Differences

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--- assembling_long_read_data [2017/11/09 13:10] – 129.173.88.84
+++ assembling_long_read_data [2018/01/08 14:51] (current) – 129.173.88.84
@@ Line 5: / Line 5: @@
 When you have your porechopped reads in fastq and fasta formats, try out the following assemblers:
-Programs: ABruijn ([[https://github.com/fenderglass/ABruijn]]), Canu ([[http://canu.readthedocs.io/en/latest/quick-start.html]]), smartdenovo ([[https://github.com/ruanjue/smartdenovo]]), miniasm ([[https://github.com/lh3/miniasm]])
+Programs: ABruijn ([[https://github.com/fenderglass/ABruijn]]), Flye ([[https://github.com/fenderglass/Flye]]), Canu ([[http://canu.readthedocs.io/en/latest/quick-start.html]]), smartdenovo ([[https://github.com/ruanjue/smartdenovo]]), miniasm ([[https://github.com/lh3/miniasm]])
 **ABruijn**
@@ Line 28: / Line 28: @@
 /scratch2/software/ABruijn-1.0/bin/abruijn /path/to_your_fasta /path/to_an_output_directory <estimated coverage> --platform nano --threads 10
+</code>
+Abruijn has been replaced by **Flye** as of January 2018! Example usage:
+<code>
+#!/bin/bash
+#$ -S /bin/bash
+. /etc/profile
+#$ -cwd
+#$ -pe threaded 16
+#$ -o leg
+source /scratch2/software/python-2.7-env/bin/activate
+unset PYTHONPATH
+flye --nano-raw Acas_merged_pc_fl.fastq --genome-size 45m --out-dir Acas_filtlongFlye --threads 16 --iterations 3 --min-overlap 3000
 </code>
 **Canu**
@@ Line 71: / Line 87: @@
 The simplest and the fastest of all the assemblers here. First, self-map the fasta file using minimap2:
 <code>
-minimap2 -x ava-ont reads.fa reads.fa | gzip -1 > reads.paf.gz
+minimap2 -x ava-ont reads.fq reads.fq | gzip -1 > reads.paf.gz
 </code>
@@ Line 83: / Line 99: @@
 awk '/^S/{print">"$2"\n"$3}' in.gfa | fold > out.fa
 </code>
+----
 The Unicycler Github page ([[https://github.com/rrwick/Unicycler]]) has nice examples of how good, alright, and terrible graphs look like.