README.md update

README.md

@@ -99,8 +99,7 @@ Mitofinder needs several files to run depending on the method you have choosen (
 
 **TIP 1**: If you hesitate between two or more mitogenomes to use as references, you can put them together in the same reference file. MitoFinder will use all of them to find mitochondrial signal and will choose the best matching one for each gene during the annotation step.  
 **TIP 2**: If you want to assemble several mitogenomes for which you have different references, you can group them together in the same reference file. This allows to use MitoFinder in a loop and does not affect the result because MitoFinder will select the best matching reference for the annotation step.  
-**TIP 3**: If you have a large number of reads, from a whole genome sequencing for example, you can pre-filter mitochondrial reads using [mirabait](http://manpages.ubuntu.com/manpages/xenial/man1/mirabait.1.html). This will considerably reduce the computation time. However, this can be done only if you have a reference mitogenome for a closely related species because MIRA uses mapping to select reads and this method is rather stringent.  
-If you don't have the mitogenome of a closely related species you can just use a random subset of your read data (~ 15/20 Millions of reads) to reduce computation time and assemble the mitogenome. Of course, in doing so, you could lose some of the information if the mitogenome coverage is unsufficient.  
+**TIP 3**: If you have a large number of reads, from a whole genome sequencing for example, you can pre-filter mitochondrial reads using [mirabait](http://manpages.ubuntu.com/manpages/xenial/man1/mirabait.1.html). This will considerably reduce the computation time. However, this can be done only if you have a reference mitogenome for a closely related species because MIRA uses mapping to select reads and this method is rather stringent. If you don't have the mitogenome of a closely related species you can just use a random subset of your read data (~ 15/20 Millions of reads) to reduce computation time and assemble the mitogenome. Of course, in doing so, you could lose some of the information if the mitogenome coverage is unsufficient.  
 
 # OUTPUTS 
 ### Result folder