio/30936562]]) creates an
+The Flye assembler ([[Kolmogorov and coll., 2018|biblio/30936562]]) creates an
 A-Bruijn (assembly) graph from draft contigs using long error-prone reads,
 untangles the graph by resolving repeats, and then uses it to refine the
 contings and increase their accuracy.  (The predecessor of Flye, ABruijn, was
-reported by [[Istace and coll.  (2017)|biblio/28369459]] to be able to assemble
+reported by [[Istace and coll. (2017)|biblio/28369459]] to be able to assemble
 mitochondrial genomes, unlike Canu and other assemblers.)
 
 After assembly, the contigs can be further polished with Racon ([[Vaser, Sović,
@@ -20,7 +20,7 @@ Nagarajan and Šikić, 2017|biblio/28100585]]).
 
 When coverage is too low for efficient reference-free assembly, related
 references can be used as a guide.  The Ragout software ([[Kolmogorov and
-coll., 2014|biblio/24931998), [[Kolmogorov and coll., 2018|biblio/30341161]])
+coll., 2014|biblio/24931998]]), [[Kolmogorov and coll., 2018|biblio/30341161]])
 can take multiple reference genomes to guide the assembly of one target.
 Polymorphisms unique to the target genome can be recovered, but chromosome
 fusions are typically hard to detect.  Compared to version 1, version 2 infers
@@ -38,7 +38,7 @@ Relase notes of HM2 version 20180603 suggest to use “_HapCUT2 or other phasing
 tools to get the high-quality haplotype assembly based on the reference haploid
 assembly_”.
 
-SALSA (Simple AssembLy ScAffolder, [Ghurye and coll., 2017|biblio/28701198]])
+SALSA (Simple AssembLy ScAffolder, [[Ghurye and coll., 2017|biblio/28701198]])
 takes Hi-C data and contigs as input and scaffolds them under the hypothesis
 that most contact points are due to local (same-chromosome) proximity.  Version
 2 of SALSA uses unitigs and the assembly graph as input ([[Ghurye and coll.,